Supply Chain Forecasting

On Medha IT Works' Retail ERP – Our Enterprise Resource Planning Product

At Medha IT Works, we enable learners to apply cutting-edge AI and ML techniques within real business environments. As part of our advanced AI/ML learning program, learners will undertake a capstone project titled:

Forecasting Demand, Inventory & Supply Chain Bottlenecks Using AI & ML

This project will be executed within Retail ERP, our all-in-one enterprise-grade platform that unifies procurement, inventory, store operations, sales, and accounting — giving learners the opportunity to apply intelligent forecasting models across the entire retail value chain. .

Project Overview for Learners

In this hands-on project, learners will build predictive models using AI and ML techniques to:

The project aims to help businesses:

Predict future sales with better accuracy

Plan inventory and restocking efficiently

Align promotions and pricing strategies with forecasted demand

Forecast product demand across categories, stores, and regions

Predict inventory shortages, stockouts, and overstock scenarios

Identify potential supply chain bottlenecks based on supplier performance and transit patterns

These insights will empower businesses to proactively plan procurement, distribution, and stocking strategies.

Retail ERP: Your Intelligent Learning Ground

Retail ERP by Medha IT Works is a comprehensive platform designed to handle every aspect of retail operations — from supply chain logistics to billing and financial consolidation. Learners will work with a unified data environment to simulate real-world forecasting and automation challenges.

Core Modules Learners Will Work With

Product & Category Management

Work with structured product data including SKUs, pricing, variants, and categories across store locations.

Inventory Management

Access real-time stock levels, stock aging data, and movement logs to feed into ML models.

Warehouse Management

Forecast delays and capacity utilization by analyzing inbound/outbound trends and bin-level availability.

Procurement Management

Use purchase patterns, supplier lead times, and historical delays to improve purchase planning accuracy.

Store Management

Analyze store-wise sales velocity and footfall patterns to guide store-level replenishment.

Sales & Billing

Leverage transactional sales data for demand forecasting by time, geography, and product mix.

Supplier Management

Monitor vendor reliability, delivery time deviations, and lead time patterns to predict supply chain risks.

Accounts Management

Evaluate the financial impact of forecasted stock imbalances or delayed shipments on business cash flow.

AI/ML Use Case Benefits: What Learners Will Achieve

By completing this project in Retail ERP, learners will:

Work with interconnected ERP datasets that reflect real business complexity

Develop ML models using regression, classification, and time-series algorithms

Simulate bottleneck alerts and demand-driven procurement planning

Learn how AI improves operational resilience and inventory efficiency

Bridge business context with intelligent automation

Final Outcome:

A fully operational AI/ML model embedded in the Retail ERP environment, delivering demand forecasts, inventory risk alerts, and supply chain bottleneck predictions — giving businesses the foresight to plan smarter, reduce losses, and operate efficiently.