Project 1
Forecasting and Replenishment Decision System
A retail decision system designed to reduce overstock risk and prevent missed sales when demand visibility is weak.
It forecasts product-level demand and converts those signals into replenishment guidance that planners can act on.
Portfolio simulation designed to reflect real retail planning workflows
Business problem
Retail teams often face overstock and missed sales because demand signals are fragmented across spreadsheets and backward-looking reports.
The challenge is deciding what to replenish, when to move inventory, and how much to send with enough confidence to support weekly planning.
What this system does
Forecast demand
Estimates near-term product demand using sales patterns and promo context.
Apply decision rules
Checks stock position, lead time, and priority rules before action is taken.
Recommend replenishment
Produces clear guidance on what to replenish, when, and in what quantity.
System overview
Step 1
Sales + Promo Data
Sales history, promo plans, and stock signals brought into one view.
Step 2
Analytics Layer
Raw inputs cleaned and structured for consistent downstream use.
Step 3
Feature Engineering
Recent trends, promo effects, and product behavior turned into model inputs.
Step 4
Demand Forecast
Future demand estimated at product level over the planning horizon.
Step 5
Decision Layer
Business rules applied to convert demand into practical actions.
Step 6
Replenishment Recommendation
Final guidance surfaced for review by planners and buyers.
Sales and promo signals move through analytics and feature engineering, then into demand forecasting, decision logic, and replenishment guidance.
Key components
Analytics Foundation
Prepares clean sales, product, and promo inputs so downstream modeling starts from a reliable base.
Feature Engineering
Transforms raw signals into inputs that improve forecast quality.
Demand Forecasting
Estimates product-level demand using recent performance and promo context.
Decision Logic
Applies stock position, lead time, and priority rules to determine practical replenishment actions.
Replenishment Output
Converts forecast results into recommended buy quantities teams can review and execute.
Outputs
Forecast table
Shows expected demand by product over the planning window.
Replenishment recommendation
Summarizes suggested quantities and timing for replenishment.
Variant decision view
Highlights which variants need action first based on business priority.
Business impact
Lower overstock risk
Helps avoid overbuying by aligning replenishment more closely to demand.
Better availability
Improves in-stock performance for products with stronger demand.
Faster planning decisions
Gives teams ready-to-review guidance instead of starting from manual spreadsheets.
Code & Implementation
This project includes a full end-to-end implementation covering data generation, feature engineering, demand forecasting, and replenishment decision logic.
The repository is structured to reflect a production-style workflow, including modular pipelines, parameterized configurations, and business-driven decision layers.