Big Data’s Impact on Supply Chains


Big data—the vast amounts of information that businesses, companies and organizations collect and store about inventory, operations, customers, vendors, sales and other transactions—and the challenges associated with strategically processing it play a key role in the operation of today’s most successful supply chains.

Companies that figure out how best to harness the capture, storage, management and analysis of big data are the ones that will reap the greatest benefits from it, agree Scott Sopher, principal at Deloitte Consulting LLP, the firm’s National Service Line Leader for Supply Chain & Manufacturing Operations, and Sam Pearson, principal at Deloitte and leader of the firm’s Supply Chain Analytics practice.

Sopher and Pearson were part of the team that developed the MHI 2014 Annual Industry Report: Innovations That Drive Supply Chains as well as part of the team currently working on the MHI 2015 Annual Industry Report to be released at ProMat 2015.

The 2014 report noted that big data feeds a company’s ability to conduct analytics activities, such as visualization and predictive modeling. Those activities provide a means to better manage costs and risk while improving operational agility and service quality.

Visualization and modeling

What makes big data so useful to supply chains, says Sopher, are advances in the computing power required to translate the information into meaningful insights.

“The ability to harness and manage big data has improved with the ongoing development of new technologies, new analytic tools and faster processing capabilities,” he notes. “All of these advances help companies make the data real, and make it easier for executives to understand.”

According to Sopher, the process of visualization via big data allows a company’s C-suite to examine a potential problem, test different hypotheses, and grasp potential outcomes. Thusly informed, corporate leadership can take appropriate action.

The value of visualization is in the ability to render data in new and innovative ways that hadn’t been previously considered in order to find insights. “Companies are no longer limited to looking in the rearview mirror to see what happened after the fact. Instead, now they can take their data, and in some cases, pair it with third-party data to do predictive modeling and scenario analysis,” adds Pearson.

For example, Pearson suggests that companies use big data to consider a variety of “what if?” scenarios, particularly as supply chains across all industries continue to become more global in terms of both suppliers and customers.

“If demand doubles in Asia, how can a supply chain be modeled to continue to deliver high quality service levels? Do I need to develop new supply bases in China or India?” he posits. “Predictive modeling lets supply chain managers manage inventory better, plan more reliable transportation networks and reduce variability in lead times. This can enhance service levels, lower costs and improve the bottom line.”

By Carol Miller, MHI Vice President of Marketing and Communications Services

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