Big Data and Supply Chain Management

          Big data analytics applied to supply chain management is one of the more discussed topics in recent years.  Big data as defined by Gartner (2016), “ . . . as high volume, high velocity and/or high variety information assets that enable enhanced insight, decision-making, and process automation” (para. 1).  This can give a firm an advantage over the competition as well as lead to increased efficiency.  Data analytics as explained by Davenport and Harris (2007), “ . . . the extensive use of data , statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions” (p. 7).  From the definitions one can see that big data analytics is a powerful tool that will be a trendsetter for businesses of all types.  All of which means greater profits for the stakeholders and businesses.

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          Big data has become an asset for businesses and companies because it helps to quantify large amounts of information and process it to get meaningful results.  It has also been described as the 5Vs: volume, variety, velocity, veracity, and value (Wamba, Akter, Coltman, and Ngai, 2015).  Volume refers to the extent or large amount of data that is placing stress on modern storage devices.  Variety refers to the way the data is gathered in diverse ways, i.e. sensors, social media, Internet of Things, etc.  Velocity means the speed in which the data is generated and delivered.  Veracity refers to the quality of the data and that some of it’s sources, such as social media, might not be the most reliable.  It needs to be valid, dependable data so that it can be used.  Value refers to the fact that the data has economic value to the business.

          In the world of technology, supply chains are supported by advanced networking know how which according to Wang, Gunasekaran, Ngai, and Papadopoulos (2016) include, “ . . . sensors, tags, tracks, and other smart devices, which are gathering data on a real-time basis” (p. 104).  These devices and others gather data that can be used to explain complex situations in supply chain management which might not be discovered because of limited information.  Also advances in computing and cloud computing allow retrieval, storage, sharing, distribution, and analysis of data to be done cheaper and more efficiently.  The data collected provide valuable information that when extracted helps facilitate data-driven decision making.  In fact it has been reported by McAfee, Brynjolfsson, Davenport, Patil and Barton (2012) that, “ . . . businesses that used big data analytics to help drive the decision making process have a 5-6% greater productivity and profitability” (p. 62).  The decisions made are providing valuable insight on how a corporation can better compete in today’s data-rich, fast-paced world.  “Even though improved efficiency and logistical decisions are made big data analysis is still considered to be in it’s infancy” (Wang et al., 2016, p. 99).

          It is not surprising that supply chains have been changed by the implementation and application of big data analysis.  Some of the advantages of the use of big data is lower operational costs, greater customer satisfaction, and improved reaction time of supply chain management.  One of the important factors is using big data analysis in supply chain in the first place.  Supply chain managers need to be aware of the benefits of interpreting and using data.  The amount of data available today is enormous and collecting and processing it is essential to derive the greatest good.  Dealing with the actual amount of data that can be garnered can be a struggle.  According to Waller and Fawcett (2013), “application of quantitative and qualitative methods from a variety of disciplines in combination with Supply Chain Management (SCM) theory to solve relevant SCM problems and predict outcomes, taking into account data quality and availability issues” (p. 79).  Even though it has been slow to catch on big data analytics it appears that many companies have yet to embrace it even though the literature supports it’s use.  It seems supply chain managers are slowly becoming more dependent on big data for identifying trends, inventory monitoring, optimization of production, and process improvement.

          The current rise in the usage of big data analytics is because of the to capture and use vast amounts of data and analytical techniques are becoming more beneficial.  Despite this many supply chain administrators have hesitated to make any major investments in big data analytics.  The evolution is still in it’s early stage for big data analytics, but progress is being made in the ability to process and analyze data sets in a variety of business situations.  It is suggested by Chen, Preston, and Swink (2015), “Similarly, we suggest that insights developed through big data analytics (BDA) usage create opportunities for organizations to reconfigure their resources in ways that are in greater alignment with trends and shifts in both demand and supply markets” (p. 13).  These insights should lead to the better use of assets over time.  Imagine that analysis of point of sale (POS) data that leads to better pricing/service offerings for certain customer segments and increased customer satisfaction.  The administration would use such data processing to allow their companies to better anticipate and take advantage of quickly growing business opportunities.

          With the change in what is considered normal in Supply Chain Management, especially with globalization, uncertainty in supply and increased demand, complexity of networks, process risk, weather extremes, large risks, and Big Data means that supply chain subtleties have changed drastically.  Predictive Analytics which can occur from the crunching of big data can particularly be beneficial to a business.  As stated by Schlegel (2014), “According to APICS (the Association for Operations and Supply Chain Professionals), Predictive Analytics includes a multitude of techniques from statistics, data mining, and even game theory to analyze current and historical facts to make a prediction in regard to the future.  In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities.  Models find relationships among the factors that allow assessment of risk or potential associated with a certain set of conditions guiding decision making for a specific action” (para. 4).  Still it has been rarely used in supply chain management.  It is still a new technology that has the ability to disrupt the current practices of supply chain.  It is raising the bar for business performance and cutting waste.     

          The above synopsis demonstrates that big data analytics use is highly pertinent in dynamic environments because BDA has been shown to raise a businesses capacity to find new information and gain insight.  It is important that the direct benefit which includes such things as cost savings on operation and other internal efficiencies coming from, a decrease in paperwork, data mistakes, and error rates.  If applied correctly the benefits also include such things as better working conditions, reduction in costs, offering of new products, new revenue streams, and so forth.  It probably depends on supply chain management administrator’s views on big data analytics whether they get implemented or not.  There still seems to be a lack of certainty, reluctance, or lack of knowledge about the benefits and how to proceed with big data analytics.              

References

Chen, D., Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems. 32(4), 4-39.

Davenport, T. H., Harris, J. G. (2007). Competing on analytics: The new science of winning.   Boston: Harvard Business Press.

Gartner. (2016). IT glossary. Retrieved from https://www.gartner.com/it-glossary/big-data/

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D., & Barton, D. (2012). Big data. The Management Revolution. Harvard Bus Rev, 90(10), 61-67.

Schlegel, G. L. (2014). Utilizing big data and predictive analytics to manage supply chain risk. Journal of Business Forecasting, 33(4), 11–17. Retrieved from http://search.ebsco host.com/login.aspx?direct=true&db=nor&AN=101065842&site=ehost-live

Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and Big Data: A revolution that will transform supply chain design and management.  Journal of Business Logistics, 34(2), 77-84.

Wamba, S. F., Akter, S., Coltman, T., & Ngai, E. W. T. (2015). Information technology-enabled supply chain management. Prod. Plann. Control 26 (12), 933-944.

Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in  logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110.