Price Optimization, Demand Forecasting & Inventory Planning

This workshop moves from clean demand estimation to profit-aware pricing and inventory decisions you can defend with backtests and governance. You leave with a small, working blueprint: data prep patterns, models with uncertainty, profit surfaces under constraints, and rollout guardrails.

Elasticity Profit Curves Time Series Inventory Policy Governance

Outcomes

  • Segmented elasticities with uncertainty bands and promotion controls.
  • Profit curves and price ladders subject to MAP, stock, and SLA constraints.
  • Forecasts (daily/weekly) with seasonality, events, and halo effects.
  • Inventory policies (s,S / Min-Max) tuned to service levels and lead times.
  • Rolling backtests, leakage checks, and a decision log for auditability.
Demand curves, profit surface, and inventory policy chart
Price ladder and constrained optimum

Price Optimization

We estimate own-price and cross-price elasticities per SKU/segment, then convert them into profit surfaces. The recommendation engine returns prices that maximize contribution margin under policy and contractual constraints.

  • Log-linear and regularized fits; hierarchical pooling for sparse SKUs.
  • Cross-effects and cannibalization captured at category level.
  • Constraints: MAP, contracts, inventory, and fairness rules.

Elasticity Cross-Price Constraints

Demand Forecasting

Point forecasts are not enough. We produce calibrated intervals and cost-aware loss metrics that reflect stockout vs. overstock penalties.

  • Seasonality, trend, holiday/event regressors; promo flags and price.
  • Models: ETS/ARIMA, gradient boosting, and hierarchical reconciliation.
  • Evaluation: rolling origin, MAE/MAPE/PINBALL loss; drift monitoring.

Time Series Exogenous Regressors Uncertainty

Forecast plot with prediction intervals
Inventory policy chart with service levels

Inventory Prediction & Policy

Forecasts flow into inventory policies that respect lead times and service levels. We size safety stock from forecast error, not guesswork.

  • Service-level targets → safety stock from residual distributions.
  • (s,S) and Min-Max policies; multi-echelon considerations.
  • Scenario analysis: price change, supplier delay, demand shocks.

Safety Stock Service Level Scenario Testing

Modules

01

Data Readiness

Sales, prices, costs, returns, inventory, promos.

  • Calendar and event features without leakage.
  • Outliers, missingness, and promo masking.
  • SKU/region/channel segmentation keys.
02

Demand Modeling

Elasticities with uncertainty.

  • Own-/cross-price effects and halos.
  • Regularization and hierarchical pooling.
  • Profit sensitivity analysis.
03

Forecasting

Intervals that reflect real costs.

  • ARIMA/ETS vs. boosting with regressors.
  • Rolling windows; drift detection.
  • Cost-weighted metrics.
04

Optimization

Price ladders under constraints.

  • Profit surfaces and argmax selection.
  • MAP, stock, and SLA constraints.
  • Price change caps and guardrails.
05

Inventory Policy

Service levels, safety stock.

  • (s,S) / Min-Max tuning from residuals.
  • Lead-time and multi-echelon effects.
  • Exception alerts and overrides.
06

Backtesting & Governance

Decisions you can defend.

  • Time-based splits and leakage checks.
  • Approval workflow and audit trail.
  • Rollback plan and KPI alarms.

Case Snapshot

A regional retailer with volatile promos used blunt markdowns and routinely stocked out. We modeled elasticities by category and channel, built price ladders with dynamic floors, and tuned inventory policy to target service levels.

  • +9–14% margin on targeted SKUs with volume held.
  • −15–20% stockouts via safety-stock re-sizing.
  • 2-week path from model to dashboard with approvals.
Before/after margin and stockout rates
Tooling stack diagram

Toolkit & Deliverables

We work in your stack. Typical components:

  • Python (pandas, statsmodels/xgboost), optimization (cvxpy/scipy).
  • Data: PostgreSQL/BigQuery; orchestration via dbt/Luigi.
  • Dash/BI outputs with approval gates and change logs.

Python PostgreSQL dbt Dash/BI

Who it’s for

Retail & eCom

High SKU counts, promos, MAP constraints.

  • Category pricing and markdown cadence.
  • Promo impact and cannibalization control.
  • Service-level inventory targets.

Subscription & SaaS

Tiers, discounts, and churn tradeoffs.

  • Price tests vs. upgrade friction.
  • ARPU vs. retention curves.
  • Fairness and communication policy.
Bring one dataset. Leave with a pricing & inventory blueprint and a backtest harness.
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