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The Impact of Big Data Analytics on Supply Chain Management

By Richard Olawoyin, Ph.D, CSP, Oakland University and Don Hutchison, Macomb Community College

The technological advancements of Industry 4.0 have giving companies around the globe the tools necessary to analyze and harness unprecedented volumes of data, across various entities of the enterprise both inside and outside the four walls, presenting unique sets of opportunities and challenges never before envisioned.

Today, businesses are confronted with the difficulty of analyzing data collected from different supply chain units and obtaining vital information for making data-driven decisions. Supply chain processes involve the flow of goods and services, operational information and financial flows. Managing these complex transactions efficiently is often the key to business success.

In the past, these flows were in sequence from the suppliers to the consumers. The sequence has become non-linear because of the real-time availability and accessibility of data that exists now across the supply chain and simultaneous in the present era of industrial transformation. The inherent complexities of supply chain processes make it imperative to use Big Data analytics to process the large amount of data from convoluted supply chain operations.

Big Data and supply chain management (SCM) are mutually reinforcing. The ability to mine large amounts of structured and unstructured data accelerates the rise of Big Data analytics for transforming the data from hindsight analysis to oversight of the business processes. The volume of data collected from numerous processes and domains within a business, and the velocity at which new data are generated, necessitate the use of Big Data analytics to explore the impact of such data on SCM. Used wisely, Big Data analytics can boost efficiency in the supply chain, which ultimately benefits the consumers, while simultaneously increasing profit opportunities for the enterprise.

The introduction of Big Data analytics has transformed traditional analytics that focus on the past (descriptive and diagnostic) to being able to predict or prescribe what will happen in the future. The simulation and representation of the entire end-to-end supply chain can provide crucial information needed for a robust analysis. This additional information can lead to more efficient and adaptive supply chains. With a robust oversight in place, companies can feel empowerment from the shop floor to the top floor and the boardroom will have the necessary tools to make decisions that will grow capital gains for their investors. Businesses with efficient oversight will use predictive and preemptive analytics to identify unknowns before customers realize they have specific needs. Meticulous supervision involves good governance and mitigating damaging hazards to the enterprise in both short and long terms.

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