DC Velocity - Tip Sheet 2012

ter decision making in supply chain management is often hindered by the inability of managers and front-line personnel to understand and apply analytical models.

We have encountered several companies that had considerably upgraded the analytical capabilities of their information systems (for example, by adding advanced planning and optimization modules for enterprise resource planning [ERP] systems) but had made no changes in associated personnel or their analytical skills. As one supply chain manager told us, “We need only half the people to do the work with these new tools, but they need to be twice as smart.” For supply chain personnel to become smarter about analytics, they must be educated about analytics and their implications, retrained, or in some situations even replaced.

There are a variety of approaches to achieving the desired level of analytical literacy. The motor carrier Schneider National, for example, has developed a simulation-based game to communicate the importance of analytical thinking in dispatching trucks and trailers. The goal of the game is to minimize variable costs for a given amount of revenue while maximizing the driver’s time on the road. Decisions to accept loads or move empty trucks are made by the players, who are aided by decision-support tools. Schneider uses the game to help its own personnel understand the value of analytical decision aids, to communicate the dynamics of the business, and to change the mindset of employees from “order takers” to “profit makers.” Some Schneider customers have also played the game.

Another way to facilitate the understanding of supply chain analytics is through simpler applications with narrow functionality. Increasingly referred to as “analytical apps,” these tools are similar to the applications found on smartphones. They support a single decision and often are industry-specific. Several business intelligence and analytics software vendors are introducing them, and they promise to make the use of analytics much simpler and available to users who do not have extensive analytical or technological skills. Analytical apps that have already been developed for supply chain

functions include tools for supplier evaluation, inventory performance analysis, transportation analytics, and transportation contract compliance. There undoubtedly will be many others over the next several years.

Perhaps the only way to guarantee the use of analytics in supply chain management is to embed them into supply chain-oriented systems and processes. No human would be involved in the decision unless there is an exception. For example, certain supply chain decisions made at least partially on the basis of statistics and probability (such as available-to-prom-ise inventory, or the likelihood that an ordered product will be returned by the customer) could be embedded in an order management system. Vendors of ERP systems expect to have such capabilities in the next several years.

THE FUTURE OF SUPPLY CHAIN ANALYTICS The use of such tools as ERP systems, the Internet, RFID, and telematics is becoming more common, and more organizations are generating considerable amounts of high-quality data. Now that companies have more and better data than ever before, it is only natural that they would begin to use it to analyze, optimize, and make predictions about their supply chains.

The most common analytical activities thus far have been descriptive—straightforward reports about what has happened in the past. But in future supply chains, we expect to see more prediction and even prescription—that is, optimization and testing models that tell supply chain managers what they should do to improve performance.

Employing emerging supply chain technologies and process improvements has always been an important path to competitive advantage. We believe the next major approach to supply chain-based competition will involve the extensive use of analytics. ●