We are entering a new era in storytelling—an era where intellectual property (IP) is no longer a static artifact, but a dynamic system.
For decades, creators and producers have relied on a traditional pipeline: a novel gets written, a script gets optioned, a pitch gets made, and—if it gains traction—the scramble begins. Suddenly, that single creative work must become a franchise. It must adapt to new formats, new teams, and new audiences. Lore must be patched. Canon must be invented on the fly. Tone is at risk. And the original voice—the spark that made it valuable in the first place—often gets diluted or lost.
The more successful the story, the more fragile the structure beneath it becomes.
At Design-Science, we believe it’s time for a new model. One that treats narrative IP not as a finished artifact to retrofit, but as a platform to be engineered from the start. We call this model the Canon Engine—a method of world-definition that turns stories into scalable systems.
At the heart of the Canon Engine is a structured artifact we call the World Kernel. Unlike traditional worldbuilding, which often focuses on aesthetic backdrops or encyclopedic lore, the World Kernel encodes the rules, roles, values, and tensions of a narrative universe in a way that allows it to generate infinite stories without loss of coherence or voice. It’s not just where the story happens—it’s why the story must happen.
This paper introduces the Canon Engine and the World Kernel as a new narrative infrastructure—designed not just for authors and screenwriters, but for agents, publishers, producers, educators, and innovation labs looking to build IP that can adapt without breaking.
We’ll show why today’s IP model is failing, what a world-first approach enables, how the Canon Engine works, and what kinds of IP futures become possible when we define the world before the story.
Welcome to the future of narrative architecture.
Welcome to the shift—from canon to content.
The Current Problem with IP
Storytelling has never been more valuable—or more volatile.
The rise of streaming platforms, global publishing deals, transmedia experiences, and generative content tools has created an unprecedented demand for high-quality, adaptable IP. Every publisher, studio, and creative team is on the hunt for “the next big thing”—not just a story that resonates, but a world that lasts.
Yet the way most IP is developed today is fundamentally misaligned with that goal.
The Traditional Path
Whether it starts as a novel, a script, a memoir, or a branded content initiative, the process is typically linear:
A compelling piece of work is created—by an author, screenwriter, or creative team
It gets optioned, picked up, or pushed toward adaptation
Stakeholders ask: “Can this be a series? A franchise? A world?”
The creative team scrambles to build supporting lore, tone bibles, and brand guidelines—often after the fact
If the IP is successful, new creators join the process: co-writers, directors, illustrators, editors, showrunners
Without a structured canon or world logic, continuity starts to wobble: tone shifts, contradictions emerge, and audience trust is strained
This is what we call the retcon trap—retroactive continuity that drains time, energy, and creative fidelity from a story that was never meant to be stretched that far.
The Cost of Fragile IP
When IP lacks structural support, it breaks under pressure. The symptoms are familiar:
Inconsistent tone across adaptations
Conflicting lore between formats
Brand dilution over time
Creative fatigue for the original team
Slower development for each new installment
Limited scalability across languages, cultures, and use cases
For publishers and producers, this means more risk.
For authors and creators, it means losing control over their voice.
For audiences, it often means disengagement.
What’s Missing?
Ironically, the more successful a story is, the more pressure it faces to become something it was never built to support.
What’s missing is not more content.
What’s missing is narrative infrastructure.
The kind of foundational logic that lets a world:
Sustain multiple voices without fracture
Generate new stories without lore drift
Adapt to new formats without losing tone
Invite co-authorship without creative chaos
We don’t just need better stories.
We need better systems for stories to live in.
That’s where the Canon Engine comes in.
The Paradigm Shift: World Before Story
For most of history, stories have come first.
A novel is written, a screenplay is drafted, a pilot is shot—and only then does the process begin of wrapping structure, branding, and continuity around that story. Worldbuilding, if it happens at all, is layered on after the fact: a map here, a character guide there, a style doc for licensing partners.
But in a world where stories must scale—across formats, authors, geographies, and technologies—that approach no longer works.
What we propose is a paradigm shift:
Start not with the story.
Start with the world.
What It Means to Go “World First”
We’re not talking about fantasy-style worldbuilding—naming planets or creating magic systems for their own sake.
We’re talking about world-definition as infrastructure: encoding the rules, roles, tensions, and symbolic logic that govern a narrative space—before the first scene is written.
“It’s not just where the story takes place—it’s why the story must happen.”
In a well-defined world:
Roles are logically emergent
Tensions arise from systems, not tropes
Character arcs are structurally supported
Every new story feels like a natural continuation, not a bolt-on
This approach doesn’t just make stories more consistent.
It makes them easier to generate, collaborate on, and adapt—without sacrificing tone or coherence.
Reversing the IP Pipeline
Traditional Model:
Book → Pitch → Adaptation → Expansion (scramble)
New Model (Canon Engine):
World Kernel → Story Path → Multi-Format Output → Canon-Aligned Scale
Instead of a single book spawning forced adaptations, we begin by building a world capable of generating many books, many shows, many experiences—each rooted in the same logic.
It’s the difference between writing a great song… and composing the musical key that dozens of songs can live in.
The Canon Engine Philosophy
At Design-Science, we call this IP-framework a Canon Engine.
It’s a way of structuring narrative systems so that canon becomes:
A unique, creative substrate
A growth engine
A collaboration layer
A protective boundary for authorial voice
With a Canon Engine in place, you don’t just write a story.
You launch a living world—one that’s ready to adapt, scale, and evolve.
What Is a World Kernel?
At the heart of the Canon Engine is the World Kernel—a structured, foundational definition of a narrative world that makes stories scalable, tone-consistent, and canon-compliant from the very beginning.
It’s not a style guide.
It’s not a pitch deck.
It’s not lore for lore’s sake.
The World Kernel is a generative, logic-based narrative substrate that encodes what makes the world work—so stories can emerge from it organically, and collaborators can build inside it safely.
Think of the World Kernel as:
A design system for narrative
A blueprint for story potential
A protocol for multi-author integrity
A scaffold that holds tone, logic, and tension in place
Just as software has architectures…
Just as brands have identity systems…
Narrative IP needs world structure that governs growth.
What’s Inside a World Kernel?
Systemic Logic
Rules that define what is possible, allowed, or taboo
Sources of power, influence, tension, and limitation
The consequences of action within the world
Roles & Archetypes
Emergent character functions based on the world’s logic
What kinds of people/agents naturally exist here?
Who upholds the system, and who challenges it?
Core Tensions
Structural conflicts that generate story—beyond surface drama
These are renewable: they support multiple stories, not just one
Tone & Symbolic Language
The aesthetic DNA of the world
Visual and emotional cues
Symbol systems, idioms, metaphors, iconography
Canonical Artifacts
Snippets of the world “in action”:
Job postings, social posts, laws, rituals, news headlines
Dialogue samples, signage, maps, documentation
These serve as training data for tone, format, and culture
Why the World Kernel Matters
With a World Kernel in place:
Authors can generate coherent stories faster
Adaptations can stay true without micromanagement
New creators can enter the world without breaking canon
AI tools can support story ideation without undermining voice
Producers get a narrative platform, not a single-use script
It is the operating system of a storyworld.
And it protects the most important thing: the unique creative signature of the original author, now empowered to scale.
The Process: Canon-to-Content
The transition from story to scalable IP begins with a fundamental reframing:
You’re not just writing a story. You’re engineering a world that makes stories inevitable.
The Canon-to-Content process is a repeatable, modular method for constructing the World Kernel, identifying viable narrative arcs, and preparing your IP for cross-format expression and long-term growth.
This isn’t a production pipeline.
It’s a creative scaffolding—built for clarity, adaptability, and scale.
The Canon-to-Content Workflow
Phase 1: World Audit
We begin with what already exists—whether that’s a story idea, a full manuscript, a screenplay, or even just a pitch.
The questions we ask:
What’s the underlying system that governs this world?
What patterns emerge in its roles, power structures, or logic?
Is there already an implicit canon waiting to be made explicit?
Output:
📄 Diagnostic map of the IP’s world logic and tension structure
Phase 2: Kernel Design
Here we build the World Kernel—the backbone of scalable, canon-safe storytelling.
We define:
Rules of the world
Emergent roles and archetypes
Core symbolic tensions
Constraints that generate meaningful story
Visual, tonal, and thematic boundaries
Output:
📘 Canon Engine: Full World Kernel document + diagrammatic scaffolding
Phase 3: Narrative Thread Mapping
Now that the world is structurally sound, we map the possible stories it can support.
This includes:
Primary arc(s): the main book, series, or story already underway
Adjacent arcs: side stories, character perspectives, alt timelines
🧵 Canon Threads Matrix: Core arcs + story node mapping
Phase 4: Artifact Layer Development
To make the world “feel real,” we generate artifacts that might exist inside it.
These serve as:
Tone and format guides for adaptation
Visual and narrative inspiration
Training assets for teams or AI tools
Examples:
In-world ads, warnings, contracts
Dialogue samples, social media posts, graffiti
Government docs, school curriculum, HR training memos
Output:
📂 World Artifact Pack: 5–10 immersive samples
Phase 5: Content Generation
With a World Kernel and story threads defined, we can generate:
Sample chapters, scripts, or scenes
Adaptation treatments or episodic breakdowns
Visual storyboards or concept art briefs
Interactive or AI-enabled story prompts
Output:
🎬 Canon Content Kit: “Starter media” for producers, co-authors, designers
Why This Process Matters
It creates:
Confidence for producers and publishers
Safety for authors and originators
Clarity for new collaborators
Consistency for every adaptation
Accelerated, high-fidelity story development
It turns narrative into infrastructure—with all the creative soul intact.
Generative Tooling Integration
AI can write. But without structure, it writes noise.
Generative AI models have opened a firehose of content.
But without canon & context, they create pastiche. Approximation. Artifacts based on the past.
The Canon Engine solves this by doing what AI alone cannot:
It encodes intentional structure, then provides clear boundaries and symbolic logic so that generative tools can create within a coherent narrative system.
This transforms AI from a novelty to a precision co-author.
How It Works
The World Kernel includes canonical inputs that are explicitly engineered for generative use:
Tone and Symbol Layers
Distilled metaphors, aesthetics, idioms, and values
Ensures that AI-generated text sounds native to the world
Dialogue + Lore Prompts
Samples and snippets pre-formatted for style conditioning
Used as fine-tuning anchors or prompt context in large language models
Artifact Templates
Formats for job posts, logs, signage, rituals, public notices
Enable quick generation of “in-world” documents with world-safe content
Narrative Constraint Maps
AI sees what kinds of plots, roles, and outcomes are permitted
Prevents off-brand, canon-breaking storylines
Prompt Engineering Frameworks
Custom prompt recipes tied to World Kernel structure
Guide AI to create:
Episodic content
Storyboard beats
Story arcs within defined moral/structural logic
Dialogue across roles and social classes
Why This Matters
Without a World Kernel, generative tools hallucinate stories based on training data from a broken past.
With the Canon Engine, AI becomes a world-anchored partner.
This makes it possible to:
Generate storyboards, scenes, and characters in canon
Co-create with multiple authors or teams without tone drift
Launch responsive storytelling interfaces for readers or learners
Enable customized experiences for individuals, teams, or classrooms
Application Examples
Use Case
Generative Benefit
Screenwriter ideation
Auto-generate beat sequences inside canon
Publisher platforms
Personalized short stories from a shared world
Educators
Scenario training based on world roles and dilemmas
Brand experiences
World-native campaigns that never go off-message
Game narrative
NPC dialogue, lore events, side quests that fit the world
With the Canon Engine in place, AI isn’t guessing.
It’s creating with fidelity, with coherence, and with world permission.
It’s not just for novelists, or studios, or futurists.
It’s a platform-agnostic method for structuring narrative IP at scale—whether you’re building a fictional universe, designing immersive training, or turning a thought-leadership book into a movement.
Here are four illustrative examples that show how this system plays out in the wild:
Case 1: The Script That Became a World
Before:
An accomplished screenwriter has a gripping sci-fi pilot, rich in tone and premise—but the world is thinly defined, and the production studio wants a franchise.
Canon Engine Use:
We extract the implicit logic from the pilot
Build a full World Kernel around the cultural, technological, and ethical systems
Identify six viable spinoff arcs based on emergent roles (e.g. a surveillance priesthood, a rebel botanist underground)
Create a canon-safe adaptation grid: short-form series, visual novel, interactive companion site
After:
What began as a single script becomes a multi-format IP property—with built-in consistency, visual tone packs, and a roadmap for audience engagement across platforms.
Case 2: The Author With One Book—and Infinite Potential
Before:
A literary author releases a haunting novel set in a semi-alternate world with its own language quirks and laws. A publisher wants a series, but fears the world is too delicate to expand.
Canon Engine Use:
We define the symbolic logic: what holds power, what generates tension, what roles recur
Establish tone constraints to protect voice
Design two adjacent narratives in different formats (short story, film treatment)
Provide a guide for future co-authors or adaptations
After:
The world holds. The voice is preserved. And the author becomes a franchise architect—without compromise.
Case 3: A Nonfiction Book That Became a Simulated Learning World
Before:
A business thinker writes a best-selling book about future leadership in networked organizations. Interest grows in making it a course, a podcast, even a VR experience—but there’s no narrative spine.
Canon Engine Use:
We construct a symbolic world where the book’s principles are lived: roles, tensions, dilemmas
Define new in-world roles: “Signal Interpreters,” “Decision Lattices,” “Ethical Firebreaks”
Package the world as an immersive learning IP with built-in curriculum pathways
After:
The book evolves into a learning universe, adaptable across industries—and the author owns a structured, generative world that extends their impact and licensing value.
Case 4: A Publisher Seeks Adaptation-Ready Titles
Before:
A boutique literary agency wants to expand its most promising properties for TV/film, but their manuscripts aren’t built for scale—and “worldbuilding” feels too genre-specific.
Canon Engine Use:
We retrofit World Kernels behind 2–3 promising works
Extract canon logic, define voice tone constraints
Generate story arc maps, pilot-ready treatments, and canon-aligned visual guides
Train the agency’s editorial team on identifying “scalable world logic” in new submissions
After:
The agency now offers World-Ready IP as a service—equipping their authors to pitch not just a story, but an ecosystem.
Each of these examples shares a single shift:
From storytelling as output… to world-definition as infrastructure.
That’s the unlock. That’s the leverage.
That’s the future.
IP Ownership Framework
Creative work isn’t just emotional—it’s legal.
Writers, producers, and partners rightly want to know:
“Who owns what? What can I protect? How much control do I keep?”
The Canon Engine is built to empower creators, not displace them.
We’ve designed the model with flexibility, transparency, and respect for original voice at its core.
Whether you’re an author, screenwriter, or brand strategist—if you bring the idea, you retain the crown.
You own your story.
We help you build the system that protects it—and scales it.
How Ownership Is Structured
Ownership in the Canon-to-Content process is designed around four layers:
1. Original Creative Work
Owned 100% by the author or client
Examples:
A novel manuscript
A TV pilot
A memoir
A branded campaign outline
This is your IP. You brought it into the world. You keep it.
2. World Kernel Artifact
Jointly negotiable based on how it was built:
Situation
Ownership Path
Client-funded (based on their story)
Client owns full Kernel
Co-developed (new world, shared input)
Co-ownership, revenue share, licensing
Design-Science–initiated
DS owns; client licenses or adapts
The World Kernel is a structured world system, derived from your idea or created alongside you. It can be assigned, shared, or licensed based on intent.
3. Canon Engine Methodology & Tools
Owned by Design-Science
These are proprietary:
Canon definition frameworks
Structural logic templates
Narrative scaffolds and process IP
AI prompt guides and generation layers
This is our narrative infrastructure, and how we help you scale—safely.
Design-Science scales value through frameworks—not ownership grabs.
“IP doesn’t have to be zero-sum. When you define the canon first, everyone can build without breaking the story.”
Use Cases by Domain
The Canon Engine is not confined to genre fiction or screenwriting. It works anywhere a world, a message, or a story ecosystem must remain coherent—across formats, time, and teams.
Here are key verticals where Canon-to-Content unlocks new value:
Fiction & Publishing
Before:
Authors pitch single titles, and if successful, scramble to extend with sequels, spinoffs, or adaptations—often without consistent lore or tone.
After:
Authors define their world once using the Canon Engine
Publishers gain a ready-to-scale IP asset
Literary agents pitch not just stories, but franchise-ready worlds
Multi-book arcs, anthology series, or co-authorship models become structurally supported
Example:
A debut fantasy novel becomes a five-book epic, a character-driven short story collection, and an adaptation-ready animated anthology—all staying canon-consistent.
Film, TV & Transmedia
Before:
One pilot or film script sets off a chain reaction of reactivity—lore expansion, tone confusion, worldbible creation under pressure.
After:
Producers start with a World Kernel, not just a treatment
Visual style, character logic, and plot density are pre-structured
Adaptations across animated, live-action, docu-fiction, or interactive are mapped and canon-verified
Co-creation across writers’ rooms becomes frictionless
Example:
A sci-fi pilot becomes the seed of a serialized mainline, a documentary-style companion series, and an alternate-timeline short—all generated from a shared canon core.
Learning & Development / Enterprise Simulations
Before:
Corporate training and education content feel bolted-on, inconsistent, and too generic. Immersive learning lacks story coherence.
After:
A “Learning World” is defined with roles, cultural logic, and tension frameworks
Training scenarios become narrative episodes inside that world
Simulation experiences feel meaningful, immersive, and connected
Upskilling modules gain repeat engagement and narrative memory
Example:
A leadership program is reimagined as a live, evolving fictional workplace—where each training is a new “episode” in a shared world, complete with fictional roles and workplace drama arcs.
Nonfiction & Thought Leadership
Before:
A compelling business, social, or philosophical book ends up as static material—hard to adapt or extend meaningfully.
After:
The principles of the work are encoded in a narrative universe
Roles, systems, and tensions are embedded in fictional settings
Stories are created that model the future instead of simply describing it
Books become platforms for content ecosystems
Example:
A nonfiction title on ethical tech leadership becomes a short-story collection, a podcast, a simulation world, and an MBA case library—each set in the same canonical logic.
Education, Civic, and Cultural Institutions
Before:
Museums, NGOs, and schools struggle to create narrative engagement that persists across audiences and programs.
After:
A world is built around their mission or historical insight
Artifacts, roles, and perspectives are structured canonically
Interactive media, AR exhibits, and classroom experiences stay unified
Story-based learning becomes scalable and participatory
Example:
A museum exhibit on global migration expands into a world-narrative simulation, letting students step into roles over time and culture—powered by a Canon Engine-driven platform.
In every domain, the shift is the same:
From one-off content to a narrative system.
From IP risk to IP resilience.
The Future of IP Is Infrastructure
In the past, great stories were enough.
A single book could launch a movement. A script could become a franchise. A film could generate billions in cultural equity. But that success often came with a cost—retroactive worldbuilding, fragile lore systems, broken tone, creative burnout, and a long tail of reactive scrambling to hold the pieces together.
That model is breaking.
The content economy is accelerating.
Audiences want more, faster, and across more platforms.
Creators need tools to scale without compromise.
Studios need worlds, not just titles.
Educators and innovators need immersive systems, not standalone messages.
In short:
IP needs infrastructure.
Narrative Infrastructure = Canon Engine
The Canon Engine doesn’t replace creative genius.
It amplifies it by giving it a durable, extensible form.
With it:
Stories become reproducible without becoming generic
Worlds can be licensed, co-authored, extended safely
Voice is preserved across format, author, and medium
IP becomes a living platform, not a static product
This isn’t about more content.
It’s about more coherence, more capacity, and more control—for creators, producers, educators, and publishers alike.
The Canon Engine Is Just the Beginning
The shift to narrative infrastructure opens the door to:
AI-assisted story generation within canon
Simulation and training experiences based on IP worlds
Audience-customized content threads that stay true
Branded, adaptive learning systems
Transmedia worlds that operate like operating systems
When canon comes first, the possibilities multiply.
And your story becomes a world with gravity—capable of pulling in stories, creators, and audiences for years to come.
What Next?
If you’re a creator, we can help you become a franchise architect.
If you’re a publisher or agent, we can help future-proof your authors.
If you’re a studio, we can deliver not just scripts—but systems.
If you’re an educator or strategist, we can build a world where learning feels lived.
A primary mission of Design-Science is to Design & Commercialize New Products. So this is kind of a big deal. The first “product/company” launched by Design-Science!
Since the early ideas of 2021 as a ‘management simulation game’, to the rapid acceleration of AI-oriented computing in 2023, INDUSTRY 5, or ‘i5’ is now a commercially viable Product! (or Services as Software, quite possibly)
In fact, i5 is even its own Company – looking for investment for growth!
You’ve already seen (i5 Demo v0.2) how i5 harmonizes the roles of Human Users, multiple AI Agents, and the INDUSTRY-5.0 platform.
Now, we’ll explore how WidgetCo, a global manufacturer, leverages i5 to orchestrate its supply chain operations.
Start:
To start, on the left we can see all of the capabilities of i5’s AI-Agents; including comprehensive ‘end-to-end’ Processes, and Role-Specific Orientations.
On the right, we see the i5 overview of current financials, operational metrics, AI-driven analytics, and explanations.
Together, i5 can orchestrate updates to demand forecasts, production plans, and procurement.
Let’s explore how i5 brings speed, precision, and intelligence to Demand-Driven Planning.
Demand Forecaster:
The process starts with the Demand Forecaster Agent analyzing WidgetCo’s current demand forecast, inventory levels, and relevant context.
Context includes external influences like market conditions, seasonal trends, or other constraints, each with varying probabilities and confidence levels.
The agent suggests incremental adjustments to the demand forecast based on new information.
After review, the user approves the forecast adjustments – and the agent proceeds to post these forecast updates to i5.
The adjusted forecast is now live, setting the stage for the next step.
Production Planner:
With the updated forecast, the Production Planner Agent evaluates current production plans and identifies incremental requirements.
The agent summarizes findings, proposing adjustments to meet the new demand.
Upon user approval, the agent updates i5 with the incremental production requirements, ensuring transparency and traceability.
Procurement Agent:
Next, the Procurement Agent accesses the Bill-of-Materials to calculate requirements for the updated production plan. The agent determines and specifies the required quantities and delivery dates for each material. On user approval, the agent posts the new material requirements to WIDGET-co’s Demand Graph for procurement.
Explanation:
To conclude the process, the Agents provide a comprehensive explanation of what actions were taken, why they were recommended, and how they were executed.
With i5, you’ll never have to guess, “Why was that decision made?”
Summary:
To summarize, i5 transformed a familiar demand-driven planning process into a seamless flow—from forecast adjustments to production alignment to procurement.
i5 delivers Prescriptive Analysis AND Action, with Precision and full Transparency.
Traditional processes that take days or weeks—characterized by manual workflows, silos, and other inefficiencies—are completed in minutes with i5.
NŪMŪV is a design-experiment exploring the truly autonomous movement of people AND cargo.
The PURPOSE of NUMUV is a comprehensive, anticipatory ‘re-imagining’ of cross-modal transport that could help challenge current assumptions, and prove-out qualitatively and quantitatively what an ideal future could be.
The INTENT of NUMUV is a system that could radically improve how the world moves.
the NUMUV Objective or ‘Job to be Done1see Job to be Done theory‘
By the year 2050, HOW can the world move 3x the volume of people and cargo, and do so…
transparently?
efficiently?
comfortably?
predictably?
safely?
NUMUV Design Hypothesis:
An Autonomous robotic vehicle / transport system is fundamentally different than a ‘Driverless Car’
Just like a modern car is fundamentally different than a ‘horseless carriage’
Decoupling the ‘cabin’ from the ‘conveyance’ enables a completely new system.
The cabin for people does not need to be the same as the conveyance platform / chassis
It can be much lighter and stronger, and most importantly – infinitely customizable.
Make Transfers ‘Transparent’
Transfers between modes are the main ‘value leak’ – waste in time & cost
Orient on the discrete Person / Product being moved
to inform the system, not multiple intermediaries
Remove all complication unrelated to core function
It’s very easy to add-complexity
Should be an ‘open’ design – to enable clear-focused innovation opportunities for existing Companies and Infrastructure (faster, at less cost)
Transport designers can focus on unique new form-factors within the system (removes ‘congestion’), vs unique standalone vehicles (adds ‘congestion’)
Running a NUMUV Simulation to determine VALUE (Cost:Benefit)
To make NUMUV come alive, so we can understand the differences in volume, time, and cost – versus today, our lNDUSTRY 5.0 supply-chain & logistics solution leverages the NUMUV concept in a realistic way, modeling the movement of people & cargo within real-world physical-economic constraints of Cost, Capacity and Time – and of course, the laws of Physics.
NOTE: this example lesson / game-plan was generated by INDUSTRY 5.0
Two teams within a company will compete in a turn-based simulation to optimize the demand-supply network over a multi-year period.
Your company’s i5 Admin may act as a ‘Facilitator’ for innovation and experimentation activities by constructing various scenarios with unexpected disruptions.
The game will be facilitated by an instructor who introduces variability and volatility events. The winning team will be the one that achieves the best comprehensive design and demonstrates superior anticipatory skills.
Game Setup
Team A
Focused on efficiency and cost reduction.
Team B
Focused on sustainability and resilience.
Facilitator
Configures and introduces unexpected events (environmental, political, economic changes).
Game Rounds
Each round represents a multi-month period, resulting in multiple rounds over a multi-year period.
Initial Setup
Baseline Data
Current state of the supply chain network (inventory levels, supplier contracts, logistics providers, market conditions). Initial KPIs (efficiency, sustainability, resilience metrics).
Objectives
Improve overall supply chain performance. Anticipate and respond to unexpected events. Balance efficiency, sustainability, and resilience.
Round 1: Initial Assessment and Strategy
Team A:
Analyze current inventory levels and supplier performance. Identify cost-saving opportunities – e.g., renegotiate supplier contracts, optimize logistics routes
Team B:
Assess sustainability practices of suppliers and logistics providers. Develop plans to improve resilience. – e.g., diversify suppliers, invest in renewable energy sources
Facilitator:
THEN, BEFORE NEXT ROUND – INTRODUCES AN UPCOMING ENVIRONMENTAL REGULATION CHANGE THAT WILL TAKE EFFECT IN 12 MONTHS.
Round 2: Implementing Initial Strategies
Team A:
Renegotiate contracts with key suppliers to secure lower prices. Implement a new inventory management system to reduce holding costs.
Team B:
Begin transitioning to suppliers with better sustainability practices. Invest in renewable energy projects for key facilities.
Facilitator:
ANNOUNCES AN UNEXPECTED SURGE IN DEMAND DUE TO A NEW MARKET TREND. TEAMS MUST ADJUST THEIR STRATEGIES ACCORDINGLY.
Round 3: Responding to Demand Surge
Team A:
Increase production capacity to meet the surge in demand. Expedite shipping schedules with logistics providers.
Team B:
Scale up production sustainably, ensuring minimal environmental impact. Strengthen partnerships with logistics providers to ensure timely delivery.
Facilitator:
INTRODUCES A GEOPOLITICAL EVENT THAT DISRUPTS SUPPLY CHAINS IN A KEY REGION.
Round 4: Managing Geopolitical Disruption
Team A:
Identify alternative suppliers to mitigate the impact of the disruption. •Increase safety stock levels to buffer against supply chain uncertainties.
Team B:
Evaluate the resilience of current suppliers and logistics routes. •Implement contingency plans for supply chain continuity.
Facilitator:
ANNOUNCES AN ECONOMIC DOWNTURN THAT AFFECTS CONSUMER SPENDING.
Round 5: Adapting to Economic Downturn
Team A:
Adjust production schedules to align with reduced demand. Implement cost-cutting policies across the supply chain.
Team B:
Focus on maintaining sustainability initiatives while managing costs. Explore new markets to offset the decline in consumer spending.
Evaluation and Scoring
Winner Announcement:
The facilitator announces the winning team, highlighting their strategies and how they effectively anticipated and managed unexpected events.
Debrief:
Teams discuss their approaches, what worked well, and areas for improvement. The facilitator provides feedback and insights into how the game mirrored real-world supply chain challenges and opportunities.
Conclusion
Metrics:
Efficiency: Cost savings, inventory turnover, order fulfillment cycle time. Sustainability: Carbon footprint, energy consumption, waste reduction. Resilience: Supplier risk score, logistics risk score, ability to recover from disruptions.
Final Scores:
Each team’s performance is assessed based on the metrics, and points are awarded accordingly. The team with the highest overall score wins, demonstrating the best comprehensive design and anticipatory skills.
By following this step-by-step approach, INDUSTRY 5.0 by Design-Science provides a dynamic and engaging way for participants to develop and apply advanced supply chain management strategies, leveraging both predictive analytics and real-time decision-making.
The exciting near-term potential of INDUSTRY 5.0 is a game-like experience for teams to experiment and innovate.
Imagine a platform where your teams can simulate real-world scenarios in a safe and controlled environment. While the long-long term goal is for i5 to be powerful tool for optimizing supply chains. In the near-term, it’s an innovative sandbox for your teams to play, experiment, and explore new strategies without any real-world risks.
Experimentation and Innovation
Safe Experimentation: Teams can use i5 to run ‘what-if’ scenarios, testing out different supply chain strategies and seeing the potential outcomes in real-time. Whether it’s adjusting inventory levels, experimenting with new suppliers, or trying out different transport routes, the platform provides a risk-free environment to innovate.
Gamified Learning: The game-like interface of i5 makes learning engaging and fun. Teams can compete to optimize their supply chains, earn points for efficiency, and even participate in friendly competitions. This gamification helps to foster a culture of continuous improvement and innovation.
Collaborative Problem-Solving: i5 allows for multi-party collaboration, where different departments or even different companies can work together to solve complex supply chain challenges. This collaborative approach not only enhances problem-solving skills but also encourages the sharing of best practices and innovative ideas.
Real-Time Feedback: The real-time data and feedback provided by i5 are invaluable. Teams can instantly see the impact of their decisions, allowing for quick adjustments and iterative improvements. This immediate feedback loop accelerates learning and innovation.
Scenario Planning: With i5, teams can simulate various future scenarios, from sudden spikes in demand to unexpected supply chain disruptions. By preparing for these possibilities in advance, companies can build more resilient and adaptive supply chains.
Driving Innovation
Strategic Decision-Making: The insights gained from experimenting with i5 can inform strategic decision-making. Companies can identify the most effective strategies and scale them up in the real world, driving significant improvements in efficiency and competitiveness.
Continuous Improvement: The platform’s continuous learning environment encourages teams to constantly seek out new ways to optimize and innovate. This culture of continuous improvement is crucial for staying ahead in a rapidly evolving market.
Cross-Functional Innovation: i5 facilitates cross-functional innovation by breaking down silos and enabling collaboration across different parts of the organization. This holistic approach ensures that innovations are aligned with overall business goals and can be implemented seamlessly.
Conclusion
INDUSTRY 5.0 is not just a tool for managing supply chains—it’s a powerful platform for fostering innovation and driving continuous improvement.
By providing a game-like, collaborative, and data-driven environment, i5 empowers your teams to experiment, learn, and innovate like never before.
Since 2021, I’ve had a bold, audacious vision to re-imagine, and re-invent how ‘supply chain’ is done – to meet the anticipated capabilities, needs, and demands of 2050.
With nearly three decades of experience designing supply chain solutions based on the technology and business constructs of the 20th century, I believe there is a better way forward.
Can we achieve the promises of Industry 4.0 with mid-20th century approaches? In my opinion, no way.
So, we designed a completely new system and method from the ground up. This new approach is faster, easier, inherently resilient, and still delivers on the business goals and objectives you expect – but with incredible efficiency for a prosperous and sustainable future.
Sooner the better, right?
Our approach is to leapfrog the Industry 4.0 hype and start from scratch. To reach that goal, we have to start somewhere, and that ‘somewhere’ is crucial.
Complexity is good; complication is not. So, we discarded the layers of accumulated complications first.
Unfortunately, in the meantime, companies will continue throwing time and money at doing the same things the same way, hoping for better results. Will adding AI on top of these old systems actually improve anything? If past ‘bolt-on’ approaches are any indication, it may ‘check a box’, but the long-term answer is likely no.
We want to go further, faster. This can only be accelerated by companies that want to be part of this vision to achieve real results sooner.
We would love to hear your ideas and vision for the future and explore how we can get there together, faster.
Here is a ‘packaged’ version of the INDUSTRY 5.0 v0.2 Demo in July 2024.
i5 Demo v0.2 July 2024
Imagine a dynamic, game-like environment for planning, managing, executing, and visualizing demand, supply, and movement.
Today, we’ll see multiple entities in a demand : supply network, interacting in real-time, represented by ai agents for sales, purchasing, and booking.
The agents listen to i5 Supply : Demand Graph updates, negotiate based on their company policies, and aim to match new demand with available supply, and autonomous transport capacity1see NŪMŪV.
Once all conditions are met, the Purchasing-Agent commits to the purchase and generates a Smart-Agreement in i5, securing each multi-party transaction.
The buyer controls the transaction. Each company has visibility into respective aspects of smart-agreements.
As new agreements are made, i5 continuously updates projected inventory forecasts, viewable anytime and utilized for planning future demand-supply scenarios.
