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From data to big data in production research: the past and future trends

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  • Yong-Hong Kuo
  • Andrew Kusiak

Abstract

Data have been utilised in production research in meaningful ways for decades. Recent years have offered data in larger volumes and improved quality collected from diverse sources. The state-of-the-art data research in production and the emerging methodologies are discussed. The review of the literature suggests that production research enabled by data has shifted from that based on analytical models to data-driven. Manufacturing and data envelopment analysis have been the most popular application areas of data-driven methodologies. The research published to date indicates that data mining is becoming a dominant methodology in production research. Future trends and opportunities for data-driven production research are presented.

Suggested Citation

  • Yong-Hong Kuo & Andrew Kusiak, 2019. "From data to big data in production research: the past and future trends," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4828-4853, August.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:15-16:p:4828-4853
    DOI: 10.1080/00207543.2018.1443230
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    Cited by:

    1. Li, Mingxing & Huang, George Q., 2021. "Production-intralogistics synchronization of industry 4.0 flexible assembly lines under graduation intelligent manufacturing system," International Journal of Production Economics, Elsevier, vol. 241(C).
    2. Yan, Yimo & Chow, Andy H.F. & Ho, Chin Pang & Kuo, Yong-Hong & Wu, Qihao & Ying, Chengshuo, 2022. "Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    3. Ray Qing Cao & Dara G. Schniederjans & Vicky Ching Gu, 2021. "Stakeholder sentiment in service supply chains: big data meets agenda-setting theory," Service Business, Springer;Pan-Pacific Business Association, vol. 15(1), pages 151-175, March.
    4. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    5. Sadia Samar Ali & Rajbir Kaur, 2022. "Exploring the Impact of Technology 4.0 Driven Practice on Warehousing Performance: A Hybrid Approach," Mathematics, MDPI, vol. 10(8), pages 1-22, April.
    6. Osinga, Sjoukje A. & Paudel, Dilli & Mouzakitis, Spiros A. & Athanasiadis, Ioannis N., 2022. "Big data in agriculture: Between opportunity and solution," Agricultural Systems, Elsevier, vol. 195(C).
    7. Nikolaos Schizas & Aristeidis Karras & Christos Karras & Spyros Sioutas, 2022. "TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review," Future Internet, MDPI, vol. 14(12), pages 1, December.
    8. Sharina Tajul Urus & Intan Waheedah Othman & Zarinah Abdul Rasit & Noraizah Abu Bakar & Sharifah Nazatul Faiza Syed Mustapha Nazri, 2023. "Beyond the Hype of Big Data Analytics Deployment: Conceptualization and Challenges Epistemology," Business and Economic Research, Macrothink Institute, vol. 13(2), pages 74-111, December.
    9. Choi, Tsan-Ming, 2020. "Innovative “Bring-Service-Near-Your-Home” operations under Corona-Virus (COVID-19/SARS-CoV-2) outbreak: Can logistics become the Messiah?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    10. Bai Liu & Shuyan Guo & Bin Ding, 2020. "Technical Blossom in Medical Care: The Influence of Big Data Platform on Medical Innovation," IJERPH, MDPI, vol. 17(2), pages 1-17, January.
    11. Ailian Zhang & Mengmeng Pan, 2020. "“Smart Process” of Medical Innovation: The Synergism Based on Network and Physical Space," IJERPH, MDPI, vol. 17(11), pages 1-17, May.
    12. Joe Zhu, 2022. "DEA under big data: data enabled analytics and network data envelopment analysis," Annals of Operations Research, Springer, vol. 309(2), pages 761-783, February.
    13. Dutta, Pankaj & Choi, Tsan-Ming & Somani, Surabhi & Butala, Richa, 2020. "Blockchain technology in supply chain operations: Applications, challenges and research opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    14. Hisham Alidrisi, 2021. "Measuring the Environmental Maturity of the Supply Chain Finance: A Big Data-Based Multi-Criteria Perspective," Logistics, MDPI, vol. 5(2), pages 1-24, April.
    15. Khan, Waqar Ahmed & Ma, Hoi-Lam & Ouyang, Xu & Mo, Daniel Y., 2021. "Prediction of aircraft trajectory and the associated fuel consumption using covariance bidirectional extreme learning machines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    16. Alnoor Bhimani, 2020. "Digital data and management accounting: why we need to rethink research methods," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 31(1), pages 9-23, April.
    17. Nitin S. Solke & Pritesh Shah & Ravi Sekhar & T. P. Singh, 2022. "Machine Learning-Based Predictive Modeling and Control of Lean Manufacturing in Automotive Parts Manufacturing Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(1), pages 89-112, March.
    18. Margherita Bernabei & Marco Eugeni & Paolo Gaudenzi & Francesco Costantino, 2023. "Assessment of Smart Transformation in the Manufacturing Process of Aerospace Components Through a Data-Driven Approach," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(1), pages 67-86, March.

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