The concept of supply-chain analytics has been around for a long time in one form or another, says Wadwha. Companies have drawn on data from their enterprise resource planning systems to diagnose problems in their supply chains. In the last 10 years, however, technology has come to pay a much larger role in the development of more sophisticated analytical tools.

For the first time, says Wadwha, businesses can look at data, patterns and trends and answer the fundamental question: "What if I do this? What is the outcome going to be?"

The change is in both the quantity and quality of data. Without a doubt, says Wadwha, the sheer volume of available data has risen. So has the value of that information, and a company's ability to make intelligent decisions based on it. For the first time, it can predict outcomes, based on more than just historical experience.

Analytics can be of great value to the supply-chain community, Wadwha said. Companies with global manufacturing and multiple sales channels can use the tool to successfully introduce a new product or plot pricing strategies. "In the past," he says, "trying to answer this kind of question was very cumbersome. It could take many days."

Today, companies can look at multiple data streams and begin answering key questions, or mapping "what-if" scenarios, almost immediately.

The rate of adoption of modern-day supply-chain analytics varies across industries. Healthcare and retail are ahead of the electronics and industrial sectors, according to Wadwha. One big obstacle is the need to break through old assumptions about the level of data that is needed to make key decisions. In addition, companies need to employ "well-rounded business people," not just statisticians, to interpret trends and identify patterns.

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Keywords: supply chain, supply chain management, supply chain planning, supply chain forecasting, supply management, retail supply chain