Development of an Intuitive Demand Forecasting Dashboard for Retail Supply Chain Optimization

The client faces inefficiencies in their product demand forecasting, which currently relies heavily on manual data entry and analysis through Excel spreadsheets. This process is time-consuming, error-prone, and hampers timely decision-making, leading to increased operational costs and reduced responsiveness to market changes.

A large retail chain with global operations specializing in consumer products, seeking to streamline demand forecasting processes.

• Automate and unify demand forecasting processes within a user-friendly interface to reduce manual workload and errors
• Improve forecasting accuracy through better data visualization and filtering capabilities
• Accelerate data processing and page load times from multiple seconds to under 3 seconds
• Enable dynamic and customizable data presentation via pivot table views
• Facilitate seamless data import/export between systems and spreadsheets
• Enhance security with secure protocols and user access controls
• Ensure scalability and performance through modern technology stack and cloud deployment

• A dynamic sidebar with product parameters (type, location, department, brand) that can be hidden or shown based on user preference
• Autocomplete dropdowns for quick selection of product parameters, minimizing scrolling
• Migration of frontend from legacy framework to modern JavaScript frameworks (e.g., React, Vite JS) for faster load times
• Page load time reduction from approximately 15 seconds to under 3 seconds
• Loading indicators during data processing to improve user experience
• Advanced filtering options, including multiple filters per data column (color, size, order, cost, etc.)
• Pagination to manage large data sets efficiently
• A pivot view feature allowing users to customize table rows, columns, and filters for complex data analysis
• Ability to import data from Excel files with frontend validation and error/success notifications
• Export functionality to Excel for large data transfer
• Data protection features to prevent accidental modifications (e.g., undo functionality)

• Excel file import/export
• Cloud deployment via Docker and AWS or equivalent cloud services
• Secure protocol enforcement (HTTPS)
• Backend data management system (e.g., PostgreSQL)

• Page load time under 3 seconds for large data sets
• High scalability to handle increasing data volume and user load
• Robust security with HTTPS and user access controls
• Reliability with consistent uptime and error handling
• Ease of maintenance through containerized deployment (Docker)

The implementation of this demand forecasting platform is expected to significantly reduce operational costs by automating manual data entry and analysis, increase employee productivity through advanced filtering and data visualization features, and accelerate forecasting cycles by reducing page load times from 15 seconds to under 3 seconds. Additionally, the system will enhance data accuracy and security, providing a scalable foundation for future growth and more responsive decision-making processes.