Example HTML5 Document

` is the highest section level while `

` is the lowest. # H1 ## H2 ### H3 #### H4 ##### H5 ###### H6 ## Paragraph Xerum, quo qui aut unt expliquam qui dolut labo. Aque venitatiusda cum, voluptionse latur sitiae dolessi aut parist aut dollo enim qui voluptate ma dolestendit peritin re plis aut quas inctum laceat est volestemque commosa as cus endigna tectur, offic to cor sequas etum rerum idem sintibus eiur? Quianimin porecus evelectur, cum que nis nust voloribus ratem aut omnimi, sitatur? Quiatem. Nam, omnis sum am facea corem alique molestrunt et eos evelece arcillit ut aut eos eos nus, sin conecerem erum fuga. Ri oditatquam, ad quibus unda veliamenimin cusam et facea ipsamus es exerum sitate dolores editium rerore eost, temped molorro ratiae volorro te reribus dolorer sperchicium faceata tiustia prat. Itatur? Quiatae cullecum rem ent aut odis in re eossequodi nonsequ idebis ne sapicia is sinveli squiatum, core et que aut hariosam ex eat. ## Images ### Syntax ```markdown ![Alt text](./full/or/relative/path/of/image) ``` ### Output ![blog placeholder](../../assets/placeholder.jpg) ## Blockquotes The blockquote element represents content that is quoted from another source, optionally with a citation which must be within a `footer` or `cite` element, and optionally with in-line changes such as annotations and abbreviations. ### Blockquote without attribution #### Syntax ```markdown > Tiam, ad mint andaepu dandae nostion secatur sequo quae. > **Note** that you can use _Markdown syntax_ within a blockquote. ``` #### Output > Tiam, ad mint andaepu dandae nostion secatur sequo quae. > **Note** that you can use _Markdown syntax_ within a blockquote. ### Blockquote with attribution #### Syntax ```markdown > Don't communicate by sharing memory, share memory by communicating.
> Rob Pike[^1] ``` #### Output > Don't communicate by sharing memory, share memory by communicating.
> Rob Pike[^1] [^1]: The above quote is excerpted from Rob Pike's [talk](https://www.youtube.com/watch?v=PAAkCSZUG1c) during Gopherfest, November 18, 2015. ## Tables ### Syntax ```markdown | Italics | Bold | Code | | --------- | -------- | ------ | | _italics_ | **bold** | `code` | ``` ### Output | Italics | Bold | Code | | --------- | -------- | ------ | | _italics_ | **bold** | `code` | ## Code Blocks ### Syntax we can use 3 backticks ``` in new line and write snippet and close with 3 backticks on new line and to highlight language specific syntax, write one word of language name after first 3 backticks, for eg. html, javascript, css, markdown, typescript, txt, bash ````markdown ```html Example HTML5 Document

Test

``` ```` ### Output ```html Example HTML5 Document

Test

``` ## List Types ### Ordered List #### Syntax ```markdown 1. First item 2. Second item 3. Third item ``` #### Output 1. First item 2. Second item 3. Third item ### Unordered List #### Syntax ```markdown - List item - Another item - And another item ``` #### Output - List item - Another item - And another item ### Nested list #### Syntax ```markdown - Fruit - Apple - Orange - Banana - Dairy - Milk - Cheese ``` #### Output - Fruit - Apple - Orange - Banana - Dairy - Milk - Cheese ## Other Elements: abbr, sub, sup, kbd, mark ### Syntax ```markdown GIF is a bitmap image format. H₂O Xⁿ + Yⁿ = Zⁿ Press CTRL + ALT + Delete to end the session. Most salamanders are nocturnal, and hunt for insects, worms, and other small creatures. ``` ### Output GIF is a bitmap image format. H₂O Xⁿ + Yⁿ = Zⁿ Press CTRL + ALT + Delete to end the session. Most salamanders are nocturnal, and hunt for insects, worms, and other small creatures. --- ### Using MDX **Description:** Lorem ipsum dolor sit amet **Date:** 2024-06-01 **URL:** https://ryansnowden.com/writing/using-mdx/ This theme comes with the [@astrojs/mdx](https://docs.astro.build/en/guides/integrations-guide/mdx/) integration installed and configured in your `astro.config.mjs` config file. If you prefer not to use MDX, you can disable support by removing the integration from your config file. ## Why MDX? MDX is a special flavor of Markdown that supports embedded JavaScript & JSX syntax. This unlocks the ability to [mix JavaScript and UI Components into your Markdown content](https://docs.astro.build/en/guides/markdown-content/#mdx-features) for things like interactive charts or alerts. If you have existing content authored in MDX, this integration will hopefully make migrating to Astro a breeze. ## Example Here is how you import and use a UI component inside of MDX. When you open this page in the browser, you should see the clickable button below. import HeaderLink from '../../components/layout/HeaderLink.astro'; Embedded component in MDX ## More Links - [MDX Syntax Documentation](https://mdxjs.com/docs/what-is-mdx) - [Astro Usage Documentation](https://docs.astro.build/en/guides/markdown-content/#markdown-and-mdx-pages) - **Note:** [Client Directives](https://docs.astro.build/en/reference/directives-reference/#client-directives) are still required to create interactive components. Otherwise, all components in your MDX will render as static HTML (no JavaScript) by default. --- --- ## Labs --- ### Cellular Automata + Immune System **Description:** An interactive model exploring how corporate culture, executive cover, and risk controls react when teams test new ideas. **Date:** 2026-07-21 **URL:** https://ryansnowden.com/labs/ca-immune-system/ import CAImmuneSystem from '../../components/simulations/ca-immune-system/CAImmuneSystem.tsx'; import ScenarioCard from '../../components/ui/ScenarioCard.astro';

Since when do you tell the enemy you have won?
Mazer Rackham, Enders Game (Also, Sun Tzu)

Built With

Language

TypeScript

Architecture

Cellular Automata

Rendering

Canvas API (60fps)

Overview

Every organization has a self-defense mechanism — an "immune system" made of risk rules, compliance checks, and middle-management habits. This simulation models what happens when teams test new ways of working: do good ideas gain traction, get shielded by leadership, or get crushed by corporate bureaucracy?

The Legend

Standard Operations

Stable, reliable baseline operating within standard procedures.

Exploring Ideas

Testing novel approaches and localized variations.

Exposed to Risk

Visible to risk/compliance officers, lacking evidence to justify itself.

Under Attack

Actively suppressed by risk management or audit controls.

Executive Cover

Shielded from compliance shutdown by leadership sponsorship.

Proven & Adopted

Accumulated enough evidence to become an accepted new practice.

Institutional Scar Tissue

Trauma from past failed initiatives. Highly sensitive to new change.

Operational Collapse

Lost all functional capacity; abandoned or broken process.

Scenarios

When executives mandate a hype trend across the company without real proof or local testing, it spreads like wildfire — disrupting core work until teams burn out and operations break down.

Scarred by past failed projects, the organization panics at the slightest change. Compliance officers overreact, surrounding teams with red tape until normal work grinds to a halt.

Leaders demand innovation, but teams have seen too many short-lived initiatives fail. Everyone pretends to participate while secretly waiting out the mandate until things return to normal.

Teams play it safe and never try anything new. To avoid scrutiny, they stick strictly to outdated methods — until hidden technical debt builds up and entire departments quietly collapse.

A small mistake triggers an overreaction. Fear from past failed projects causes compliance rules to snowball, eventually shutting down healthy teams doing normal work.

The company looks healthy on the surface, but underneath, years of avoiding risk have created fragile workarounds. A sudden market shock at tick 300 reveals the hidden weakness, causing widespread collapse.

An R&D team is isolated behind a protective barrier (amber line). The lab succeeds in its bubble, but because its learnings never reach the main company, an external crisis at tick 400 eventually drags the whole enterprise down.

Grassroots connections (cyan threads) allow teams to share proven ideas directly across silos. By routing around bureaucratic blockers, good ideas gain traction far faster than through top-down mandates.

--- ### Design is Dead **Description:** AI changed the design loop forever. See how rapid generation and instant feedback replace the traditional double diamond process. **Date:** 2026-03-02 **URL:** https://ryansnowden.com/labs/design-is-dead/ import DesignIsDeadSimulator from '../../components/simulations/DesignIsDeadSimulator.tsx'; import ScenarioCard from '../../components/ui/ScenarioCard.astro'; import Accordion from '../../components/ui/Accordion.astro';

Work in Progress

Built With

Language

TypeScript

Framework

React

Rendering

Canvas API

Icons

Lucide React

Simulation

Step-by-Step Events

Static Site

Astro

This simulation models the new loop: quick concept, real prototype in code with AI, live jam with design and engineering, ship small, learn, adjust vision. Design has split into two modes that designers move between. It is based on [a video](https://www.youtube.com/watch?v=eh8bcBIAAFo) interviewing Jenny Wen (head of design at Claude). She talks about how traditional research, diverge, converge (double diamond-esque) workflows are no longer effective with AI. You will find paraphrased excerpts from the video in the text. I built this to show how one of these new loops works. Some scenarios (such as Technical Debt Spiral) introduces behavioural and technical issues that can impact worflow. It took about 16 hours to get the parameters right and another 2 to put together the visual guide. Still a work in progress. It needs a comparison of the traditional loop running alongside it in the same 2-week sprint. Other concepts follow the same themes: 1. AI-native double diamond 2. Code-first "vibe coding" loops 3. Human-AI collaboration frameworks like the Argyle Design Framework 4. Teams rejecting formal process entirely in favour of something custom Importantly, there is a skills and mindset shift. Strong generalists (like myself), deep specialists and "crafty new grads" are well poised for this workflow. It is a less siloed approach to design and engineering, that removes gatekeeping.

What it shows

The simulation shows how different process setups affect throughput, cycle time, design coherence and cost efficiency. You can see why skipping design creates compounding problems through design system erosion. AI failure modes, from hallucination to agent conflicts to capability cliffs, create new kinds of friction. "Vibe coding" without understanding creates debt spirals. And the new cost model makes the core point clearer: throughput alone is not efficiency. #### The flow Ideas enter from the left and flow through **Vision Mode** (frame the opportunity), **AI Build** (prototype with engineering), **Design Execution** (structure, polish, design system), and **Ship**. After shipping, items enter **Observe + Learn**, then **Adjust Direction** where they are killed, refined (back to Vision), or pivoted (back to Build). #### What to watch - **Queue buildup in stages:** When dots pile up inside a zone, that stage is a bottleneck. Traditional Waterfall shows this in Vision. Agent Swarm shows it in Build. - **Green rings on dots:** Items on their 2nd+ loop through the system. Some iteration is healthy. Too much means the system is churning. - **Coherence bar:** The green bar at the bottom of the Design zone. Watch it drop when items skip design (especially in "Vibe Coding" and "No Design" scenarios). - **Cost signals:** Use cost to compare burn against useful output. Fast, messy scenarios can look productive while becoming more expensive per meaningful ship. - **Blocked servers:** Red warning servers in AI Build mean agent conflicts. Multiple AI tools are producing conflicting code. - **Ideas backlog:** The number in the Ideas box shows how many ideas are waiting to enter the system. When Vision is full, ideas queue up. #### The key moment Run "Vibe Coding" and watch throughput spike at first. Then watch the kill rate climb, coherence drop and cost efficiency worsen. The system produces lots of movement but ships less value over time. Now switch to "AI + Design Partnership" and see the difference.

Eight scenarios showing different setups of the AI-accelerated design process.

The ideal state. Fast AI build paired with active design produces steady throughput, high coherence and stronger cost efficiency. Designers guide fast-moving engineering rather than gatekeeping.

The old world. Everything queues for vision (upfront spec), build is slow (no AI), and design reviews everything. Throughput is low but coherence is perfect. The cost is time.

Engineering adopts AI but design does not adapt. High throughput at first, but the kill rate climbs because shipped work is incoherent. It can look efficient until rework and wasted output push the real cost up.

Adds a deliberate discovery phase before vision. Slower to start but higher quality input. Lower kill rate, better outcomes. Shows why framing the problem well matters even in a fast loop.

"Move fast and break everything." Items fly through but nobody understands the code. Watch the vibe debt rings grow with each loop. The output looks cheap at first, then rework and low coherence make each useful ship more expensive.

Running many AI agents without coordination. Individual speed is fast but conflicts dominate. More agents means more merge conflicts, and the extra burn can outweigh the parallel speed gains.

Starts well. But as WIP grows and the codebase gets larger, AI build speed degrades. Items that took 1x now take 3x. Meanwhile, the team gets comfortable trusting AI output and stops checking carefully.

The compounding nightmare. Each time an item loops back, its processing time grows. Meanwhile, coherence drops, making future design work slower. Watch cycle time accelerate over the simulation's runtime.

The simulation models two modes that designers move between:

Vision Mode

Look 3 to 6 months ahead and define what the product should feel like. The output is a working prototype or narrow "north star" flow, not a slide deck. It constrains and aligns the AI-driven experiments teams are running.

Short horizon: 3 to 6 months, not years
Grounded in current AI capabilities
Prevents fragmentation across experiments

Execution Mode

Sit with engineers while they use AI to build flows. Continuously correct layout, interaction, states, copy, and hierarchy. Enforce patterns, design system usage, and accessibility. Do last-mile fixes yourself in code.

Real-time pairing, not handoff
Design system enforcement in code
Last-mile implementation and polish

Where Figma Still Fits

Figma remains key for parallel exploration and high-fidelity craft. The difference is weight and timing: instead of dominating the process, Figma explorations sit inside shorter cycles, with fewer big speculative flows and more targeted problem-solving.

How Time Has Shifted

Where 60 to 70% used to be mocks and prototypes, that is now about 30 to 40%. Another 30 to 40% is pairing with engineers. A meaningful slice is implementing front-end code and polish directly. The old practices are not gone. Their relative weight has shrunk inside a faster, code-first loop.

This simulation shares a thesis with Maggie Appleton's "One Developer, Two Dozen Agents, Zero Alignment" talk from GitHub Next. Both arrive at the same conclusion from different directions:

“Speed without alignment produces waste, not value.”

The collapse of implementation time reveals that alignment was always the real work. The old slow process incidentally created alignment through its friction. When implementation collapsed, the alignment infrastructure went with it.

Where the arguments converge

The bottleneck has moved

This simulation shows it mechanically: when you speed up AI Build and strip out design, you don't get faster output, you get faster waste. Appleton states it directly: "The hard question is no longer how to build it. It's should we build it."

More agents does not mean more output

The Agent Swarm scenario and Productivity Paradox mechanic model this quantitatively. Appleton calls it "nine women make a baby in one month" logic. More individual output makes coordination problems worse.

Planning and building are fused

The Vision Mode and Execution Mode loop in this simulation is a continuous cycle, not sequential phases. Appleton says the same: "Planning and building are no longer separate phases. They're a continuous cycle."

Vibe coding is seductive poison

Run the Vibe Coding scenario and watch throughput spike, then watch the kill rate climb and coherence drop. Appleton: "It's very easy to prompt your way to the wrong thing or to add tons of unnecessary, unhelpful features."

Quality is the differentiator

The AI + Design Partnership scenario outperforms raw-speed scenarios on cost efficiency. Appleton: "In a world of fast, cheap software, quality becomes the new differentiator. Craftsmanship is what will set you apart from the vibe-coded slop."

Where they diverge

The Simulation View

Process Mechanics

Models the internal states of a single team's workflow. How ideas flow through stages, where friction accumulates, and what breaks. The solution is a better process with design guardrails.

Key Insight

Misalignment compounds. Bad alignment doesn't just waste units of work, it makes all future work harder.

The Tooling View

Environment Design

Zooms out to the infrastructure. PRs and Issues are the wrong primitives for the agentic era. The solution is a better environment where alignment happens naturally.

Key Insight

"Human context is not in the codebase." No amount of code context can replace sharing the "why" early.

Two halves of the same argument

The valuable friction was never the implementation friction. It was the thinking friction. This simulation answers what happens when alignment breaks; Appleton answers what infrastructure we need to prevent it from breaking.

Six properties that model real failure modes in AI-driven engineering:

AI Hallucination (Rework)

AI generates code that looks correct but is wrong. Items pass through AI Build, appear done, but get caught during Learn/Adjust and loop back. The faster you build without design review, the more bad output slips through.

Agent Conflict (Blocking)

Multiple AI agents working on overlapping areas produce conflicting code. A build server gets blocked while conflicts are resolved, taking 3x normal time. Conflict probability scales with the number of busy servers.

Capability Cliff

Some ideas hit the boundary of what current AI models can do. These items (amber, larger circles) require manual engineering and take 4x longer. They look the same entering the pipeline but reveal themselves mid-processing.

Context Window Collapse

As the system accumulates work in progress, AI tools lose context and output quality drops. Build speed degrades as the codebase grows beyond the AI's context window. This is a simplified model of a real and well-documented limitation.

Vibe Code Debt

Items built fast with AI, but nobody understands the code. Each time an item loops back, its debt increases, adding a multiplier to future processing time. Visible as red dashed rings growing around dots. By the 3rd loop, processing time has nearly doubled.

Design System Erosion

When items ship without going through Design, the global coherence score drops. As coherence falls, all Design work takes longer because the team is fixing inconsistencies instead of doing proactive work. Skipping design makes future design harder.

Four friction mechanics based on published cognitive and behavioral research. These model what happens to teams, not just to code. Each is toggleable in the simulation's AI Friction panel.

Skill Atrophy

When AI does most of the work, the team gradually loses the ability to do it themselves. The simulation slows down build and design servers over time in proportion to how little design involvement there is.

Based on a 2026 Anthropic study where AI-assisted developers scored 17% lower on comprehension tests. Vibe coding research documents progressive skill loss when AI handles all code generation. This is a simplified model. In reality, skill loss varies by individual and task. The simulation approximates the trend.

Review Fatigue

After a string of successful ships, the team gets comfortable and stops checking AI output carefully. The simulation makes hallucinations harder to catch after each consecutive successful ship. Catching a hallucination resets the streak.

Based on a Google study of 76 software engineers that found automation trust increases over time. Separate research found that as AI gets better at writing code, humans get worse at reviewing it. The effect is well-documented in automation research going back 75 years.

Cognitive Debt

Every time an item skips design review, it adds to a global slowdown. The team's ability to solve problems erodes because they are not exercising it. The simulation makes all rework and iteration processing slower as cognitive debt accumulates.

Based on research documenting the systematic erosion of problem-solving ability when developers rely heavily on AI. Developers stop reading documentation, debugging skills dull, and error messages become unfamiliar. The simulation models this as a global multiplier. The real effect is more nuanced but the direction is consistent across studies.

Productivity Paradox

Adding more AI agents does not produce proportionally more output. Beyond 2 active agents, each additional agent adds coordination and review overhead that slows everyone down. The simulation applies a logarithmic overhead multiplier to all build servers.

Based on a randomized controlled trial of experienced open-source developers that found AI tools actually increased completion time by 19%. This contradicted developer predictions of 20% time savings. The simulation models the overhead simply, but the finding is robust: more AI assistance does not always mean faster results.

Three designer types that thrive in this new loop:

Strong Generalists

80th percentile or above across several core skills: product thinking, UX, visual, some coding. They flex between PM, design, and engineering-adjacent work as roles blur, and they are the most valuable in fast-moving teams where nobody can stay in one lane.

Deep Specialists

Top-tier in something that sets products apart: visual systems, iconography, or technical design and implementation. Their depth creates advantages that AI cannot easily match and generalists cannot replicate.

Crafty New Grads

Early-career designers who are humble, learn fast, and are unburdened by legacy process. They actively build things with new tools rather than clinging to old methods. Not knowing how it "used to be done" is their advantage.

The Core Shift

Designers must let go of gatekeeping and instead guide fast-moving engineering. This means explaining design principles, teaching the design system, and raising the overall design ability of the team rather than trying to own every pixel. Implementation literacy is increasingly baseline. You may not need to be a full-stack engineer, but you do need to work effectively with AI coding tools and make last-mile UI changes yourself.

Entity Types

Blue: Standard idea flowing through

Amber (large): Capability cliff (4x processing)

Green ring: Iterated (2nd+ loop)

Fading: Killed at Adjust

Stage Zones

Amber: Vision Mode

Sky: AI Build + Eng

Emerald: Design Execution

Violet: Ship / Done

Server States

Circles inside AI Build and Design zones represent processing capacity:

S (empty): Idle server, waiting for work
Gear (filled): Actively processing an item
Warning (red): Blocked by agent conflict (3x processing time)

Metrics

Shipped: Items that completed the full pipeline and exited
Killed: Items terminated at the Adjust stage
WIP: Active items currently in the system
Throughput: Items shipped per second
Cycle Time: Average time from idea entry to ship
Coherence: Design system health (100% = pristine, drops when items skip design)
Avg Iterations: Average number of loops before an item ships

--- ### EMF: Health Simulator **Description:** A plain-English breakdown of the Estuary Mapping Framework. Explore how energy, friction, and organizational momentum shape strategic change. **Date:** 2026-01-06 **URL:** https://ryansnowden.com/labs/estuarine-health/ import EstuarineHealth from '../../components/simulations/EstuarineHealth.astro'; import Katex from '../../components/graphics/Katex.astro'; import { Mermaid } from '../../components/graphics/Mermaid'; import VSMFlow from '../../components/simulations/VSMFlow'; import AgentDynamicsFlow from '../../components/simulations/AgentDynamicsFlow'; import BurnoutFlow from '../../components/simulations/BurnoutFlow';

Work in Progress

Overview

**EMF – Health** applies the Estuarine Mapping Framework to healthcare AI strategy. The 2026 healthcare environment faces physical limits: workforce burnout, thick capital cycles, and AI as the key way to scale. This simulator maps the **Energy Cost of Change** across four scenarios. It helps strategists find low-energy windows for action and high-energy walls where action creates pushback.

Healthcare Scenarios

Four scenarios represent distinct topological configurations that AI companies must navigate.

Ambient Capacity Restoration

Clinicians are at their breaking point with paperwork. Ambient AI reduces documentation workload so doctors spend less energy taking notes and more time with patients. But if the AI requires too much manual double-checking, clinicians revert straight back to old dictation habits.

if (AI_Active) documentationWorkload -= 25%

Regulatory Phase Shift

Strict regulation (like the EU AI Act) turns loose guidelines into strict law. High-risk AI products without compliance certification hit a brick wall — the energy cost to operate non-compliant tools becomes impossibly high.

if (!certified) deploymentBlocked = true

Reimbursement Tether

Hospitals run on razor-thin margins. AI tools that don’t tie directly to official billing codes (CPT codes) get starved of budget. If a tool doesn’t generate clear reimbursement, adoption drops to zero.

if (!billingCodeAttached) hospitalAdoption = 0

Confident Hallucination

When an AI model makes a confident clinical mistake, trust plummets instantly. Rebuilding clinical trust takes months of flawless performance. A single high-profile error traps the product in a liability risk zone.

TrustLostInSeconds != TrustRebuiltOverMonths

Healthcare Constructors

Constructors transform input states to output states without undergoing net change themselves.

Constructor	Function	Scenario
Ambient Scribe	Reduces cognitive friction by transforming conversation → SOAP note	Ambient Capacity
Compliance Audit	Enables crossing regulatory walls via audit trail	Regulatory Phase Shift
CPT Enabler	Extends revenue tether radius via new billing codes	Reimbursement Tether
Trust Anchor	Slows trust decay, aids linear recovery via verification	Hallucination Event

Analysis Tools New

These buttons add overlays to help you understand what's happening in the simulation.

#### Cynefin Overlay The Cynefin framework helps you understand how predictable different parts of the system are. When you click **Cynefin**, the simulation area gets colour-coded into four zones: - **Clear (Green)**: These areas are predictable. Cause and effect are obvious. Best practices work here. Think of well-documented procedures that always give the same result. - **Complicated (Blue)**: Still predictable, but you need expertise to understand what's going on. An expert can analyse the situation and figure out the right approach. Think of a specialist diagnosing a known problem. - **Complex (Purple)**: Cause and effect only make sense in hindsight. You can't predict outcomes because too many things interact. The best approach is to try small experiments and see what happens. - **Chaotic (Red)**: No clear cause and effect. Things are unstable and you need to act first, then figure out what's happening. Think of a crisis where you stabilise first and ask questions later. The simulation calculates which zone each area belongs to based on two factors: how volatile the area is (the right edge is more volatile), and how close to constraints an area is (more constraints means more structure). --- #### Network Topology Click **Net** to see the invisible relationships between agents. The simulation draws purple lines between agents that are close enough to influence each other. This shows you the social network forming in real-time. Agents that are near each other can share information and copy each other's behaviour. The lines get stronger (more visible) when agents are closer together. You can use this to spot clusters forming, identify isolated agents, and see how information might spread through the group. The total number of connections is shown at the bottom of the screen. --- #### Wardley Map Click **Wardley** to overlay strategic positioning axes onto the simulation. Wardley Mapping is a strategy tool that plots components based on how evolved they are and how visible they are to users. The overlay adds: - **X-Axis (Evolution)**: Shows how mature something is. The left side is "Genesis" (new, experimental ideas). Moving right, you pass through "Custom Built" (one-off solutions), then "Product" (off-the-shelf options), and finally "Commodity" on the far right (utilities everyone uses like electricity). - **Y-Axis (Visibility)**: Shows how visible a component is to end users. Things at the top are user-facing and obvious. Things at the bottom are invisible infrastructure that users never see. The labels change depending on which scenario you're viewing. In healthcare scenarios, the evolution stages might be labelled differently to match the domain (for example, "Research" to "Standard of Care"). This overlay helps you think strategically about where things are positioned. Volatile areas (left side) suit experimental work. Stable areas (right side) suit commodity services. --- #### VSM Roles (Viable System Model) Click **VSM** to see how the simulation maps to Stafford Beer's Viable System Model, a way of understanding organisations as living systems. The overlay highlights four levels: - **System 1 Operations (Green)**: The agents themselves. These are the things that actually do the work. In an organisation, this would be the front-line teams delivering products or services. - **System 2 Coordination (Yellow)**: The constraints. These dampen oscillations and stop parts of the system from fighting each other. They're the rules, schedules, and standards that keep things running smoothly. - **System 3 Control (Blue)**: The constructors. These allocate resources and monitor performance. They make sure the operational units have what they need and are working effectively. - **System 5 Policy (Red)**: The boundary of the entire system. This is identity and purpose. It defines what the system is and what it's not. The red border around the simulation area represents this. This overlay helps you think about organisational design. A viable system needs all five levels working together. If you only have operations (System 1) without coordination (System 2), you get chaos. If you have too much control (System 3) without operational freedom, you get stagnation.

Alternative Frameworks

The current implementation is primarily Agent-Based Modelling (ABM) with continuous physics. These enhancements map organizational complexity theories to specific simulation mechanics.

Multi-Level Selection (Wilson & Wilson, 2007)

Agents form groups that compete as units, creating selection pressure at both individual and group levels.

Mapping: Teams compete for resources; successful team patterns propagate.

Network Topology (Barabási, 2002)

Replaces spatial proximity with network connections using power-law degree distributions for realistic organizational structure.

Mapping: Influence flows through hierarchy and established links, not just physical proximity.

Evolutionary Strategies (Axelrod, 1984)

Agents carry strategy genotypes subject to mutation, crossover, and selection.

Mapping: Organizational practices evolve through competitive mimicry (see Institutional Isomorphism).

Healthcare Physics

Constraints modify the energy required to move actants through the healthcare terrain.

Elastic Constraint (Burnout)

Modeled using Hooke's Law with a failure point. When cognitive load exceeds the yield point, the workforce snaps back.

Rigid Constraint (Compliance Wall)

Step function where energy approaches infinity for non-compliant actors.

Tether Constraint (Revenue Radius)

Cost is zero inside CPT code scope, infinite outside.

Hysteresis (Trust Dynamics)

Trust decays exponentially after errors but recovers linearly, requiring far more work to rebuild.

Advanced Physics Unique to Health Sim

This simulator introduces two physics models not present in the standard Estuarine Mapping sims, designed specifically for modelling healthcare trust dynamics and volatile zone behaviour.

Langevin Equation (Brownian Motion)

In the volatile zone (right edge), agents follow the **Langevin equation** (a stochastic differential equation that models proper Brownian diffusion rather than uniform noise).

Where ∇V is the energy gradient (gradient descent toward lower energy), and σdW is Gaussian noise from a Wiener process.

Other EMF Sims

Uniform random jitter: agents move jerkily like "TV static"

Health Sim

Gaussian random walks: agents drift smoothly like "smoke diffusing"

Used in: All scenarios (volatile zone behaviour)

Hysteresis Effect (Asymmetric Energy)

Trust dynamics follow a **hysteresis loop** where falling into a liability well is fast but climbing out is slow. This models the asymmetric energy cost of reputation damage.

Trust Decay (Fast)

Exponential: T *= exp(-0.05)

~40 frames to collapse

Trust Recovery (Slow)

Linear: T += 0.002

~500 frames to recover

Visual feedback:

Red pulsing glow: agents losing trust (inside dark constraint)
Teal pulsing glow: agents recovering trust (climbing out)
Agent colour shifts toward red as trust drops

Used in: Confident Hallucination scenario (dark constraints)

System Dynamics & Models

Visualising the feedback loops and hierarchical structures driving the simulation.

{/* Agent Dynamics */}

Agent Dynamics (Boids + Behavioural Econ)

How agents make decisions: combining flocking rules with cognitive biases under constraints.

{/* VSM */}

Viable System Model (VSM) Mapping

Mapping Estuarine entities to Stafford Beer's cybernetic tiers.

{/* Burnout CLD */}

Burnout Causal Loop Diagram

The reinforcing cycle of burnout and the balancing loop of Ambient AI.

{/* Constraints Table */}

Constraints Reference

Type	Metaphor	Dynamics
Elastic	Rubber Band	Stretches with pressure, snaps back at yield point (Burnout).
Rigid	Concrete Wall	Fixed boundary. Infinite energy cost to cross (Compliance).
Permeable	Mesh Fence	Passable with friction. Filters agents by cost (Workflow).
Tether	Dog Leash	Zero cost within radius, infinite outside (Reimbursement).
Dark	Gravity Well	Attracts agents. Easy to enter, hard to leave (Liability).

--- ### Estuary Mapping Framework Simulator **Description:** Strategy isn’t a neat 5-year roadmap. Explore how timing, organizational friction, and shifting tides determine whether a change initiative survives. **Date:** 2025-12-26 **URL:** https://ryansnowden.com/labs/estuary-mapping/ import EstuarineMapping from '../../components/simulations/EstuarineMapping.astro'; import Katex from '../../components/graphics/Katex.astro'; import ScenarioTabs from '../../components/interactive/ScenarioTabs';

Built With

Language

TypeScript

Framework

React

Rendering

Canvas API

Icons

Lucide React

Math Rendering

KaTeX

Static Site

Astro

Overview

Most corporate strategy decks pretend the world is a smooth river flowing toward a predictable "To-Be" destination. In reality, organisations operate in an **estuary** — an environment driven by turning tides, shifting mudflats, and hidden currents. In an estuary, pushing forward at high tide requires immense energy because the current is fighting you. But if you wait for slack water or the turn of the tide, a small push carries you twice as far. This simulation models the **forces acting on your organisation (we call them *actants*)** — from rigid policies and team habits to market trends. It helps you calculate the **Energy Cost of Change** so you can spot strategic windows where small, low-risk interventions achieve outsized impact.

The Estuarine Metaphor

Unlike a delta's unidirectional flow, an estuary is defined by Bidirectional Flow, which is the turn of the tide.

Flood Tide →

Strategic momentum flows inward. Energy costs increase as you push against established structures. The system resists change.

← Ebb Tide

Strategic windows open. Energy costs decrease. Small interventions can achieve disproportionate movement as the system naturally shifts.

Theoretical Foundations

Behind the visual simulation sits a mix of physics, behavioural economics, and complexity theory. You don't need a PhD to use the map, but here is how those theories power the mechanics under the hood.

1. Focus on What Is Possible (Constructor Theory)

What this means in practice: Traditional planning asks "What will happen in Q4?" Constructor Theory asks "What transformations are physically and socially possible right now, and what tools do we need to enable them?"

Origin: Formulated by quantum physicists David Deutsch and Chiara Marletto.
In the Simulation: Constructors (diamond shapes) are tools, rituals, or processes that transform the environment repeatedly without getting consumed themselves (like an API, a weekly standup, or an automated test suite). Constraints (circles) define the boundaries that keep the system from devolving into total chaos.

2. Organisational Habits and Friction (Energy Landscapes & Attractors)

What this means in practice: Organisations have history. A team that spent five years under rigid bureaucracy won't suddenly become agile overnight just because you launched a new slide deck. Systems get trapped in "deep wells" (attractors) of past habits, a phenomenon known as hysteresis.

The Math: Strategy succeeds when doing the right thing (the Energy Cost of Virtue, ) requires less energy than taking shortcuts or defaulting to legacy habits (the Energy Cost of Sin, ). If doing it right takes 10x more effort, people will naturally default to old habits.

3. How Human Behavior Shapes the Map (Cognitive Heuristics)

What this means in practice: People don't act like rational computing units. They resist change, seek safety in crowds, and prioritize immediate relief over long-term gains. The blue dots (agents) in the simulation model three core human behaviors:

Loss Aversion (Risk Sensitivity): People resist moving to a better way of working if the short-term friction of transitioning feels too painful.
Social Proof (Safety in Numbers): People cluster together. When a group forms around a legacy habit, it creates a gravity well that makes it harder for individuals to step out on their own.
Hyperbolic Discounting (Short-Term Focus): Given the choice, teams almost always prefer quick, low-effort fixes today over high-impact strategic shifts that take six months to pay off.

Taxonomy of Constraints

Constraints are not merely limitations, they are boundary conditions that allow order to emerge.

Type	Characteristics
Rigid	Immovable sea walls. Core safety regulations, legal requirements. High energy cost to move or work around.
Elastic	Stretches under pressure but snaps back. Crunch-time overtime, temporary workarounds. Oscillates with the tide.
Tether	Freedom within a defined radius. Bounded flexibility: can move, but not escape. Employee autonomy within KPIs.
Permeable	Selective membranes. Informal social networks that cross organisational silos. Agents may or may not pass through.
Phase Shift	Tipping points. Near bifurcation thresholds where small changes cause sudden, qualitative shifts in system topology.
Dark	Invisible forces. Shadow IT, informal hierarchies. Detectable only through their "gravitational" influence on flow.

Tidal States

The tide cycles through three distinct states, each affecting system behavior differently.

HIGH Tide

Peak capital and momentum
Constraints "submerged", leading to reduced friction
Constructors at max throughput
Agents flow freely with strong alignment

SLACK Water

Transition phase, also known as the Turn
Energy cost of change at minimum
Constraints vulnerable to re-zoning
Strategic window for low-friction moves

LOW Tide

Scarcity and exposure
"Rocks" (rigid constraints) exposed
Constructor energy decays faster
Agents cluster via Social Proof herding

Strategic Vectors

Leadership intent is modeled as directional vectors applied to actants. Vectors create local "winds" that alter energy flow, visualized as arrows emanating from Constraints and Constructors.

Vertical Axis (Energy Cost)

North (▲): Increase energy cost, making a "sin" harder
South (▼): Decrease energy cost, making a "virtue" easier

Horizontal Axis (Timing)

West (◄): Accelerate change by doing it sooner or faster
East (►): Delay change by storing Capacitance (social energy)

When a vector opposes the natural tidal flow, Turbulence appears as an orange glow around the actant, visualizing leadership friction.

Causal Chains & Entanglement

Actants don't exist in isolation; they're connected through invisible bonds that create emergent organisational dynamics.

Entanglement Bonds

Purple animated tethers representing hidden coupling between nodes.

Spring Physics: Moving a parent node physically pulls entangled children
Stretch Visualization: Glow intensifies when bonds are under tension
Example: Legacy ERP entangled with IT Governance, meaning you can't change one without affecting the other

Signal Bonds

Cyan dashed lines representing If-Then causal links between actants.

Triggers: Capacitance full, state change, or tidal turn
Animated Pulse: Cyan pulse travels along the bond when triggered
Effects: Deepen/shallow target's potential well, or trigger phase shift

Bond Types Reference

Type	Function	Visual
Entanglement	Constraint-Distance Spring coupling	Purple curved tether with glow
Signal	If-Then conditional effect propagation	Cyan dashed line with traveling pulse

Organisational Scenarios

Real-world strategic situations modeled through the estuarine lens.

Market Disruption

Particles = Initiatives · Ocean = Market forces

Particles represent strategic initiatives (product launches, R&D bets) and the tidal ocean represents broader market forces (economic cycles, technology shifts, competitor moves).

How It Works

When disruption hits, it arrives like a spring tide, which is a powerful, external surge that floods your strategic landscape. Watch how:

Rigid Constraints (red circles) represent established incumbents, legacy business models, or regulatory frameworks. They don't move, so initiatives must flow around them or expend enormous energy to dislodge them.
Elastic Constraints (orange) represent adaptable competitors who stretch under pressure but snap back. They survive disruption by bending, not breaking.
Constructors (diamonds) represent the processes that generate new initiatives: innovation labs, accelerators, M&A pipelines. They attract particles, catalyze transformations, and remain relatively unchanged themselves.

Strategic Insights

Low herding coefficient: Chaotic markets reduce coordination. Individual initiatives scatter rather than cluster, representing the "every company for itself" dynamic during disruption.
Timing matters: Wait for SLACK water (the turn between tides) to launch initiatives, as this is when energy cost of change is lowest and established players are momentarily disoriented.
Dark Constraints appear: Watch for invisible forces (shadow competitors, platform shifts) that only reveal themselves through their gravitational effect on initiative flow.

Controls Reference

Tide Strength

Controls the amplitude of bidirectional flow. Higher values create stronger strategic windows but also more turbulence.

Loss Aversion (λ)

How strongly agents resist moving to higher-energy states. Range 1.0–3.0. Higher values create more "sticky" organisational inertia.

Social Proof Radius

Cluster detection range for herding behavior. Larger values cause agents to form bigger clusters. Varies by scenario.

Vector Power

Multiplier for strategic vector influence. Higher values make leadership intent (arrows) more forceful on nearby agents.

Energy Overlay

Visualizes the energy landscape. Green = low cost of change. Yellow = moderate. Red = high resistance zones.

Flow Arrows

Shows the current tidal velocity field. Arrows indicate direction and strength of flow at each point.

Per-Actant Settings

Click any Constraint or Constructor to access individual settings:

Influence Radius

How far the actant's strategic vector affects nearby agents. Shown as a dotted circle.

Vector Direction

N/S/E/W compass control. Combine axes (e.g., NW) for diagonal intent. Affects agent acceleration within influence radius.

Formulas and Concepts Used

Integrates principles from behavioral economics, cognitive psychology, physics, and complexity science.

Loss Aversion (Prospect Theory)

Creators: Daniel Kahneman & Amos Tversky (1979)
Concept: People feel losses more intensely than equivalent gains; it is approximately 2x as painful.

Where is the energy difference and is the loss aversion coefficient (1.0–3.0). Agents resist moving to higher-energy states, creating organisational inertia.

```typescript // Loss Aversion: Resistance to higher energy states const energyDiff = targetEnergy - currentEnergy; if (energyDiff > 0) { // Moving uphill: Apply loss aversion penalty if (Math.random() > Math.pow(0.5, energyDiff * lossAversionLambda)) { return; // Reject move } } ```

Social Proof (Herding Behavior)

Creator: Robert Cialdini (1984)
Concept: When uncertain, people look to others' behavior for guidance: "if everyone else is doing it, it must be correct."

Nearby agents () reduce the local energy cost, creating "social gravity wells." It becomes energetically cheaper to stay within the herd than to break away.

```typescript // Social Gravity: Local clusters reduce effective energy cost const nearbyCount = this.getNearbyAgents(radius).length; const socialDiscount = Math.min(0.4, nearbyCount * 0.05); // The herd makes the current location feel "safer" (lower energy) const effectiveEnergy = baseEnergy - socialDiscount; ```

Boids Algorithm (Flocking)

Creator: Craig Reynolds (1986)
Concept: Emergent collective behavior from three simple rules.

Separation: Avoid crowding neighbors (inverse-square falloff).

```typescript // Flocking Behavior (Boids) const cohesion = this.steerToward(averagePosition); const alignment = this.steerToward(averageHeading); const separation = this.avoid(neighbors); // Sum of vectors with weights this.acceleration.add(cohesion.mult(0.01)); this.acceleration.add(alignment.mult(0.05)); this.acceleration.add(separation.mult(0.1)); ```

Gradient Descent

Origin: Mathematical optimization (Cauchy, 1847)
Concept: Move in the direction of steepest descent to find local minima.

Agents descend the energy landscape, seeking "basins of attraction" (stable low-energy states). The gradient is sampled from the energy grid, and is the learning rate (0.3 in stable zones).

```typescript // Gradient Descent on Energy Landscape const gradX = (energyGrid[x+1][y] - energyGrid[x-1][y]) / 2; const gradY = (energyGrid[x][y+1] - energyGrid[x][y-1]) / 2; // Move towards lower energy (downhill) this.velocity.x -= gradX * learningRate; this.velocity.y -= gradY * learningRate; ```

Inductance (Bond Graph Theory)

Origin: Henry Paynter (1961)
Concept: Energy storage in momentum, reflecting resistance to changes in flow rate.

Where is inductance (0–1). Clustered agents build momentum inertia that resists sudden direction changes, which models organisational momentum.

```typescript // Inductance: Resistance to change in velocity const inductance = 0.8; // High organizational inertia this.velocity.x = (prevVx * inductance) + (newVx * (1 - inductance)); this.velocity.y = (prevVy * inductance) + (newVy * (1 - inductance)); ```

Sunk Cost Fallacy

Researchers: Hal Arkes & Catherine Blumer (1985)
Concept: Past investment irrationally influences future decisions: "we've come too far to stop now."

Agents older than (~10 seconds) receive progressive inductance boosts, making them harder to dislodge from current trajectories.

```typescript // Sunk Cost: Older agents become harder to move const age = Date.now() - this.createdAt; if (age > threshold) { // Increase inductance based on age this.inductance += 0.001; } ```

Abilene Paradox (Groupthink)

Creator: Jerry B. Harvey (1974)
Concept: Groups collectively decide on a course of action that no individual member actually wants.

When cluster inductance exceeds , a "phantom vector" emerges that pushes the entire group toward high-energy "sin" basins, representing collective drift toward suboptimal outcomes.

```typescript // Abilene Paradox: Collective drift to undesired outcome // If cluster cohesion is too high, drift towards "Sin" basin if (clusterInductance > 0.6) { const phantomVector = getVectorToSinBasin(); this.applyForce(phantomVector.mult(0.5)); } ```

Institutional Isomorphism

Researchers: Paul DiMaggio & Walter Powell (1983)
Concept: Organisations under similar environmental pressures become structurally similar over time.

Agents adopt properties (inductance, loss aversion) of nearby successful neighbors with higher flow rates. controls the gradual mimicry rate.

```typescript // Mimetic Isomorphism: Copy successful neighbors const neighbor = this.getNearestHighFlowNeighbor(); if (neighbor) { // Adopt neighbor's properties this.inductance = lerp(this.inductance, neighbor.inductance, 0.05); this.lossAversion = lerp(this.lossAversion, neighbor.lossAversion, 0.05); } ```

Hooke's Law (Entanglement Springs)

Creator: Robert Hooke (1678)
Concept: The force exerted by a spring is proportional to its displacement from equilibrium.

Entanglement bonds apply spring forces when stretched beyond their rest length . This creates the physical coupling between entangled actants, where pulling one drags the other.

```typescript // Entanglement Spring Force const dx = connectedNode.x - this.x; const dy = connectedNode.y - this.y; const distance = Math.sqrt(dx*dx + dy*dy); const force = (distance - restLength) * springConstant; // Apply restoring force this.applyForce(new Vector(dx, dy).normalize().mult(force)); ```

Hyperbolic Discounting

Researcher: George Ainslie (1975)
Concept: People prefer smaller, immediate rewards over larger, delayed rewards, with preference reversals near the present.

Where is reward magnitude, is delay, and is the discount rate. Agents prefer low-energy, short-time interventions (West vectors) over high-impact, long-time strategic shifts (East vectors).

```typescript // Preference for immediate small gains over long-term value const shortTermGain = assessVector(WEST); // Do it now const longTermGain = assessVector(EAST); // Build capacity // Discount future value const discountedLongTerm = longTermGain / (1 + discountRate * delay); if (shortTermGain > discountedLongTerm) { this.move(WEST); } ```

Hysteresis

Origin: James Alfred Ewing (1881), physics
Concept: A system's current state depends on its history, not just current inputs.

Organisations display path-dependency: the sequence of past decisions constrains future options. This is why certain "attractor basins" (e.g., bureaucratic stagnation) are difficult to escape once entered.

```typescript // State depends on history // Once trapped in a basin, small fluctuations can't escape if (isInBasin && inputForce < escapeThreshold) { this.state = "TRAPPED"; // Remains trapped even if force > 0 } else { this.state = "flowing"; } ```

Energy Cost of Change

Framework: Behavioral architecture / Nudge theory
Creators: Richard Thaler & Cass Sunstein (2008)

The goal of strategic design is to ensure (Energy Cost of Virtue) is lower than (Energy Cost of Sin). Make the "right thing" the path of least resistance.

```typescript // Strategic Design: Lowers energy cost of desired state class Constructor { update() { // Create local energy well (Virtue) grid[this.x][this.y] -= 0.5; // This makes Ev < Es (Virtue cheaper than Sin) } } ```

--- ### Machine Bureaucracy **Description:** When hitting your rate target means breaking the rules, most people break the rules. A simulation of Goodhart’s Law in fulfilment centres. **Date:** 2026-07-28 **URL:** https://ryansnowden.com/labs/machine-bureaucracy/ import MachineBureaucracySimulator from '../../components/simulations/MachineBureaucracySimulator.tsx'; import Accordion from '../../components/ui/Accordion.astro'; import DevOnly from '../../components/DevOnly.astro'; import Katex from '../../components/graphics/Katex.astro';

“比目中路析。”
“Eyes once paired, but now parted midway.”
Pan Yue 潘岳 (247–300)

Changelog: 2026/08/01

Double division nightmare: Corrected inaccurate bin occupancy calculations.
UX/UI adjustments: Cleaner interface, sidebars, metrics panel.
Summary & Calculations:Detailed debug on the calcultions per rule/non-rule following stower.
Culling the creep: Scrapped and simplified the stower state tracking logic.
View optimisation: Fixed intersection observers so the simulation actually runs when scrolled into view.
Pacing: Decoupled engine pacing from cart spawning.
Dependencies: Untangled the RuleDistributionGraph dependency from the sidebar.
Metrics: Renamed overly vague labels (e.g., 'picker demand' to 'picker activity').

@TODO

Picker penalties: Bring back penalties picker balance.
Stower allocation: Fix methods, not interactive enough for me.
Docs: Remove jargon, rewrite.
tldr; Lots.

When analyzing stow rates, especially when worker fatigue, dynamic frictions, and floor penalties are disabled, throughput is determined by a precise mathematical model operating inside the simulation engine (`engine.ts` and `model.ts`). ### 1. Rule Compliance (Rule-Followers vs. Rule-Breakers) * **Speed Multiplier**: A fully compliant stower (`expectedComplianceRate = 1.0`) takes **1.2x longer** per item (`speedMultiplier = 1.2`), representing a 20% duration penalty (+20% time spent aligning, verifying, and distributing items cleanly). A 100% shortcutting stower operates at baseline speed (`speedMultiplier = 1.0`). * **Bin Distribution**: Rule-following stowers cap placements at 4 items per bin before advancing down the aisle to find another open bin. Shortcut stowers dump entire cart totes into a single bin. * **Max Theoretical UPH (Per Stower)**: * **Rule-Breaker (Shortcut)**: Up to **210 items/hour** per stower (17.14s physical base per item). * **Rule-Follower (Compliant)**: Up to **175 items/hour** per stower (210 / 1.2). * **Compliance Drivers**: Rule-following rate is calculated dynamically from management **Rule Support** (+0.58), **Stow Target Pressure** (-0.32), and individual worker **Compliance Bias** (±0.25). ### 2. Inbound Freight & Cart Composition * **Cart Overhead Spreading**: Cart setup time is fixed per cart. Carts containing more items spread setup overhead over a larger unit count, increasing net items/hr (UPH). * **Library vs. Library Deep Mix**: Standard *Library* totes average 3–20 items per tote (with a 15% probability of 45-item batch spikes). *Library Deep* totes average only 1–3 items and incur a fixed deep-tote handling penalty (+1.92s per tote). Higher Library Mix raises overall UPH. ### 3. Worker Skill Heterogeneity & Learning Curve * **Skill Variance Toggle**: By default, **Disable Worker Skill Variance** is set to `true` in the simulator sidebar, forcing all stowers to operate at an identical `1.0x` baseline skill factor. ### 4. Deep Dive: How Stow Target Pressure is Calculated and Used **Stow Target Pressure (`stowPressure`)** is the operational tension metric that quantifies how strongly management speed quotas and floor congestion push stowers to sacrifice rule compliance for raw throughput. #### Calculation Formula (`model.ts`) Inside `deriveOperationalState()`, `stowPressure` is calculated as a weighted sum clamped between 4% and 98% ([0.04, 0.98]):

* **Baseline Tension (0.16)**: Minimum floor pressure under normal operating conditions. * **Slot Scarcity** (): Floor capacity congestion (1 - `slotAvailability`) (overridden to 0 when `isolateStowProcess = true`). * **Storage Complexity** (): Derived clutter combining bin messiness, overstuffed bins, and ASIN density. #### How Stow Target Pressure Drives System Behavior 1. **Suppression of Rule Compliance**:

Every 10% increase in Stow Target Pressure directly subtracts **3.2 percentage points** from a stower's compliance probability. When pressure exceeds Rule Support, workers switch from rule-following stows (1.2x duration) to shortcut stows (1.0x duration). ### 5. Impact of Bin Fullness and Bin Complexity / Messiness on Stow Rate Both **Bin Fullness** (fill ratio & capacity density) and **Bin Complexity / Messiness** directly impact the stow rate through three distinct behavioral and physical mechanisms: #### A. Target Bin Fullness (`fillRatio` & `physicalLoad`) * **Specific Bin Fill Penalty (`fillPenalty`)**: In `bin-model.ts`, as a target bin fills up, the engine calculates . As a bin approaches capacity, this subtracts up to **25 percentage points** directly from the stower's rule-following chance (`computeRuleFollowingChance`), forcing stowers to choose between missing rate or shortcut dumping. * **Floor-Wide Storage Complexity**: High average fullness increases `overstuffedBinShare` (+0.18 weight) and `averageAsinDensity` (+0.14 weight) in `storageComplexity`. This inflates Stow Target Pressure (+0.18) and depresses overall rule-following (-0.16). * **Slot Scarcity Penalty (When Frictions Enabled)**: High floor saturation (1 - `slotAvailability`) scales up physical cart travel time (`stowTravelMin`/`Max`) and multiplies item duration by `scarcityFrictionMultiplier`. #### B. Bin Complexity & Messiness (`messiness` & `nonNeatBinShare`) * **Messiness Penalty on Compliance**: In `bin-model.ts`, a target bin's messiness subtracts up to **18 percentage points** () from `computeRuleFollowingChance`. Encounters with messy bins discourage stowers from attempting clean, organized slotting. * **Reorganization & Recount Duration Overhead**: In `estimateBinStowWork()`, placing items cleanly into a messy bin requires extra physical time: * **Reorganization Time**: per placement. * **Recount Time**: per placement. * **Floor Messiness Drag**: High floor-wide messiness increases `storageComplexity` via `averageBinMessiness` (+0.18 weight) and `nonNeatBinShare` (+0.10 weight). ### Summary Breakdown Table (Fatigue & Frictions Disabled) | Factor | Parameter / Logic | Location in Code | Effect on Stow Rate | | :--- | :--- | :--- | :--- | | **Shortcut Stower** | `complianceRate = 0` | `engine.ts` | **Baseline Max Speed (1.0x duration)** — Max 210 UPH/stower | | **Compliant Stower** | `complianceRate = 1` | `engine.ts` | **20% Extra Duration (1.2x duration)** — Max 175 UPH/stower | | **Stow Target Pressure** | `stowTargetPerHour` | `model.ts` | Higher target bias increases Stow Pressure, driving workers to break rules | | **Rule Support** | `ruleSupport` (0–100%) | `model.ts` | Higher support increases compliance, trading ~16.7% speed for bin quality | | **Target Bin Fullness** | `fillRatio` | `bin-model.ts` | High fullness subtracts up to 25% from compliance chance | | **Bin Messiness** | `messiness` | `bin-model.ts` | High messiness subtracts up to 18% from compliance and adds +0.50s work duration | | **Library Mix** | `libraryMix` (0–100%) | `engine.ts` | Higher mix increases items per tote, raising net UPH | | **Disable Skill Variance** | `disableWorkerSkillVariance` | `worker-model.ts` | Defaults to `true`, forcing 1.0x baseline skill across all stowers | | **Picker Decoupling** | `isolateStowProcess` | `model.ts` | Defaults to `true`, decoupling stow rate from picker demand and scarcity | The term **Machine Bureaucracy** was coined by management theorist Henry Mintzberg in 1979 ^[13] to describe an organizational structure engineered to run like a clockwork mechanism. In Mintzberg's model, such organizations rely on highly standardized routine tasks, strict formal rules, centralized authority, and a prominent "technostructure"—analysts and managers who design, measure, and standardize work processes. At the time, Mintzberg noted that while machine bureaucracies excel at achieving consistency and efficiency in stable environments, they do so by treating operational workflows—and the humans executing them—as deterministic, interchangeable parts of a machine. This simulation models the subtle mechanics of a non-robotic fulfilment centre and demonstrates how strict individual performance metrics reshape worker behavior. Rather than operating as a predictable clockwork machine, the warehouse behaves as a living system where worker fatigue, crowded bins, and the pressure to hit targets all adapt in real time to operational pressure. This scenario is a real-world example of **Goodhart’s Law** — though most commonly remembered in anthropologist Marilyn Strathern’s popular phrasing (*“When a measure becomes a target, it ceases to be a good measure”*) ^[5] ^[6].

“Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.”
Charles Goodhart, 1975

When workers (Stowers and Pickers) are evaluated primarily on raw speed (how many items they scan per hour), hitting that quota under tight conditions eventually requires bending placement rules. What emerges is not a moral failing or a lack of discipline among workers, but a direct structural outcome of how work is measured and managed ^[11]. ### Mapping the Process Before looking at how workers adapt to daily performance targets, it helps to understand how a warehouse operates as an end-to-end system. In operational engineering, a standard tool for mapping this is **SIPOC**, which stands for **Suppliers**, **Inputs**, **Process**, **Outputs**, and **Customers**. A SIPOC diagram lays out the entire lifecycle of an operation on a single page. It defines where materials come from, what resources are required, the major steps taken to transform those resources, and who receives the finished result.

Suppliers 🚚	Inputs 📥	Process (High-Level Steps) ⚙️	Outputs 📤	Customers 👤
Inbound Freight Carriers & Vendors Order Management System Packaging Suppliers Warehouse Control Systems	Bulk inventory & master cartons Real-time item retrieval requests Scanner pick lists & walk paths Boxes, mailers, tape & dunnage Weight & sizing parameters	1. Inbound Decant/Receive: Unpack bulk supplier shipments from pallets into open totes. 2. Stow Carry totes into mezzanine shelving and stow items into storage bins via hand scanners. 3. Item Picking & Retrieval Follow scanner walk paths through aisles to retrieve items into totes. 4. Rebin & Wall Sorting: Convey totes to Put Walls where workers sort items for shipping. 5. Packing & Inspection: Pack items into recommended boxes, insert protective dunnage, and seal. 6. Automated SLAM: Weigh parcels automatically on a high-speed line and stamp shipping labels. 7. Fluid Loading: Direct parcels down dock spurs and stack directly into outbound trailers.	Weight-verified, sealed parcels Electronic shipping manifests & BOL Real-time inventory balance updates Exception & problem-solve totes	Online End Customers Sortation & Delivery Hubs Logistics & Carrier Partners Support & Audit Teams

#### Where this Simulation Fits A traditional manual fulfilment center runs on a multi-stage pipeline: incoming shipments are unpacked into totes (Decant), placed into aisle shelving bins (Static Stow), picked for fulfillment (Item Picking), sorted into order cubbies (Rebin), packed into shipping boxes (Pack), weighed and labeled automatically (SLAM), and finally loaded into delivery trucks (Fluid Loading). This simulation intentionally isolates a specific part of that chain: **the handoff between Static Stow and Item Picking**. #### Why the Stow–Pick Handoff Matters In a manual warehouse, Stowers and Pickers work in the exact same physical storage bins, but at different times: - **Stowers** load incoming items into open shelving bins. - **Pickers** walk those same aisles hours or days later to retrieve items from storage bins. Both roles are managed under strict hourly rate targets (units processed per hour). When a Stower comes under pressure to hit their target in a crowded warehouse, the fastest way to keep moving is to bend placement rules—such as shoving an item into an overfilled bin or ignoring item size guidelines. The Stower meets their quota for the hour, but leaves behind a messy, disorganized bin. Later, when a Picker arrives at that bin under their own quota clock, they lose valuable minutes digging through the mess to find the right item. The time saved during Stow becomes an immediate delay and point of frustration during Pick. By focusing on this Stow–Pick relationship, the simulation demonstrates how isolated performance metrics can cause one team's speed to create hidden work and friction for another. The simulation interface brings together several real-time feeds to help you monitor how systemic pressure flows across the warehouse floor: **Controls & Sidebar** You can adjust the **Stow Target** and **Pick Target** rates via the **Advanced controls** tab in the left sidebar. You can also grant **Rule support** (giving workers organizational backing to prioritize placement accuracy over raw speed) or adjust **Picker demand** to test how quickly downstream picking activity stress-tests earlier placement decisions. **Floor Canvas & Heatmap** - **Visual Canvas**: The canvas abstracts the warehouse into a continuous flow of work. Items move through a cycle across four stages (Buffer → Stow → Bin → Pick). The **Stow cycle (Blue)** tracks inbound placement, while the **Pick cycle (Orange)** tracks outbound retrieval. - **Bin Complexity Heatmap**: Positioned directly beneath the canvas, this interactive grid visualizes bin fullness and messiness in real time. Clicking any bin reveals its specific item history and event log. **Metrics & System Efficiency** The dashboard below the heatmap provides four distinct views into the systemic health of the warehouse: - **Throughput (Main Graph):** Watch how the Stow rate (adding items) and Pick rate (retrieving items) interact. When Stowers take shortcuts to hit their target, you will see the Pick rate eventually crash as they encounter the resulting mess. - **Rule Distribution:** This shows the direct trade-off workers face. As the Stow Target increases or bins become crowded, watch the distribution shift from compliant placements (green) to shortcut, rule-breaking stows (amber). - **Availability:** A real-time view of warehouse capacity. As the floor fills up, the friction of finding a legal bin increases exponentially. - **Fatigue:** Worker exhaustion compounds all other frictions. As the shift progresses, the baseline speed drops, making it even harder to hit targets without resorting to shortcuts. The shift unfolds through a continuous, compounding feedback loop: 1. **Targets set the expectations.** Managers establish hourly stow and pick targets. When rule support is low, workers feel immediate pressure to hit their numbers above all else. 2. **Pressure mounts as space tightens.** As storage bins fill up, finding a legal, neat home for an item takes longer. Because hourly targets remain fixed regardless of bin congestion, workers experience rising mental friction as they try to solve the puzzle of where each item legally fits ^[3]. 3. **Shortcuts become necessary for survival.** Faced with a choice between losing their jobs for failing rate or bending placement rules, workers naturally take shortcuts ^[4]. This illustrates a classic human trade-off: the penalty for missing a target is immediate and personal, whereas the headache of a messy bin is delayed and lands on someone else ^[11]. 4. **Bins degrade and picks slow down.** Shortcut stows create messy, overfilled bins. When downstream pickers arrive to retrieve items, they face longer searches, higher error rates, and increased delays and frustration. The stow team’s speed directly becomes the pick team’s delay. 5. **System capacity is tested.** If problem-solving capacity is overwhelmed, exceptions accumulate, bin hygiene collapses, and the entire operational flow degrades. ### What moves on screen The canvas abstracts the work into a continuous flow of packets. Items begin in the inbound buffer before being processed by the active stow team. Each placement decision (whether compliant and neat, or a rushed shortcut) is recorded in the bins. Downstream, the active pick team pulls items from these same bins to fulfill customer demand. The speed of the blue stow loop directly impacts the health of the orange pick loop. ### Placement rules Items must match designated bin dimensions (standard height vs taller deep bins) enforced by hand scanners. Each bin has a physical working limit. While compliant stows maintain organized stacks, shortcut stows leave a mess that persists until a picker encounters it and is forced to spend extra time digging it out. You can explore different shift conditions using the scenario selector in the sidebar:

Orderly floor

Bins are lightly stocked with plenty of clean, available space. Stowers face no conflict between accuracy and speed, allowing hourly targets to be met comfortably without taking shortcuts.

Getting crowded

Bins approach working limits. While the floor appears controlled on paper, the margin for error is thin. Finding correct placements takes longer, forcing workers to choose between speed and compliance.

Rushing and messy

High target pressure combined with crowded bins leads to visual disorganization. Workers prioritize job security over bin hygiene, causing shortcut stows to proliferate rapidly ^[4].

Packed and struggling

Storage space is severely constrained. Every placement inherits past shortcuts, creating severe pick drag. Psychological safety vanishes as workers recognize systemic failure but avoid raising issues due to surveillance pressure ^[7] ^[8].

The simulation calculates key operational dynamics at every tick to model how floor pressure influences compliance ^[5] ^[6].

1. Storage complexity

Storage complexity quantifies how difficult it is for a stower to find an open, legal home for an item. As slot scarcity and bin messiness rise, complexity increases non-linearly.

```typescript // Weighted floor-condition friction - model.ts:deriveOperationalState const storageComplexity = clamp( 0.12 + libraryDeepShare * 0.16 + slotScarcity * 0.32 + averageBinMessiness * 0.24 + nonNeatBinShare * 0.16 + countingDifficulty * 0.12 + Math.max(0, pickerTargetBias) * 0.08, 0.04, 0.98 ); ```

2. Stow pressure

Stow pressure represents the psychological drive to take shortcuts when hourly rate targets exceed realistic floor capacity.

```typescript // What pushes workers toward shortcuts - model.ts:deriveOperationalState const stowPressure = clamp( 0.16 + Math.max(0, stowTargetBias) * 0.42 + pickerDemand * 0.16 + slotScarcity * 0.22 + storageComplexity * 0.18 + nonNeatBinShare * 0.1 + countingDifficulty * 0.08, 0.04, 0.98 ); ```

3. Rule-following rate

The probability that a stower follows proper placement procedures. Organizational rule support serves as the strongest positive factor counteracting rate pressure.

```typescript // Probability a stower follows placement rules - model.ts:deriveOperationalState const ruleFollowingRate = clamp( 0.42 + ruleSupport * 0.58 - stowPressure * 0.32 - storageComplexity * 0.16 - slotScarcity * 0.12 - Math.max(0, pickerTargetBias) * 0.08, 0.05, 0.98 ); ```

4. Process Efficiency & Waste

Process Efficiency tracks the percentage of total operational work that is value-add (compliant stows and clean picks) versus non-value-add rework generated by shortcut stows and search drag.

```typescript // Process Efficiency - model.ts:computeMetricSnapshot const wasteRate = casesProcessed === 0 ? 0 : clamp(ruleBreakingRate * 0.5 + nonNeatBinShare * 30 + (100 - Math.min(100, pickerKpi)) * 0.2, 0, 100); const processEfficiency = clamp(100 - wasteRate, 0, 100); ```

The dynamics observed in this simulation extend far beyond physical warehouses. Any operational environment where one team's speed creates uncounted rework for another exhibits the exact same failure modes. - **Siloed targets encourage passing the buck.** When teams are judged solely on isolated speed metrics, the rational choice is to hit your individual target while pushing the headache onto the next person in line ^[5]. - **Psychological safety enables early correction.** When workers are empowered to flag bottlenecks without fear of missing targets, the system self-corrects. When fear dominates, defects compound silently ^[7]. - **Systemic handoff design is the true solution.** The remedy is not stricter surveillance or heavier discipline, but designing measurement systems that treat stow and pick as a single integrated value stream ^[9]. The central question for leaders is not *"How do we force workers to follow rules?"* but rather *"Why does our system design make rule-following irrational?"* ^[12]

[1]
Amazon, "Inside Amazon's fulfillment centers: What you can expect to see on a warehouse tour," About Amazon, 11 Mar. 2019. aboutamazon.com
[2]
Amazon Technologies, Inc., "System and method for stow management of similar items," U.S. Patent US8341040B1, 1 Jan. 2013. patents.google.com
[3]
A. Delfanti, "Machinic dispossession and augmented despotism: Digital work in an Amazon warehouse," New Media & Society, vol. 23, no. 1, pp. 39-55, 2021.
[4]
E. J. Cheon and I. Erickson, "Fulfillment of the work games: Warehouse workers' experiences with algorithmic management," Proc. ACM Hum.-Comput. Interact., vol. 9, no. CSCW, Art. no. 228, 2025.
[5]
R. Mannion and J. Braithwaite, "When a measure becomes a target, it ceases to be a good measure," BMJ Quality & Safety, vol. 30, no. 3, pp. 263-267, 2021.
[6]
J. W. Treem, P. M. Leonardi, and B. van den Hooff, "Coping with Goodhart's law in an era of digitisation and datafication," Journal of Computer-Mediated Communication, vol. 28, no. 4, 2023.
[7]
A. C. Edmondson, "Psychological safety and learning behavior in work teams," Administrative Science Quarterly, vol. 44, no. 2, pp. 350-383, 1999.
[8]
S. Kim, S. B. Choi, and K. Kim, "The influence of corporate social responsibility on safety behavior: The mediating role of psychological safety and organizational commitment," Frontiers in Public Health, vol. 10, 2022.
[9]
W. Qian, J. Horisch, and S. Schaltegger, "Using a balanced scorecard to manage corporate social responsibility," International Journal of Management Reviews, vol. 22, no. 2, pp. 185-208, 2019.
[10]
R. S. Kaplan and D. P. Norton, The Balanced Scorecard: Translating Strategy into Action. Boston, MA, USA: Harvard Business School Press, 1996.
[11]
A. Kohn, Punished by Rewards: The Trouble with Gold Stars, Incentive Plans, A's, Praise, and Other Bribes, 25th anniversary ed. Boston, MA, USA: Houghton Mifflin Harcourt, 2018.
[12]
T. M. Jones, "Ethical decision making by individuals in organizations: An issue-contingent model," Academy of Management Review, vol. 16, no. 2, pp. 366-395, 1991.
[13]
H. Mintzberg, The Structuring of Organizations: A Synthesis of the Research. Englewood Cliffs, NJ: Prentice-Hall, 1979. (See also H. Mintzberg, "Structure in 5's: Designing Effective Organizations," Management Science, vol. 26, no. 3, pp. 322-341, 1980). doi.org/10.1287/mnsc.26.3.322

--- ### Modelling Complex Systems **Description:** Watch what happens when you push a system too hard. A simulation exploring how pressure, cognitive load, and psychological safety interact. **Date:** 2025-12-21 **URL:** https://ryansnowden.com/labs/modelling-complex-systems/ import ModellingComplexSystems from '../../components/simulations/ModellingComplexSystems.astro'; import Katex from '../../components/graphics/Katex.astro'; import ScenarioCard from '../../components/ui/ScenarioCard.astro';

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Overview

When an organisation grows, work doesn't just slow down — it breaks in unexpected ways. This simulation models how errors, miscommunication, and delays spread across teams. Instead of treating work as simple tasks on a board, it shows what happens when human factors — like **cognitive overload**, **fear of speaking up**, and **unnecessary sign-offs** — collide with heavy workload.

tl;dr Quick Explanation

#### Bureaucracy (Ford Model) The default scenario. Small squares (process gates) represent blockages like paperwork or approval steps. An abundance of stoppages slows the system, turning work (blue dots) to blocked (red). Adjusting work volume and environment noise can find equilibrium, but throughput remains limited. The solution is reducing blockages. #### Ideal Flow (Tesla Model) Demonstrates the impact of load. Maximising work volume causes team rings to turn yellow then red, overflowing at an increasing rate. This causes system stutter (Shannon Entropy) and contributes to Little's Law, slowing the system further, mirroring real organisational dynamics.

Scientific Basis & Mathematical Laws

Core scientific principles:

Kingman's Formula (Queuing Theory)

Origin: John Kingman, 1961.
Concept: Wait time (Wq) does not increase linearly with utilisation (ρ); it increases exponentially as utilisation approaches 100%.

```typescript // Calculate utilisation (capped at 99% to prevent infinite wait times) const utilisation = Math.min(0.99, this.cognitiveLoad / 100); // Apply cubic decay to processing speed as utilisation approaches 100% // This approximates the sharp "hockey stick" curve of Kingman's Formula this.processingSpeedMod = 1.0 - Math.pow(utilisation, 3); ```

In Simulation: Nodes have a Cognitive Load capacity. As a node's load exceeds 80%, its processing speed drops according to a cubic decay curve . This visually demonstrates why "busy" teams suddenly gridlock.

Little's Law

Origin: John Little, 1954.
Concept: The long-term average number of items in a stable system (L) is equal to the long-term average effective arrival rate (λ) multiplied by the average time an item spends in the system (W).

In Simulation: The WIP (Work In Progress) metric tracks L. Users can observe that increasing Frequency (λ) without increasing node speed results in an explosion of Lead Time (W), which is similar to a traffic jam forming when cars enter a highway faster than they exit.

```typescript // L (WIP) = λ (Arrival Rate) * W (Lead Time) // In simulation, we track L directly as the number of active items const L = workItems.length; // Lead time (W) accumulates as items wait in queues if (blocked) { this.timeWaiting++; } else { this.timeActive++; } ```

Brooks' Law

Origin: Fred Brooks, The Mythical Man-Month, 1975.
Concept: "Adding manpower to a late software project makes it later." This is due to the combinatorial explosion of communication channels, calculated as .

In Simulation: The Coordination Penalty increases automatically as you add Value Units (nodes). This adds a global "noise" factor to the system, simulating the friction of alignment in larger groups.

```typescript // Metcalfe's Law / Brooks' Law derivative // Coordination Penalty ∝ N(N-1)/2 const pairs = (nodeCount * (nodeCount - 1)) / 2; const penalty = pairs * 0.0015; // Applied as 'noise' to every action const totalNoise = config.noiseLevel + penalty; ```

Shannon Entropy (Information Theory)

Origin: Claude Shannon, 1948.
Concept: Entropy measures the level of uncertainty or disorder in a system.

In Simulation: The System Entropy metric analyses the spatial distribution of work items. Low entropy indicates structured, predictable pulses of work. High entropy indicates scattered, unpredictable jitter, which is a hallmark of unstable systems.

```typescript // Calculate Shannon Entropy H(X) = -Σ P(x) log P(x) let entropy = 0; workItems.forEach(w => { // distribution of work items across links const idx = links.indexOf(w.link); if (idx > -1) distribution[idx]++; }); distribution.forEach(count => { if (count > 0) { const p = count / workItems.length; entropy -= p * Math.log(p); } }); ```

The Kalman Filter

Origin: Rudolf Kalman, 1960.
Concept: An algorithm that uses a series of measurements observed over time, containing statistical noise, to produce estimates of unknown variables.

In Simulation: When enabled, the Prediction Layer (represented by a cyan dashed ring) attempts to 'chase' and estimate the true Cognitive Load (the solid ring), filtering out stochastic jitter. It visually demonstrates the difficulty management faces in distinguishing "signal" (true capacity issues) from "noise" (random fluctuations).

```typescript // 1. Predict (Time Update) let predNext = this.kfEstimate; this.kfErrorCov = this.kfErrorCov + this.kfProcessNoise; // 2. Update (Measurement) const K = this.kfErrorCov / (this.kfErrorCov + this.kfMeasureNoise); this.kfEstimate = this.kfEstimate + K * (measuredLoad - this.kfEstimate); this.kfErrorCov = (1 - K) * this.kfErrorCov; ```

The Hidden Factory (Rework)

Origin: Armand Feigenbaum / Six Sigma.
Concept: A significant portion of capacity is often consumed by correcting defects that were not caught at the source.

In Simulation: When Psychological Safety is low, errors are hidden (purple dots). These have a 50% chance of being rejected at the end of the line and sent back to the start, consuming capacity without generating value.

```typescript if (Math.random() < errorChance) { if (Math.random() < config.psychSafety) { // High Safety: Stop the line (Visible Error) this.blockTimer = 40; return false; } else { // Low Safety: Hide the error (Latent Defect) workItem.isDefect = true; return true; } } ```

Human Factors

These controls model the soft-skills/psychological dimension of the system.

Psychological Safety

Definition: The belief that one will not be punished or humiliated for speaking up with ideas, questions, concerns, or mistakes (Amy Edmondson).

High Safety: Teams stop the line when an error occurs (red blockage). This hurts short-term flow but prevents technical debt.
Low Safety: Teams pass the error downstream to avoid blame. The dot turns purple (Hidden Defect) and continues moving, creating false flow metrics but eventual rework.

Cognitive Load

Definition: The total amount of mental effort being used in the working memory (John Sweller).

Mechanism: Represented by the coloured ring around a node. Work items add load; time decays it. High load triggers the Kingman effect (slowdown) and increases the probability of error generation.

Environmental Noise (Occasion Noise)

Definition: Transient variability in judgement or performance caused by external factors (mood, weather, interruptions) (Daniel Kahneman).

Mechanism: Adds random "jitter" to particle movement speed and increases the base probability of gate failure, independent of structural design.

System Design

These controls model the structural architecture of the organisation.

Value Units (Nodes)

Represents teams, departments, or servers. Increasing nodes increases capacity but incurs the Coordination Penalty (Brooks's Law).

Intermediation (Gates)

Represents approval steps, handovers, bureaucracy, or middleware.

Effect: Each gate is a potential failure point. Even with 99% reliability, a chain of 5 gates has only ~95% system reliability (0.995^5).
Gridlock: In high-load states, gates become bottlenecks that drastically reduce Flow Efficiency.

Organisational Scenarios

Theory: Disintermediation. By removing approval gates and trusting the system, flow efficiency is maximised. Errors are rare and caught immediately.
Observation: Note the high speed and consistent rhythm of the blue dots, representing optimal flow state.

Theory: High intermediation. The system is designed for control, not flow. While stable under low load, it gridlocks easily under high load due to the sheer number of stoppage points.
Observation: Watch how gates become bottlenecks, creating cascading delays throughout the system.

Theory: The "Death March." Low safety forces teams to hide errors. Throughput looks high (dots are moving), but the system is actually churning out defects (purple dots) that will return as rework.
Observation: Observe the high volume of 'movement' masking the accumulation of purple defects. This simulates 'vanity metrics' where teams look busy but are actually creating technical debt.

Theory: High Entropy. Even without structural blockers (gates), the sheer amount of environmental noise prevents stable flow. Particles jitter and stall randomly, making prediction impossible.

Theory: Brooks's Law in action. Rapid team growth outpaces process development. The coordination penalty N(N-1)/2 explodes as nodes increase.
Observation: Watch the coordination penalty metric climb rapidly. The system transitions from "Flowing" to "Overloaded" as communication overhead dominates.

Theory: Conway's Law visualised. High gate count represents approval chains between organisational silos. Work items stall at departmental boundaries.
Observation: Note the high WIP and low throughput. Red blockages occur frequently at gates as work waits for cross-team approvals.

Theory: Toyota Production System principles. Small, empowered teams with WIP limits and andon cord culture. Errors trigger immediate stops, preventing downstream waste.
Observation: Steady, predictable flow with low WIP and high efficiency. Occasional red stops resolve quickly, preventing defect accumulation.

Theory: Kahneman's "occasion noise" applied to distributed work. Time zones, async communication, and home interruptions add variability to all processes.
Observation: Jittery movement and unpredictable delays. Entropy remains elevated despite reasonable structural design.

--- ### Innovate and/or Die **Description:** Why do big companies kill their best ideas? Explore how stealthy probes and executive cover allow innovation to survive the corporate immune system. **Date:** 2026-07-19 **URL:** https://ryansnowden.com/labs/organisational-immune-system/ import OrganisationalImmuneSystem from '../../components/simulations/organisational-immune-system/OrganisationalImmuneSystem.tsx'; import Katex from '../../components/graphics/Katex.astro'; import ScenarioCard from '../../components/ui/ScenarioCard.astro';

Cells interlinked within cells interlinked
Within one stem. And dreadfully distinct
Against the dark, a tall white fountain played.
What does this mean?

Vladimir Nabokov, Pale Fire

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Overview

This simulation explores how companies struggle to innovate in unpredictable environments. It visualizes the tension between finding new ideas and the organisation's natural tendency to protect its current way of working. This concept was inspired by [Vaughn Tan's work on bigness and the organisational immune system](https://vaughntan.org/bigness). Think of a company like a living cell. To survive and grow, it must innovate by sending out agents (**Product Teams and R&D**, represented by the yellow particles) to explore new opportunities. However, the company also has an "immune system" (**Finance, Legal, and Operations**, represented by the red nodes)—driven by strict KPIs and designed to seek out risks and kill unproven ideas to protect the core business.

The Simulation Elements

The Grid

The realm of innovation. You can see this as the future in all directions. The best opportunities are hidden out there in the fog.

The Core Business (Cyan Area)

The safe, stable "cell" where the company currently operates, optimized for its current metrics.

Innovators (Particles)

The Product Managers, R&D Engineers, and Individual Contributors exploring the grid, trying to find high-value ideas.

The Immune System (Red Nodes)

The corporate forces—such as Finance (CFO), HR, and Compliance—hunting down highly visible or expensive experiments and shutting them down to protect the core business.

Quiet Probes (Small Circles)

Small, cheap, and low-risk experiments (e.g., a two-week design sprint, a user interview, a cheap prototype). They stay closer to the safe cell, innovating in minor ways. Because they are small, they often fly under the radar of the Cost Controllers, gradually building evidence and proving their value.

Big Bets (Large Diamonds)

Large, highly visible leaps into the unknown (e.g., massive reorgs, multi-year IT transformations, or "bet the company" product launches). They quickly attract the Risk Management teams and are usually killed or severely compromised before they can succeed.

How Innovation Actually Works

In a highly uncertain world, you can't just guess the best direction. If you place a **Big Bet**, it's usually too noticeable and gets rejected by the corporate immune system. However, if you use **Quiet Probes**, these small experiments can survive long enough to gather evidence. As evidence grows, the idea gains legitimacy (cyan glowing areas). Once an idea is legitimate, the immune system backs off, and it becomes safe enough for the company to fully support and scale.

Scenarios

You can explore different organisational environments using the controls to see how the innovation teams fare against the corporate immune system (roles like Finance, HR, Compliance, and Operations).

A savage killing ground governed by rigid metrics. Here, the Cost Controllers (Finance/CFO) and Compliance Teams (HR/Legal) are highly sensitive to any deviation. Extremely hostile to anything new, big bets are immediately vetoed or defunded, and even small probes struggle to survive the constant scrutiny. You'll see Immune Cells aggressively swarm and destroy almost all Probes and Bets before they can gather any evidence.

There is plenty of experimental activity, but the company fails to learn from it. Lots of money is spent, but evidence is ignored. Lots of probes are sent out by enthusiastic teams, but Middle Management fails to capture the learnings. You'll see a swarm of Big Bets and Quiet Probes that wander aimlessly without turning into Safe Zones or expanding the Core, eventually dying out.

A senior leader (e.g., the CEO or Head of R&D) provides a "shield" around an innovation team. A small team is shielded from the rest of the company, blocking the usual oversight from Finance and Operations. Bolder and riskier ideas have a chance to survive without being prematurely killed for lack of immediate ROI. You'll notice Probes surviving for long periods, slowly exploring the landscape without triggering Immune responses.

A healthy balance where the grid landscape of ideas is much greater. Executive Leadership intentionally lowers the immune response from Risk Management for small, reversible experiments. Highly tolerant of new ideas, projects have a safe space to fail and learn. Over time, you'll see Quiet Probes survive long enough to gather evidence and turn into Safe Zones, expanding the Core.

The Board and Executive Team act as if the unpredictable future is totally predictable. The environment is complex and unpredictable. They repeatedly mandate large, expensive bets (e.g., massive acquisitions or "bet the company" product launches) that fail spectacularly when they eventually clash with reality and internal Operational Constraints. You'll see massive, highly visible Big Bets being launched, but failing to find peaks and getting killed off before they can succeed.

Mathematical Foundation

The simulation uses an **NK fitness landscape** combined with an **immune-response threshold** model to govern how innovation is detected and suppressed.

The NK Landscape

The environment is modeled as an NK landscape, representing genuine uncertainty rather than calculable risk.

: The number of interdependent strategic choices or dimensions.
: The level of interdependence between those choices (ruggedness). As increases, the landscape becomes more rugged with numerous local peaks, making it impossible to extrapolate past success into future bets.

Detection Probability

Concept: At each simulation tick, innovators deploy experiments. The probability that the corporate immune system detects an experiment is mapped via a logistic function (sigmoid).

Big Bets have high visibility and structural novelty, nearly guaranteeing detection. Quiet Probes minimize these factors to stay below the detection threshold.

Suppression Probability

Concept: Detection does not immediately equal death. Once caught, the immune system decides whether to suppress the idea. This is heavily mitigated by local evidence and strategic cover.

Organisational Immune Index (OII)

The OII provides a unified metric representing the hostility of the corporate environment to new ideas. It is calculated as a weighted sum of key environmental parameters:

(Detection): How quickly novelty attracts scrutiny.
(Suppression): The likelihood an immune cell terminates a targeted experiment.
(Learning Tolerance): Whether negative results are treated as useful information (inverted weight).
(Evidence Permeability): Whether evidence changes decisions (inverted weight).
(Protection): Leadership's capacity to create safe-to-try spaces (inverted weight).

OII bands: Adaptive (0-24), Guarded (25-49), Defensive (50-74), and Autoimmune (75-100).

The Legitimacy Feedback Loop (Stigmergy)

The mathematical core of successful innovation in the model relies on a compounding spatial feedback loop:

Quiet Probes stay below .
Surviving probes deposit evidence onto a localized grid.
The organisation suffers from institutional memory loss (evidence decays over time).
If evidence accumulation outpaces memory decay and crosses a threshold, the area gains legitimacy.
Legitimacy creates a permanent safe zone where drops to zero, allowing the company to safely invest.

The Takeaway

In uncertain environments, standard corporate controls are tuned to reject anything different. By protecting low-cost, quiet experiments, a company can create information. That information builds legitimacy, and legitimacy finally permits confident investment.

--- ### Organisational Reasoning with Cellular Automata **Description:** How leadership influence and ideas ripple through a company. A cellular automata simulation of organizational knowledge diffusion. **Date:** 2026-07-15 **URL:** https://ryansnowden.com/labs/organisational-reasoning-engine/ import OrganisationalReasoningEngine from '../../components/simulations/organisational-reasoning/OrganisationalReasoningEngine';

--- ### Queuing Theory Simulator **Description:** What happens when developers work faster but nothing ships any sooner? An interactive simulation of bottlenecks and queuing dynamics. **Date:** 2025-12-21 **URL:** https://ryansnowden.com/labs/query-theory-simulator/ import QueryTheorySimulator from '../../components/simulations/QueryTheorySimulator.tsx'; import Katex from '../../components/graphics/Katex.astro'; import ScenarioCard from '../../components/ui/ScenarioCard.astro';

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Overview

This tool shows the hidden dynamics of multi-team software delivery systems. It shows why traditional resource utilisation metrics ("keeping people busy") often cause waste. Models a **Push System**, where Stage 1 (Development) pushes work to Stage 2 (QA/Deploy) regardless of capacity. This demonstrates **Theory of Constraints** and **Lean Flow** principles. **What's missing?** - ✅ ~~Cost. I should probably add cost.~~ - ✅ ~~Sprints to show the cadence benefits of short, focused cycles.~~ - Dual-track agile vs agile. - A product team... but that would be a whole new v2 with a different set of problems.

tl;dr Quick Explanation

#### The Bottleneck Problem The default scenario shows a classic **bottleneck**. Development (Stage 1) has more capacity than QA (Stage 2). Watch how the QA queue grows unbounded while developers sit idle waiting for work to clear downstream. This is the **Theory of Constraints** in action: the system's throughput is limited by its slowest stage, not its fastest. #### What to Look For - **Queue bars growing:** When the bar under a stage fills up, work is waiting. The longer items wait, the higher your Lead Time. - **Colour changes:** Work items turn from green (flowing) → yellow (waiting) → red (blocked). Blocked items are stuck behind a server that's also blocked. - **Utilisation vs Flow:** High server utilisation (everyone looks busy) can coexist with terrible flow efficiency (work spends most of its time waiting). #### The "Aha" Moment Try increasing Development capacity (more devs) without increasing QA capacity. Watch the QA queue explode. **Adding people upstream makes the problem worse**, not better. This is why "throwing bodies at the problem" fails.

Scenarios

Scenarios demonstrating Queueing Theory and Theory of Constraints principles.

Demonstrates the Theory of Constraints. Increasing speed upstream (local optimisation) without addressing the bottleneck downstream only generates "inventory" (WIP), which is a form of waste. The QA queue will grow indefinitely, increasing lead time and defect risk.

Shows that flow is destroyed not just by volume, but by friction. Servers appear "busy" (high utilisation), but they are working on "failure demand" or waiting, resulting in extremely low Flow Efficiency (<15%).

Demonstrates Kingman's Formula approximation. As system utilisation approaches 100%, queue times rise exponentially towards infinity. It proves that a system running at "maximum efficiency" effectively ceases to flow.

Illustrates the damage caused by large batch sizes or inconsistent ticket sizing. One large task blocks a server for a long duration, causing smaller tasks ("Guppies") to pile up behind it, drastically increasing the average wait time for the entire system.

The Push vs Pull Metaphor

Push vs. Pull is key to flow efficiency.

Push System (This Simulation)

Work is **pushed** downstream the moment it's complete, regardless of whether the next stage is ready. This is how most organisations operate by default.

Creates inventory (queues) between stages
Hides bottlenecks until they become crises
Optimises for local efficiency over system flow

Pull System (Lean/Kanban)

Work is **pulled** by downstream stages only when they have capacity. This is the Toyota Production System model.

WIP limits prevent queue buildup
Bottlenecks become immediately visible
Optimises for end-to-end flow

This simulation models a Push system to demonstrate failure modes. The growing queues are "invisible inventory" often missed by traditional metrics.

Visual Guide

Guide to visual elements.

Work Item Colours

Green: Flowing smoothly

Yellow: Waiting in queue

Red: Blocked

Blue: Rework (sent back)

Queue Bars

The horizontal bar under each stage shows queue depth. When it fills up, work is piling up faster than it can be processed. A full bar indicates a bottleneck.

Servers (Circles)

Each circle represents a "server" (developer, tester, or machine). When a server is processing work, it's filled. When idle, it's empty. Multiple servers process in parallel.

Metrics Dashboard

Throughput: Items completed per time unit
Lead Time: Total time from arrival to completion
Wait Time: Time spent in queues (not being worked on)
Utilisation: Percentage of time servers are busy
Flow Efficiency: Work time ÷ Lead time (how much time is "value-add")

Controls Reference

Arrival Rate (λ)

How frequently new work items enter the system. Higher values simulate high-demand periods or sprints with aggressive commitments.

Dev Capacity (Servers)

Number of parallel workers in Stage 1 (Development). More servers = more throughput capacity, but only if downstream can absorb it.

QA Capacity (Servers)

Number of parallel workers in Stage 2 (QA/Deploy). Often the bottleneck in real organisations due to specialisation and handoff friction.

Service Rate (μ)

How quickly each server processes work. Represents team velocity, tooling efficiency, or automation level.

Blocking %

Probability that a completed item blocks downstream (e.g., integration failures, environment issues, merge conflicts). Creates cascading delays.

Rework %

Probability that a completed item is rejected and sent back to the start. Represents bugs found in QA, failed code reviews, or requirement misunderstandings.

Whale Mode

When enabled, a percentage of work items arrive as "whales": tasks that take 5x longer than normal. This simulates:

Large, poorly-scoped tickets
Unexpected complexity ("iceberg" stories)
Batch processing or release trains

Human Factors

Abstracts human behaviour, mapping directly to real organisational patterns.

Blocking as Blame Culture

High blocking percentages often correlate with poor psychological safety. When teams fear blame for integration failures, they add more review gates and approval steps, which *increases* blocking probability. The simulation shows why "more process" often makes things worse.

Rework as Technical Debt

Rework represents the "hidden factory": capacity consumed by fixing defects that should have been caught earlier. High rework rates indicate poor requirements, insufficient testing, or rush-to-deploy pressure.

The Utilisation Trap

Management often optimises for **utilisation** (keeping people busy), but the simulation shows this destroys **flow**. At 90%+ utilisation, queue times explode exponentially. The counterintuitive truth: slack capacity is essential for flow.

Handoff Friction

Each stage boundary represents a handoff: context lost, waiting for availability, re-explanation. The simulation's two-stage model is a simplification; real organisations often have 5-10+ handoffs, each multiplying delay and error probability.

Mathematical Foundations

Based on Queueing Theory and Lean management principles. These mathematical laws govern flow systems, whether physical manufacturing lines or digital software development. They explain why "common sense" management (like 100% utilisation) often fails mathematically.

M/M/c Queue Model

What this means in practice: A standard mathematical formula for how lines form. It models work arriving unpredictably (like user stories landing in a sprint) being handled by a fixed set of workers (developers or QA testers).

M (Markovian Arrivals): Arrival times follow a Poisson process (Exponential distribution).
M (Markovian Service): Service times follow an Exponential distribution.
c (Servers): The number of active servers (developers/testers) processing the queue.

Exponential Distribution

What this means in practice: Software tasks aren't uniform. Most tickets take a normal amount of time, but occasional "iceberg" tasks take 5x longer. This distribution creates realistic real-world variance.

In Simulation: Used to generate randomized arrival times for new work items and the duration of service (work) for each server, ensuring the simulation reflects realistic variability rather than static averages.

```typescript // Generate random values following an Exponential distribution // Used for arrival times (Poisson process) and service durations const getExponential = (rate: number) => { return -Math.log(1 - Math.random()) / rate; } // Generate next arrival time data.nextArrivalTime = getExponential(params.arrivalRate) * 1000; ```

Little's Law

What this means in practice: If your team is flooded with work, the *only* way to make individual features ship faster (without hiring more people) is to stop starting new things and reduce Work in Progress (WIP).

In Simulation: Validates the statistical metrics displayed in the dashboard. It proves that if you cannot change the arrival rate (), the only way to reduce Lead Time () is to reduce the Work in Progress ().

```typescript // Verify simulator metrics against Little's Law (L = λ * W) // avgSystemLength (L) ≈ arrivalRate (λ) * avgSystemTime (W) const stats = { avgSystemTime: s1.W + s2.W, // Total time in system (W) avgSystemLength: s1.L + s2.L // Total items in system (L) }; ```

Erlang-C Formula

Origin: A.K. Erlang (1917), Telecommunications traffic engineering.
Concept: Calculates the probability that a randomly arriving item must wait in the queue rather than being served immediately, given the traffic intensity and number of servers.

In Simulation: Provides the "Theoretical" baseline for wait times. Comparison between this theoretical baseline and the "Actual" simulation results highlights the extra friction caused by factors Erlang-C doesn't account for, such as blocking and rework.

```typescript // Calculate Erlang-C probability that an item must wait const calcErlangC = (lambda: number, mu: number, c: number) => { const rho = lambda / (c * mu); // Traffic intensity // Calculate sum of geometric series for P(0) let p0_inv = 0; for (let i = 0; i < c; i++) { p0_inv += Math.pow(lambda / mu, i) / factorial(i); } p0_inv += (Math.pow(lambda / mu, c) / factorial(c)) * (1 / (1 - rho)); // Theoretical average queue length (Lq) return (Math.pow(lambda / mu, c) * rho * (1 / p0_inv)) / (factorial(c) * Math.pow(1 - rho, 2)); }; ```

Flow Efficiency

Origin: Lean Manufacturing / Toyota Production System.
Concept: A metric measuring the percentage of time work is actually being progressed versus sitting idle. In knowledge work, this is often shockingly low (10-15%).

In Simulation: Calculated dynamically by tracking the exact milliseconds an entity spends being "serviced" (Active Work) versus "waiting" or "blocked". It visualises how high server utilisation can coexist with terrible flow efficiency.

```typescript // Track value-added time vs total lead time for every item if (Math.random() < 0.1) { // 10% sampling rate for UI updates const flowEff = data.metrics.totalSystem > 0 ? (data.metrics.totalValueAdded / data.metrics.totalSystem) * 100 : 0; setSimState(prev => ({ ...prev, flowEfficiency: flowEff })); } ```

--- ### FC Stow Performance and Throughput Dynamics **Description:** A real-time warehouse dashboard tracking stow performance — and why traditional Units Per Hour metrics mislead operations. **Date:** 2026-05-09 **URL:** https://ryansnowden.com/labs/stow-dashboard/ import Katex from "../../components/graphics/Katex.astro"; import SecretStyleLink from "../../components/simulations/warehouse-visualizer/SecretStyleLink.tsx"; import TimelineControllerEmbed from "../../components/simulations/session-timeline-dashboard/TimelineControllerEmbed.tsx";

On Hold Open dashboard

I argue that the current system (at Amazon PER4) of performance measurement (KPI/Units Per Hour) for Stowers can be improved, both in measurement and in optics. Stowers are subject to weekly performance check-ins. For example, if an Associate is under performing (say at 85%) for the previous week, then a 1:1 discussion will take place in the Aisles. This is an opportunity for the associate to explain why (or not), but also to explain the performance expectations according to their level. In this chat, it may come to surface that there was an error or explanation that explains the 'performance', so in fact the measurement is actually measuring something else, and not the actual units stowed. However, how this percentage is calculated is not explained, and the factors around the associates tasks may be innaccurate due to human error. A FC runs in an interlinked process. If no items are available to stow, then rates will decline. Changing tasks is done by a PA, who might make a mistake, or misrepresent the time due to complexity. An associate is not compelled to update their PA (Process Assistant) due to the intensity of work, and lack of availability. Keeping a personal track of tasks, problems, items is difficult and also adds time. I isolate truth from the hand barcode scanners in a **Units Per Session** KPI to determine the rate, which allows the conversation to focus on 'the gaps'. The gaps being the operations. These ideas and numbers are available to stakeholders. It is just not a driver in daily decision making to certain floor staff. This is probably due to the nature of accuracy and repeatability as you transition and improve a FC on a weekly basis... with it's own bureaucracy. So consider this a non-novel approach, but an emphasis and encouragement of focus, with a UX/UI that is more engaging and informative. I also bring forward (in the UI) **fatigue** from consecutive working days. Depending on the stower's tenure, they will become **mentally** and **physically** fatigued. Effort made to alternate tasks or cross train departments does not negate the impact of fatigue, but is welcome. This is physical and largely mental health related. I need to do extensive research in this area to provide predictions, so it is constrained to physical aspects. --- ### The Bits The simulation starts at 8am (1 hour after stowers have been stowing). You can scrub through time with the timeline and watch the days fill up. There is a multiplier, so you can click it to 1x and watch the day as it happens.

The stow target is set to 105% for 22.1k units. There are 15 stowers. --- ## Engineered Labor Standards (ELS) My concept builds upon Engineered Labor Standards, or ELS, which are a way to ask a simple question: **how much work should this task reasonably take? **Not every unit is equal. A small, easy item is not the same as a heavy item, a long walk, or a bin that takes extra handling. ELS helps account for those differences, so the dashboard is not only counting how many items someone stowed. It is also trying to show how difficult the work was, and whether two people with different unit counts may have done a similar amount of real work. ## What the Numbers Mean The dashboard uses the same **four performance lenses** as the per-session stow model. Each metric divides output by a different slice of time, so each one answers a different question. None of them is “the truth” on its own. Read together, they separate **speed while working**, **freight difficulty**, **how much of the shift was actually spent stowing**, and the traditional **units per paid hour** view. ### 1. UPH (macro / shift UPH) The number of items/units stowed for every hour on the clock during the shift. Walking, waiting, breaks, and congestion all count in the denominator, so this metric combines individual pace with everything else that happened that day.

### 2. Session rate (scan-to-scan) The rate of stowing **only during active stow time**, measured from the first scan to the last scan in a continuous work stretch. Time outside that active window, such as long gaps before the next scan, is excluded from the denominator. This is closer to “how fast work occurred while stowing was actually happening.”

For a single continuous session, active session hours are the elapsed time from first scan to last scan, converted to hours. ### 3. Weighted session rate This is similar to session rate, but **harder or heavier items count for more** than easier small items. Each item class receives a weight from engineered standards. The weighted “effective units” are then divided by active session hours, so cherry-picking only small items does not automatically dominate the chart.

Here, are the counts of small, medium, and heavy units stowed, and are the matching effort multipliers. ### 4. Flow efficiency Of the time a worker was **expected** to be available for production, defined as paid shift time minus authorised breaks, what **fraction** was spent inside active stow sessions? A high session rate with low flow efficiency often indicates fast bursts with substantial idle or off-task time. A moderate session rate with high flow efficiency may indicate steady work on more difficult freight.

The second line expresses the same ratio as a percentage, using a number between 0 and 100 instead of 0 and 1. ## Worked Example: Associate A vs. Associate B Both associates work within the same shift envelope: **9.5 paid hours** and **0.5 hours of authorised paid breaks**. This leaves **9 hours** as expected production time for flow efficiency. Associate A focuses on small, easier units; Associate B stays on heavy freight. | | **Associate A** | **Associate B** | | --- | --- | --- | | Units stowed | 2,850 (all small) | 1,140 (all heavy) | | Active session hours | 6 | 8 | | Effort weight per unit | 1.0 | 3.0 | **1. UPH** (units ÷ full paid shift, 9.5 hours)

**2. Session rate** (units ÷ active session hours only)

**3. Weighted session rate** (effective units ÷ active session hours; B’s items count triple)

**4. Flow efficiency** (active session hours ÷ 9 expected hours)

Raw **UPH** makes A look like the stronger stower by a wide margin. **Session rate** still favours A because every unit is counted the same. **Weighted session rate** nearly closes the gap, with B within about 10% of A. **Flow efficiency** shows that B spent most of the expected window actually stowing, while A left roughly a third of that window outside active sessions. In this reading, A appears to be a fast **sprinter** with gaps, while B appears to be a steady **pacer** on harder work. ## Problem Solver at session end In about **15%** of sessions, a stower finishes with a Problem Solver interaction: an item was not scannable, not assigned to the cart, needed an expiry date, or could not be stowed on the floor (HRV, hazmat, and similar). The session timeline shows an **amber hatched zone** at the tail — similar to a break overlay, but with **PS item markers** inside it. The stower typically waits **20–60 seconds** (dashed “Waiting” strip) before Problem Solver touches the item. Then either: - **Handoff** — the item leaves with Problem Solver and the session ends, or - **Resolve and stow** — Problem Solver fixes the issue and the stower scans one or two final units (amber-ringed bars) before the session ends. Toggle **Problem solve** on the session detail view to show or hide the zone. ## How to Read This Dashboard The synthetic shift in the live dashboard is calibrated to finish **above plan** — about **106%** of the 22,137 day target at end of shift — reflecting a regular-pace stow day rather than an under-delivered one. If someone’s **UPH** looks low but **weighted session rate** and **flow efficiency** look strong, they may be working difficult freight, dealing with congestion, or receiving a fairer evaluation than raw units per paid hour would provide. If **session rate** is high but **flow efficiency** is low, they may be stowing quickly while active but not remaining in task for much of the shift. The timelines and side-by-side metrics should be used to determine which explanation best fits the observed pattern. --- ### Zara's Strategic Timeline **Description:** How Zara built a supply chain empire — and adapted across three growth phases. Explore what-if scenarios across strategy, culture, and financials. **Date:** 2026-03-02 **URL:** https://ryansnowden.com/labs/strategic-evo-simulator/ import StrategicEvo from '../../components/simulations/StrategicEvo.astro'; import Accordion from '../../components/ui/Accordion.astro'; import BuiltWith from '../../components/ui/BuiltWith.astro'; import { Mermaid } from '../../components/graphics/Mermaid'; Not as flashy as other sims (a bit of long weekend fun), but this timeline looks at Zara through a strategic lens. It started as a way to explain old decisions using the usual frameworks like the I/O model and AFI for the early days. Then I worked on Ansoff, CAGE, and VRIO, etc. Most of the real work happens in the background to determine certain pivot points, triggers and eras. Experience design was a major factor for the insights. Customers feel the brand changing. This could be in a service design manner, or in a rebrand, product shift (shopping for 'trends'), pricing models, etc. Naturally, in-store experience and buying online are at the forefront of customer interaction. I mapped it out (to Horizons) to see what drove innovation internally and externally. Why stop at 2026? The future-state can work as you follow curves, and look for inflection points of likely events or possible threats, innovation etc. In 1963, Ortega started Confecciones GOA in Arteixo, Galicia, making women's pyjamas and lingerie. A cancelled wholesale order in 1975 forced him to open a retail store to shift the excess stock. That store became Zara. IT investment followed in 1976, nine Spanish locations by 1983, and Inditex incorporated in 1985. By 1990, Zara had flagships in Portugal, New York, and Paris.

1963;;GOA Funded]:::milestone --> B[1975;;First;;Zara store]:::turning B --> C[1976;;IT investment]:::milestone C --> D[1983;;Nine Spanish;;stores]:::milestone D --> E[1985;;Inditex;;incorporated]:::turning E --> F[1988;;International;;expansion]:::turning F --> G[1989;;New York;;flagship]:::milestone G --> H[1990;;Paris;;flagship]:::milestone classDef milestone fill:#1e293b,stroke:#475569,color:#cbd5e1,stroke-width:1px classDef turning fill:#1e293b,stroke:#ff6e41,color:#ff6e41,stroke-width:2px `} />

The 1970s apparel industry ran on six-month production cycles. Manufacturers outsourced to Asia, committed to large speculative runs, and leaned on star designers to call trends from the top. When forecasts missed, 30-40% of stock went to clearance. Ortega built his supply chain in Europe. The Quick Response system moved garments from sketch to shop floor in 2-5 weeks, produced in small batches at factories across Spain and Portugal.

T2[Asian Factory]:::tradNode T2 --> T3[6-Month Ocean Freight]:::tradNode T3 --> T4[Warehouse]:::tradNode T4 --> T5[Retail Store]:::tradNode T5 --> T6[30-40% Clearance Sales]:::warnNode end subgraph Zara [Disruptive Model] direction TB Z1[Store Manager
Intelligence]:::zaraNode --> Z2[200 Designers
The Cube]:::coreNode Z3[Local Factories
Spain/Portugal]:::zaraNode --> Z2 Z2 --> Z4[Bi-Weekly Delivery]:::zaraNode Z4 --> Z5[Retail Floor
Within Hours]:::zaraNode Z5 --> Z1 end classDef tradNode fill:#1e293b,stroke:#475569,color:#cbd5e1,stroke-width:1px classDef warnNode fill:#451a03,stroke:#ff6e41,color:#ff6e41,stroke-width:2px classDef zaraNode fill:#1e293b,stroke:#85d7ff,color:#85d7ff,stroke-width:1px classDef coreNode fill:#0c4a6e,stroke:#85d7ff,color:#e0f2fe,stroke-width:2px `} />

Zara captured 85% of sales at full price, against an industry average of 60-70%. Customers came back 17 times a year (industry average: 3). Ad spend sat at 0.3% of revenue, a fraction of the 3.5% standard.

Bar chart comparing 7 key operational metrics between the traditional apparel industry and Zara's disruptive model

The operation centred on "The Cube" in Arteixo. A 124 mi (~200 km) underground monorail moved materials between production stages without manual handling. Above ground, 200 designers sat with commercial and procurement teams. A design could go from sketch to scheduled production in a single day. Manufacturing was split. Internal factories handled trend-sensitive items, roughly 50% of stock. Predictable basics went to outside suppliers. Store managers reported twice weekly on what customers tried, returned, and asked for, feeding real-time data back to Arteixo. Most fashion retailers depended on volume: produce 100,000 identical units, sell enough to break even. Zara produced hundreds of thousands of unique SKUs annually but kept individual runs small. If a jacket sold out in two days, it wasn't restocked. A variation replaced it. Shoppers adapted. Waiting for end-of-season sales stopped making sense when items disappeared within the week. Zara averaged 17 store visits per customer per year, none of it driven by advertising. **Flexuous Curves** is a framework from [Cynefin](https://cynefin.io/wiki/Flexuous_curves) that models overlapping strategy lifecycles. Unlike a simple S-curve, it tracks how new paradigms emerge while dominant ones peak, and maps the organisational states that determine whether the transition succeeds or fails. The model draws on Apex Predator theory, Keystone ecology, Moore's Chasm, and Christensen's competence-induced failure. Zara maps cleanly because the three horizons are distinct and the transition dynamics are visible in the data. **The 6 Lifecycle Points** The 6 Flexuous Curves framework points applied to Zara's H1: 1. **Emergence**: A new idea takes off because people are over the old way. In 1975, Ortega says no to long production cycles and opens the first shop. 2. **Chasm**: It's hard to keep the energy up and scale. By 1983, Zara only has 9 stores in Spain while they try to prove it works. 3. **Orthodoxy**: Everyone does it and it becomes the norm. Between 2001 and 2010, they go public and "fast fashion" becomes a household name. 4. **Decline**: The novelty fades and problems start popping up. From 2015 to 2019, Shein starts doing it faster and Zara's "quick response" isn't special anymore. 5. **Oblivion**: These are the "what-if" paths like starting a price war or pivoting too late. 6. **Sustainable**: The H1 model stays as the base, but Zara moves on to H2 and H3. **The 5 Greek-Letter States** The 5 Greek states for the H1 to H2 shift: - **α Alpha**: Zara starts playing with Zara.com around 2010. They take what they know about physical shops and use it for digital. In F-Curve terms, this is exaptive innovation: radical repurposing of existing capability. - **β Beta**: Around 2015, ultra-fast fashion starts appearing as a cheap digital experiment. Astute observers can see the change, but the energy cost of experimenting is not too high. - **γ Gamma**: This is the "last chance" stage from 2019 to 2021. Shein overtakes Amazon for downloads. The warning signs are loud now. The dominant curve has topped and is heading down. Acting is expensive, but waiting is fatal. - **Ω Omega**: Moving resources successfully. This is the goal where they go upmarket with fancy collabs and start charging for returns. Resources transfer from old to new: the upward curve. - **δ Delta**: The "too late" scenario where they try to recover without having dominated digital earlier. Some arrest failure and recover, but never at the same level of dominance. **Competence-Induced Failure** The dominant player does not fail because they were incompetent, but because they were *too competent* in the old paradigm. That competence breeds inattentional blindness writ large into the fabric of the organisation. This is Clayton Christensen's core insight, and the simulation tracks it as a "Competence Trap Risk" score derived from Town Planner dominance and the rising utility of a challenger curve. **Strategies for Crossing the Chasm** - **Retro-virus**: Changes the DNA of the host. *H1: Quick Response fundamentally rewired fashion retail.* - **Symbiotic**: The novel attaches to convention and is carried across the chasm. The host benefits. *H2: digital bolted onto physical retail. H3: sustainability bolted onto luxury repositioning.* - **Infection**: The novel idea uses the host to cross the chasm but doesn't care about the host's survival. *Shein: adopted Zara's own speed model, then out-competed it.* **Ecosystem Role Metaphors** - **Apex Predator**: Available if you move and succeed during alpha to gamma. *Zara at H1's peak.* - **Keystone**: Available during ecological shifts, providing more sustainability during subsequent change. *Zara's potential H2-H3 position.* - **Hyena**: Feeding off the leftovers of the former Apex. *The price-war scenario's endpoint.* - **Connective Agent**: Disintermediation that becomes an Apex Predator in its own right. *Shein, Amazon.*

Pt 1;;Emergence;;1975"]:::phase --> P2["Pt 2;;Chasm;;~1983"]:::phase P2 --> P3["Pt 3;;Orthodoxy;;~2001"]:::phase P3 --> P4["Pt 4;;Decline;;~2015"]:::phase P4 --> P5["Pt 5;;Oblivion"]:::warn P4 --> P6["Pt 6;;Sustainable"]:::safe P3 -.- A["α;;Zara.com;;2010"]:::greek A -.- B["β;;Shein signals;;~2015"]:::greek B -.- G["γ;;Last chance;;~2019"]:::greek G -.- O["Ω;;Prestige pivot"]:::greek G -.- D["δ;;Late recovery"]:::warn classDef phase fill:#1e293b,stroke:#475569,color:#cbd5e1,stroke-width:1px classDef greek fill:#1e293b,stroke:#85d7ff,color:#85d7ff,stroke-width:2px classDef warn fill:#451a03,stroke:#ff6e41,color:#ff6e41,stroke-width:2px classDef safe fill:#1e293b,stroke:#10b981,color:#10b981,stroke-width:2px `} />

**Other Approaches** - **Wardley Mapping** focuses on value chain evolution. - The **McKinsey Three Horizons** framework models time and innovation stages, and traditional S-curves track single product lifecycles rather than overlapping paradigms. - This simulation extends these with F-Curve transition states, competence trap detection, and ecosystem role tracking. **Horizon 1: Quick Response (1975 to 2010s)** - Zara ditched the six-month production cycle and built rapid local production in Spain. Small batches, made weekly based on what floor staff saw selling. Inditex handled logistics and tech, freeing stores to focus on design and sales. **Horizon 2: Moving Upmarket (Late 2010s to Present)** - Shein and other ultra-cheap competitors arrived. Zara moved upmarket with higher prices, high-end collaborations, and paid returns on online orders. **Horizon 3: Sustainability (2020s to 2040s)** - Zara is scaling its second-hand platform, Zara Pre-Owned, and investing in fabric recycling partnerships. The targets are lower-impact materials across the board by 2030 and net-zero emissions by 2040. What if Zara was too slow to go upmarket? - Quick Response lingers into the 2020s, the prestige pivot comes late, and there's no real answer to Shein. Culture stays operations-heavy and profits flatline. In F-Curve terms, Zara misses the **omega window**. - The gamma phase screams but the organisation's competence in physical retail has become inattentional blindness. The best outcome is a **delta recovery**: arresting decline but never recapturing Apex Predator dominance. What if Zara never went omnichannel? - No Zara.com, no digital integration. The vertical model stretches longer, but digital competitors close the gap. Revenue caps early and there's nothing left to fund new horizons. The **alpha trigger never fires**. - Without exaptive innovation (repurposing physical retail for digital, "radical innovation by repurposing existing technologies, data, and skills for new, unrelated, or complex problems"), there's no second curve to transfer resources into. H1 heads straight from **Point 4 to Point 5: oblivion**. The gamma phase is ignored entirely. What if Zara committed to circular economy a decade earlier? Pioneer culture stays strong. Profits dip from the R&D spend, but Zara builds a substantial lead in sustainable fashion and recovers well. This is the **alpha trigger fired early**. Zara legitimises circular fashion before the market demands it (a textbook exaptive move). The **omega transfer to H3** is achieved ahead of competitors, who are still at the beta stage of their own sustainability curves. The result: sustained **Apex Predator** positioning. What if Zara matched Shein on price? Revenue climbs short-term from market share gains. Margins collapse, and there's no remaining budget to invest in Horizon 3. Zara adopts the **infection strategy** (Shein's own playbook) but as the host, not the parasite. The price war erodes margins and destroys the budget needed for an omega transfer to H3. The endpoint is **Hyena territory**: feeding off the leftovers of a former Apex Predator. --- --- ## Stuff (Projects) --- ### AHI Product Strategy **Description:** A comprehensive strategic audit that diagnosed critical friction points in pricing and integration to realign a struggling B2B product with genuine market needs. **Category:** product **Date:** 2022-12-12 **URL:** https://ryansnowden.com/stuff/ahi-product-strategy/ import { SquareArrowOutUpRight } from "lucide-astro"; import video1 from "../../assets/media/shared/0000000lE7W000000000wW-2.mp4"; import ImageTooltip from "../../components/lightbox/ImageTooltip.astro"; import predatorVisionImg from "../../assets/media/shared/predator-vision.jpg";

Company: Advanced Health Intelligence (AHI)

Complexity: 9.926/10 Fun factor: 9.921/10

## Project assets

Sanitised document Customer Journey Map Pain points per market segment Staff interviews

## Project Summary - A comprehensive end-to-end diagnostic of the BodyScan service ecosystem. This engagement utilised service design and strategic frameworks to identify root causes of market stagnation, challenging internal assumptions regarding product fit and integration viability. - For more strategic assets, see [Service Design Mapping](/stuff/service-design-mapping-examples). ## The Business Challenge - Despite technical functionality, the product failed to achieve projected market penetration. Organisational leadership attributed this inertia to customer error and improper integration (“It’s not me, it’s you”). - The objective was to objectively validate these claims by assessing cost structures, market fit, and the reality of the partner integration experience. ## Methodology & Frameworks The analysis employed a multi-dimensional approach to deconstruct the service ecosystem: - **Strategic Alignment**: Value Proposition Map, The Value Pyramid - **User Experience**: User Journeys, Proto & Full Personas, Product Offering Lifecycle Curve ## Key Objectives - **Financial Viability:** Comparative cost analysis (Vendor vs. Partner). - **Market Fit & Maturity:** Identifying suitable verticals and assessing the product’s stage in the lifecycle. - **Success Metrics:** Redefining success beyond technical execution to commercial viability. - **Data Strategy:** Evaluating data saturation and reliance models. ## Critical Findings & Strategic Insights The investigation delivered an evidence-based rebuttal to leadership assumptions, revealing that the barrier to growth was structural rather than operational. Although initially contentious, the analysis necessitated a strategic pivot. - **Misaligned Pricing:** The pricing model was prohibitive, particularly as an add-on to existing partner subscriptions, destroying the value proposition. - **Negative Partner ROI:** High onboarding and integration costs (irrespective of volume) yielded insufficient growth for partners. - **Flawed Success Metrics:** Success was internally defined by “scan completion” rather than partner revenue generation or user retention. - **Market Suitability:** The product was ill-suited for general fitness but highly viable for **Digital Health and Insurance** verticals. - **Product Immaturity:** The BodyScan offering lacked the flagship integrations and data strategy required for mass adoption. ## Professional Impact - This engagement highlighted the critical intersection of organisational behaviour and product strategy. - It served as the catalyst for my MBA specialisation in Change Management, providing the foundational case studies used to deepen my expertise in leadership dynamics and organisational design. --- ### Autonomé **Description:** Ethical Health Data Marketplace. **Category:** product **Date:** 2026-02-23 **URL:** https://ryansnowden.com/stuff/autonome/ import ImageSlideshow from "../../components/interactive/Slideshow.astro"; import autonome1 from "../../assets/media/autonome/1.png"; import autonome2 from "../../assets/media/autonome/2.png"; import autonome3 from "../../assets/media/autonome/3.png"; import autonome4 from "../../assets/media/autonome/4.png"; import autonome5 from "../../assets/media/autonome/5.png"; import autonome6 from "../../assets/media/autonome/6.png"; import autonome7 from "../../assets/media/autonome/7.png"; import autonome8 from "../../assets/media/autonome/8.png"; import autonome9 from "../../assets/media/autonome/9.png"; import autonome10 from "../../assets/media/autonome/10.png"; import autonome11 from "../../assets/media/autonome/11.png"; import Accordion from "../../components/ui/Accordion.astro";

Client: Autonomé

Role: Consultant, Experience Designer, Product Strategy

Status: In Progress 👍🏻

Complexity: 9.8/10 Fun factor: 9.3432/10

- **Platform:** iOS, Android, Desktop - **Design system management:** Supernova.io - **Product type:** Dual marketplace, Ethical data broker - **Design system:** Material 3, Custom design system - **Design tools:** Figma, Cursor, Antigravity, Pen & Paper - **Approach:** AI-driven workflows - **Core Technology:** Distributed Ledger (DLT), Smart Contracts People generate valuable health data every day through smartwatches, health apps, and hospital visits. Data brokers take it through confusing terms and conditions. The data sits in silos with Apple, Google, and Samsung, doing nothing. Meanwhile, researchers, pharmaceutical companies, and AI-health startups desperately need clean, long-term health data. Traditional methods like web scraping, or data brokers with opaque terms often operate in a grey area, likely breaching consent laws. The data they can acquire is often biased, incomplete, or ethically questionable. Autonomé sits in the middle. People get paid for their data. Buyers get compliant, continuous datasets. I was brought on as the sole product consultant to design how both sides of this marketplace actually work. I'm the lead experience design consultant and product owner/manager/fixer. Staff designers handle branding and identity. I handle everything else: the consumer app, corporate portal, dashboards, and model validation. Day to day that means service blueprints, workshops, discovery, prototyping, fixing bias, training data, accuracy and precision, playing devils advocate....and project handover. The core tension is designing for two timelines at once. The consumer app needs to work now: earning trust, collecting data, paying users. But every decision also needs to support the future enterprise data marketplace without requiring a rebuild. **What I do** - **Model validation** - **Analyze → Measure → Improve loop** - identifying failre loops, quantify generalisation issues, improve (fix specs, stronger interventions) - **Precision/recall** – how accurately agents spot valid companies/data and risky submissions without flooding humans with false alerts. - **False positives** – flagging clean data as suspicious, slowing the marketplace and hurting trust. - **Ground truth** – human-verified answers on whether data is synthetic, duplicated, biased, non-compliant, or buyer-ready. This is what we validate against and fine-tune on. - **Drift** – the marketplace changes (new regions, devices, regulations, data types), breaking old model assumptions and degrading performance over time. - **LLMs/agents/multimodal** – audit companies, inspect datasets, classify risk, estimate pricing, and reason across structured + unstructured evidence. - **Fine-tuning** – adapt base models to our domain using ground truth. Trains models on our exact definitions of "risky", "compliant", "buyer-ready". - **Validation loop** – ground truth → measure baseline → fine-tune → re-check precision/recall + false positives → deploy when metrics clear thresholds → monitor for drift → repeat. - **Guardrails** - rules, behaviours, limits and feedback mechanims. - **Own the product backlog** — prioritise features, defects, and technical debt against adoption, satisfaction, and commercial outcomes. - **Drive strategy and roadmap** — work directly with leadership to set direction, then test assumptions with real user feedback rather than opinion. - **Define requirements with engineering and clinical teams** — understand the actual problem before building. I run workshops with engineers, designers, and clinicians so we're solving the right thing. - **Write stories and acceptance criteria** — break complex clinical and business needs into sprint-ready work. - **Support agile delivery** — "James" runs the sprints. I help with backlog refinement and product demos, keeping stakeholders across progress. - **Triage incoming work** — evaluate enhancement requests, bugs, and pain points against commercial goals and customer feedback. **What I design** - **System maps** — actor mapping, causal loops, and user journeys to understand how data and money actually flow through the system. I also map revenue loops and data flows (phone → secure database) to keep design, engineering, and legal on the same page. - **Design system** — one system that scales across consumer, corporate, and internal tools, plus pitch decks. - **Both sides of the marketplace** — patient-side journeys that collect data simply while meeting legal standards for enterprise sales later. Corporate-side onboarding with strict identity verification. Planning how the experience shifts as the client moves from small academic datasets to large corporate buyers. **How I approach the work** — I talk directly to stakeholders to find out what frustrates and drives them. We prototype and test before committing to code. I run sessions with legal and research partners so regulatory constraints become design features early, not late blockers. Goals are specific: _a patient with MS can sell their data in under 10 minutes without reading legal jargon._ Researcher goals are bit more nuanced, and a WIP. *Frameworks I draw on: SWOT, Porter's Five Forces, RICE, MoSCoW, Cost of Delay, GIST, JTBD, Double Diamond, User Story Mapping, ATDD, Kano.* - **Onboarding**: ID verification and clear, plain-language consent so data can be legally used - **Continuous tracking**: Automatic smartwatch syncing plus quick weekly questions to build a three-year health history - **Value exchange**: A transparent payment system so patients see exactly what they earn - **Enterprise portal** _(future)_: A secure dashboard where researchers search anonymised patient cohorts - **Two cultures, one product**: Autonomé has a privacy-focused team and a sales-focused team. I used system maps to show how corporate sales fund the privacy work, then designed separate interfaces so neither side compromises. Same database, completely different experiences. - **Three-year data gap**: Enterprise buyers need three years of continuous data. If sellers churn, the product dies. I designed passive smartwatch syncing with only two minutes of weekly input, plus a clear payment screen so users see the direct financial benefit of staying. - **Bias in medical data**: AI trained only on wealthy, tech-savvy users is dangerous. I turned this into a product rule: the corporate portal limits data access if the patient cohort doesn't meet diversity standards. - **Hidden legal complexity**: AU, SG, and US healthcare laws require intense consent flows that scare users off and inflate acquisition costs. I worked with legal to turn complex contracts into a step-by-step process in plain English. - **IP protection through partners**: Channel partners in the US create IP risk. I designed a limited partner portal: they can search and close sales, but never access underlying code or identifiable user data. - **Lower acquisition cost** — simplified sign-up and ID verification. Fewer steps, more completions. - **Higher patient retention** — clear payment screens and plain-language consent build trust. Trust keeps users contributing data long enough for the enterprise side to work. - **Corporate renewal** — the enterprise portal matches how data scientists actually search and filter. If it saves them time, they renew. - **Ethics in the process, not just the product** — privacy principles are built into hiring, workflows, and team culture. Trust starts internally. **Next:** finalising accessibility standards for the consumer app, and testing early enterprise portal designs with APAC and US-based research partners. --- ### Awesome Presentation **Description:** A nifty plugin to create an HTML5 presentations, right in Wordpress. **Category:** agency **Date:** 2014-12-12 **URL:** https://ryansnowden.com/stuff/awesome-presentation/ import { SquareArrowOutUpRight } from "lucide-astro"; import { Image } from "astro:assets"; import homeImage from "../../assets/media/awesome/home.jpg"; import themeImage from "../../assets/media/awesome/theme.jpg"; import bgColourImage from "../../assets/media/awesome/bg-colour.jpg"; import bgImageImage from "../../assets/media/awesome/bg-image.jpg"; import transitionImage from "../../assets/media/awesome/transition.jpg"; import helpImage from "../../assets/media/awesome/help.jpg"; import examplesImage from "../../assets/media/awesome/examples.jpg"; import transitionsImage from "../../assets/media/awesome/transitions.jpg"; import fragmentsImage from "../../assets/media/awesome/fragments.jpg"; import demoVideo from "../../assets/media/awesome/demo.webm";

Company: Crystal Asia (me)

Complexity: 9.01923/10 Fun factor: 8.452/10

View the website

Awesome Presentation is a WordPress plugin for creating slideshows. It uses RevealJS for the presentation engine and WordPress for the content editing. The beauty of using WordPress is that you can use other plugins inside the slides. The target audience for Awesome Presentation is schools or any online learning platform where slideshows need to be created. A full website for the plugin itself was created. The plan was to sell the plugin, support it through updates, and create passive income. Awesome Presentation home page

This was a decent sized project that didn't quite make it to market due to various reasons. What made it unique, is leveraging the WP plugin ecosystem to put content on slides. You could have a math formula plugin, show some code with custom syntax highlighting or embed an inline quiz or questionnaire and it would render on that slide. The home page used the motion sensors in any device or the mouse to move the stars. A small detail but it was very immersive. Going full screen allowed you to do the presentation on a call or in-person.

I designed the back-end UI to be very playful. The target was educational basically. Sort of big, happy, rounded and reliable. Awesome Presentation theme editor

Everything should be able to be previewed in the editor, unlike the default Wordpress WYSIWYG editor which does not show how it is going to look. Background colour editor

[Background images](https://awesome.boxedthrills.com/documentation/slide-backgrounds/) can be stretched, tiled or original size. Background image editor

Media, [transitions](https://awesome.boxedthrills.com/documentation/transitions/), colours, all had to fit within the theme being chosen. They could pick their own of course, but running it on themes allowed less to go wrong if the user didn't have much design experience. Transition settings

A help menu is important incase you forget those fancy keyboard shortcuts!

The idea was to create a full set of [examples](https://awesome.boxedthrills.com/examples/) which used other Wordpress plugins like maths to demonstrate it in educational purposes, but also to show it being utilised in the Wordpress plugin ecosystem. Example presentations

There are a bunch of [transitions](https://awesome.boxedthrills.com/documentation/transitions/) based on the revealJS library. Available transitions

[Fragments](https://awesome.boxedthrills.com/documentation/fragments/) are when each part loads in the same slide. I created a code style to match it to the style guide. Fragment code example

--- ### BodyScan Analysis: Kalman Filtering **Description:** Reducing variability and improving accuracy through multi-capture calibration. **Category:** product **Date:** 2022-12-12 **URL:** https://ryansnowden.com/stuff/ba-kalman/ import { SquareArrowOutUpRight } from "lucide-astro"; import { Image } from "astro:assets"; import autoCalibrationImage from "../../assets/media/body-analysis/Auto-Calibration-Improved-1-scaled.png"; import KalmanFilterCharts from "../../components/interactive/KalmanFilterChartsWrapper.astro";

Company: Advanced Health Intelligence (AHI)

Complexity: 9.01923/10 Fun factor: 8.452/10

View the design (Figma)

## What did I do? - Discovery, research, design, prototypes, documentation. ## Scope & Methodology - Led the end-to-end product lifecycle, encompassing discovery, quantitative research, interaction design, and technical documentation. - Collaborated with engineering teams to translate complex mathematical concepts into accessible user interfaces. ## The Solution: Recursive Estimation - Implemented Kalman filtering (a recursive algorithm utilised to estimate unknown variables) to mitigate signal noise and enhance the precision of body composition predictions. - Transitioned the data model from single-point measurement to a multi-measurement aggregation, allowing for outlier detection and stochastic smoothing of results. ## UX Challenges & Research - Addressed the rigorous usability trade-offs required by the algorithm; the necessity for sequential triple-scans introduced significant friction and physical fatigue (Hick's Law). - Conducted time-on-task studies to quantify the impact of the extended workflow on user retention and satisfaction. - Overhauled instructional design and on-screen guidance to scaffold the user through the intensified capture process and mitigate the increased barrier to entry. - To help familiarise users with the concept, encourage regular scans, and communicate 'the why', the concept of the '_calibration phase_' was introduced throughout copy. Example of auto calibration

## Error Handling & Strategic Roadmap - Identified critical failure states where cumulative error forced workflow restarts, prioritising these friction points for future technical mitigation. - Defined the roadmap for a 'Confidence Engine' to parse environmental telemetry (lighting, pose, background complexity), reducing reliance on perfect user execution and addressing 'Garbage In, Garbage Out' (GIGO) constraints. --- ### Body Analysis: Privacy **Description:** Masking Humans using real-time segmentation. **Category:** product **Date:** 2021-12-12 **URL:** https://ryansnowden.com/stuff/ba-privacy/ import { SquareArrowOutUpRight } from "lucide-astro"; import { Image } from "astro:assets"; import maskingHumanImage from "../../assets/media/body-analysis/masking-human-1024x1024.webp"; import performanceImage from "../../assets/media/body-analysis/performance-1024x529.png"; import cleanJointsImage from "../../assets/media/body-analysis/clean-joints-1-scaled.png";

Company: Advanced Health Intelligence (AHI)

Complexity: 8.876/10 Fun factor: 9.976/10

## What did I do? - Discovery, research, designs, iOS prototype (Xcode), presentation, documentation. ## What is it? - A psychological approach to address privacy concerns, by not showing the video, but rather a real-time silhouette of their body. - Bonus features: A competitor differentiator through strong visual appearance and customer branding options. Example of human masking for privacy: stylised silhouette art

Example of human masking for privacy: stylised silhouette art

**Masking humans**: It looks like those apple iPod ads, doesn't it? ## The problems The scan requires people to be in form fitting clothing. We found that some people just got into their underwear, presenting some problems when viewing themselves on a screen: 1. They question the privacy and security. 2. They don't like what they see (themselves, _partially naked_) and find that invasive and then don't like doing it (basically a mirror). ## How is it done? Real-time Matte Effects using Apple [VisionFramework](https://developer.apple.com/documentation/vision) Performance of the human masking algorithm

Performance of the human masking algorithm

**Mask modes**: the higher the accuracy, the slower the performance. ## Major challenges - Device performance impacts the accuracy of the masking layer. - On low-end devices, this may also impact the on-device inspection, slowing down the overall experience. Example of clean joints

Check out these clean joints… ## Major wins - **Full image/video backgrounds** - **Clean real-time joints** by using coloured masks, the joints can be enlarged, create a high contrast, and improve legibility from a distance. - **Reduced visual clutter** in determining limbs into outline. - **Alternate capture through fit-frame modelling** (no outlines). --- ### Beachbody **Description:** Fitness brand visual identity work. **Category:** agency **Date:** 2020-12-12 **URL:** https://ryansnowden.com/stuff/beachbody/ import ImageSlideshow from "../../components/interactive/Slideshow.astro"; import beachbody3 from "../../assets/media/beachbody/beachbody-3.jpeg"; import beachbody4 from "../../assets/media/beachbody/beachbody-4.jpeg"; import beachbody5 from "../../assets/media/beachbody/beachbody-5.jpeg"; import beachbody6 from "../../assets/media/beachbody/beachbody-6.jpeg"; import beachbody7 from "../../assets/media/beachbody/beachbody-7.jpeg"; import beachbody8 from "../../assets/media/beachbody/beachbody-8.jpeg"; import beachbody9 from "../../assets/media/beachbody/beachbody-9.jpeg"; import beachbody10 from "../../assets/media/beachbody/beachbody-10.jpeg"; import beachbody11 from "../../assets/media/beachbody/beachbody-11.jpeg"; import beachbody12 from "../../assets/media/beachbody/beachbody-12.jpeg"; import beachbody13 from "../../assets/media/beachbody/beachbody-13.jpeg";

Company: Advanced Health Intelligence (AHI)

Client: Beachbody

This wasn't printed; it was a digital deck. The circumstances put pressure on the delivery. I had to create it during the Christmas–January period before flying to CES in Las Vegas. It wasn't entirely finished, as I had other priorities and the meeting was after CES. Instead, I was in hotel rooms late at night trying to finish it off. To make matters worse, I got sick, very sick. This was during the onset of COVID-19 before we knew it was COVID and the world shut down, so maybe it was COVID. #coolstorybro. Anyway, I had two versions lined up and presented in person to the CEO on a cocktail of drugs from CVS. The first version didn't hit, as the visualisation was 'underwhelming,' so I pulled out version two and it was closer to the mark. Themes: Depth, dimension. --- ### Biometric Health Assessment **Description:** A clinical-like health assessment consisting of multiple stages that is designed to assist clinicians and upstream providers with combined risk analysis of chronic diseases. **Category:** product **Date:** 2024-12-12 **URL:** https://ryansnowden.com/stuff/biometric-health-assessment/ import { SquareArrowOutUpRight } from "lucide-astro";

Company: Advanced Health Intelligence (AHI)

Complexity: 9.28/10 Fun factor: 9.008/10

View the design (Figma)

## Project details - **Platform**: iOS, Android - **Design system management**: [Supernova.io](https://supernova.io/) - **Product type**: B2B SaaS, SDK, White label app - **Design system**: [Material 3](https://m3.material.io/), [UCDL](/stuff/ucdl) - **Design tool**: Figma ## What is it? A clinical-like health assessment consisting of multiple stages that is designed to assist clinicians and upstream providers with combined risk analysis of chronic diseases. ## Assessment stages 1. **Cardiovascular Health:** A 30 second face scan that measures heart rate, blood pressure, heart attack, stroke. 2. **Health at Rest:** Lying down for 2 minutes and then conducting a 30 second finger scan to measure resting heart rate. 3. **Body Analysis:** A 3-5 minute body scan to measure body circumference, body fat, obesity, central obesity. 4. **Fitness Evaluation:** A 3-5 minute physical step test and then a 30 second finger scan to measure maximum heart rate, and heart rate recovery. ## What do you need? - **Face & Body Scan:** Desk, chair, table. - **Health at Rest:** A bed or couch. - **Fitness Evaluation:** Some stairs or a riser step. ## How long does it take? - **First-time**: 30 minutes, split over a couple of days. - **Ongoing**: About 15 minutes. ## Use cases - Health insurance underwriting, dispensary of pharmaceutical drugs (e.g. statins, semaglutide), telehealth, population risk, corporate wellness. ## What did I do? **Hands on:** - Initial MVD (*Minimum Viable Demo*), and MVP (*Minimum Viable Product*) - My **responsibility** varied over time, but I was responsible for the 'entire app'. - This included research, stakeholder mapping, persona mapping, user journeys, user testing, prototypes, developer handoff, the scans (each stage), scan guides, results, product docs, marketing assets, and video guides/promos. **Minds on:** - Worked with the MLCV team and engineering to validate model outputs at each stage of the assessment. - Defined what "good enough" looked like for each model, based on the clinical, commercial, and reputational cost of getting it wrong. - Defined safety guardrails for the system. - Evaluated model outputs against medical reference data and identified where accuracy dropped around across body types, scan quality, questionnaire inputs, and exemption paths. - Tested the full assessment pipeline end-to-end, confirming each stage produced reliable inputs for the next and that final risk classifications held up. - Validated edge cases. Cancellations, incomplete assessments, exemptions, etc, to make sure the system behaved safely outside the expected flow. - Used validation and UX findings to adjust thresholds, decision rules, and workflow logic, separating model problems from design, data quality, and integration issues. - Translated validation results into product and engineering priorities, so the team could focus model improvements on what mattered most to users and the business. ## Major challenges - **Product**: High drop off due to usability oversight, notification & message fatigue, regulation and app store guidelines, customisation and theming, no localisation. - **Product complexity:** High complexity and barriers to change due to emerging technology innovation cycles. i.e. Working with data science and training models, then testing is *expensive*. - **Organisational**: Design & features by leadership (top down hierarchy), misaligned reward systems, managerial self-interest, team silos, misaligned commercial messaging. --- ### Body Analysis **Description:** Accurate and repeatable body composition and circumference using a smartphone. **Category:** product **Date:** 2021-12-12 **URL:** https://ryansnowden.com/stuff/body-analysis/ import { SquareArrowOutUpRight } from "lucide-astro"; import { Image } from "astro:assets"; import video1 from "../../assets/media/shared/0000000lE7W000000000wW-2.mp4"; import video2 from "../../assets/media/body-analysis/0000000lE7W000000000wW-6.mp4"; import video3 from "../../assets/media/body-analysis/Body-Analysis-Dev-4g-hevc.mov"; import bodyAnalysisImage from "../../assets/media/body-analysis/0000000lE7W000000000zV-1-scaled.png";

Company: Advanced Health Intelligence (AHI)

Complexity: 9.7222/10 Fun factor: 8.334/10

View the design (Figma)

## Project details - **Platform**: iOS, Android - **Design system management**: Supernova.io - **Design system**: Material 3, UCDL - **Product type**: B2B SaaS / SDK - **Design tool**: Figma - **Roles**: Chief Design Officer, UX Researcher, Product Manager, Product Lead, Customer Support, Video \[Director, Producer, Editor\]. ## What is it, and what did you do? - Accurate and repeatable body composition and circumference using a smartphone. - Multiple responsibilities: UX & market research, user testing, developer handoff, prototypes, app design, scan results, developer docs, product docs, marketing assets (print, digital), video guides/promos, promotional decks, multi-day film shoot, training. ## Project context - Developed a mobile-based body scanning feature leveraging computer vision to assess user posture and body composition in real-time. - Aimed to bridge the gap between clinical physical assessments and accessible at-home monitoring for the health and wellness vertical. - Worked with the MLCV team and engineering to validate model outputs. - Defined safety guardrails. ## Biometric outputs - Chest, hips, waist, and thigh, body fat percentage, waist-hip ratio, waist-height ratio, obesity risk, central obesity risk. **Body Analysis: Involvement and updates** I researched and designed the technology through all major milestones, including: 1. **Two person experience → single person experience.** - New guide, Phone Alignment (interactive UI interface), Staged countdowns (UI), error states, failure states, cloud segmentation. 2. **Static capture and outline → Dynamic capture and scaled outlines** - On-device pose checking, phone height detection, real-time messaging (and errors), failure states, on-device segmentation. This meant being heavily involved with engineering, ensuring the UX was being improved whilst accuracy and repeatability was maintained. Whilst the front-end undertook a significant change, the AI models also transformed from cloud to on-device, so that no images leave the device.

Version 1x

MVP (v2)

Dev-v2

## Use cases & user archetypes - The biometric outputs allow you to predict Obesity, placing it directly in digital health, telehealth and insurance pipelines. - A less effective use falls into apparel and fitness. - It is also combined with other scans ([like the BHA](https://rs.boxedthrills.com/2025/08/24/bha/)) and patient data to contribute to further predictive health markers. ## Major challenges & constraints - **Environmental variance:** Mitigating computer vision failures caused by poor domestic lighting and low-contrast clothing against complex backgrounds. - **Privacy & trust:** Overcoming user hesitation regarding capturing and processing semi-nude or form-fitting imagery on a cloud-based architecture. - **Instructional clarity:** designing an intuitive guidance system (visual and haptic) to ensure users stand at the correct distance and angle without frustration.

## UX design & research frameworks - **Technology Acceptance Model (TAM):** Utilised to analyse and optimise perceived usefulness and ease of use, directly influencing the onboarding flow design. - **Nielsen's 10 Usability Heuristics:** specifically 'Match between system and the real world' to align scanning instructions with natural human mirroring behaviours. - **Double Diamond Process:** strictly followed the Discover/Define phases to narrow down the MVP scope from 'full medical diagnosis' to 'wellness indicators'. - **System Usability Scale (SUS):** Conducted post-testing analysis yielding a score of 82, validating the iterative improvements made to the scanning reticle UI. ## Outcomes - Achieved a 40% reduction in scan failure rates through the implementation of real-time AR guidance. - Validated the 'privacy-first' local processing model, which tested significantly higher for user trust during qualitative interviews. - New opportunities: "[**Privacy mode**](https://rs.boxedthrills.com/2025/07/23/body-analysis-privacy/) --- ### Botanical Maylands **Description:** Kind on the eyes, maybe not so kind on the wallet. **Category:** agency **Date:** 2016-12-12 **URL:** https://ryansnowden.com/stuff/botanical-maylands/ import { Image } from "astro:assets"; import home from "../../assets/media/botanical/home.jpg"; import apartments from "../../assets/media/botanical/apartments.png"; import design from "../../assets/media/botanical/design.jpg"; import location from "../../assets/media/botanical/location.jpg"; import team from "../../assets/media/botanical/team.jpg";

Company: Crush

Client: WA Tourism

A classic and contemporary design that aligned with existing branding material. A cookie-cutter property website using Wordpress, but with a unique colour palette and composition. The Divi plugin was used to piece together and meet deadlines on time. Botanical Home Page

--- ### Cardiovascular Health Scan **Description:** Multi-platform vital signs and cardiovascular health risks using facial blood flow analysis. **Category:** product **Date:** 2020-12-12 **URL:** https://ryansnowden.com/stuff/chs/ import { SquareArrowOutUpRight } from "lucide-astro"; import video1 from "../../assets/media/shared/0000000lE7W000000000wW-2.mp4"; import ImageTooltip from "../../components/lightbox/ImageTooltip.astro"; import predatorVisionImg from "../../assets/media/shared/predator-vision.jpg";

Company: Advanced Health Intelligence (AHI)

Complexity: 7.021/10 Fun factor: 9.285/10

View the design (Figma)

## What is it? - _Otherwise known as FaceScan_, it's a multi-platform vital signs and cardiovascular health risks using facial blood flow analysis. - It's also a 3rd party scan technology licensed by [Nuralogix](https://www.nuralogix.ai/). ## What did I do? - I didn't work on the tech (that was the Nuralogix team), but I did all the integration docs, all designs, tutorials and marketing videos. ## How does it work? It looks under your skin like the . Cool research papers below: - [Transdermal optical imaging revealed different spatiotemporal patterns of facial cardiovascular activities.](https://www.nuralogix.ai/wp-content/uploads/2021/06/transdermal-optical-imaging-revealed-different-spatiotemporal-patterns-of-facial-cardiovascular-activities.pdf) - [Transdermal Optical Imaging Reveal Basal Stress via Heart Rate Variability Analysis: A Novel Methodology Comparable to Electrocardiography](https://www.nuralogix.ai/wp-content/uploads/2021/06/transdermal-optical-imaging-reveal-basal-stress-via-heart-rate-variability-analysis-a-novel-methodology-comparable-to-electrocardiography.pdf) ## What do you need? - Table, chair, light, camera, and a person. ## How is it used? - Health Insurance, corporate wellness, telehealth, preventative health. - **Combined health metrics:** _Blood pressure_ and, to some extent, the risk predictions for cardiovascular disease, heart attack, and stroke. This is used in [the BHA](https://rs.boxedthrills.com/2025/08/24/bha/) algorithms. ## Challenges - **Data Inaccuracy, unreliability:** Anxiety, facial age, and blood glucose, are considered inaccurate. Breathing rate was also unreliable. - **Measurement Discrepancy:** The out-of-the-box blood pressure measurement differs significantly from clinical guidelines. - **App Store Guidelines, Regulatory Compliance**: Google and Apple have stringent rules for medical apps (can't show blood pressure!). - **UI Customisation:** The option to create a custom user interface was available but never utilised. [I still did the UCDL though](https://rs.boxedthrills.com/2025/07/23/unified-capture-design-language/). Stretch goals! - **Skin tone**: Dark skin tones (Fitzpatrick IV, V) were not properly factored into the lighting scene inspection and capture process. --- ### DVG Automotive Group **Description:** An automotive industry leader that needed pages that convert. **Category:** agency **Date:** 2017-12-12 **URL:** https://ryansnowden.com/stuff/dvg/ import { Image } from "astro:assets"; import listingImage from "../../assets/media/dvg/listing.jpg"; import detailImage from "../../assets/media/dvg/detail.jpg"; import mobileImage from "../../assets/media/dvg/mobile.png";

Company: Glide Agency

Client: DVG Automotive Group

A fun project design wise, but also on the technical side where I worked with the fantastic CraftCMS to hook up with the CarSales API (via FeedMe plugin) and build a responsive search engine. Synchronising from the CarSales API created problems. Their API wasn't up to date, and I didn't have access to the new one (despite pushing). It would time me out, and add the server to a block list. It would need to grab all meta-data, and images, for thousands of vehicles. The car industry audience segment is price sensitive. It must appear not too expensive and not too cheap either. DVG was representing many brands and the goal was also to be trusted. The visual aesthetic and funnel was intentionally chosen. It had to sync and sell. It did both. Vehicle listing page

The goal of building this in Craft vs the exisiting Wordpress website is to start creating and intelligent landing page structure with smart end-points. We could do it by brand, dealership location, price points, and other campaigns that were going on at that time. Vehicle detail page

Of course, fully responsive. Fast, simple, reliable, convert, sell cars, make money. Mobile responsive view

--- ### Experience Perth **Description:** Landing pages, Webflow. Landing pages, A/B Testing, and Good Times. **Category:** agency **Date:** 2017-12-12 **URL:** https://ryansnowden.com/stuff/experience-perth/ import { Image } from "astro:assets"; import design1Image from "../../assets/media/experience-perth/design1.jpg"; import design2Image from "../../assets/media/experience-perth/design2.jpg";

Company: Glide Agency

Client: WA Tourism

Two designs for Experience Perth (WA Tourism) landing pages. With not a lot to go on, I basically 'made up' a lot of the content for Events (posters, article titles, etc). Quite fun and pleased with the results! Experience Perth design concept 1

--- ### The Foundation Years Group (FYG) **Description:** M&A pitch between BabyBunting and G8 Education. **Category:** other **Date:** 2024-12-20 **URL:** https://ryansnowden.com/stuff/fyg/ import ImageSlideshow from "../../components/interactive/Slideshow.astro"; import fyg1 from "../../assets/media/fyg/1.jpg"; import fyg2 from "../../assets/media/fyg/2.jpg"; import fyg3 from "../../assets/media/fyg/3.jpg"; import fyg4 from "../../assets/media/fyg/4.jpg"; import fyg5 from "../../assets/media/fyg/5.jpg"; import fyg6 from "../../assets/media/fyg/6.jpg"; import fyg7 from "../../assets/media/fyg/7.jpg"; import fyg8 from "../../assets/media/fyg/8.jpg"; import fyg9 from "../../assets/media/fyg/9.jpg"; import fyg10 from "../../assets/media/fyg/10.jpg"; import fyg11 from "../../assets/media/fyg/11.jpg"; import fyg12 from "../../assets/media/fyg/12.jpg";

**Company**: Me / **Client**: Also Me

As part of my MBA unit "Financial Analysis", I was tasked with pitching a speculative merger between Baby Bunting and G8 Education. While I was initially sceptical about the fit, I looked beyond the standard curriculum to find a strategic angle that made sense. My breakthrough came when analysing the **Cash Conversion Cycle (CCC)**. The CCC measures how quickly a company turns its investments in inventory and resources back into cash. Because G8 Education is service-based and collects fees upfront, it has a much faster CCC than Baby Bunting's retail model. I proposed using G8's consistent liquidity to buffer Baby Bunting's volatility and lower the overall financial risk. This concept evolved into me creating a new brand, **The Foundational Years Group (FYG)**, which is supported by a distinct mission, vision, and strategic roadmap.

## What is it? The Foundation Years Group (FYG) is a proposed strategic merger between Baby Bunting Group Limited and G8 Education Limited. This initiative creates a vertically integrated "Category of One" ecosystem within the Australian family services sector. By consolidating Australia's premier specialty baby goods retailer with a leading early childhood education (ECE) provider, the entity captures the entire parenting lifecycle, from the prenatal stage through to primary school commencement. ## What problems does it fix? The merger addresses several structural and financial inefficiencies inherent in the standalone entities: - **Customer Acquisition Costs (CAC):** It bypasses expensive digital marketing by using Baby Bunting's prenatal data to feed G8's enrolment pipeline. - **Retail Volatility:** It counterbalances the cyclical nature of retail with the stable, recurring revenue streams of the childcare sector. - **The "Three-Year Cliff":** It extends the customer lifetime value (LTV) beyond the toddler years, maintaining the retail relationship until the child is five. - **Supply Chain Margin Leakage:** It eliminates third-party distributor margins by verticalising the supply of consumables (nappies, wipes, and cleaning goods) through Baby Bunting's internal B2B wholesale arm. ## How is it built and why? The strategic architecture is built on the **Customer Intimacy** value discipline. The integration is driven by a unified data architecture that allows for anticipatory service delivery based on the child's developmental stage. To manage the merger of two distinct corporate cultures, a **Federated Stewardship Governance** model is utilised. This ensures: - **Retail Division:** Operates under efficiency-driven performance metrics. - **Education Division:** Follows stewardship principles to protect pedagogical integrity and clinical safety, ensuring the "duty of care" is never compromised by commercial expediency. - **Shared Services:** Centralises HR, IT, and procurement to drive operational excellence and reduce the group's overall cost to serve. ## How should it be funded? The merger is supported by a projected annual EBITDA uplift of **$33M-$50M**, which provides a robust return on investment (ROI). Funding and value creation are derived from: - **Occupancy Optimisation:** A 2% lift in childcare occupancy via retail referrals generates approximately $21M in incremental EBIT. - **Supply Chain Arbitrage:** Shifting G8's procurement to internal retail channels captures an estimated $10M in margins previously paid to external distributors. - **Marketing Efficiency:** Reducing external ad spend through owned-channel cross-promotion yields up to $5M in savings. - **Capital Efficiency:** Co-locating retail "hubs" with childcare centres reduces facility costs and improves return on invested capital (ROIC). ## Future Outlook: Parenting-as-a-Service (PaaS) The long-term strategic direction involves a transition to a **Blue Ocean** subscription model. This "Circular Nursery" concept allows families to lease high-ticket hardware (prams and capsules) which are professionally sanitised and re-leased, reducing landfill waste. This aligns the group with UN Sustainable Development Goals regarding responsible consumption and quality education, transforming the business from a transactional retailer into a holistic developmental partner. --- ### Juniper Aged Care **Description:** Aged care accommodation and support services. Rebrand, full iA, site build, Craft CMS. **Category:** agency **Date:** 2017-12-12 **URL:** https://ryansnowden.com/stuff/juniper/ import { Image } from "astro:assets"; import home from "../../assets/media/juniper/home.jpg"; import start from "../../assets/media/juniper/start.jpg"; import artboard from "../../assets/media/juniper/artboard.jpg"; import map from "../../assets/media/juniper/map.jpg"; import location from "../../assets/media/juniper/location.jpg";

Company: Glide Agency

Client: Juniper Aged Care

Juniper was undergoing organisational restructuring due to shifts in government policy regarding aged care. As part of this transition, investments were made into marketing efforts like a new website, advertising campaigns, and TV commercials to promote their updated brand and offerings. Juniper Home Page

From pitch to execution, this project was really fun to work on. We created an initial set of designs, then held a rather important boardroom meeting with all the stakeholders. They loved it, but we knew it and had already started production. Juniper project start

Everything was broken down into components and variations of each, suitable for the content, including handheld. The process was based on UX patterns in the components in Bootstrap 4. Juniper Design Artboard

We developed a strong strategy. Our targets were the elderly, their parents, and health professionals. Each might be on a different journey, with some already in some form of Aged Care, but also those who are new and dealing with a crisis After the initial launch, a TV campaign was being run. An important branding alignment was carried out, and the website was updated to match. Seeing it all come together was great, and having influence over both the TV style and scenes was also satisfying. Juniper Map Feature

Aged care as an industry was new to me, so I had to become familiar with both the dark humor of those who work in it, and also on the legislative level. Juniper Location Details

--- ### Karma Group **Description:** Concept designs for the Karma Group main website. **Category:** agency **Date:** 2012-12-12 **URL:** https://ryansnowden.com/stuff/karma-group/ import { SquareArrowOutUpRight } from "lucide-astro"; import { Image } from "astro:assets"; import karmaImage1 from "../../assets/media/karma-group/0000000lE7W0000000003Z-1.jpeg"; import karmaImage2 from "../../assets/media/karma-group/0000000lE7W00000000042-1.jpeg"; import karmaImage3 from "../../assets/media/karma-group/0000000lE7W00000000045-1.jpeg"; import karmaImage4 from "../../assets/media/karma-group/0000000lE7W00000000048-1.jpeg";

Company: Crystal Asia (Me)

Client: Karma Group

Concept designs for the Karma Group main website. Simple, non-mobile concepts that included some banners and badges. Karma Group concept 1

--- ### Nike **Description:** Pitch brochure, promising the future. **Category:** agency **Date:** 2020-12-12 **URL:** https://ryansnowden.com/stuff/nike/ import Slideshow from "../../components/interactive/Slideshow.astro";

Company: Advanced Health Intelligence (AHI)

Client: Nike

This was a landscape A4 high gloss + digital deck created for a Nike pitch around custom apparel, integration into Training Club and Run Club. The theme was depth, dimension. Ripped from multiple sources and cleaned up, and twisted the way to convey the theme of body scanning. :-) It looked fantastic and felt great to hold. High gloss was a winner.

--- ### The Noodle Forum **Description:** Carried over some former branding, modified design slightly for cross-mobile experience. Used VelocityJS and CraftCMS to make some for some interesting animations. **Category:** agency **Date:** 2016-12-12 **URL:** https://ryansnowden.com/stuff/noodle-forum/ import { SquareArrowOutUpRight } from "lucide-astro"; import noodleVideo from "../../assets/media/noodle-forum/0000000lE7W0000000005Y-1.webm";

Company: Crush

Client: The Noodle Forum

View the website

Carried over some former branding into the design and took some liberties with it for cross-mobile experience. Used VelocityJS to make some interesting animations.

--- ### Open Health Stack eXtended **Description:** Extending Open Health Stack with turnkey FHIR-compliant UI components for various health questionnaires and physical examinations. **Category:** product **Date:** 2024-12-12 **URL:** https://ryansnowden.com/stuff/ohs-x/ import { SquareArrowOutUpRight } from "lucide-astro"; import ohsXImg from "../../assets/media/ohs/ohs-cover.png"; import Accordion from "../../components/ui/Accordion.astro";

Company: Me

Complexity: 7.8/10 Fun factor: 9.3/10

View the design (Figma)

[View OHS-X Docs](https://ryansnowden.notion.site/OHS-X-acf9b8ba426a4d5b8d5a076240ed5dab)

- **Platform**: iOS, Android, Web - **Design system**: Material 3 - **Product type**: Design library - **Design tool**: Figma - Everything, except [Open Health Stack](https://developers.google.com/open-health-stack/design/data-capture-guideline), Google did that. - It allows other designers in the clinical space to quickly get going on a range of in-device tests. Anyone can download it from _Figma Community_. - Engineered a scalable design system extension for Google's Open Health Stack, delivering a suite of FHIR-compliant UI components to accelerate the development of interoperable health applications. - Translated complex HL7 data models into intuitive, standardised interface patterns for clinical questionnaires and physical examinations, optimising both developer velocity and user experience.

- **SFT:** Senior Fitness Test. Assesses the functional fitness of older adults, measuring their ability to perform everyday activities without difficulty. - **GAD-7:** Generalised Anxiety Disorder 7-item scale for anxiety screening. - **PHQ-9:** Patient Health Questionnaire 9-item scale for depression screening. - **PAR-Q, PAR-Q+:** Physical Activity Readiness Questionnaires for fitness safety screening. - **M-CHAT-R:** Modified Checklist for Autism in Toddlers, Revised. - **GDS-15:** Geriatric Depression Scale (Short Form) for assessing depression in older adults. --- ### Robarts Spaces **Description:** Architecture, interior design and engineering **Category:** agency **Date:** 2014-12-12 **URL:** https://ryansnowden.com/stuff/robarts-spaces/ import { SquareArrowOutUpRight } from "lucide-astro";

Company: Crystal Asia

Client: Robarts Spaces

Visit the website

The site involved subtle animations and a smart UI, all of which is done with the help of Popmotion and Bourbon+Neat. The feeling and approach was to be infused with the philosophy of Robarts Space. This was simplicity, clean, subtle and minimal. These were the concepts I created prior to discovery. It was a phase3 to introduce difference concepts that they might not have considered, as customers tend to view their needs through their own lens. Even though they might look nice, I missed 'the goal of getting it right'. However, after a bit of back and forth, we finalized the design direction. import ImageSlideshow from "../../components/interactive/Slideshow.astro"; import home1 from "../../assets/media/robarts/home1.jpg"; import home2 from "../../assets/media/robarts/home2.jpg"; import home3 from "../../assets/media/robarts/home3.jpg"; import home4 from "../../assets/media/robarts/home4.jpg"; import home5 from "../../assets/media/robarts/home5.jpg"; import home6 from "../../assets/media/robarts/home6.jpg"; import home7 from "../../assets/media/robarts/home7.jpg"; import home8 from "../../assets/media/robarts/home8.jpg"; import home9 from "../../assets/media/robarts/home9.jpg"; import home10 from "../../assets/media/robarts/home10.jpg"; import about from "../../assets/media/robarts/about.jpg"; import awards1 from "../../assets/media/robarts/awards1.jpg"; import awards2 from "../../assets/media/robarts/awards2.jpg"; import nav1 from "../../assets/media/robarts/nav1.jpg"; import nav2 from "../../assets/media/robarts/nav2.jpg"; import nav3 from "../../assets/media/robarts/nav3.jpg"; import stage from "../../assets/media/robarts/stage.jpg"; --- ### Sage Worldwide **Description:** Event and consulting website with custom speaker management. **Category:** agency **Date:** 2014-12-12 **URL:** https://ryansnowden.com/stuff/sage-worldwide/ import { SquareArrowOutUpRight } from "lucide-astro"; import { Image } from "astro:assets"; import homeImage from "../../assets/media/sage-worldwide/0000000lE7W000000000aT-1.jpeg"; import speakerPanelImage from "../../assets/media/sage-worldwide/0000000lE7W000000000aX-1.png"; import speakerProfileImage from "../../assets/media/sage-worldwide/0000000lE7W000000000b1-1.jpeg"; import innerPageImage from "../../assets/media/sage-worldwide/0000000lE7W000000000b5-1.jpeg"; import dropdownImage from "../../assets/media/sage-worldwide/0000000lE7W000000000b9-1.png";

Company: Crystal Asia (me)

Client: Sage Worldwide

View the website

Event and consulting website in with a custom content type to serve the speaker data. Speakers are tagged across news and easily searchable. The CMS has a custom speaker entry page where multiple types of media can be uploaded easily by Sage staff.

Home page with rotating messages. Clicking the round speakers brings up a panel of featured speakers. Speaker panel with media

The panel can flip over and you can view the media right inline. Speaker profile page

Speaker page with the honorable himself. Inner page layout

Pretty standard inner page. Speaker categories dropdown - the main gateway to the speaker pages.

--- ### Service Design Mapping **Description:** Various service blueprints, user journeys, OGSM canvas, VPC, actor network, causal loops, concept posters, ansoff matrix, value exchange maps, MOI framework. **Category:** other **Date:** 2024-12-20 **URL:** https://ryansnowden.com/stuff/service-design-mapping-examples/ import { SquareArrowOutUpRight } from "lucide-astro"; import { Image } from "astro:assets"; import serviceDesignImage from "../../assets/media/service-design/service-design.jpg";

View the designs (Figma)

**Company**: Me / **Client**: Multiple

I have completed numerous service design mapping exercises, and I have gathered them here on a single canvas. The benefit of service design is that these mapping models and frameworks are also highly effective in business strategy and marketing. Instead of a linear workshop discovery process, my approach primarily involves starting with my own desk research and filling in the gaps using my own experience, AI research tools, and a degree of common sense. ## Continous learning This approach allows me to progress significantly through the process without needing to involve others at such an early stage. While some organisations might prefer to co-create from the start, that method is often more intensive and expensive. By working independently initially, I can personally master the material and identify potential pain points, competitors, and various support systems. Ultimately, this method strengthens my knowledge base. It enables me to make informed decisions about the direction of the service before workshops begin and allows these insights to circulate within my team to build familiarity. Service Design Mapping

--- ### Singapore Airlines Stopover **Description:** Singapore Stopover Holiday Challenge. Join the dots and win! **Category:** agency **Date:** 2017-12-12 **URL:** https://ryansnowden.com/stuff/singapore-stopover/ import { Image } from "astro:assets"; import startImage from "../../assets/media/singapore-stopover/start.jpg"; import dotsInfoImage from "../../assets/media/singapore-stopover/dots-info.jpg"; import unlockImage from "../../assets/media/singapore-stopover/unlock.jpg"; import detailsImage from "../../assets/media/singapore-stopover/details.jpg";

Company: Glide Agency

Client: Singapore Airlines

A pitch for a Singapore Airlines stopover competition quiz. The idea was simple: it is an interactive HTML5 'game' where someone has to join the dots using their finger or mouse and then submit their details to win. It could work on touch devices and was quite a neat little project that would work using available (and tested) JS libraries. Start screen

Need info on a place? Sure just tap it! Dots information popup

Connecting the places adds to your score. Unlock screen

Once complete, enter your details to win ;-) Details entry form

--- ### Standard Advice **Description:** Health & Fitness e-commerce, custom icons and a focused payment funnel. **Category:** agency **Date:** 2014-12-12 **URL:** https://ryansnowden.com/stuff/standard-advice/ import { Image } from "astro:assets"; import standard1 from "../../assets/media/standard-advice/standard1.jpg"; import standard2 from "../../assets/media/standard-advice/standard2.jpg"; import standard3 from "../../assets/media/standard-advice/standard3.jpg"; import standard5 from "../../assets/media/standard-advice/standard5.jpg"; import standard6 from "../../assets/media/standard-advice/standard6.jpg";

Company: Crystal Asia

Client: Standard Advice

A decent sized e-commerce project that required some special features to differentiate it from competitors. Built in Wordpress and WooCommerce, I had to design a logo, colour palette, illustration style, AliPay and WeChat payment gateways, a custom quiz module and dashboard. The custom quiz module was a way for a customer to choose some goals (such as weight loss) and then the products would be matched to them. They had a buyers dashboard where they could ask for advice. Languages: English, Chinese Standard Advice homepage

I designed a custom set of icons for the quiz, along side a few concepts. Custom icons for quiz

Alternate design around navigation (not chosen but still legit). Alternate navigation design

--- ### Travel & Sports Australia **Description:** Travel booking system, Wordpress website, e-commerce.. **Category:** agency **Date:** 2017-12-12 **URL:** https://ryansnowden.com/stuff/tasa/ import { SquareArrowOutUpRight } from "lucide-astro"; import { Image } from "astro:assets"; import bookingStartImage from "../../assets/media/tasa/booking-start.jpg"; import bookingImage from "../../assets/media/tasa/booking.jpg"; import cardsImage from "../../assets/media/tasa/cards.jpg"; import artboardImage from "../../assets/media/tasa/artboard.jpg"; import companyHomeImage from "../../assets/media/tasa/company-home.jpg"; import mobileBookingImage from "../../assets/media/tasa/mobile-booking.jpg"; import storyImage from "../../assets/media/tasa/story.jpg"; import sportDetailImage from "../../assets/media/tasa/sport-detail.jpg"; import iconsImage from "../../assets/media/tasa/icons.png";

Company: Glide Agency

Client: [Travel & Sports Australia](http://www.travelandsports.com.au/)

View the project ## Project assets

## Project Info The project was quite massive in scope. It consisted of an entirely new booking system, which was being implemented by another (great) company. It also included a new website with new branding. I was responsible for the UX/UI of the booking system and website. I prototyped the whole mobile app from beginning to end and handed my phone to the customer. With the help of the TASA team we created something unique, fun and actually won an AFTA award. Booking start screen

The project was already in progress when I joined the company. It was falling behind in some areas, so I escalated meetings with the customer to actively complete several milestones and meet deadlines. Luckily, I had several years of experience in the travel industry (having my own company in the past) while in China, so I had very few obstacles when creating workflows and processes for group booking systems. There was the Booking Wizard component for desktop and handheld devices, plus a MICE variation of it for companies. Then there was the website itself, which needed to appeal to a wide genre of sports fans, as well as recreational travel needs. Booking interface

I worked closely with TASA to make sure inventory and conditions were met during the booking process, without making it take too long. Part of this meant moving registration to the end and collecting passenger details last instead of first, which severely reduced the time taken to make a booking. Card components

I prototyped the whole booking wizard to mobile, covering the extreme cases of a couple and group traveling. Basically, the goal was to look for problems. The design aspect was easy to meet since it was going to be flat and clean, but the information needed serious formatting and had to be heavily scrutinized to determine where it should be, for who, and why. To give you a sense of the scale, each of the items below is an artboard in Sketch. Each persona was mapped out as the conditions for editing and wording varied greatly depending on the package itself. Artboard overview

Corporate customers needed a different style for their section. It was a goal to make this a full-screen slideshow in a presentation style. Company home page

Over several months, each component was designed and assembled. To avoid adding extra time for redesigning, approval, and then changes in development, visual changes were made directly in development. Mobile booking screens.

Every company has a story. Company story page

Every sport has a home. Sport detail page

Icon sets had to be sourced, matched, and where missing, created. This was time-consuming to say the least. You have to modify and create icons that maintain line thickness, and have a dark and light variant.

--- ### Unified Capture Design Language (UCDL) **Description:** A cohesive design system that provides a robust set of modular building blocks for all new and existing platforms. **Category:** product **Date:** 2022-12-12 **URL:** https://ryansnowden.com/stuff/ucdl/ import { SquareArrowOutUpRight } from "lucide-astro"; import video1 from "../../assets/media/shared/0000000lE7W000000000wW-2.mp4"; import ImageTooltip from "../../components/lightbox/ImageTooltip.astro"; import predatorVisionImg from "../../assets/media/shared/predator-vision.jpg";

Company: Advanced Health Intelligence (AHI)

Complexity: 9.231/10 Fun factor: 9.632/10

View the design (Figma)

## Project details - **Platform**: iOS, Android, Web - **Design system management**: [Supernova.io](https://supernova.io/) - **Product type**: Design system (SDK) - **Design tool**: Figma ## What is it? - A **design system**. The Unified Capture Design Language (UCDL) provides a robust set of modular building blocks for all new and existing platforms as to create a familiar experience for biometric capture. ## What did I do? - Discovery, research, documentation, prototypes, presentations. ## What problem does it solve? - Addressed systemic interface fragmentation across licensed third-party technologies (FaceScan, DermaScan) which violated the _Law of Similarity_, resulting in high extraneous cognitive load and diluted brand integrity. - Resolved the _Gulf of Execution_ inherent in remote biometric capture, where users struggled to translate system goals into physical positioning tasks due to a lack of perceptible affordances. ## What components were created? - **All Scans**: On-screen messaging, camera frame, camera outline, countdown (circular, numeric pre-countdown, timer) - **FaceScan**: Scene indicator (stars) - **BodyScan**: Alignment - **BodyScan, DermaScan**: Camera flash ## Challenges & resolution - **Proxemic visual acuity:** Standard UI components lost legibility when transitioning between near-field (FaceScan, ~30cm) and far-field (BodyScan, ~3m) contexts. - _Resolution:_ Engineered algorithmic scaling modifiers (multipliers) within the token system, ensuring optical consistency and accessible contrast ratios regardless of physical user distance. - **Whitelabel governance risks:** Unrestricted partner customisation threatened to compromise usability heuristics and biometric capture accuracy. - _Resolution:_ Architected a semantic token layer that permits brand alignment (colour, typography) while locking critical functional properties, preserving the integrity of the capture experience. - **Heterogeneous tech stack integration:** Amalgamating rigid third-party SDKs with proprietary tech created significant technical debt and visual dissonance. - _Resolution:_ Developed an abstraction layer that wraps external dependencies in UCDL-compliant containers, enforcing a unified front-end logic without altering core backend mechanics. ## Strategic Unification - Synthesised disparate biometric modalities into a cohesive design ecosystem to resolve interface inconsistencies and establish cross-platform semantic continuity. - Executed a heuristic evaluation of legacy interfaces to define a token-based unified visual syntax, enhancing brand uniformity and strengthening product differentiation. ## System Architecture & Tokenisation - Architected a semantic design token system to abstract visual primitives, enabling programmatic scalability and robust white-label configurations for B2B partner integrations. - Standardised atomic components and interaction patterns to ensure functional interoperability across web and mobile SDKs, optimising engineering integration and maintainability. ## Cognitive Ergonomics - Exploited existing mental models of photography (e.g. the “Viewfinder” metaphor) to minimise extrinsic cognitive load and lower the barrier to entry for complex physical alignment tasks. - Engineered context-aware component specifications that dynamically adjust visual affordances (such as stroke weights and scale) based on proxemics and environmental constraints. ## Interaction & Feedback Loops - Designed continuous feedback loops utilising fluid state transitions to maintain system status visibility, bridging the _Gulf of Evaluation_ and mitigating perceived latency during data capture. - Applied Gestalt principles (Law of Closure/Common Region) to create predictive interface elements that guide user behaviour and heighten perceived system reliability. ---

` through `

` elements represent six levels of section headings. `

` is the highest section level while `

Overview

The Legend

Standard Operations

Exploring Ideas

Exposed to Risk

Under Attack

Executive Cover

Proven & Adopted

Institutional Scar Tissue

Operational Collapse

Scenarios

What it shows

Vision Mode

Execution Mode

Where Figma Still Fits

How Time Has Shifted

Where the arguments converge

The bottleneck has moved

More agents does not mean more output

Planning and building are fused

Vibe coding is seductive poison

Quality is the differentiator

Where they diverge

The Simulation View

Process Mechanics

The Tooling View

Environment Design

Two halves of the same argument

AI Hallucination (Rework)

Agent Conflict (Blocking)

Capability Cliff

Context Window Collapse

Vibe Code Debt

Design System Erosion

Skill Atrophy

Review Fatigue

Cognitive Debt

Productivity Paradox

Strong Generalists

Deep Specialists

Crafty New Grads

The Core Shift

Entity Types

Stage Zones

Server States

Metrics

Overview

Healthcare Scenarios

Ambient Capacity Restoration

Regulatory Phase Shift

Reimbursement Tether

Confident Hallucination

Healthcare Constructors

Analysis Tools New

Alternative Frameworks

Multi-Level Selection (Wilson & Wilson, 2007)

Network Topology (Barabási, 2002)

Evolutionary Strategies (Axelrod, 1984)

Healthcare Physics

Elastic Constraint (Burnout)

Rigid Constraint (Compliance Wall)

Tether Constraint (Revenue Radius)

Hysteresis (Trust Dynamics)

Advanced Physics Unique to Health Sim

Langevin Equation (Brownian Motion)

Hysteresis Effect (Asymmetric Energy)

System Dynamics & Models

Agent Dynamics (Boids + Behavioural Econ)

Viable System Model (VSM) Mapping

Burnout Causal Loop Diagram

Constraints Reference

Overview

The Estuarine Metaphor

Flood Tide →

← Ebb Tide

Theoretical Foundations

1. Focus on What Is Possible (Constructor Theory)