1️⃣ Objective
Design and deliver a Retail Demand Forecasting & Assortment Optimization Platform that produces accurate, probabilistic SKU×store forecasts, recommends assortments and replenishment policies, simulates supply scenarios, and provides planners with actionable dashboards to improve on-shelf availability and reduce inventory cost.
Key Goals:
✨ Accurate SKU×store demand forecasts (daily/weekly) with quantile estimates for uncertainty-aware planning.
✨ Optimized assortments per store using demand, profitability and space constraints.
✨ Replenishment recommendations and reorder suggestions to meet service targets while reducing carrying cost.
✨ Promotion & price impact analysis to quantify lift and plan better promotions.
✨ Scenario simulation (supplier delays, demand spikes) to stress-test policies.
✨ Operational dashboard for planners with exportable recommendations to ERP/WMS.
2️⃣ Problem Statement
Retailers struggle with stockouts, overstocks, and suboptimal assortments because forecasts are inaccurate, uncertainty isn’t considered, and optimization is manual. This leads to lost sales, high carrying costs, and poor customer experience.
This project builds a data-driven platform to forecast demand reliably, quantify uncertainty, and recommend assortments and replenishment policies to balance availability and cost.
3️⃣ Methodology
The project will follow a staged approach:
✨ Step 1 — Data Ingestion: Ingest POS, inventory, catalog, pricing, promotions, and external signals (holidays, weather) into a data lake.
✨ Step 2 — Data Cleaning & Features: Clean histories, create SKU hierarchies, rotational features, lag features, price elasticities and promo flags.
✨ Step 3 — Forecasting: Train ensembles (Prophet / LightGBM / LGBM/Gradient boosting) with hierarchical reconciliation and backtests; produce quantile forecasts for uncertainty.
✨ Step 4 — Assortment & Optimization: Feed forecasts to an optimization engine (OR-Tools or ILP) to recommend assortments and replenishment policies under business constraints.
✨ Step 5 — Promotion Lift & Uplift: Use uplift or causal models to estimate promo impact and incorporate into planning.
✨ Step 6 — Simulation: Run Monte Carlo / scenario simulations for supplier delays, demand spikes and carry trade-offs to validate decisions.
✨ Step 7 — Dashboard & Integration: Build Streamlit/Tableau dashboards and expose recommendations via API/CSV to ERP/WMS for execution.
4️⃣ Dataset
Sources:
✨ POS / Transactional sales (SKU×store×timestamp)
✨ SKU master & catalogue (category, dimensions, profit margin)
✨ Inventory & receiving logs (on-hand, lead times)
✨ Pricing, promotions & markdown history
✨ Store attributes, footfall, local events, weather and holidays
Data Fields:
5️⃣ Tools and Technologies
6️⃣ Evaluation Metrics
✨ Forecast Accuracy (MAPE / RMSE): SKU×store forecast error measured per horizon.
✨ Service Level / Fill Rate: % of demand met without stockout.
✨ Stockout Reduction: Decrease in stockout incidents after implementation.
✨ Inventory Turns / Carrying Cost: Improvement in turns and reduced holding cost.
✨ Promotion ROI / Uplift: Measured increase in incremental revenue from optimized promos.
✨ Forecast Bias & Coverage: Systematic under/over-forecasting and quantile coverage.
✨ Planner Adoption: % of planners using recommendations and time saved per planning cycle.
7️⃣ Deliverables
8️⃣ System Architecture Diagram
Input: POS & Sales Data
Historical transactional data, promotions, store performance metrics.
Market & External Data
Competitor pricing, macroeconomic indicators, weather, social trends.
Inventory & Product Data
Stock levels, lead times, product hierarchy, lifecycle status.
Data Cleaning & Feature Engineering
Normalization, outlier detection, creating time-series features and lag variables.
Demand Forecasting Models
ML models (e.g., ARIMA, Prophet, XGBoost) predicting demand at SKU/Store level.
Optimization Engine
Mathematical optimization for optimal inventory allocation and product assortment sizing.
Assortment Recommendations
Which products to stock, space planning, and localization strategies.
Predictive Demand Reports
Forecasted sales volumes, expected stock-outs, and order quantity suggestions.
Visualization & UI Layer
Interactive dashboards for analysts to review forecasts and optimization results.
Final Outcome: Actionable Retail Strategies
Optimized Ordering, Reduced Waste, Increased Revenue & Customer Satisfaction.
Input: POS & Sales Data
Historical transactional data, promotions, store performance metrics.
Market & External Data
Competitor pricing, macroeconomic indicators, weather, social trends.
Inventory & Product Data
Stock levels, lead times, product hierarchy, lifecycle status.
Data Cleaning & Feature Engineering
Normalization, outlier detection, creating time-series features and lag variables.
Demand Forecasting Models
ML models (e.g., ARIMA, Prophet, XGBoost) predicting demand at SKU/Store level.
Optimization Engine
Mathematical optimization for optimal inventory allocation and product assortment sizing.
Assortment Recommendations
Which products to stock, space planning, and localization strategies.
Predictive Demand Reports
Forecasted sales volumes, expected stock-outs, and order quantity suggestions.
Visualization & UI Layer
Interactive dashboards for analysts to review forecasts and optimization results.
Final Outcome: Actionable Retail Strategies
Optimized Ordering, Reduced Waste, Increased Revenue & Customer Satisfaction.
9️⃣ Expected Outcome
✨ Reduced stockouts and improved on-shelf availability through better forecasts and replenishment.
✨ Lower inventory carrying costs and higher inventory turns via optimized replenishment and assortments.
✨ Higher promotion ROI by identifying high-lift promos and avoiding ineffective discounts.
✨ Measurable forecast accuracy gains (MAPE/RMSE reduction) and calibrated uncertainty estimates for safer planning.
✨ Production-ready deployment with scheduled pipelines, monitoring and a planner-facing dashboard enabling faster decision cycles.