Three warehouses. Three regions. Three hundred SKUs. One legacy allocation logic that looked reasonable on average and leaked margin every single week. I rebuilt the allocation engine and recovered roughly four million dollars a year, with no new inventory and no increase in warehouse capacity — just a smarter planning decision built on probabilistic demand modeling.
I engineered the entire case study from the ground up — designed the data architecture, built the simulation pipelines, analyzed the risk vectors, implemented the Monte Carlo simulation, and formulated the linear programming model.
Plans to an average week. Overstocks slow movers, starves the volatile lines, and gives back margin the moment one region runs hot.
Plans against the bad week, not the average week. Absorbs a +30% East region surge with no manual replan.
A 54% reduction in weekly operating cost. Roughly $3.98M annualized, recovered from the same data by making a smarter decision against it.
Mid-market operators do not lose margin because demand is unknowable. They lose it because decisions are made against averages that hide risk.
The legacy plan took one average week of demand per product, split it across warehouses by historical share, and shipped whatever the orders called for. On paper it balanced. In the real business it never did.
Margin leaked through dead stock in the West, expedited freight into the East, write-downs on slow movers, and lost sales on the volatile lines. No single failure was loud. Hundreds of small mismatches compounded every week.
The fix was never a sharper forecast. It was a sharper decision.
The legacy plan prepared for an average week, completely missing the fact that a handful of highly volatile products were causing the largest stockouts in the catalog.
At the same time, a +30% demand surge was silently bleeding money in the East region. Averaged across the whole network, the signal vanished. On the ground, it was the story.
A small group of products swung so widely week to week that any single average was meaningless. The cost of getting them wrong dominated the entire plan.
Instead of planning against one expected number, the engine simulates ten thousand realistic versions of next week, built from historical demand patterns and the modeled East region surge.
The output is not a prediction. It is the honest range of weeks the business will actually live through, expressed in dollars and risk instead of a single point estimate.
Three numbers matter. A normal week. A bad week the business should plan against. And the rare week it must survive.
This engine answers one core question: How much of each SKU should be positioned in each warehouse so total weekly cost is minimized under real operating constraints?
The optimization engine did not just produce a cheaper answer. It produced a more realistic allocation policy — one that respected physical limits, protected high-risk demand, and converted uncertainty into an operational decision.
On the same data and the same warehouse limits, the optimized plan lands at $65,580.72 a week against $142,224.92 for the baseline. The gap is not a better forecast. It is the value of allocating against the right picture of demand.
The infrastructure stays boring on purpose, so the decision layer can carry the weight. Statistics on NumPy and SciPy. Simulation on a vectorized Monte Carlo pipeline. Allocation solved with PuLP and CBC.
On the same network, with the same warehouse limits and the same demand patterns, the difference was not more inventory or a more complicated forecast. It was a better decision framework. By translating volatility into probabilistic planning thresholds and optimizing allocation under real operating constraints, I turned a fragile weekly planning process into a robust decision system that recovered roughly $3.98M in annualized margin.
Every figure, every constraint, and every line of the optimization lives in the repository, written to be read.