Today’s businesses are powered by a broad, deep, and ever-expanding river of data powers. That’s doubly true of supply chain operations. Unfortunately, many organizations are struggling when it comes to leveraging the wide range of supply-chain-relevant data now available to them.
The primary—and best understood—sources of supply chain data are the core business systems on which companies run their main operations. As noted in an earlier post, supply chain operations interact with and depend on almost all of a company’s main business units.
As such, supply chain managers must be able to collect, filter, and analyze data drawn from corporate ERP, accounting, inventory and warehouse, sales and marketing, transportation management, and other back-end systems. Ensuring that you have the data management tools and capabilities needed to fully tap into these internal data sources is no trivial task, however, and often requires the expertise of key vendors and consultants.
The second source of relevant data is also a familiar one: your network of suppliers. Companies need to get as much real-time visibility into the operations of key suppliers as possible—everything from production rates and pricing schedules to quality control statistics.
Another category of supplier-associated data has lately been gaining higher priority: the cybersecurity practices and overall risk profile of suppliers. Many companies have suffered data breaches traced back to flaws in their suppliers’ cyberdefenses. Choosing between two different suppliers these days may hinge on their respective cyberrisk profiles as much as on their on-time-delivery and component quality records.
Beyond the internal systems of a company and the data it can garner about its suppliers is another large and growing universe of data that is relevant to supply chain operations. Probably the most notable sources of this data are Internet of Things (IoT) devices and sensors. IoT devices are being used to monitor and control factory floor operations, manage warehouse inventories, track shipment locations, and guide delivery drivers to the most efficient routes.
Other data that relates to supply chain operations can come from weather forecasts (dangerous storms may disrupt shipments), political actions (think tariffs), and even social media feeds. If the popularity of one of your products is exploding—or tanking—in online discussion groups and tweets, your supply chain managers should know about it.
Clearly, the wealth of data now available carries its own challenges and risks. Companies are most familiar with tapping the structured data residing in relational databases and data warehouses, but some of the most valuable data is likely to be the raw, unstructured data now being collected in massive data lakes. These largely untapped volumes of raw data can be the source of valuable insights, but sorting through the data lakes to identify insights, patterns, and trends has only recently become technically and economically practical.
Making sense of such voluminous and varied data—often in near real time—to make better business decisions is in many ways the most challenging element of the data management process. Fortunately, big data analytics—increasingly aided by artificial intelligence technologies—is helping companies harness this valuable business resource. Those advances are the subject of a future post.
Learn how GEP can help you digitally transform your procurement and supply chain operations at www.gep.com.