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Big Data Analytics in Supply Chain Management

Anvesh Rasamalla, [email protected]

BIS 625 Research in Information Systems

College of Business Administration, Department of Business Information Systems

Central Michigan University

Mount Pleasant, Michigan 48858

USA

Keywords:

Big Data, Analytics, Supply chain Management, Logistics and Operational Efficiency

Background:

An issue which is on the minds of many supply chain management (SCM) professionals is how to deal with huge amounts of data, and how to leverage and apply analytics. This challenge is a direct result of the ease with which data have been able to be collected generating unprecedented volume, variety, and velocity of data (Hazen, Boone, Ezell, & Jones-Farmer, 2014). Big data is data that exceeds the processing capacity of traditional database systems. The data is too big, moves too fast, or doesn't fit the strictures of conventional database architectures (Wang & Alexander, 2015) Supply chains today are heavily instrumented – sensors, tags, trackers, and other smart devices are collecting data in real time on a wide variety of business processes. Gartner estimates that by 2020, there will be around 26 Billion such devices in the supply chain monitoring and connecting supply chain operations (Sanders & Ganeshan, 2015). Achieving a competitive level of global supply chain excellence cannot be achieved without data-driven, end to-end operations. Manufacturers will collect data from Point of Sale (POS), Global Positioning System (GPS) and RFID data, to data emitted by equipment sensors. Walmart is an early adopter of data-driven supply chains. It optimizes all its supply chain decisions-from customer fulfillment to inventory tracking and automatic purchase (Sanders, 2014).

Figure 1 clearly displays the Exponential growth of data in the past 6 years and also prediction of the data growth in the coming 5 years as per predictive analysis.

Currently, more than 90% of all data that exists in the world was created just two years ago (Schlegel, 2014). Estimates put the amount of data in existence at this time at more than a zettabyte or a trillion gigabytes (Anonymous, 2011). A straightforward way to apply Big Data analytics in a business environment is to increase the level of efficiency in operations. This is simply what IT has always been doing – accelerating business processes – but Big Data analytics effectively opens the throttle (Martin Jeske, 2013).

Big Data in Supply Chain Management

Supply Chain Management is an approach, which increases the efficiency of the entire business process, starting with the raw material and ending with the finished product delivered to the customer (Elmuti, Khoury, Omran, & Abou-Zaid, 2013). Increasing globalization, diversity of the product range, and increasing customer awareness are making the market(s) highly competitive thereby forcing different supply chains to adapt to different technologies on a continuous basis (Vanteddu, Chinnam, & Gushikin, 2011). For many years, supply chain decision makers have used analytics to help design facility networks, determine economic order quantities, and define safety stock parameters. However, these are not Big Data efforts. They are generally narrow and mostly piecemeal, and most are applied situationally rather than systemically (Pearson, 2014). The field of supply chain management (SCM) has been relatively slow in studying social media and big data for research and practice (Chae, 2015) The success of any supply management program is also largely dependent upon the ability to access, organize, and analyze data  (Russo, Confente, & Borghesi, 2015). Good data quality management can be used as one of the useful activity to seek out operational efficiencies for cost reduction, leading to increased margins and, consequently, increased profits. By using Big Data Analytics  organizations can  create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and therefore expose variability and boost performance (McKinsey Global Institute, 2011).The tools used to analyze complex datasets are just as important as the data themselves (Saey, 2015). According to Accenture Today, because of the widespread use of digital technologies, companies are collecting ever-greater amounts of data—and, as a result, need even more powerful ways to make sense of that data. Big data analytics fills that need. Big data have the potential to revolutionize supply chain dynamics (Waller & Fawcett, 2013). The positive impact of business analytics capabilities on supply chain performance is also confirmed in several empirical studies Moreover, Brown et al and Waller and Fawcett indicate the disruptive potential of data-driven decision-making for business and operations management in particular (B. Brown, 2011). the main challenge to managers is to identify an analytic infrastructure that could harvest big data to support firms innovation (Tan, Zhan, Ji, Ye, & Chang, 2015).

Source: http://www.dhl.com/content/dam/downloads/g0/about_us/innovation/CSI_Studie_BIG_DATA.pdf

According to Wang& Alexander Big Data offers the following benefits in supply chain management and business administration.

• Provide more accurate operational information and enable timely correction or supplier change.

• Improve transparency of information and give greater visibility throughout the supply chain.

• Uncover defects in products/services in the supply chain, give early warning and avoid recalls.

• Minimize inventory and supply chain risk using big data analytics.  

• Higher operational efficiencies (Wang & Alexander, 2015).

Research Questions and Objectives:

• What is the current status of using Big Data in Supply chain and logistics?

• Does Big Data increases operational efficiency in supply chain and logistics.

• How can we avoid ‘out of stock' problems using big data in supply chain.

• How to handle huge stream of data evolving from fleet of vehicles, RFID, geo tags etc.

Methods:

Research Design: The research design includes literature review, Analysis of Big data in current implementations, case studies, conducting surveys/ interviews. Initially we analyze Logistics firms using Big Data and the Logistics firms which are using traditional ways to run supply chain. According to the MIT-SAS research, about 10% of firms surveyed have experts who have become Analytic Innovators who leverage advanced analytics to re-think the business and innovate processes and products (Milliken, 2014).  First we study the comparison between the process involved in the supply chain with and without the role of Big Data. A case study will be done, by comparing an existing Logistics and supply chain procedures and that of the supply chain firm using Big Data. Questionnaires are prepared and face to face interviews were conducted. An analysis is made based on all the secondary data and interviews.

Participants: As the research involves Logistics and supply chain firms like DHL, FedEx and Walmart can be used as a case study in the paper to understand the use and benefits of Big Data in detail. So, it would be easy to meet the required persons and get permissions for conducting surveys and questionnaires for my research.

Techniques: The technique involved in the process is all about how to achieve a better collaboration in all  the stages of the project from schematic design to final form and overall out-come of the results based on it, an analysis can be made how Big Data is useful in supply chain procedures.

Time Scale:

References:

Anonymous. (2011). Big Data-Big Challenges, Big Opportunities. Database Trends and Applications, 25(1), 12-14.

B. Brown, M. C., J. Manyika. (2011). Are you ready for the era of ‘big data'? McKinsey Quarterly, 4, 24-35.

Chae, B. (2015). Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research. International Journal of Production Economics, 165, 247-259. doi

Elmuti, D., Khoury, G., Omran, O., & Abou-Zaid, A. S. (2013). Challenges and Opportunities of Health Care Supply Chain Management in the United States. Health Marketing Quarterly, 30(2), 128-143. doi:10.1080/07359683.2013.787885

Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80. doi:

Martin Jeske, M. G., Frank Weiß. (2013, Dec 2013). BIG DATA IN LOGISTICS A DHL perspective on how to move beyond the hype.   

Milliken, A. L. (2014). Transforming Big Data into Supply Chain Analytics. The Journal of Business Forecasting, 33(4), 23-27.

Pearson, M. (2014). Strengthen the supply chain with Big Data analytics. Logistics Management (2002), 53(10), 24.

Russo, I., Confente, I., & Borghesi, A. (2015, 2015/09//

Sep 2015). Using big Data in the Supply Chain Context: Opportunities and Challenges, Kidmore End.

Saey, T. H. (2015, 2015 Feb 07). Big Data, Big Challenges. Science News, 187, 23-27.

Sanders, N. R., & Ganeshan, R. (2015). Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”. Production and Operations Management, 24(11), 1835-1836. doi:10.1111/poms.12516

Schlegel, G. L. (2014). Utilizing Big Data and Predictive Analytics to Manage Supply Chain Risk. The Journal of Business Forecasting, 33(4), 11-17.

Tan, K. H., Zhan, Y., Ji, G., Ye, F., & Chang, C. (2015). Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph. International Journal of Production Economics, 165, 223-233.

Vanteddu, G., Chinnam, R. B., & Gushikin, O. (2011). Supply chain focus dependent supplier selection problem. International Journal of Production Economics, 129(1), 204-216.

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. doi:10.1111/jbl.12010

Wang, L., & Alexander, C. A. (2015). Big Data Driven Supply Chain Management and Business Administration. American Journal of Economics and Business Administration, 7(2), 60-67.

 Sanders, N.R., 2014. Big Data Driven Supply Chain Management: A Framework for Implementing Analytics and Turning Information into Intelligence, 1st End. Pearson Education, Upper Saddle River, ISBN-10: 0133801284, pp: 262.

BIG DATA IN LOGISTICS A DHL perspective on how to move beyond the hype

December 2013

McKinsey Global Institute, “Big data: The next frontier for innovation, competition, and productivity”. 2011.

Accenture, 2014. Big Data analytics in supply chain: hype or here to stay? Technical Report, Accenture Company.

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