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In most large supply chain transformations, the conversation starts with platforms, features, requirements and timelines. But after working through nearly 1,700 user stories across the end-to-end supply chain, one insight stood out clearly:
Scale is not the problem. Lack of structure is.
Many programs deliver hundreds of well-written user stories. Each one makes sense in isolation. Yet when you step back and ask a simple question — “What decision does this enable, and what outcome does it drive?”—the answer is often unclear.
That’s where value starts to leak.
The hidden issue with traditional delivery
Typical delivery models organize work around tools or modules. Over time, user stories become:
- Tool-centric instead of decision-centric
- Siloed across functions
- Hard to prioritize, govern, or explain in business terms
The result is familiar: the system goes live, functionality exists, but value realization lags.
Starting with decisions, not features
We took a different approach in our implementations. Instead of structuring work around platforms, we anchored everything around supply chain functional areas and the decisions planners make across demand, supply, inventory, deployment, scheduling and logistics. Within each area, we identified a finite set of core decisions: allocation trade-offs, replenishment triggers, buffer placement, plan stability, and service vs. cost choices.
Those decisions, not features became the foundation.
DEU: Decision Enablement Unit
To connect strategy to execution, we introduced DEU.
Each DEU:
- Anchors to a single planner decision
- Lives within one functional area
- Bundles configuration, data, integration, and capability
- Has explicit KPI impact
Across the full scope, this resulted in:
- ~49 DEU
- ~245 features
- ~1,700 user stories
It is a large number but governed, and finite.
Why structure changes everything
Every user story became fully traceable:
Business outcome → KPI → Decision → DEU → Feature → User story
Example is illustrated below:
Example: Finished Goods Inventory Optimization
1. Business Outcome Reduce working capital tied up in inventory without hurting customer service.
2. KPIs
- Inventory Turns ↑
- Average Days of Inventory ↓
- Customer Service Level (OTIF / Fill Rate) ≥ 98%
3. Decision where and how much safety stock should be held across the distribution network?. This is a planner decision, made periodically and adjusted when variability or demand patterns change.
4. DEU (Decision Enablement Unit)
Network Safety Stock Placement Decision
This DEU enables planners to decide:
- At which nodes (plant, DC, regional DC) buffers should exist
- What target buffer levels should be, based on variability and service targets
DEU scope includes:
- Decision logic for safety stock calculation
- Variability inputs (demand, supply, lead time)
- Service-level policies
- Planner override capability
- KPI visibility (inventory vs service trade-off)
5. Features (under this DEU):
- Safety Stock Calculation Logic
- Statistical buffer calculation based on demand and lead-time variability
- Service Level Policy Configuration
- Ability to define service targets by SKU / location / customer segment
- Buffer Visualization Dashboard
- View recommended vs approved safety stock by node
- Planner Override & Approval Workflow
- Allow planners to adjust buffers with reason codes
- ERP Integration for Target Stock Levels
- Publish approved safety stock to execution systems
6. User Stories (under one feature)
Feature: Safety Stock Calculation Logic
- As a supply planner, I want the system to calculate safety stock by SKU–location using demand and lead-time variability, so that I can meet service targets with minimal excess inventory.
- As a supply planner, I want to simulate the impact of changing service levels on safety stock, so that I can understand the inventory–service trade-off before approving buffers.
- As a planning manager, I want to see which SKUs contribute most to inventory value, so that I can challenge buffer levels where working capital risk is high.
This approach eliminated ambiguity, reduced rework, and aligned business and delivery teams around why something existed not just what was being built. Adoption, data readiness, integration risk, and delivery phasing were designed in from the start.
Where AI adds value
AI becomes more powerful when applied to a well-structured decision system. Once the decision model was clean and connected, AI became a true force multiplier. It could:
- Instantly show KPI impact when scope changes
- Highlight which stories matter most based on value
- Surface data, integration, or adoption risks early
- Help teams navigate thousands of stories with context
- Preserve institutional knowledge across phases and teams
This isn’t about replacing consultants. It’s about amplifying judgment, speed, and quality.
The real differentiation
Many teams can implement a planning platform. Far fewer can explain how hundreds of stories collectively drive service, inventory, cost, and resilience. Our differentiation isn’t the number 1,700. It’s the coherence behind it—and the ability to apply AI meaningfully on top of that structure.
A simple litmus test
If you can’t answer:
“Which decision does this story enable, and which KPI does it move?”
Then the story probably shouldn’t exist. That question has fundamentally changed how we design and deliver supply chain transformations.
