🔷 The Situation
The business experienced a sudden 20% uplift in demand across five SKUs.
At first glance, higher demand appears positive. However, increased volume under constrained supply can quickly turn into lost profitability if not managed strategically.
Production relies on two critical raw materials: RM1 and RM2.
After capacity evaluation:
- Total RM1 required to fulfill full demand: 37,800 units
- Available RM1 capacity: 32,000 units
- RM2 capacity: Sufficient
The gap clearly indicated that RM1 would restrict production.
This transformed the challenge from:
“Can we meet demand?”
to
“How do we allocate limited RM1 to maximize financial outcome?”
🔷 My Thought Process
1️⃣ Confirm the True Constraint
Before making allocation decisions, I validated which resource was actually limiting output.
Only RM1 showed a deficit.
RM2 had adequate capacity.
This step prevented unnecessary complexity and ensured the model focused on the real bottleneck.
2️⃣ Shift from Volume Thinking to Profit Thinking
Under normal conditions, demand fulfillment drives planning.
Under constraint, the metric changes.
Instead of asking:
“How much can we produce?”
I asked:
“Where does each unit of RM1 generate the highest return?”
Each SKU was evaluated based on:
- Contribution margin
- Margin generated per unit of constrained resource
- RM1 consumption per unit
This reframed the decision around economic efficiency.
3️⃣ Rank and Allocate
After comparing profitability per constrained unit, I prioritized SKUs that generated higher contribution per unit of RM1.
One SKU (SKU2) stood out as:
- Lower contribution margin
- Higher relative RM1 consumption
- Lowest return per unit of constrained input
Under shortage conditions, continuing to allocate RM1 to that SKU would dilute overall profitability.
So instead of spreading shortages evenly across the portfolio, the model concentrated the shortage impact where it had the least financial damage.
🔷 Strategic Outcome
This structured allocation approach resulted in:
- 91% service level across the portfolio
- Full fulfillment of higher-margin SKUs
- Controlled lost sales limited to the least profitable SKU
- Preservation of overall contribution margin
Rather than chasing 100% fulfillment, the model protected profit integrity.
Live Scenario Optimization Model (Excel): Access the Working Model
Scenario Model Insight & Action Plan: Review the Analytical Reasoning
🔷 What This Demonstrates
This project highlights a core supply chain principle:
When a bottleneck exists, optimization must revolve around the constraint.
Equal allocation may appear fair — but it is rarely optimal.
Targeted allocation based on financial return ensures the constrained resource delivers maximum value.
🔷 Forward-Looking Actions
To reduce future exposure:
Immediate Focus
- Understand root cause of demand surge
- Evaluate short-term RM1 sourcing flexibility
Planning Enhancement
- Revisit RM1 safety stock policies
- Incorporate constraint scenarios into S&OP discussions
Strategic Improvements
- Explore dual sourcing options
- Periodically review resource bottlenecks
- Integrate scenario modeling into routine planning
🔷 Closing Perspective
This case reinforced an important insight:
In constrained environments, operational decisions must align with financial strategy.
Volume does not equal value.
Allocation discipline protects profitability.
This model reflects my approach to combining data analysis with strategic supply chain thinking.
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