Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

The use of big data analytics for forecasting business trends is gaining momentum among professionals. At the same time, supply chain risk management is important for practitioners to consider because it outlines ways through which firms can allay internal and external threats. Predicting and addressing the risks that social issues cause in the supply chain is of paramount importance to the sustainable enterprise. The aim of this research is to explore the application of big data analytics in mitigating supply chain social risk and to demonstrate how such mitigation can help in achieving environmental, economic, and social sustainability. The method involves an expert panel and survey identifying and validating social issues in the supply chain. A case study was used to illustrate the application of big data analytics in identifying and mitigating social issues in the supply chain. Our results show that companies can predict various social problems including workforce safety, fuel consumptions monitoring, workforce health, security, physical condition of vehicles, unethical behavior, theft, speeding and traffic violations through big data analytics, thereby demonstrating how information management actions can mitigate social risks. This paper contributes to the literature by integrating big data analytics with sustainability to explain how to mitigate supply chain risk.

[1]  Erik Hofmann,et al.  Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect , 2017, Int. J. Prod. Res..

[2]  David L. Olson,et al.  Business Analytics for Supply Chain: a Dynamic-Capabilities Framework , 2013, Int. J. Inf. Technol. Decis. Mak..

[3]  Vipin Chaudhary,et al.  Big Data in Finance , 2016 .

[4]  Daniel E. O'Leary.  D.E. O'LEARY , 2011 .

[5]  J. M. Whipple,et al.  Strategic Alliance Success Factors , 2000 .

[6]  Sun-Kyoung Park,et al.  Sustainable Development Plan for Korea through Expansion of Green IT: Policy Issues for the Effective Utilization of Big Data , 2015 .

[7]  Dursun Delen,et al.  Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud , 2013, Decis. Support Syst..

[8]  Amy Z. Zeng,et al.  How many suppliers are best? A decision-analysis approach , 2004 .

[9]  Benjamin T. Hazen,et al.  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 , 2014 .

[10]  Bert De Reyck,et al.  The Value of Capacity Sizing Under Risk Aversion and Operational Flexibility , 2013, IEEE Transactions on Engineering Management.

[11]  Stefan Seuring,et al.  From a literature review to a conceptual framework for sustainable supply chain management , 2008 .

[12]  Yingli Wang,et al.  Using e‐business to enable customised logistics sustainability , 2007 .

[13]  Andreas Norrman,et al.  Ericsson’s Proactive Supply Chain Risk Management-approach After a Serious Supplier Accident , 2004 .

[14]  Ray Y. Zhong,et al.  Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors , 2017, Int. J. Prod. Res..

[15]  Kim Hua,et al.  Harvesting Big Data to Enhance Supply Chain Innovation Capabilities : An Analytic Infrastructure Based on Deduction Graph , 2016 .

[16]  Benjamin T. Hazen,et al.  Supply chain social sustainability for developing nations: Evidence from India , 2016 .

[17]  Hing Kai Chan,et al.  Guest Editorial Big Data Analytics: Risk and Operations Management for Industrial Applications , 2016, IEEE Trans. Ind. Informatics.

[18]  R. Klassen,et al.  Social issues in supply chains: Capabilities link responsibility, risk (opportunity), and performance , 2012 .

[19]  H.L. Lee,et al.  Aligning Supply Chain Strategies with Product Uncertainties , 2002, IEEE Engineering Management Review.

[20]  Roland W. Scholz,et al.  Sustainable Digital Environments: What Major Challenges Is Humankind Facing? , 2016 .

[21]  O. Tang,et al.  Identifying risk issues and research advancements in supply chain risk management , 2011 .

[22]  Y. Sheffi,et al.  A supply chain view of the resilient enterprise , 2005 .

[23]  Reuben E. Slone.  Leading a supply chain turnaround. , 2004, Harvard business review.

[24]  Computer Staff,et al.  The Machine That Changed the World , 1992 .

[25]  L. G. White,et al.  Administrative Behavior , 2019, Managing Development in the Third World.

[26]  J. Ruiz Moreno.  [Organizational learning]. , 2001, Revista de enfermeria.

[27]  M. Lynne Markus,et al.  Power, politics, and MIS implementation , 1987, CACM.

[28]  Chetan S. Sankar,et al.  Cross-Border Process Innovations: Improving the Fit Between Information Processing Needs and Capabilities , 2015 .

[29]  Sameer Kumar,et al.  Before and after disaster strikes: A relief supply chain decision support framework , 2013 .

[30]  Christine Nadel,et al.  Case Study Research Design And Methods , 2016 .

[31]  Gilvan C. Souza,et al.  Supply Chain Analytics , 2016 .

[32]  J. Brett,et al.  Managing multicultural teams. , 2006, Harvard business review.

[33]  Fabrice Dupros,et al.  Collaborative simulation and scientific big data analysis: Illustration for sustainability in natural hazards management and chemical process engineering , 2014, Comput. Ind..

[34]  Peter F. Drucker,et al.  The Changed World Economy , 1986 .

[35]  M. Fine,et al.  Qualitative and quantitative methods: When stories converge , 1987 .

[36]  Angappa Gunasekaran,et al.  The impact of big data on world-class sustainable manufacturing , 2015, The International Journal of Advanced Manufacturing Technology.

[37]  Vipin Kumar,et al.  Trends in big data analytics , 2014, J. Parallel Distributed Comput..

[38]  Daniel E. O'Leary,et al.  The Use of Social Media in the Supply Chain: Survey and Extensions , 2011, Intell. Syst. Account. Finance Manag..

[39]  Hing Kai Chan,et al.  Recent Development in Big Data Analytics for Business Operations and Risk Management , 2017, IEEE Transactions on Cybernetics.

[40]  O. Tang,et al.  Manufacturing facility location and sustainability: A literature review and research agenda , 2014 .

[41]  Yelena Yesha,et al.  Workshop on Analytics for Big Data Generated by Healthcare and Personalized Medicine Domain , 2012, CASCON.

[42]  Nada R. Sanders,et al.  The Emerging Role of the Third‐Party Logistics Provider (3PL) as an Orchestrator , 2011 .

[43]  Min Xia,et al.  Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs , 2012, Knowl. Based Syst..

[44]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[45]  S. Chopra,et al.  Reducing the Risk of Supply Chain Disruptions , 2014 .

[46]  T. Bonoma.  Case Research in Marketing: Opportunities, Problems, and a Process , 1985 .

[47]  Sunghae Jun,et al.  A Patent Analysis for Sustainable Technology Management , 2016 .

[48]  J. Morland,et al.  A Case for the Case Study , 1991 .

[49]  M. Jelinek,et al.  Innovation as the strategic driver of sustainability: big data knowledge for profit and survival , 2013, IEEE Engineering Management Review.

[50]  G. Hult,et al.  Bridging organization theory and supply chain management: The case of best value supply chains , 2007 .

[51]  J. Rowley.  Using case studies in research , 2002 .

[52]  Robert K. Yin,et al.  Qualitative Research from Start to Finish , 2010 .

[53]  Charles M. Judd,et al.  Combining Process and Outcome Evaluation. , 1987 .

[54]  Bin Shen,et al.  Forecast Information Sharing for Managing Supply Chains in the Big Data Era: Recent Development and Future Research , 2017, Asia Pac. J. Oper. Res..

[55]  Fei Tao,et al.  Big Data in product lifecycle management , 2015, The International Journal of Advanced Manufacturing Technology.

[56]  R. Grant.  Chapter 8 – Prospering in Dynamically-Competitive Environments: Organizational Capability as Knowledge Integration , 1999 .

[57]  Christopher S. Tang.  Perspectives in supply chain risk management , 2006 .

[58]  Amit P. Sheth,et al.  From Data to Actionable Knowledge: Big Data Challenges in the Web of Things , 2013, IEEE Intell. Syst..

[59]  Wolfgang Kersten,et al.  Where Do We Go From Here? Progressing Sustainability Implementation Efforts Across Supply Chains , 2013 .

[60]  D. Wood.  Corporate Social Performance Revisited , 1991 .

[61]  P. Schoemaker,et al.  Strategic assets and organizational rent , 1993 .

[62]  Benjamin T. Hazen,et al.  Back in business: operations research in support of big data analytics for operations and supply chain management , 2016, Annals of Operations Research.

[63]  Hau L. Lee,et al.  Mitigating supply chain risk through improved confidence , 2004 .

[64]  T. Davenport.  Competing on analytics. , 2006, Harvard business review.

[65]  J. Halamka.  Early experiences with big data at an academic medical center. , 2014, Health affairs.

[66]  A. Gunasekaran,et al.  Social sustainability in the supply chain: Construct development and measurement validation , 2016 .

[67]  Shahriar Akter,et al.  How ‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study , 2015 .

[68]  Mohamed Mohamed Naim,et al.  A supply chain diagnostic methodology: determining the vector of change , 2002 .

[69]  Peter Trkman,et al.  The impact of business analytics on supply chain performance , 2010, Decis. Support Syst..

[70]  Elliot Liebow,et al.  Tally's Corner: A Study of negro Streetcorner Men , 1967 .

[71]  Beatriz de la Iglesia,et al.  Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer , 2015, JMIR medical informatics.

[72]  A. Raman,et al.  Aligning incentives in supply chains. , 2004, Harvard business review.

[73]  R. Grant.  Toward a Knowledge-Based Theory of the Firm,” Strategic Management Journal (17), pp. , 1996 .

[74]  K. B. Hendricks,et al.  An Empirical Analysis of the Effect of Supply Chain Disruptions on Long‐Run Stock Price Performance and Equity Risk of the Firm , 2005 .

[75]  M. Taisch,et al.  Sustainable manufacturing: trends and research challenges , 2012 .

[76]  Benjamin T. Hazen,et al.  Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda , 2016, Comput. Ind. Eng..

[77]  Donald S. Siegel,et al.  Creating and Capturing Value , 2011 .

[78]  C. Mangano.  Risky business. , 2003, The Journal of thoracic and cardiovascular surgery.

[79]  Hau L. Lee.  The triple-A supply chain. , 2004, Harvard business review.

[80]  Mihalis Giannakis,et al.  The intellectual structure of the supply chain management discipline: A citation and social network analysis , 2012, J. Enterp. Inf. Manag..

[81]  A. Gunasekaran,et al.  Big data analytics in logistics and supply chain management: Certain investigations for research and applications , 2016 .

[82]  K. Eisenhardt.  Building theories from case study research , 1989, STUDI ORGANIZZATIVI.

[83]  Angappa Gunasekaran,et al.  Big Data and supply chain management: a review and bibliometric analysis , 2018, Ann. Oper. Res..

[84]  Gunno Park,et al.  Sustaining Innovative Success: A Case Study on Consumer-Centric Innovation in the ICT Industry , 2016 .

[85]  Uta Jüttner.  Supply chain risk management: Understanding the business requirements from a practitioner perspective , 2005 .

[86]  Pankaj Chandra,et al.  The Logistics Sector in India: Overview and Challenges , 2007 .

[87]  G. Huber.  Organizational Learning: The Contributing Processes and the Literatures , 1991 .