5 Ways Big Data Can Help Retail Supply Chains

Retail / eCom | Sectors   |   
Published June 27, 2013   |   

The supply chain is a critical component of a retailer’s business. This article identifies fives ways in which big data can enhance a retailer’s supply chain processes.

1. Real-time Delivery Management

Tracking delivery of shipped goods has improved over the years. Big data helps improve it even further by enabling real-time delivery management that analyzes weather, traffic, and truck location feeds to determine the exact time of delivery. Sensors can be used to track high-priced items in the shipment. Services from several logistics vendors, such as FedEx SenseAware, let you control your supply chain by providing real-time information on the shipment’s environmental conditions as it travels, its location, whether it has been opened or not, and more.

This capability helps retailers who are shipping perishable or high-priced products or who need to keep track of their shipments for some other reason like customer delivery appointments. The good news is that the solution can be implemented without requiring significant changes to the existing supply chain setup as the bulk of the work is focused around integrating with different data feeds.

2. Improved Order Picking

Order picking is a labor-intensive process. If the orders can be picked faster, they can be shipped faster, resulting in better order fulfillment. Large retailers use a variety of automated mechanisms to pick the orders quickly, but with big data even the smaller retailers can improve their order-picking process.

Big data solutions allow data from different sources like orders, product inventory, warehouse layout, and historical picking times to be analyzed together based on the rules defined by the retailer to improve the overall picking process. The solutions also enable running the improved order picking process in simulation mode. This results in minimal impacts to warehouse or store operations as the order picking process can be optimized in simulation mode, by tweaking various parameters and settings, before rolling out the final process to the warehouses and stores.

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