Optimizing Cash on Delivery (COD) for Retail Success
Industry:
Retail
Technology Used:
Angular 7 (front-end development with TypeScript)
Node.js (back-end development)
Python (data analysis and machine learning)
Google OR Tools (optimization algorithms)
R Programming Language (statistical analysis)
Oracle 12c Database
Client:
A leading New York City-based retailer with a strong market presence and 30 years of experience. Recognizing the need for improved profitability and customer satisfaction, they sought to optimize their COD process.
Requirement:
Network Complexity:Optimizing a network with diverse transportation modes, inventory levels, and delivery timelines proved difficult.
Limited Visibility:Gaining real-time insights into inventory, transportation status, and demand patterns remained a hurdle.
Resource Allocation:Balancing cost-efficiency with service levels and ensuring optimal resource allocation across locations presented an ongoing challenge.
Solution Delivered:
Advanced Supply Chain Visibility Platform:We developed a platform integrating data from various systems, offering real-time insights into inventory levels, transportation status, and demand patterns.
Predictive Analytics & Optimization:By leveraging advanced analytics and demand forecasting tools, we predicted demand patterns, optimized inventory levels, and minimized stockouts or excess inventory.
Collaborative Communication Tools:We implemented communication tools to facilitate seamless communication and coordination among supply chain stakeholders.
Results:
The client was able to get the following results –
Enhanced Visibility:The client gained real-time visibility into their entire supply chain, enabling better monitoring of inventory, transportation, and demand.
Optimized Logistics Planning:Scenario generation capabilities and optimization tools facilitated more efficient and cost-effective operations.
Increased Agility:The client gained the ability to respond rapidly to supply chain changes such as demand shifts, transportation disruptions, or market trends.