While the basics of retail logistics remain the same, the game of delivering delight to customers has had a massive makeover. The eCommerce surge, smartphone adoption, and internet penetration have changed the way people purchase, supply, sell, or transport goods. These global shifts have opened up new possibilities for businesses engaged in the sale of goods and have had far-reaching impacts on the traditional retail landscape. This is driving the need for the centralization of warehouses and large distribution centers, automated fulfillment systems, rapid delivery, and smart stakeholder management.
However, all such advanced functionalities and management capabilities require robust strategies and reliable data insights across the entire retail ecosystem. Apart from helping businesses identify the key areas for improvement, such bankable analytics can help them automate and optimize their processes and resources in a highly intelligent and continuous manner.
Let us have a quick look at four such crucial analytics use cases in retail logistics for operational excellence.
The inability to have exact visibility over the rider/vehicle movements makes it hard for retailers to control, manage, and monitor field operations. They cannot check whether a rider is taking the planned route or whether the reason cited for the delay is valid or genuine or not. This leads to increased costs of field operations, staffing, transportation, etc.
Analytics on rider movements, vehicle movements, and deliveries coupled with advanced technological offerings, such as real-time tracking, can help overcome these and many other challenges. Retailers can leverage automated workflows to ensure optimized order allocation, and efficient rider management and trap any fake delivery attempts. They can also set key metrics and calculate the number of times any rider has deviated from expected or system-generated behavior and use the information to optimize incentivization and performance measurement.
Delayed pickups from warehouses, delayed deliveries or drop-offs at consolidation centers, and inefficient handling of FTL/LTL shipments – retailers find themselves struggling with change management. The inability to optimize the end-to-end movements spurs disruptions leading to delays and higher logistics costs.
Predictive analytics capabilities help businesses accurately predict whether deliveries can get delayed or get completed done on or before time. Predictive and dynamic ETAs for multiple stakeholders ensure effortless change management via streamlined communication between both parties. For example, once a rider is out for delivery of orders on a particular route, all the customers for those orders will be notified well in advance to ensure first-attempt delivery and to reduce empty miles traveled for re-attempts. This improves logistics planning significantly and also makes operations management better and more proactive.
It is very common for retailers to work with multiple third-party logistics providers for different delivery and logistics requirements. However, the higher the number of logistics service providers, the more confusing and chaotic managing them all becomes. Owing to the lack of data insights into the delivery and logistics operations, deliveries, and performance benchmarking, retailers are unable to allocate orders to the most profitable and reliable 3PL.
Analytics help retailers compare the individual performance of every 3PL provider with the others and identify the most profitable logistics partner as well. They can negotiate better deals, and enjoy an upper hand at the same. They can also enter more profitable commitments, such as volume-based commitments, cost-efficient freelance driver onboarding, working with a mixed fleet, and more. Hence, they can always prioritize such 3PLs and optimize their logistics costs.
The face of modern retail is diverse, and so are the types of delivery windows that call for order fulfillment optimization. However, it is impossible to achieve this without having a solid strategy for warehouse and inventory management, which, yet again, relies on customer/purchase data.
Analytics-powered platforms for retail logistics management identify the customer/delivery hotspots, which can help retailers plan strategically for warehousing and inventory. Further, data analytics also helps in optimizing the movements among all the warehouses/fulfillment centers.
Analytics, coupled with automation stemming from the latest technologies and advanced computing, can help retailers shrink the last mile delivery costs by 12%, reduce the order-to-ship time by 12%, increase on-time dispatch by 28%, and deliver NPS by 26%. This powerful duo can help retailers drive highly efficient and optimized logistics operations, ensure on-time deliveries, and gain granular visibility over asset movements. Hence, it emerges as a powerful tool to leverage industry disruptions and rise above them.