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Dominos – Predictive Purchase Order System

Project Overview

This project implements a predictive purchase order system for Domino's Pizza, forecasting future ingredient demand based on historical sales data to minimize waste and avoid stock-outs.

Objectives

  • Analyze historical pizza sales data to identify demand patterns
  • Build time-series forecasting models to predict future sales
  • Generate automated purchase orders based on predicted demand and ingredient requirements
  • Optimize inventory management

Tools & Technologies

  • Python – Pandas, NumPy, Statsmodels, Prophet / ARIMA
  • Scikit-learn – Machine learning models
  • Matplotlib / Seaborn – Visualization
  • Streamlit – Dashboard (optional)

Project Structure

04_Dominos_Predictive_Purchase/
├── README.md
├── notebooks/          # EDA, forecasting, and order generation notebooks
├── app.py              # Optional Streamlit dashboard
├── data/               # Sales and ingredient datasets
└── models/             # Saved forecasting models

Approach

  1. Data Preprocessing – Clean sales data, handle missing values, engineer time features
  2. Forecasting – Train ARIMA / Prophet models on pizza sales time series
  3. Purchase Order Generation – Map forecasted sales to ingredient quantities
  4. Evaluation – Measure forecast accuracy using MAE, RMSE

Source Repository

yasararafath-s/Dominos_predictive_purchase_order_system

📁 Project code files to be added.