IDEAS home Printed from https://ideas.repec.org/a/spr/jbecon/v88y2018i5d10.1007_s11573-017-0889-x.html
   My bibliography  Save this article

Privacy-preserving condition-based forecasting using machine learning

Author

Listed:
  • Fabian Taigel

    (Chair of Logistics and Quantitative Methods at University of Würzburg)

  • Anselme K. Tueno

    (Product Security Research, SAP SE)

  • Richard Pibernik

    (Chair of Logistics and Quantitative Methods at University of Würzburg)

Abstract

As machines get smarter, massive amounts of condition-based data from distributed sources become available. This data can be used to enhance maintenance management in several ways, such as by improving maintenance demand forecasting and spare parts and capacity planning. Regarding the former, machine learning techniques promise substantial benefits for forecasting the demand for spare parts over conventional techniques that are commonly used. While development and implementation of these techniques is difficult, practical applications pose another important challenge to providers of maintenance, repair, and overhaul services. Their customers are reluctant to provide access to sensitive real-time data because of privacy concerns, and even more so when their data is stored and processed in the cloud. In this paper we describe an application for privacy-preserving forecasting of demand for spare parts based on distributed condition data. It combines machine learning techniques—more specifically, decision-tree classification—with order-preserving encryption. The application is appropriate whenever planning for spare parts for the maintenance of condition-monitored machinery is needed, and it is particularly suitable for cloud-based implementation.

Suggested Citation

  • Fabian Taigel & Anselme K. Tueno & Richard Pibernik, 2018. "Privacy-preserving condition-based forecasting using machine learning," Journal of Business Economics, Springer, vol. 88(5), pages 563-592, July.
  • Handle: RePEc:spr:jbecon:v:88:y:2018:i:5:d:10.1007_s11573-017-0889-x
    DOI: 10.1007/s11573-017-0889-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11573-017-0889-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11573-017-0889-x?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dekker, Rommert & Pinçe, Çerağ & Zuidwijk, Rob & Jalil, Muhammad Naiman, 2013. "On the use of installed base information for spare parts logistics: A review of ideas and industry practice," International Journal of Production Economics, Elsevier, vol. 143(2), pages 536-545.
    2. Vinayak Deshpande & Ananth V. Iyer & Richard Cho, 2006. "Efficient Supply Chain Management at the U.S. Coast Guard Using Part-Age Dependent Supply Replenishment Policies," Operations Research, INFORMS, vol. 54(6), pages 1028-1040, December.
    3. van Wingerden, E. & Basten, R.J.I. & Dekker, R. & Rustenburg, W.D., 2014. "More grip on inventory control through improved forecasting: A comparative study at three companies," International Journal of Production Economics, Elsevier, vol. 157(C), pages 220-237.
    4. Sharad Barkataki & Hassan Zeineddine, 2015. "On achieving secure collaboration in supply chains," Information Systems Frontiers, Springer, vol. 17(3), pages 691-705, June.
    5. Deloux, E. & Castanier, B. & Bérenguer, C., 2009. "Predictive maintenance policy for a gradually deteriorating system subject to stress," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 418-431.
    6. Ghobbar, A.A & Friend, C.H, 2002. "Sources of intermittent demand for aircraft spare parts within airline operations," Journal of Air Transport Management, Elsevier, vol. 8(4), pages 221-231.
    7. Kurz, Julian, 2016. "Capacity planning for a maintenance service provider with advanced information," European Journal of Operational Research, Elsevier, vol. 251(2), pages 466-477.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hui Ma & Xuyang Guo & Yuan Ping & Baocang Wang & Yuehua Yang & Zhili Zhang & Jingxian Zhou, 2019. "PPCD: Privacy-preserving clinical decision with cloud support," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-17, May.
    2. Lulin Xu & Zhongwu Li, 2021. "A New Appraisal Model of Second-Hand Housing Prices in China’s First-Tier Cities Based on Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 617-637, February.

    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. Van der Auweraer, Sarah & Boute, Robert N. & Syntetos, Aris A., 2019. "Forecasting spare part demand with installed base information: A review," International Journal of Forecasting, Elsevier, vol. 35(1), pages 181-196.
    2. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    3. Yongquan, Sun & Xi, Chen & He, Ren & Yingchao, Jin & Quanwu, Liu, 2016. "Ordering decision-making methods on spare parts for a new aircraft fleet based on a two-sample prediction," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 40-50.
    4. Zhu, Sha & Jaarsveld, Willem van & Dekker, Rommert, 2020. "Spare parts inventory control based on maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    5. Zhu, Sha & Dekker, Rommert & van Jaarsveld, Willem & Renjie, Rex Wang & Koning, Alex J., 2017. "An improved method for forecasting spare parts demand using extreme value theory," European Journal of Operational Research, Elsevier, vol. 261(1), pages 169-181.
    6. Van der Auweraer, Sarah & Zhu, Sha & Boute, Robert N., 2021. "The value of installed base information for spare part inventory control," International Journal of Production Economics, Elsevier, vol. 239(C).
    7. Dinis, Duarte & Barbosa-Póvoa, Ana & Teixeira, Ângelo Palos, 2019. "A supporting framework for maintenance capacity planning and scheduling: Development and application in the aircraft MRO industry," International Journal of Production Economics, Elsevier, vol. 218(C), pages 1-15.
    8. Dombi, József & Jónás, Tamás & Tóth, Zsuzsanna Eszter, 2018. "Modeling and long-term forecasting demand in spare parts logistics businesses," International Journal of Production Economics, Elsevier, vol. 201(C), pages 1-17.
    9. Feng, Haofang & Zhang, Sheng Hao & Zhang, Yong, 2023. "Managing production-inventory-maintenance systems with condition monitoring," European Journal of Operational Research, Elsevier, vol. 310(2), pages 698-711.
    10. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    11. Dudu Guo & Pengbin Duan & Zhen Yang & Xiaojiang Zhang & Yinuo Su, 2024. "Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-BiLSTM)-Attention-Based Prediction of the Amount of Silica Powder Moving in and out of a Warehouse," Energies, MDPI, vol. 17(15), pages 1-22, July.
    12. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    13. Boutselis, Petros & McNaught, Ken, 2019. "Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context," International Journal of Production Economics, Elsevier, vol. 209(C), pages 325-333.
    14. van Wingerden, E. & Basten, R.J.I. & Dekker, R. & Rustenburg, W.D., 2014. "More grip on inventory control through improved forecasting: A comparative study at three companies," International Journal of Production Economics, Elsevier, vol. 157(C), pages 220-237.
    15. Małgorzata Jasiulewicz-Kaczmarek & Katarzyna Antosz & Ryszard Wyczółkowski & Dariusz Mazurkiewicz & Bo Sun & Cheng Qian & Yi Ren, 2021. "Application of MICMAC, Fuzzy AHP, and Fuzzy TOPSIS for Evaluation of the Maintenance Factors Affecting Sustainable Manufacturing," Energies, MDPI, vol. 14(5), pages 1-30, March.
    16. Zhao, Xuejing & Fouladirad, Mitra & Bérenguer, Christophe & Bordes, Laurent, 2010. "Condition-based inspection/replacement policies for non-monotone deteriorating systems with environmental covariates," Reliability Engineering and System Safety, Elsevier, vol. 95(8), pages 921-934.
    17. P Baraldi & M Compare & A Despujols & E Zio, 2011. "Modelling the effects of maintenance on the degradation of a water-feeding turbo-pump of a nuclear power plant," Journal of Risk and Reliability, , vol. 225(2), pages 169-183, June.
    18. Awasthy, Prakash & Hazra, Jishnu, 2020. "Collaboration under outcome-based contracts for information technology services," European Journal of Operational Research, Elsevier, vol. 286(1), pages 350-359.
    19. Zhao, Xufeng & Qian, Cunhua & Nakagawa, Toshio, 2013. "Optimal policies for cumulative damage models with maintenance last and first," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 50-59.
    20. Keedy, Elias & Feng, Qianmei, 2012. "A physics-of-failure based reliability and maintenance modeling framework for stent deployment and operation," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 94-101.

    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:spr:jbecon:v:88:y:2018:i:5:d:10.1007_s11573-017-0889-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.