Unlocking causal relations of barriers to big data analytics in manufacturing firms

PurposeThis study initially aims to identify the barriers to the big data analytics (BDA) initiative and further evaluates the barriers for knowing their interrelations and priority in improving the performance of manufacturing firms.Design/methodology/approachA total of 15 barriers to BDA adoption were identified through literature review and expert opinions. Data were collected from three types of industries: automotive, machine tools and electronics manufacturers in India. The grey-decision-making trial and evaluation laboratory (DEMATEL) method was employed to explore the cause–effect relationship amongst barriers. Further, the barrier's influences were outranked and cross-validated through analytic network process (ANP).FindingsThe results showed that “lack of data storage facility”, “lack of IT infrastructure”, “lack of organisational strategy” and “uncertain about benefits and long terms usage” were most common barriers to adopt BDA practices in all three industries.Practical implicationsThe findings of the study can assist service providers, industrial managers and government organisations in understanding the barriers and subsequently evaluating interrelationships and ranks of barriers in the successful adoption of BDA in a manufacturing organisation context.Originality/valueThe paper is one of the initial efforts in evaluating the barriers to BDA in improving the performance of manufacturing firms in India.

[1]  Ray Y. Zhong,et al.  Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives , 2016, Comput. Ind. Eng..

[2]  Manoj Kumar Tiwari,et al.  Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach , 2005, Comput. Ind. Eng..

[3]  Irwin Brown,et al.  Challenges to the Organisational Adoption of Big Data Analytics: A Case Study in the South African Telecommunications Industry , 2015, SAICSIT '15.

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

[5]  Shahriar Akter,et al.  How to improve firm performance using big data analytics capability and business strategy alignment , 2016 .

[6]  Joao Quariguasi Frota Neto,et al.  Mapping the market for remanufacturing: An application of “Big Data” analytics , 2020 .

[7]  E. Yadegaridehkordi,et al.  Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach , 2018, Technological Forecasting and Social Change.

[8]  M. Snir,et al.  Big data, but are we ready? , 2011, Nature Reviews Genetics.

[9]  Shahriar Akter,et al.  Big data analytics and firm performance: Effects of dynamic capabilities , 2017 .

[10]  Winston M. Tellis,et al.  APPLICATION OF A CASE STUDY METHODOLOGY , 1997 .

[11]  Sunil Tiwari,et al.  Big data analytics in supply chain management between 2010 and 2016: Insights to industries , 2018, Comput. Ind. Eng..

[12]  Surya Prakash Singh,et al.  Modeling big data enablers for operations and supply chain management , 2018 .

[13]  Shahriar Akter,et al.  Modelling quality dynamics, business value and firm performance in a big data analytics environment , 2017, Int. J. Prod. Res..

[14]  Angappa Gunasekaran,et al.  Agile manufacturing practices: the role of big data and business analytics with multiple case studies , 2018, Int. J. Prod. Res..

[15]  T. Oliveira,et al.  Assessing business value of Big Data Analytics in European firms , 2017 .

[16]  Jatinder Singh,et al.  An intelligent approach to Big Data analytics for sustainable retail environment using Apriori-MapReduce framework , 2017, Ind. Manag. Data Syst..

[17]  James J. H. Liou,et al.  Developing a hybrid multi-criteria model for selection of outsourcing providers , 2010, Expert Syst. Appl..

[18]  A. Gunasekaran,et al.  Can big data and predictive analytics improve social and environmental sustainability? , 2017, Technological Forecasting and Social Change.

[19]  Angappa Gunasekaran,et al.  Vision, applications and future challenges of Internet of Things: A bibliometric study of the recent literature , 2016, Ind. Manag. Data Syst..

[20]  Muhittin Sagnak,et al.  A systematic literature review with bibliometric analysis of big data analytics adoption from period 2014 to 2018 , 2020, J. Enterp. Inf. Manag..

[21]  Kan Wu,et al.  Smart spare parts management systems in semiconductor manufacturing , 2017, Ind. Manag. Data Syst..

[22]  A. Gunasekaran,et al.  The role of Big Data in explaining disaster resilience in supply chains for sustainability , 2017 .

[23]  Niraj Kumar,et al.  Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice , 2017, Transportation Research Part E: Logistics and Transportation Review.

[24]  Jing Zhang,et al.  Efficient jobs scheduling approach for big data applications , 2018, Comput. Ind. Eng..

[25]  R. Shankar,et al.  Modelling critical success factors for sustainability initiatives in supply chains in Indian context using Grey-DEMATEL , 2018 .

[26]  Matthias Messer,et al.  Decision data model in virtual product development , 2018, Comput. Ind. Eng..

[27]  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 .

[28]  V. Krotov,et al.  Addressing barriers to big data , 2017 .

[29]  Rakesh D. Raut,et al.  Factors affecting the adoption of cloud of things , 2019, J. Syst. Inf. Technol..

[30]  Z. Zainal.  Case Study As a Research Method , 2007 .

[31]  Dong-Hee Shin,et al.  Demystifying big data: Anatomy of big data developmental process , 2016 .

[32]  Rakesh D. Raut,et al.  To identify the determinants of the CloudIoT technologies adoption in the Indian MSMEs: structural equation modelling approach , 2019, Int. J. Bus. Inf. Syst..

[33]  Benjamin T. Hazen,et al.  Big data and predictive analytics for supply chain and organizational performance , 2017 .

[34]  Rakesh D. Raut,et al.  To identify the determinants of the CloudIoT technologies adoption in the Indian MSMEs: structural equation modelling approach , 2019, Int. J. Bus. Inf. Syst..

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

[36]  Ejaz Ahmed,et al.  Big Data Analytics in Industrial IoT Using a Concentric Computing Model , 2018, IEEE Communications Magazine.

[37]  Joey F. George,et al.  Toward the development of a big data analytics capability , 2016, Inf. Manag..

[38]  David S. Cochran,et al.  Big data analytics with applications , 2014 .

[39]  Jiunn-I Shieh,et al.  Evaluating performance criteria of Employment Service Outreach Program personnel by DEMATEL method , 2010, Expert Syst. Appl..

[40]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[41]  A. Gunasekaran,et al.  Contingency factors and complementary effects of adopting advanced manufacturing tools and managerial practices: Effects on organizational measurement systems and firms' performance , 2018, International Journal of Production Economics.

[42]  Surabhi Verma,et al.  Perceived strategic value-based adoption of Big Data Analytics in emerging economy: A qualitative approach for Indian firms , 2017, J. Enterp. Inf. Manag..

[43]  Qinghua Zhu,et al.  Evaluating green supplier development programs with a grey-analytical network process-based methodology , 2014, Eur. J. Oper. Res..

[44]  Mahdi Fahmideh,et al.  Big data analytics architecture design - An application in manufacturing systems , 2019, Comput. Ind. Eng..

[45]  Lakshman S. Thakur,et al.  A big data MapReduce framework for fault diagnosis in cloud-based manufacturing , 2016 .

[46]  Pamela Baxter,et al.  Qualitative Case Study Methodology: Study Design and Implementation for Novice Researchers , 2008 .

[47]  Rakesh D. Raut,et al.  Mediating role of cloud of things in improving performance of small and medium enterprises in the Indian context , 2020 .

[48]  Hsin-Hung Wu,et al.  A DEMATEL method to evaluate the causal relations among the criteria in auto spare parts industry , 2011, Appl. Math. Comput..

[49]  José Raúl Romero,et al.  On the Need of Opening the Big Data Landscape to Everyone: Challenges and New Trends , 2018 .

[50]  Syed Mithun Ali,et al.  Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh , 2019, Comput. Ind. Eng..

[51]  Rakesh D. Raut,et al.  Linking big data analytics and operational sustainability practices for sustainable business management , 2019, Journal of Cleaner Production.

[52]  Wouter Joosen,et al.  Trustworthy data-driven networked production for customer-centric plants , 2017, Ind. Manag. Data Syst..

[53]  Petri T. Helo,et al.  Big data applications in operations/supply-chain management: A literature review , 2016, Comput. Ind. Eng..

[54]  Cristiano Ciappei,et al.  Ambidextrous organization and agility in big data era: The role of business process management systems , 2018, Bus. Process. Manag. J..

[55]  Amin Vafadarnikjoo,et al.  A grey DEMATEL approach to develop third-party logistics provider selection criteria , 2016, Ind. Manag. Data Syst..

[56]  Addo-TenkorangRichard,et al.  Big data applications in operations/supply-chain management , 2016 .

[57]  Guo Chao Peng,et al.  Barriers of embedding big data solutions in smart factories: insights from SAP consultants , 2019, Ind. Manag. Data Syst..

[58]  Anders Skoogh,et al.  A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines , 2018, Comput. Ind. Eng..

[59]  Shaofeng Liu,et al.  Sustainable knowledge-based decision support systems (DSS): perspectives, new challenges and recent advance , 2017, Ind. Manag. Data Syst..

[60]  M. Hacksteiner,et al.  Strategic energy management in mechanical series production: An industrial use-case , 2019 .

[61]  Sepideh Ebrahimi,et al.  Data analytics competency for improving firm decision making performance , 2018, J. Strateg. Inf. Syst..

[62]  Ravi Shankar,et al.  Multi-criteria evaluation of real-time key performance indicators of supply chain with consideration of big data architecture , 2019, Comput. Ind. Eng..

[63]  Winston Tellis,et al.  Introduction to Case Study , 1997 .

[64]  Dianne Hall,et al.  Understanding the Factors Affecting the Organizational Adoption of Big Data , 2018, J. Comput. Inf. Syst..

[65]  Francis Rousseaux,et al.  BIG DATA and Data-Driven Intelligent Predictive Algorithms to support creativity in Industrial Engineering , 2017, Comput. Ind. Eng..

[66]  Muhammad Shiraz,et al.  Big Data: Survey, Technologies, Opportunities, and Challenges , 2014, TheScientificWorldJournal.

[67]  S. Seuring,et al.  Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management , 2017 .

[68]  Rakesh D. Raut,et al.  To investigate the determinants of cloud computing adoption in the manufacturing micro, small and medium enterprises , 2019, Benchmarking: An International Journal.

[69]  Ratna Babu Chinnam,et al.  An HMM and polynomial regression based approach for remaining useful life and health state estimation of cutting tools , 2019, Comput. Ind. Eng..