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Retail Demand Forecasting System Enhancement and Modernization

n-ix.com

Retail

Business Challenge

The client's existing demand forecasting process relies heavily on manual data aggregation and calculations using Excel, leading to inefficiencies, inaccuracies, and increased operational costs. The current web application, built on Flask, suffers from slow loading times and limited scalability, hindering timely and effective decision-making. Merchandising and planning experts are burdened by manual tasks and lack of a streamlined, intuitive interface for accessing and analyzing demand data.

About the Client

A leading global luxury retail chain specializing in exclusive clothing, accessories, and home products, operating in over 100 countries.

Project Objectives

Develop a user-friendly and intuitive UI for the demand forecasting application.
Improve the speed and accuracy of the demand forecasting process.
Reduce manual effort and operational costs associated with forecasting.
Enhance data accessibility and analysis capabilities for merchandising and planning teams.
Modernize the application architecture for improved performance and scalability.

Functional Requirements

Dynamic product parameter filtering (product type, location, department, brand).
Type-ahead functionality for product parameter selection.
Multiple simultaneous filters on various data columns (color, size, order, cost, etc.).
Pivot view functionality for customizable data table analysis.
Data import from Excel documents.
Pagination for handling large datasets.
Data validation and error handling.
Return function to prevent accidental data modification.
Export to Excel functionality.

Preferred Technologies

React

AWS

PostgreSQL

Vite JS

Material UI

TypeScript

Key Non-Functional Requirements

High performance and fast page load times (target: < 3 seconds).
Scalability to handle large datasets and increasing user load.
Enhanced security (HTTPS protocol).
User-friendly and intuitive interface (UI/UX).

Expected Business Impact

The project is expected to significantly improve the efficiency and accuracy of the demand forecasting process, leading to reduced operational costs, increased employee productivity, and better inventory management. The modernization of the application will enhance the client's ability to respond quickly to market changes and optimize product availability, ultimately contributing to increased revenue and profitability.