Big data analysis was heralded as having the ability to offer big insights. It was the tool that was going to allow logistics and supply chain managers to identify patterns and connections hidden in piles of data stored in different formats in disparate computer systems throughout the extended supply chain. It was going to help managers identify new ways to save money, cut costs, boost revenue, and so forth.

In the world of information technology (IT) jargon, software that proposes solutions to problems is dubbed "prescriptive analytics." Although this might sound like the answer to a supply chain executive's dreams, the reality is that, for now at least, few if any companies are doing big data analysis in the supply chain with prescriptive analytics.

That was the assessment of Michael Burkett, a vice president for supply chain research at Gartner Inc. Burkett made his remarks at the Supply Chain World North America 2014 conference in a presentation titled "Big data and supply chain analytics: Separating fact from fiction."

Despite the slow uptake of prescriptive analytics, other types of big data analysis are starting to make headway in the supply chain. Most experts classify analytics into one of four categories. The most common is what's known as "descriptive analytics," insights into what happened in the past and why. Nowadays, most logistics software applications, such as warehouse management and transportation management systems, come with some type of descriptive analytics. Because descriptive analytics has been in use for decades, it's generally not considered to be fodder for big data analytics, whose purpose is to explore uncharted information territory.

Three other types of business analytics are considered perfect fits for big data examination. One is the aforementioned prescriptive analytics. Another is "diagnostic analytics," which offers insight into why a procedure went awry. For example, diagnostic analysis could determine why shipments from a particular carrier frequently fail to arrive on time at a customer's facility. A third category is "predictive analytics," in which the software foretells what will happen in the supply chain. For example, companies could use information on weather forecasts to determine what consumers might buy, prompting them to stockpile certain products or ship them to stores for in-stock availability. Aircraft and automobile makers could use sensor-based data monitoring equipment to predict when certain components might fail and take steps to have repair parts on hand at the distribution center.

Burkett says companies are already experimenting with diagnostic and predictive analytics in logistics and supply chain applications. So what's keeping them from dabbling in prescriptive analytics? The reason, according to Burkett, is that the underlying technology isn't quite ready for prime time. It needs further development, particularly in the area of artificial intelligence, which would be required to help come up with the solutions.

As for what's ahead for big data analysis, Burkett believes that logistics and supply chain managers will ultimately be able to use the results of this type of analysis to persuade upper management to adopt new courses of action. "Supply chain professionals struggle to bring evidence to the table," he said. "Analytics can do this."