Intuition-led pricing
What to charge was gut feel, with no model behind it.
Decision & Commercial Optimization
B2B Footwear Pricing, Inventory & Supplier Decision System
How a B2B footwear commerce company replaced gut-feel pricing and ordering with statistical decision models, then shipped the products that put them in operators' hands.
17%Margin improvement
22%Less excess stock
48%Less pricing decision time
Pricing and inventory decisions shared one economic outcome but ran through separate processes
Price affected demand and margin; demand affected stock needs; cost, logistics, and inventory exposure set the price range. Manual analysis slowed decisions, leaving outcomes judgment-dependent.
Client brief: Improve margin and stock control while reducing the time required to reach a defensible pricing decision.
Unmanaged stockout risk
Order quantity and safety stock were guesswork, unmodeled against demand.
Slow, manual decisions
Every pricing and stock decision meant rebuilding the analysis from scratch.
Maximize contribution margin while minimizing excess stock and pricing decision time
The model balances price, demand, and inventory exposure in one shared economic objective.
- Objective — maximize expected contribution after purchase cost, logistics, discounting, holding cost, and stockout risk while reducing decision-processing time.
- Decision variables — price, discount boundary, order quantity, reorder point, safety stock, product allocation, and review threshold.
- Constraints — demand and elasticity assumptions; minimum order quantity; supplier capacity and reliability; lead time; service-level target; working-capital limit; logistics and storage capacity; commercial approval boundaries.
Statistical pricing and inventory decision optimization
Demand and cost modeling
Demand-and-price analysis estimates the price-volume-contribution relationship, combined with landed-cost modeling covering purchase, logistics, duty, handling, and inventory carrying cost.
Price-boundary optimization
Identifies commercially viable price ranges under margin and demand constraints.
Order-quantity optimization
Balances ordering cost, holding cost, service level, and stock exposure.
Two more steps close the loop: safety-stock modeling reacts to demand and lead-time variability, not fixed intuition; decision rules with exception thresholds automate standard pricing while escalating material deviations for human review, validated by scenario analysis of the margin-volume-inventory-capital trade-off.
The gains show up in margin, inventory, and decision speed.
Contribution margin17% improvement
Better price boundaries, landed-cost visibility, and demand-sensitive decisions improve contribution across the same commercial base.
Excess stock22% reduction
Inventory levels respond to demand variability, service requirements, and replenishment economics instead of fixed buffers.
Pricing decision time48% reduction
Standard calculations, shared assumptions, exception thresholds, and scenario outputs replace repeated manual analysis and approval loops.
The business keeps final control of every decision.
Explainable, traceable models
Models stay explainable to commercial and operations users; operators see which variables changed a recommendation.
Human approval, staged rollout
Human approval remains for prices and material inventory commitments, starting with one product family or category.
Exceptions and monitoring
Exception thresholds flag high-impact decisions, separate model output from realized demand and margin, and track contribution margin, excess stock, decision time, forecast error, and service level.
Rigorous decision models beat a heavier AI approach, and they earn trust faster.
The project sharpens decision economics and speed, giving buyers clearer margin trade-offs, lower inventory exposure, and a repeatable process that skips rebuilding the analysis each time.
Best fitB2B operators or marketplaces making recurring pricing and replenishment decisions with margin pressure, excess inventory, or slow approval cycles.
Optimize one pricing and inventory decision cycle
Bring product-level demand, price history, costs, lead times, order history, stock policy, and the current approval workflow.