Skip to main content

Advertisement

SpringerLink
Log in

Big data analytics in mitigating challenges of sustainable manufacturing supply chain

  • Published:
Operations Management Research Aims and scope Submit manuscript

Abstract

Manufacturing Supply Chain (MSC) becomes more complex not only from the business viewpoint but also for environmental care and sustainability. Despite the current progress in realizing how Big Data Analytics (BDA) can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major research gap in the storyline relating to factors of Big Data-based operations in managing several forms of SMSC operations. This study attempts to fill this major research gap by studying the key challenges of using Big Data in SMSC operations obtained from IoT devices, group behavior parameters, social networks, and ecosystem frameworks. Big Data Analytics (BDA) is receiving more attention in management, yet there is relatively little empirical research available on the topic. The authors use the multi-criteria strategy employing analytic hierarchy process (AHP) and grey relational analysis (GRA) methodology due to the dearth of comparable information at the junction of BDA and MSC. The presented multi-criteria strategy findings add to the body of understanding by identifying eleven critical criteria and five associated challenges (Financial, Quality, Operation, Technical, and Logistics) related to the emergence of Big Data Analytics from a corporate and supply chain perspective. The findings reveal that product safety barriers (C4) and lack of information sharing (C8) are the critical factor immensely surge and affect the MSC in attaining sustainability. As no empirical study has yet been presented, it is important to examine the challenges at the organizational and MSC levels with a focus on the effects of BDA implementation to achieve sustainability with enhanced customer trust and improved SMSC performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Availability of data and materials

Data can be made available on request for academic purposes.

References

  • Agrawal TK, Sharma A, Kumar V (2018) Blockchain-based secured traceability system for textile and clothing supply chain. In Artificial intelligence for fashion industry in the big data era (pp. 197–208). Springer, Singapore

  • Alharthi A, Krotov V, Bowman M (2017) Addressing barriers to big data. Bus Horiz 60(3):285–292

    Article  Google Scholar 

  • Araújo SO, Peres RS, Barata J, Lidon F, Ramalho JC (2021) Characterising the agriculture 4.0 landscape—Emerging trends, challenges and opportunities. Agronomy 11(4):1–37

  • Arunachalam D, Kumar N, Kawalek JP (2018) Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part e: Logistics and Transportation Review 114:416–436

    Article  Google Scholar 

  • Bag S, Dhamija P, Luthra S, Huisingh D (2021) "How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic", The International Journal of Logistics Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJLM-02-2021-0095

  • Bibi F, Guillaume C, Gontard N, Sorli B (2017) A review: RFID technology having sensing aptitudes for food industry and their contribution to tracking and monitoring of food products. Trends Food Sci Technol 62:91–103

    Article  Google Scholar 

  • Brinch M (2018) Understanding the value of big data in supply chain management and its business processes: Towards a conceptual framework. Int J Oper Prod Manag 38(7):1589–1614

    Article  Google Scholar 

  • Chan JW, Tong TK (2007) Multi-criteria material selections and end-of-life product strategy: Grey relational analysis approach. Mater Des 28(5):1539–1546

    Article  Google Scholar 

  • Chanchaichujit J, Balasubramanian S, Charmaine NSM (2020) A systematic literature review on the benefit-drivers of RFID implementation in supply chains and its impact on organizational competitive advantage. Cogent Business & Management 7(1):1–20

    Article  Google Scholar 

  • Chatterjee P, Chakraborty S (2014) Investigating the Effect of Normalization Norms in Flexible Manufacturing Sytem Selection Using Multi-Criteria Decision-Making Methods. J Eng Sci Tech Rev 7(3):141–150

    Article  Google Scholar 

  • Crowe TJ, Noble SJ, Machimada SJ (1998) Multi-attribute analysis of ISO 9001 registration using AHP. International Journal of Quality and Reliability Management 15(2):205–222

    Article  Google Scholar 

  • Dubey R, Gunasekaran A, Childe SJ (2018) Big data analytics capability in supply chain agility: The moderating effect of organizational flexibility. Manag Decis 57(8):2092–2112

    Article  Google Scholar 

  • Dubey R, Gunasekaran A, Childe SJ, Wamba SF, Papadopoulos T (2016) The impact of big data on world-class sustainable manufacturing. The International Journal of Advanced Manufacturing Technology 84(1):631–645

    Article  Google Scholar 

  • Dyer RF, Forman EH (1992) Group decision support with the analytic hierarchy process. Decis Support Syst 8(2):99–124

  • Fawcett SE, Waller MA (2014) Supply chain game changers—mega, nano, and virtual trends—and forces that impede supply chain design (ie, building a winning team). J Bus Logist 35(3):157–164

    Article  Google Scholar 

  • Frey CB, Osborne MA (2017) The future of employment: How susceptible are jobs to computerisation? Technol Forecast Soc Chang 114:254–280

    Article  Google Scholar 

  • Gandomi A, Haider M (2015) Beyond the hype: Big data concepts, methods, and analytics. Int J Inf Manage 35(2):137–144

    Article  Google Scholar 

  • Gangwar H, Mishra R, Kamble S (2023) Adoption of big data analytics practices for sustainability development in the e-commercesupply chain: a mixed-method study. International Journal of Quality & Reliability Management 40(4):965–989

    Article  Google Scholar 

  • Gautam R, Singh A, Karthik K, Pandey S, Scrimgeour F, Tiwari MK (2017) Traceability using RFID and its formulation for a kiwifruit supply chain. Comput Ind Eng 103:46–58

    Article  Google Scholar 

  • Govindan K, Kaliyan M, Kannan D, Haq AN (2014) Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process. Int J Prod Econ 147:555–568

    Article  Google Scholar 

  • Grover V, Chiang RH, Liang TP, Zhang D (2018) Creating strategic business value from big data analytics: A research framework. J Manag Inf Syst 35(2):388–423

    Article  Google Scholar 

  • Grover V, Lindberg A, Benbasat I, Lyytinen K (2020) The perils and promises of big data research in information systems. J Assoc Inf Syst 21(2):268–291

    Google Scholar 

  • Hamzaçebi C, Pekkaya M (2011) Determining of stock investments with grey relational analysis. Expert Syst Appl 38(8):9186–9195

    Article  Google Scholar 

  • Jarrahi MH (2018) Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Bus Horiz 61(4):577–586

    Article  Google Scholar 

  • Kamble SS, Gunasekaran A (2020) Big data-driven supply chain performance measurement system: a review and framework for implementation. Int J Prod Res 58(1):65–86

    Article  Google Scholar 

  • Kashyap A, Yadav AK, Vatsa ON, Chandaka TN, Shukla OJ (2022) "Investigation of the critical success factors in the implementation of the lean industry 4.0 in manufacturing supply chain: an ISM approach", Management of Environmental Quality, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/MEQ-04-2022-0109

  • Khan MI, Khan S, Khan U, Haleem A (2021) Modeling the Big Data challenges in context of smart cities–an integrated fuzzy ISM-DEMATEL approach. International Journal of Building Pathology and Adaptation. https://doi.org/10.1108/IJBPA-02-2021-0027

    Article  Google Scholar 

  • Kumar Dadsena K, Pant P (2023) Analyzing the barriers in supply chain digitization: sustainable development goals perspective. Oper Manag Res 1–14

  • Kumar S, Raut RD, Nayal K, Kraus S, Yadav VS, Narkhede BE (2021) To identify industry 4.0 and circular economy adoption barriers in the agriculture supply chain by using ISM-ANP. J Clean Prod 293:1–13

    Article  Google Scholar 

  • Kumar R, Kansara S, Bangwal D, Damodaran A, Jha A (2022) Motivating factors to promote tourism in India: using AHP methods. International Journal of Logistics Systems and Management 42(3):407–426

    Article  Google Scholar 

  • Kuo Y, Yang T, Huang GW (2008) The use of grey relational analysis in solving multiple attribute decision-making problems. Comput Ind Eng 55(1):80–93

    Article  Google Scholar 

  • Lamba K, Singh SP (2017) Big data in operations and supply chain management: current trends and future perspectives. Production Planning & Control 28(11–12):877–890

    Article  Google Scholar 

  • Lotfi V (1995) Implementing flexible automation: A multiple criteria decision making approach. Int J Prod Econ 38(2–3):255–268

    Article  Google Scholar 

  • Madaan J, Mangla S (2015) Decision modeling approach for eco-driven flexible green supply chain. Systemic Flexibility and Business Agility 343–364

  • Mangla SK, Luthra S, Rich N, Kumar D, Rana NP, Dwivedi YK (2018) Enablers to implement sustainable initiatives in agri-food supply chains. Int J Prod Econ 203:379–393

    Article  Google Scholar 

  • Misra NN, Dixit Y, Al-Mallahi A, Bhullar MS, Upadhyay R, Martynenko A (2020) IoT, big data and artificial intelligence in agriculture and food industry. IEEE Internet Things J 9(9):6305–6324

    Article  Google Scholar 

  • 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 128:1063–1075

    Article  Google Scholar 

  • Narwane VS, Gunasekaran A, Gardas BB (2022) Unlocking adoption challenges of IoT in Indian Agricultural and Food Supply Chain. Smart Agricultural Technology 2:1–14

    Article  Google Scholar 

  • Nguyen T, Li ZHOU, Spiegler V, Ieromonachou P, Lin Y (2018) Big data analytics in supply chain management: A state-of-the-art literature review. Comput Oper Res 98:254–264

    Article  Google Scholar 

  • Niu B, Shi M, Zhang Z, Li Y, Cao Y, Pan S (2021) Multi-objective optimization of supply air jet enhancing airflow uniformity in data center with Taguchi-based grey relational analysis. Build Environ 208(1–17):108606

    Google Scholar 

  • Ogbuke NJ, Yusuf YY, Dharma K, Mercangoz BA (2022) Big data supply chain analytics: ethical, privacy and security challenges posed to business, industries and society. Production Planning & Control 33(2–3):123–137

    Article  Google Scholar 

  • Paul T, Islam N, Mondal S, Rakshit S (2022) RFID-integrated blockchain-driven circular supply chain management: A system architecture for B2B tea industry. Ind Mark Manage 101:238–257

    Article  Google Scholar 

  • Prajapati D, Jauhar SK, Gunasekaran A, Kamble SS, Pratap S (2022) Blockchain and IoT embedded sustainable virtual closed-loop supply chain in E-commerce towards the circular economy. Comput Ind Eng 172:108530

    Article  Google Scholar 

  • Raman S, Patwa N, Niranjan I, Ranjan U, Moorthy K, Mehta A (2018) Impact of big data on supply chain management. Int J Log Res Appl 21(6):579–596

    Article  Google Scholar 

  • Ramanathan U, Subramanian N, Parrott G (2017) Role of social media in retail network operations and marketing to enhance customer satisfaction. Int J Oper Prod Manag 37(1):105–123

    Article  Google Scholar 

  • Raut RD, Yadav VS, Cheikhrouhou N, Narwane VS, Narkhede BE (2021) Big data analytics: Implementation challenges in Indian manufacturing supply chains. Comput Ind 125:103368

    Article  Google Scholar 

  • Roberts M, Hazen B (2016) Big data for omni-channel supply chain management: the need for greater focus on people and process. International Journal of Automation and Logistics 2(4):271–278

    Article  Google Scholar 

  • Rodriguez L, Da Cunha C (2018) Impacts of big data analytics and absorptive capacity on sustainable supply chain innovation: A conceptual framework. LogForum 14(2):151–161

    Article  Google Scholar 

  • Saaty TL (1980) “The analytic hierarchy process McGraw-Hill”. New York 324

  • Saaty TL (1985) Decision making for leaders. IEEE Transactions on Systems, Man, and Cybernetics 15(3):450–452

  • Saaty TL (1988) What is the analytic hierarchy process? Mathematical Models for Decision Support. Springer, Berlin, Heidelberg 109–121

    Chapter  Google Scholar 

  • Sanders NR (2016) How to use big data to drive your supply chain. Calif Manage Rev 58(3):26–48

    Article  Google Scholar 

  • Sharma M, Gupta R, Acharya P (2020) Prioritizing the critical factors of cloud computing adoption using multi-criteria decision-making techniques. Glob Bus Rev 21(1):142–161

    Article  Google Scholar 

  • Tagarakis AC, Benos L, Kateris D, Tsotsolas N, Bochtis D (2021) Bridging the Gaps in Traceability Systems for Fresh Produce Supply Chains: Overview and Development of an Integrated IoT-Based System. Appl Sci 11(16):1–16

    Article  Google Scholar 

  • Talari G, Cummins E, McNamara C, O’Brien J (2021) State of the art review of Big Data and web-based Decision Support Systems (DSS) for food safety risk assessment with respect to climate change. Trends Food Sci Technol 126:192–204

    Article  Google Scholar 

  • Tosun N (2006) Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis. The International Journal of Advanced Manufacturing Technology 28(5):450–455

    Article  Google Scholar 

  • Truong HL, Gao L, Hammerer M (2018) Service architectures and dynamic solutions for interoperability of iot, network functions and cloud resources. In Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings 1–4

  • Tsai CW, Lai CF, Chao HC, Vasilakos AV (2015) Big data analytics: a survey. J Big Data 2(1):1–32

    Article  Google Scholar 

  • Tsang YP, Choy KL, Wu CH, Ho GTS, Lam HY, Koo PS (2017) An IoT-based cargo monitoring system for enhancing operational effectiveness under a cold chain environment. Int J Eng Bus Manag 9:1847979017749063

    Article  Google Scholar 

  • van Hoek R (2019) Exploring blockchain implementation in the supply chain: Learning from pioneers and RFID research. Int J Oper Prod Manag 39(6/7/8):829–859

  • Verma P, Kumar V, Mittal A, Rathore B, Jha A, Rahman MS (2023) The role of 3S in big data quality: a perspective on operational performance indicators using an integrated approach. The TQM Journal 35(1):153–182. https://doi.org/10.1108/TQM-02-2021-0062

    Article  Google Scholar 

  • Wamba SF, Ngai EW, Riggins F, Akter S (2017) Transforming operations and production management using big data and business analytics: future research directions. Int J Oper Prod Manag 37(1):2–9

    Google Scholar 

  • Yadav G, Luthra S, Jakhar SK, Mangla SK, Rai DP (2020) A framework to overcome sustainable supply chain challenges through solution measures of industry 4.0 and circular economy: An automotive case. J Clean Prod 254:1–15

    Article  Google Scholar 

  • Yi P, Dong Q, Li W, Wang L (2021) Measurement of city sustainability based on the grey relational analysis: The case of 15 sub-provincial cities in China. Sustain Cities Soc 73:1–11

    Article  Google Scholar 

  • Zeng G, Jiang R, Huang G, Xu M, Li J (2007) Optimization of wastewater treatment alternative selection by hierarchy grey relational analysis. J Environ Manage 82(2):250–259

    Article  Google Scholar 

  • Zhang M, Pratap S, Huang GQ, Zhao Z (2017) Optimal collaborative transportation service trading in B2B e-commerce logistics. Int J Prod Res 55(18):5485–5501

    Article  Google Scholar 

  • Zhong RY, Xu C, Chen C, Huang GQ (2017) Big data analytics for physical internet-based intelligent manufacturing shop floors. Int J Prod Res 55(9):2610–2621

    Article  Google Scholar 

Download references

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Author information

Authors and Affiliations

  1. Department of Information Management, Chaoyang University of Technology, Taichung-413310, Taiwan

    Rohit Raj & Vimal Kumar

  2. Department of Strategic Management, Indian Institute of Management, Kozhikode, 673570, India

    Pratima Verma

Authors
  1. Rohit Raj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vimal Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pratima Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Rohit Raj: Wrote the paper, Collected the data, Conceived and designed the analysis, Performed the analysis. Vimal Kumar: Collected the data, Designed the analysis, Performed the analysis. Pratima Verma: Contributed data analysis tools, Performed the analysis, proofreading.

Corresponding author

Correspondence to Vimal Kumar.

Ethics declarations

Ethical approval

Not Applicable.

Consent to participate

Not Applicable.

Consent to publish

Not Applicable.

Competing interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 19 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Raj, R., Kumar, V. & Verma, P. Big data analytics in mitigating challenges of sustainable manufacturing supply chain. Oper Manag Res 16, 1886–1900 (2023). https://doi.org/10.1007/s12063-023-00408-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12063-023-00408-6

Keywords

Search

Navigation