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Framework for implementing big data analytics in Indian manufacturing: ISM-MICMAC and Fuzzy-AHP approach

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Abstract

Manufacturing firms generate a massive amount of data points because of higher than ever connected devices and sensor technology adoption. These data points could be from varied sources, ranging from flow time and cycle time through different machines in an assembly line to shop floor data collected from sensors viz. temperature, stress capability, pressure, etc. Analysis of this data can help manufacturers in many ways, viz. predict breakdown—reduction in downtime and waste, optimal inventory level—resource optimization, etc. The data may be highly voluminous, highly unstructured, coming from varied sources at a higher speed. Thus, big data analytics has become more critical than ever for the manufacturing industry to have the capability of effectively deriving business value from the vast amount of generated data. Manufacturing firms face hindrances and failures in the implementation of big data analytics. It is, therefore, necessary for the companies in the Indian manufacturing sector to identify and examine the reason and nature of barriers resisting the implementation of Big Data Analytics (BDA) to their organization. This paper explores the existing literature available to identify the barriers, categorized based on different functions of an organization. A total of 16 barriers are determined from the rigorous review of existing research. A survey is conducted on the industry experts from automobile, steel, automotive parts manufacturer, and electrical equipment industries to obtain a contextual relationship between the barriers. Interpretive Structural Modeling and MICMAC (Cross-impact matrix multiplication applied to classification) are the analytical techniques used in this research to classify the barriers into different impact levels and importance. Independent factors (barriers) have high driving power and are the key factors that were further analyzed using Fuzzy AHP to determine their comparative priority/importance. The result of this research shows that barriers related to Management and Infrastructure & Technology are the main hurdles in the implementation of big data analytics in the manufacturing industry. Six critical barriers (based on high driving power) are; lack of long-term vision, lack of commitment from top management, lack of infrastructure facility, lack of funding, lack of availability of specific data tools, and lack of training facility. Lack of commitment from top management is the most critical barrier. Research focuses on a comprehensive analysis of the barriers in implementing big data analytics (BDA) in manufacturing firms. The novelty lies in (a) finding an extensive list of barriers, (b) application domain and geography, and (c) the multi-criteria decision making technique used for finding the critical barriers to the implementation of big data analytics. The findings of this research will help industry leaders to formulate a better plan before the application of BDA in their organizations.

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Abbreviations

BD:

Big Data

BDA:

Big Data Analytics

BI:

Business Intelligence

IoT:

Internet of Things

ISM:

Interpretive Structural Technique

IT:

Information Technology

MICMAC:

Matriced’ Impacts Croise’s Multiplication Applique’e a UN Classement

SSIM:

Structural Self Interaction Matrix

SEM:

Structural Equation Modeling

References

  1. Luckow A, Kennedy K, Manhardt F, Djerekarov E, Vorster B, Apon A (2015) Automotive big data: applications, workloads, and infrastructures.  Proceedings: IEEE international conference on big data, IEEE Big Data.

  2. Wang Y, Hajli N (2017) Exploring the path to big data analytics success in healthcare. J Bus Res. https://doi.org/10.1016/j.jbusres.2016.08.002

    Article  Google Scholar 

  3. Waller MA, Fawcett SE (2013) Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J Bus Logist. https://doi.org/10.1111/jbl.12010

    Article  Google Scholar 

  4. Wang G, Gunasekaran A, Ngai EWT, Papadopoulos T (2016) Big data analytics in logistics and supply chain management: certain investigations for research and applications. Int J Prod Econ 176:98–110

    Article  Google Scholar 

  5. Schoenherr T, Speier-Pero C (2015) Data science, predictive analytics, and big data in supply chain management: current state and future potential. J Bus Logist. https://doi.org/10.1111/jbl.12082

    Article  Google Scholar 

  6. Moktadir MA, Ali SM, Kusi-Sarpong S, Shaikh MAA (2018) Assessing challenges for implementing Industry 4.0: implications for process safety and environmental protection. Process Saf Environ Prot. https://doi.org/10.1016/j.psep.2018.04.020

    Article  Google Scholar 

  7. Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manage. https://doi.org/10.1016/j.ijinfomgt.2014.10.007

    Article  Google Scholar 

  8. Tabesh P, Mousavidin E, Hasani S (2019) Implementing big data strategies: a managerial perspective. Bus Horiz. https://doi.org/10.1016/j.bushor.2019.02.001

    Article  Google Scholar 

  9. Axryd S (2019) Why 85% of big data projects fail. DNA. https://www.digitalnewsasia.com/insights/why-85-big-data-projects

  10. Gartner (2015) Gartner says business intelli-gence and analytics leaders must focus on mindsets and culture to kick start advanced analytics. In: Forbes. http://www.gartner.com/newsroom/id/3130017

  11. Marr B (2015) Where big data projects fail. Forbes. https://www.forbes.com/sites/bernardmarr/2015/03/17/where-big-data-projects-fail/#1a6e75eb239f

  12. Patrizio A (2019) 4 reasons big data projects fail—and 4 ways to succeed. In: InfoWorld. https://www.infoworld.com/article/3393467/4-reasons-big-data-projects-failand-4-ways-to-succeed.html

  13. Machado CG, Winroth M, Carlsson D et al (2019) Industry 4.0 readiness in manufacturing companies: challenges and enablers towards increased digitalization. Procedia CIRP 81:1113–1118

    Article  Google Scholar 

  14. Moktadir MA, Ali SM, Paul SK, Shukla N (2019) Barriers to big data analytics in manufacturing supply chains: a case study from Bangladesh. Comput Ind Eng. https://doi.org/10.1016/j.cie.2018.04.013

    Article  Google Scholar 

  15. Horváth D, Szabó RZ (2019) Driving forces and barriers of Industry 4.0: do multinational and small and medium-sized companies have equal opportunities? Technol Forecast Soc Change. https://doi.org/10.1016/j.techfore.2019.05.021

    Article  Google Scholar 

  16. Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Netw Appl 19:171–209

    Article  Google Scholar 

  17. Dessureault S (2016) Understanding big data. CIM Mag. https://doi.org/10.4018/978-1-5225-9750-6.ch001

    Article  Google Scholar 

  18. Laney D (2001) 3D data management: controlling data volume, velocity, and variety. Appl Deliv Strateg. https://doi.org/10.1016/j.infsof.2008.09.005

    Article  Google Scholar 

  19. Beyer MA, Laney D (2012) The importance of ‘big data’: a definition. CT Gart, Stamford

    Google Scholar 

  20. Zikopoulos PC, DeRoos D, Parasuraman K, et al (2012) Harness the power of big data

  21. Lee I (2017) Big data: dimensions, evolution, impacts, and challenges. Bus Horiz. https://doi.org/10.1016/j.bushor.2017.01.004

    Article  Google Scholar 

  22. Belhadi A, Zkik K, Cherrafi A et al (2019) Understanding big data analytics for manufacturing processes: insights from literature review and multiple case studies. Comput Ind Eng. https://doi.org/10.1016/j.cie.2019.106099

    Article  Google Scholar 

  23. Sivarajah U, Kamal MM, Irani Z, Weerakkody V (2017) Critical analysis of big data challenges and analytical methods. J Bus Res. https://doi.org/10.1016/j.jbusres.2016.08.001

    Article  Google Scholar 

  24. Tewari S, Dwivedi UD (2019) Ensemble-based big data analytics of lithofacies for automatic development of petroleum reservoirs. Comput Ind Eng. https://doi.org/10.1016/j.cie.2018.08.018

    Article  Google Scholar 

  25. Tsai CW, Lai CF, Chao HC, Vasilakos AV (2015) Big data analytics: a survey. J Big Data. https://doi.org/10.1186/s40537-015-0030-3

    Article  Google Scholar 

  26. Bag S, Pretorius JHC, Gupta S, Dwivedi YK (2021) Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technol Forecast Soc Change. https://doi.org/10.1016/j.techfore.2020.120420

    Article  Google Scholar 

  27. Kamble SS, Gunasekaran A (2020) Big data-driven supply chain performance measurement system: a review and framework for implementation. Int J Prod Res 58:65–86

    Article  Google Scholar 

  28. Li L, Hao T, Chi T (2017) Evaluation on China’s forestry resources efficiency based on big data. J Clean Prod. https://doi.org/10.1016/j.jclepro.2016.02.078

    Article  Google Scholar 

  29. Bi Z, Cochran D (2014) Big data analytics with applications. J Manag Anal 1:249–265

    Google Scholar 

  30. Li J, Tao F, Cheng Y, Zhao L (2015) Big data in product lifecycle management. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-015-7151-x

    Article  Google Scholar 

  31. Varela IR, Tjahjono B (2014) Big data analytics in supply chain management: trends and related research. 6th international conference on operations supply chain management. https://doi.org/10.13140/RG.2.1.4935.2563

  32. Hamilton RH, Sodeman WA (2019) The questions we ask: opportunities and challenges for using big data analytics to strategically manage human capital resources. Bus Horiz. https://doi.org/10.1016/j.bushor.2019.10.001

    Article  Google Scholar 

  33. Barnes SJ, Mattsson J, Sørensen F, Jensen JF (2020) Measuring employee-tourist encounter experience value: a big data analytics approach. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113450

    Article  Google Scholar 

  34. Nie X, Fan T, Wang B et al (2020) Big data analytics and IoT in operation safety management in under water management. Comput Commun. https://doi.org/10.1016/j.comcom.2020.02.052

    Article  Google Scholar 

  35. Wang J, Zheng P, Zhang J (2020) Big data analytics for cycle time related feature selection in the semiconductor wafer fabrication system. Comput Ind Eng. https://doi.org/10.1016/j.cie.2020.106362

    Article  Google Scholar 

  36. Bag S, Wood LC, Xu L et al (2020) Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resour Conserv Recycl. https://doi.org/10.1016/j.resconrec.2019.104559

    Article  Google Scholar 

  37. Ngo J, Hwang BG, Zhang C (2020) Factor-based big data and predictive analytics capability assessment tool for the construction industry. Autom Constr. https://doi.org/10.1016/j.autcon.2019.103042

    Article  Google Scholar 

  38. Ajayi A, Oyedele L, Akinade O et al (2020) Optimised big data analytics for health and safety hazards prediction in power infrastructure operations. Saf Sci. https://doi.org/10.1016/j.ssci.2020.104656

    Article  Google Scholar 

  39. Hausladen I, Schosser M (2020) Towards a maturity model for big data analytics in airline network planning. J Air Transp Manag. https://doi.org/10.1016/j.jairtraman.2019.101721

    Article  Google Scholar 

  40. Fang K, Jiang Y, Song M (2016) Customer profitability forecasting using big data analytics: a case study of the insurance industry. Comput Ind Eng. https://doi.org/10.1016/j.cie.2016.09.011

    Article  Google Scholar 

  41. Li X, Zhao X, Pu W et al (2019) Optimal decisions for operations management of BDAR: a military industrial logistics data analytics perspective. Comput Ind Eng. https://doi.org/10.1016/j.cie.2019.106100

    Article  Google Scholar 

  42. Raut RD, Mangla SK, Narwane VS et al (2019) Linking big data analytics and operational sustainability practices for sustainable business management. J Clean Prod. https://doi.org/10.1016/j.jclepro.2019.03.181

    Article  Google Scholar 

  43. Dubey R, Gunasekaran A, Childe SJ et al (2019) Can big data and predictive analytics improve social and environmental sustainability? Technol Forecast Soc Change. https://doi.org/10.1016/j.techfore.2017.06.020

    Article  Google Scholar 

  44. Dubey R, Gunasekaran A, Childe SJ et al (2018) Examining the role of big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour. J Clean Prod. https://doi.org/10.1016/j.jclepro.2018.06.097

    Article  Google Scholar 

  45. Ernst, Young (2020) Indian manufacturing firms rank big data, predictive analytics as top investment priorities. In: Hindu Business line. https://www.thehindubusinessline.com/economy/indian-manufacturing-firms-rank-big-data-predictive-analytics-as-top-investment-priority-ey/article30989322.ece

  46. Economic times (2019) Big data plays a crucial role in the manufacturing sector. Economic times.: https://economictimes.indiatimes.com/small-biz/sme-sector/big-data-plays-a-crucial-role-in-manufacturing-sector-ashutosh-sharma-secy-department-of-sc-tech/articleshow/69692549.cms?from=mdr

  47. Raut RD, Yadav VS, Cheikhrouhou N et al (2021) Big data analytics: implementation challenges in Indian manufacturing supply chains. Comput Ind. https://doi.org/10.1016/j.compind.2020.103368

    Article  Google Scholar 

  48. Arunachalam D, Kumar N, Kawalek JP (2018) Understanding big data analytics capabilities in supply chain management: unravelling the issues, challenges and implications for practice. Transp Res Part E Logist Transp Rev. https://doi.org/10.1016/j.tre.2017.04.001

    Article  Google Scholar 

  49. Gangwar H (2018) Understanding the determinants of big data adoption in India. Inf Resour Manag J. https://doi.org/10.4018/irmj.2018100101

    Article  Google Scholar 

  50. Dutta D, Bose I (2015) Managing a big data project: the case of Ramco cements limited. Int J Prod Econ. https://doi.org/10.1016/j.ijpe.2014.12.032

    Article  Google Scholar 

  51. Dremel C (2017) Barriers to the adoption of big data analytics in the automotive sector. AMCIS 2017—America’s conference on information systems: a tradition of innovation. ISBN: 9780996683142

  52. Alharthi A, Krotov V, Bowman M (2017) Addressing barriers to big data. Bus Horiz. https://doi.org/10.1016/j.bushor.2017.01.002

    Article  Google Scholar 

  53. Malaka I, Brown I (2015) Challenges to the organisational adoption of big data analytics: a case study in the South African telecommunications industry. In: ACM international conference proceeding series

  54. Shukla M, Mattar L (2019) Next generation smart sustainable auditing systems using big data analytics: understanding the interaction of critical barriers. Comput Ind Eng. https://doi.org/10.1016/j.cie.2018.04.055

    Article  Google Scholar 

  55. Yasmin M, Tatoglu E, Kilic HS et al (2020) Big data analytics capabilities and firm performance: an integrated MCDM approach. J Bus Res. https://doi.org/10.1016/j.jbusres.2020.03.028

    Article  Google Scholar 

  56. Waibel MW, Steenkamp LP, Moloko N, Oosthuizen GA (2017) Investigating the effects of smart production systems on sustainability elements. Procedia Manuf. https://doi.org/10.1016/j.promfg.2017.02.094

    Article  Google Scholar 

  57. Zhong RY, Newman ST, Huang GQ, Lan S (2016) Big Data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives. Comput Ind Eng. https://doi.org/10.1016/j.cie.2016.07.013

    Article  Google Scholar 

  58. Chkanikova O, Mont O (2015) Corporate supply chain responsibility: drivers and barriers for sustainable food retailing. Corp Soc Responsib Environ Manag. https://doi.org/10.1002/csr.1316

    Article  Google Scholar 

  59. Gorane SJ, Kant R (2015) Modelling the SCM implementation barriers. J Model Manag. https://doi.org/10.1108/jm2-08-2012-0026

    Article  Google Scholar 

  60. Shaharudin MR, Zailani S, Tan KC (2015) Barriers to product returns and recovery management in a developing country: investigation using multiple methods. J Clean Prod. https://doi.org/10.1016/j.jclepro.2013.12.071

    Article  Google Scholar 

  61. Van RM (2014) Think bigger: developing a successful big data strategy for your business. Choice Rev Online. https://doi.org/10.5860/choice.51-6848

    Article  Google Scholar 

  62. Douglas M (2013) Big data raises big questions. Gov Technol

  63. Fallik D (2014) For big data, big questions remain. Health Aff. https://doi.org/10.1377/hlthaff.2014.0522

    Article  Google Scholar 

  64. Warfield JN (1974) Developing interconnection matrices in structural modeling. IEEE Trans Syst Man Cybern. https://doi.org/10.1109/TSMC.1974.5408524

    Article  Google Scholar 

  65. Jain V, Raj T (2016) Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach. Int J Prod Econ 171:84–96. https://doi.org/10.1016/j.ijpe.2015.10.024

    Article  Google Scholar 

  66. Cagno E, Micheli GJL, Jacinto C, Masi D (2014) An interpretive model of occupational safety performance for small- and medium-sized enterprises. Int J Ind Ergon. https://doi.org/10.1016/j.ergon.2013.08.005

    Article  Google Scholar 

  67. Mathiyazhagan K, Govindan K, NoorulHaq A, Geng Y (2013) An ISM approach for the barrier analysis in implementing green supply chain management. J Clean Prod. https://doi.org/10.1016/j.jclepro.2012.10.042

    Article  Google Scholar 

  68. Digalwar AK, Giridhar G (2015) Interpretive structural modeling approach for development of electric vehicle market in India. Procedia CIRP 26:40–45

    Article  Google Scholar 

  69. Dewangan DK, Agrawal R, Sharma V (2015) Enablers for competitiveness of Indian manufacturing sector: an ISM-Fuzzy MICMAC analysis. Procedia Soc Behav Sci. https://doi.org/10.1016/j.sbspro.2015.03.200

    Article  Google Scholar 

  70. Dewangan DK, Agrawal R, Sharma V (2017) Enablers of eco-innovation to enhance the competitiveness of the Indian manufacturing sector: an integrated ISM-fuzzy MICMAC approach. Int J Bus Innov Res. https://doi.org/10.1504/IJBIR.2017.085103

    Article  Google Scholar 

  71. Sindhu S, Nehra V, Luthra S (2016) Identification and analysis of barriers in implementation of solar energy in Indian rural sector using integrated ISM and fuzzy MICMAC approach. Renew Sustain Energy Rev 62:70–88

    Article  Google Scholar 

  72. Attri R, Grover S (2017) Developing the weighted ISM-MICMAC framework for process design stage of production system life cycle. Int J Process Manag Benchmark. https://doi.org/10.1504/IJPMB.2017.080941

    Article  Google Scholar 

  73. Mangla SK, Luthra S, Rich N et al (2018) Enablers to implement sustainable initiatives in agri-food supply chains. Int J Prod Econ. https://doi.org/10.1016/j.ijpe.2018.07.012

    Article  Google Scholar 

  74. Kannan D (2018) Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process. Int J Prod Econ. https://doi.org/10.1016/j.ijpe.2017.02.020

    Article  Google Scholar 

  75. Zhou F, Lim MK, He Y et al (2019) End-of-life vehicle (ELV) recycling management: improving performance using an ISM approach. J Clean Prod. https://doi.org/10.1016/j.jclepro.2019.04.182

    Article  Google Scholar 

  76. Kaswan MS, Rathi R (2019) Analysis and modeling the enablers of green lean six sigma implementation using interpretive structural modeling. J Clean Prod. https://doi.org/10.1016/j.jclepro.2019.05.253

    Article  Google Scholar 

  77. Rana NP, Barnard DJ, Baabdullah AMA et al (2019) Exploring barriers of m-commerce adoption in SMEs in the UK: developing a framework using ISM. Int J Inf Manage. https://doi.org/10.1016/j.ijinfomgt.2018.10.009

    Article  Google Scholar 

  78. Ahmad M, Tang XW, Qiu JN, Ahmad F (2019) Interpretive structural modeling and MICMAC analysis for identifying and benchmarking significant factors of seismic soil liquefaction. Appl Sci. https://doi.org/10.3390/app9020233

    Article  Google Scholar 

  79. Moktadir MA, Dwivedi A, Ali SM et al (2019) Antecedents for greening the workforce: implications for green human resource management. Int J Manpow. https://doi.org/10.1108/IJM-07-2019-0354

    Article  Google Scholar 

  80. Kannan G, Pokharel S, Kumar PS (2009) A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resour Conserv Recycl. https://doi.org/10.1016/j.resconrec.2009.06.004

    Article  Google Scholar 

  81. Govindan K, Palaniappan M, Zhu Q, Kannan D (2012) Analysis of third party reverse logistics provider using interpretive structural modeling. Int J Prod Econ. https://doi.org/10.1016/j.ijpe.2012.01.043

    Article  Google Scholar 

  82. Mudgal RK, Shankar R, Talib P, Raj T (2009) Greening the supply chain practices: an Indian perspective of enablers’ relationships. Int J Adv Oper Manag. https://doi.org/10.1504/ijaom.2009.030671

    Article  Google Scholar 

  83. Attri R, Grover S, Dev N, Kumar D (2013) An ISM approach for modelling the enablers in the implementation of total productive maintenance (TPM). Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-012-0088-7

    Article  Google Scholar 

  84. Attri R, Dev N, Sharma V (2013) Interpretive structural modelling (ISM) approach: an overview. Res J Manag Sci 2319:1171

    Google Scholar 

  85. Dev N, Samsher KSS, Attri R (2013) GTA-based framework for evaluating the role of design parameters in cogeneration cycle power plant efficiency. Ain Shams Eng J. https://doi.org/10.1016/j.asej.2012.08.002

    Article  Google Scholar 

  86. Diabat A, Govindan K (2011) An analysis of the drivers affecting the implementation of green supply chain management. Resour Conserv Recycl. https://doi.org/10.1016/j.resconrec.2010.12.002

    Article  Google Scholar 

  87. Mandal A, Deshmukh SG (1994) Vendor selection using interpretive structural modelling (ISM). Int J Oper Prod Manag. https://doi.org/10.1108/01443579410062086

    Article  Google Scholar 

  88. Jharkharia S, Shankar R (2004) IT enablement of supply chains: modeling the enablers. Int J Product Perform Manag. https://doi.org/10.1108/17410400410569116

    Article  Google Scholar 

  89. Grover V, Chiang RHL, Liang TP, Zhang D (2018) Creating strategic business value from big data analytics: a research framework. J Manag Inf Syst. https://doi.org/10.1080/07421222.2018.1451951

    Article  Google Scholar 

  90. Bellman RE, Zadeh LA (1970) Decision-making in a fuzzy environment. Manag Sci. https://doi.org/10.1287/mnsc.17.4.b141

    Article  Google Scholar 

  91. Govindan K, Khodaverdi R, Jafarian A (2013) A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. J Clean Prod. https://doi.org/10.1016/j.jclepro.2012.04.014

    Article  Google Scholar 

  92. Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Sets Syst. https://doi.org/10.1016/0165-0114(85)90090-9

    Article  Google Scholar 

  93. Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol. https://doi.org/10.1016/0022-2496(77)90033-5

    Article  Google Scholar 

  94. Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res. https://doi.org/10.1016/0377-2217(90)90057-I

    Article  Google Scholar 

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Authors and Affiliations

  1. Management Development Institute Gurgaon, Mehrauli Road, Sukhrali, Gurgaon, 122007, India

    Amit Kumar Gupta

  2. TCS, Mumbai, 400001, India

    Harshit Goyal

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Appendices

Appendix A

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Table 21 Level partition–Iteration I
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Table 22 Level partition–Iteration II
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Table 23 Level partition–Iteration III
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Table 24 Level partition–Iteration IV
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Table 25 Level partition–Iteration V
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Table 26 Level partition–Iteration VI
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Table 27 Level partition–Iteration VII
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Table 28 Level partition–Iteration VIII
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Table 29 Level partition–Iteration IX
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Table 30 Level partition–Iteration X
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Appendix B

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Table 31 Details of expert profile and related company
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31.

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Gupta, A.K., Goyal, H. Framework for implementing big data analytics in Indian manufacturing: ISM-MICMAC and Fuzzy-AHP approach. Inf Technol Manag 22, 207–229 (2021). https://doi.org/10.1007/s10799-021-00333-9

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