IDEAS home Printed from https://ideas.repec.org/a/inm/ormsom/v22y2020i1p113-122.html
   My bibliography  Save this article

Industry 4.0: Opportunities and Challenges for Operations Management

Author

Listed:
  • Tava Lennon Olsen

    (Department of Information Systems and Operations Management, The University of Auckland Business School, Auckland 1142, New Zealand;)

  • Brian Tomlin

    (Tuck School of Business, Dartmouth College, Hanover, New Hampshire 03755)

Abstract

Industry 4.0 connotes a new industrial revolution centered around cyber-physical systems. It posits that the real-time connection of physical and digital systems, along with new enabling technologies, will change the way that work is done and therefore, how work should be managed. It has the potential to break, or at least change, the traditional operations trade-offs among the competitive priorities of cost, flexibility, speed, and quality. This article describes the technologies inherent in Industry 4.0 and the opportunities and challenges for research in this area. The focus is on goods-producing industries, which includes both the manufacturing and agricultural sectors. Specific technologies discussed include additive manufacturing, the internet of things, blockchain, advanced robotics, and artificial intelligence.

Suggested Citation

  • Tava Lennon Olsen & Brian Tomlin, 2020. "Industry 4.0: Opportunities and Challenges for Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 113-122, January.
    • Handle: RePEc:inm:ormsom:v:22:y:2020:i:1:p:113-122
    DOI: 10.1287/msom.2019.0796
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/msom.2019.0796
    Download Restriction: no
    File URL: https://libkey.io/10.1287/msom.2019.0796?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yossi Aviv, 2002. "Gaining Benefits from Joint Forecasting and Replenishment Processes: The Case of Auto-Correlated Demand," Manufacturing & Service Operations Management, INFORMS, vol. 4(1), pages 55-74, December.
    2. Knofius, N. & van der Heijden, M.C. & Zijm, W.H.M., 2019. "Consolidating spare parts for asset maintenance with additive manufacturing," International Journal of Production Economics, Elsevier, vol. 208(C), pages 269-280.
    3. Hau L. Lee & Kut C. So & Christopher S. Tang, 2000. "The Value of Information Sharing in a Two-Level Supply Chain," Management Science, INFORMS, vol. 46(5), pages 626-643, May.
    4. Saghafian, Soroush & Tomlin, Brian & Biller, Stephan, 2018. "The Internet of Things and Information Fusion: Who Talks to Who?," Working Paper Series rwp18-009, Harvard University, John F. Kennedy School of Government.
    5. Wijnmalen, Diederik J. D. & Hontelez, Jan A. M., 1997. "Coordinated condition-based repair strategies for components of a multi-component maintenance system with discounts," European Journal of Operational Research, Elsevier, vol. 98(1), pages 52-63, April.
    6. Velibor V. Miv{s}i'c & Georgia Perakis, 2019. "Data Analytics in Operations Management: A Review," Papers 1905.00556, arXiv.org.
    7. Volodymyr Babich & Gilles Hilary, 2019. "Distributed Ledgers and Operations: What Operations Management Researchers Should Know about Blockchain Technology," Post-Print hal-02005158, HAL.
    8. Niels Agatz & Paul Bouman & Marie Schmidt, 2018. "Optimization Approaches for the Traveling Salesman Problem with Drone," Transportation Science, INFORMS, vol. 52(4), pages 965-981, August.
    9. Nicole DeHoratius & Adam J. Mersereau & Linus Schrage, 2008. "Retail Inventory Management When Records Are Inaccurate," Manufacturing & Service Operations Management, INFORMS, vol. 10(2), pages 257-277, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tang, Christopher S. & Veelenturf, Lucas P., 2019. "The strategic role of logistics in the industry 4.0 era," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 1-11.
    2. Ma, Yungao & Wang, Nengmin & He, Zhengwen & Lu, Jizhou & Liang, Huigang, 2015. "Analysis of the bullwhip effect in two parallel supply chains with interacting price-sensitive demands," European Journal of Operational Research, Elsevier, vol. 243(3), pages 815-825.
    3. Karimi, Majid & Zaerpour, Nima, 2022. "Put your money where your forecast is: Supply chain collaborative forecasting with cost-function-based prediction markets," European Journal of Operational Research, Elsevier, vol. 300(3), pages 1035-1049.
    4. Kaijie Zhu & Ulrich W. Thonemann, 2004. "Modeling the Benefits of Sharing Future Demand Information," Operations Research, INFORMS, vol. 52(1), pages 136-147, February.
    5. Yao, Yuliang & Dong, Yan & Dresner, Martin, 2010. "Managing supply chain backorders under vendor managed inventory: An incentive approach and empirical analysis," European Journal of Operational Research, Elsevier, vol. 203(2), pages 350-359, June.
    6. Ouyang, Yanfeng, 2007. "The effect of information sharing on supply chain stability and the bullwhip effect," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1107-1121, November.
    7. Mark Ferguson & Michael E. Ketzenberg, 2006. "Information Sharing to Improve Retail Product Freshness of Perishables," Production and Operations Management, Production and Operations Management Society, vol. 15(1), pages 57-73, March.
    8. Yossi Aviv, 2003. "A Time-Series Framework for Supply-Chain Inventory Management," Operations Research, INFORMS, vol. 51(2), pages 210-227, April.
    9. Zhao, Xiande & Xie, Jinxing & Wei, Jerry C., 2007. "The value of early order commitment in a two-level supply chain," European Journal of Operational Research, Elsevier, vol. 180(1), pages 194-214, July.
    10. Graça, Paula & Camarinha-Matos, Luís M., 2017. "Performance indicators for collaborative business ecosystems — Literature review and trends," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 237-255.
    11. Li Chen & Hau L. Lee, 2009. "Information Sharing and Order Variability Control Under a Generalized Demand Model," Management Science, INFORMS, vol. 55(5), pages 781-797, May.
    12. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    13. Hosoda, Takamichi & Disney, Stephen M., 2006. "On variance amplification in a three-echelon supply chain with minimum mean square error forecasting," Omega, Elsevier, vol. 34(4), pages 344-358, August.
    14. Li Chen & Wei Luo & Kevin Shang, 2017. "Measuring the Bullwhip Effect: Discrepancy and Alignment Between Information and Material Flows," Manufacturing & Service Operations Management, INFORMS, vol. 19(1), pages 36-51, February.
    15. Sharad Barkataki & Hassan Zeineddine, 2015. "On achieving secure collaboration in supply chains," Information Systems Frontiers, Springer, vol. 17(3), pages 691-705, June.
    16. Tetsuo Iida & Paul Zipkin, 2010. "Competition and Cooperation in a Two-Stage Supply Chain with Demand Forecasts," Operations Research, INFORMS, vol. 58(5), pages 1350-1363, October.
    17. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 2004. "Comments on "Information Distortion in a Supply Chain: The Bullwhip Effect"," Management Science, INFORMS, vol. 50(12_supple), pages 1887-1893, December.
    18. Li, Tian & Zhang, Hongtao, 2015. "Information sharing in a supply chain with a make-to-stock manufacturer," Omega, Elsevier, vol. 50(C), pages 115-125.
    19. Sechan Oh & Özalp Özer, 2013. "Mechanism Design for Capacity Planning Under Dynamic Evolutions of Asymmetric Demand Forecasts," Management Science, INFORMS, vol. 59(4), pages 987-1007, April.
    20. Xiaolong Zhang, 2004. "Evolution of ARMA Demand in Supply Chains," Manufacturing & Service Operations Management, INFORMS, vol. 6(2), pages 195-198, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormsom:v:22:y:2020:i:1:p:113-122. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.