Academics and software companies are developing "predictive metrics" that will detect early warning signs of future problems in a supply chain. Unlike traditional metrics, which use historical data to benchmark activities, predictive metrics use information to identify a trend line and predict a shift in an activity before it happens. "Predictive metrics complement historical reporting for better decision making," said Lynda Haydamous, a project manager at the Boeing Company, in a session on that topic at CSCMP's 2010 Annual Global Conference in San Diego.

Current research on predictive metrics is attempting to determine the underlying factors that could cause a future supply chain problem. For instance, high employee turnover might be one of several causal factors indicating that a supplier may have trouble delivering quality products in the future.

Metrics work now under way is focusing on shifts in the areas of supply and demand, said Lawrence Lapide, a research affiliate at the Massachusetts Institute of Technology (MIT) Center for Transportation and Logistics. Examples of a supply shift include a supplier becoming unreliable, going out of business, or manufacturing degraded products. An example of a demand shift would be a customer dramatically increasing its orders.

The most important supply chain predictive metric, now in development, is the "Inventory Mix Quality Index," said ToolsGroup Chief Executive Officer Joseph D. Shamir. This metric indicates how an overstock or understock will affect the profit margin of a particular product; using it would allow supply chain planners to take corrective action to maintain profitability for specific stock-keeping units.

The development of predictive metrics will allow companies to manage their supply chains in alignment with their corporate objectives, making them popular with corporate executives, Shamir said. "In the future, we will see more predictive KPIs (key performance indicators)," he predicted.