1.5 Data-Driven Supply Chains
Few areas of business have been transformed by big data analytics as much as supply chain management. Same-day delivery has become nearly mandatory to modern multichannel retailing.38 As consumers, we have developed this expectation. We don’t think about it unless there is a problem. It may be that the item we ordered online doesn’t show up as scheduled or an advertised item is out of stock when we try to purchase it. Achieving a competitive level of global supply chain excellence cannot be accomplished without data-driven, end-to-end operations.
Consider companies such as Tesco. The company gathers huge amounts of customer data from its loyalty program. It then mines this data to inform decisions from promotions to strategic segmentation of customers. Amazon came early to the frontier of data analytics. The online retailer pushed the frontier using customer data to power its recommendation engine “you may also like...” based on a type of predictive modeling technique called collaborative filtering. The company continues its rapid leadership in fulfillment capabilities through data-driven decisions. Walmart was also an early adopter of data-driven supply chains. By making supply-and-demand signals visible between retail stores and suppliers, the company optimizes all its supply chain decisions—from customer fulfillment to inventory tracking (think POS data and RFID sensors) to automatic purchase orders through its supplier portal.
The number of RFID tags sensing inventories across supply chains is in the millions. In fact, the number of RFID tags sold globally is projected to rise from 12 million in 2011 to 209 billion in 2021.39 Supply chains are increasingly combining data from different systems to coordinate activities across the supply chain end-to-end. Marketing is generating huge volumes of POS data from retail stores that is automatically shared with suppliers for real-time, stock-level monitoring. RFID tags monitor inventory on shelves and in-transit coordinating with current stock levels for automatic order replenishment. Add to this data from computer-aided design, computer-aided engineering, computer-aided manufacturing, collaborative product development management, and digital manufacturing, and connect it across organizational boundaries in an end-to-end supply chain.
Even more value can be unlocked from big data when companies are able to integrate data from other sources. This includes data from retailers that goes well beyond sales. It may be promotion data, such as items, prices, and sales. It also includes launch data, such as specific items to be listed and associated ramp-up and ramp-down plans. It also includes inventory data, such as stock levels per warehouse and sales per store. This data is essential for the supply chain to deliver the items that are needed when they are needed.
Through collaborative supply chain management and planning, companies can mitigate the bullwhip effect and better smooth out flow through the supply chain. Many companies guard customer data as proprietary, but there are many examples of successful data sharing. Walmart is a great example of requiring all suppliers to use its Retail Link platform.40 The exchange and sharing of data across the extended enterprise has provided transparency and enabled coordinated cross-enterprise efforts.
Big data analytics is the game changer. It has given rise to the intelligent supply chain.