Overcoming challenges to "big data" analysis

Coordinating the flow of a supply chain requires large volumes of data from multiple software systems and devices. That's why "big data" analysis has become so attractive as a way to improve supply chain operations. Advances in computer power and software make it feasible to sift through information stored in traditional, structured databases as well as unstructured ones to make an "aha" connection that can lead to a more efficient or synchronized supply chain operation.

But getting the right data and quality for this kind of analysis isn't easy. For starters, a supply chain manager has to determine what information will be required for a valid analysis. "The company has to understand what kind of data they have to process to get the right insight," says Frode Huse Gjendem, a consultant at Accenture who does work on supply chain analytics. "You have to understand what kind of data you need. There has to be a data assessment before you can implement big data analytics."

Along with the right data, a big data analysis for a supply chain requires partners to be willing to share information, and many companies are reluctant to do so. In fact, the 2014 18th Annual Third-Party Logistics Study headed up by Dr. John Langley of Penn State highlighted this obstacle. Some 22 percent of shippers and 32 percent of third-party logistics companies (3PLs) surveyed for that research said their companies considered data to be proprietary and would not be willing to share that information with others. That's troubling, since using big data analysis to solve supply chain issues will likely require access to more than one company's data.

In order to persuade supply chain partners to share data, Gjendem says, a company should offer incentives. For example, he knows of a large original equipment manufacturer (OEM) that's supporting information sharing by providing its suppliers with the architecture for data gathering. The incentive for the suppliers is that they will be able to use the software tools provided by the OEM for analysis of their own operations.

If supply chain partners are still hesitant to exchange information, Gjendem said, another possible approach would be to use a neutral third party as a trustee to ensure data confidentiality. The trustee would then handle the big data analysis.

Big data analysis has huge potential to transform supply chains. For that to happen, though, supply chain chiefs will have to reach out to their supply chain partners to gain their cooperation in sharing the right data.