5 Ways Big Data Can Help Retail Supply Chains
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.
3. Better Vendor Management
Most retailers work with multiple vendors in their supply chain. These include drop ship vendors, 3PL (“third party logistics”) vendors, transportation vendors, and packaging vendors. Big data analytics solutions enable real-time management by reviewing vendor performance against a set of key performance indicators. These KPIs include vendor profitability, on-time service, and customer feedback and complaints. The KPIs are tracked in real-time by integrating with vendor systems, financial inputs like cost of goods, social network feeds related to vendor deliveries, and product packaging. Rules can be created to generate alerts if the KPIs do not stay within the defined range.
These real-time analytics solutions ensure that the quality of service and the profitability of your vendor business stay at the desired levels without requiring any additional effort. Vendors also benefit, as they know exactly what is required from them to continue to keep your business.
4. Automated Product Sourcing
Losing revenue due to an out-of-stock product is painful. Big data solutions help overcome this challenge by having a real-time view of the product demand, product sales, and sourcing process. Additionally, once the big data solutions are deployed, retailers can stop marking certain products as “backordered” as they always know the exact lead times for sourcing them. This also reduces the number of abandoned shopping carts. Realistic lead times are shared with the customers by displaying an accurate shipping window before the order is placed, resulting in fewer queries about the order.
Solutions for automated product sourcing analyze purchase history, product lead times, and other factors that might influence a product sale, such as a marketing promotion or weather changes. In case of multiple warehouses or drop ship vendors, the solution can work with data from different locations to determine the best way to source the products.
5. Personalized or Segmented Supply Chain
Today’s shoppers demand personalized customer service. Increasingly, they also require personalized product offerings. With big data, retailers can analyze customer interactions across all channels — social, mobile, and web — to determine how the customer is using the products they bought or will buy. For example, retailers can segment their supply chain to offer some shoppers configurable products, where they can select features like color or wired versus wireless. A different segment of shoppers could get products that are environmentally friendly, and another segment could be offered value-added features like gift-wrapping. All this can be done in real-time by matching the available products with the target customer segments. This will result in increased overall revenue and also improve profitability.
There are many other ways that big data can improve the supply chain. There is, for example, (a) vendor managed inventory that connects to your customer’s systems directly, (b) real-time financial analysis, planning and forecasting to avoid unnecessary surprises or costs, and (c) instant fulfillment for commodity products via revenue sharing with partners. Most of these improvements require an investment that must first be justified, ignoring the big data hype.