IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v33y2022i6d10.1007_s10845-021-01772-5.html
   My bibliography  Save this article

Discovering critical KPI factors from natural language in maintenance work orders

Author

Listed:
  • Madhusudanan Navinchandran

    (National Institute of Standards and Technology)

  • Michael E. Sharp

    (National Institute of Standards and Technology)

  • Michael P. Brundage

    (National Institute of Standards and Technology)

  • Thurston B. Sexton

    (National Institute of Standards and Technology)

Abstract

Optimizing maintenance practices is a continuous process that must take into account the evolving state of the equipment, resources, workers, and more. To help streamline this process, facilities need a concise procedure for identifying critical tasks and assets that have major impact on the performance of maintenance activities. This work provides a process for making data investigations more effective by discovering influential equipment, actions, and other environmental factors from tacit knowledge within maintenance documents and reports. Traditional application of text analysis focuses on prediction and modeling of system state directly. Variation in domain data, quality, and managerial expectations prevent the creation of a generic method to do this with real industrial data. Instead, text analysis techniques can be applied to discover key factors within a system, which function as indicators for further, in-depth analysis. These factors can point investigators where to find good or bad behaviors, but do not explicitly perform any anomaly detection. This paper details an adaptable procedure tailored to maintenance and industrial settings for determining important named entities within natural language documents. The procedure in this paper utilizes natural language processing techniques to extract these terms or concepts from maintenance work orders and measure their influence on Key Performance Indicators (KPIs) as defined by managers and decision makers. We present a case study to demonstrate the developed workflow (algorithmic procedure) to identify terms associated with concepts or systems which have strong relationships with a selected KPI, such as time or cost. This proof of concept uses the length of time a Maintenance Work Order (MWO) remains open from creation to completion as the relevant performance indicator. By identifying tasks, assets, and environments that have significant relevance to KPIs, planners and decision makers can more easily direct investigations to identify problem areas within a facility, better allocate resources, and guide more effective analysis for both monitoring and improving a facility. The output of the analysis workflow presented in this paper is not intended as a direct indicator of good or bad practices and assets, but instead is intended to be used to help direct and improve the effectiveness of investigations determining those. This workflow provides a preparatory investigation that both conditions the data, helps guide investigators into more productive and effective investigations of the latent information contained in human generated work logs, specifically the natural language recorded in MWOs. When this information preparing and gathering procedure is used in conjunction with other tacit knowledge or analysis tools it gives a more full picture of the efficiency and effectiveness of maintenance strategies. When properly applied, this methodology can identify pain points, highlight anomalous patterns, or verify expected outcomes of a facility’s maintenance strategy.

Suggested Citation

  • Madhusudanan Navinchandran & Michael E. Sharp & Michael P. Brundage & Thurston B. Sexton, 2022. "Discovering critical KPI factors from natural language in maintenance work orders," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1859-1877, August.
  • Handle: RePEc:spr:joinma:v:33:y:2022:i:6:d:10.1007_s10845-021-01772-5
    DOI: 10.1007/s10845-021-01772-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01772-5
    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/s10845-021-01772-5?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. Li Li & Yong Wang & Kuo-Yi Lin, 2021. "Preventive maintenance scheduling optimization based on opportunistic production-maintenance synchronization," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 545-558, February.
    2. Jyrki Savolainen & Michele Urbani, 2021. "Maintenance optimization for a multi-unit system with digital twin simulation," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1953-1973, October.
    3. Swanson, Laura, 2001. "Linking maintenance strategies to performance," International Journal of Production Economics, Elsevier, vol. 70(3), pages 237-244, April.
    4. Michael Buckland & Fredric Gey, 1994. "The relationship between Recall and Precision," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 45(1), pages 12-19, January.
    5. Andrea Saltelli, 2002. "Sensitivity Analysis for Importance Assessment," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 579-590, June.
    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. Marko Orošnjak & Dragoljub Šević, 2023. "Benchmarking Maintenance Practices for Allocating Features Affecting Hydraulic System Maintenance: A West-Balkan Perspective," Mathematics, MDPI, vol. 11(18), pages 1-30, September.

    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. Makam, Vaishno Devi & Millossovich, Pietro & Tsanakas, Andreas, 2021. "Sensitivity analysis with χ2-divergences," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 372-383.
    2. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    3. Marco Percoco, 2006. "A Note on the Inoperability Input‐Output Model," Risk Analysis, John Wiley & Sons, vol. 26(3), pages 589-594, June.
    4. Wenbin Ruan & Zhenzhou Lu & Longfei Tian, 2013. "A modified variance-based importance measure and its solution by state dependent parameter," Journal of Risk and Reliability, , vol. 227(1), pages 3-15, February.
    5. Kunz, Nathan & Chesney, Thomas & Trautrims, Alexander & Gold, Stefan, 2023. "Adoption and transferability of joint interventions to fight modern slavery in food supply chains," International Journal of Production Economics, Elsevier, vol. 258(C).
    6. Pinjala, Srinivas Kumar & Pintelon, Liliane & Vereecke, Ann, 2006. "An empirical investigation on the relationship between business and maintenance strategies," International Journal of Production Economics, Elsevier, vol. 104(1), pages 214-229, November.
    7. Yun, Wanying & Lu, Zhenzhou & Feng, Kaixuan & Li, Luyi, 2019. "An elaborate algorithm for analyzing the Borgonovo moment-independent sensitivity by replacing the probability density function estimation with the probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 99-108.
    8. Johannes Freiesleben & Nicolas Gu'erin, 2015. "Homogenization and Clustering as a Non-Statistical Methodology to Assess Multi-Parametrical Chain Problems," Papers 1505.03874, arXiv.org, revised Dec 2017.
    9. Zhang, Fan & Bales, Chris & Fleyeh, Hasan, 2021. "Night setback identification of district heat substations using bidirectional long short term memory with attention mechanism," Energy, Elsevier, vol. 224(C).
    10. Gonnet, Gaston H. & Stewart, John & Lafleur, Joseph & Keith, Stephen & McLellan, Mark & Jiang-Gorsline, David & Snider, Tim, 2021. "Analysis of feature influence on Covid-19 Death Rate Per Country Using a Novel Orthogonalization Technique," MetaArXiv 4kw2n, Center for Open Science.
    11. Alsyouf, Imad, 2007. "The role of maintenance in improving companies' productivity and profitability," International Journal of Production Economics, Elsevier, vol. 105(1), pages 70-78, January.
    12. Abdur Rahim Hamidi & Jiangwei Wang & Shiyao Guo & Zhongping Zeng, 2020. "Flood vulnerability assessment using MOVE framework: a case study of the northern part of district Peshawar, Pakistan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 101(2), pages 385-408, March.
    13. Kamoonpuri, Sana Zehra & Sengar, Anita, 2023. "Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    14. Wenbin Ruan & Zhenzhou Lu & Pengfei Wei, 2013. "Estimation of conditional moment by moving least squares and its application for importance analysis," Journal of Risk and Reliability, , vol. 227(6), pages 641-650, December.
    15. Green, Maxwell H. & Davies, Philip & Ng, Irene C.L., 2017. "Two strands of servitization: A thematic analysis of traditional and customer co-created servitization and future research directions," International Journal of Production Economics, Elsevier, vol. 192(C), pages 40-53.
    16. Pesenti, Silvana M. & Millossovich, Pietro & Tsanakas, Andreas, 2019. "Reverse sensitivity testing: What does it take to break the model?," European Journal of Operational Research, Elsevier, vol. 274(2), pages 654-670.
    17. Li, Haihe & Wang, Pan & Huang, Xiaoyu & Zhang, Zheng & Zhou, Changcong & Yue, Zhufeng, 2021. "Vine copula-based parametric sensitivity analysis of failure probability-based importance measure in the presence of multidimensional dependencies," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    18. Emanuele Borgonovo, 2006. "Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches," Risk Analysis, John Wiley & Sons, vol. 26(5), pages 1349-1361, October.
    19. Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    20. C. L. Smith & E. Borgonovo, 2007. "Decision Making During Nuclear Power Plant Incidents—A New Approach to the Evaluation of Precursor Events," Risk Analysis, John Wiley & Sons, vol. 27(4), pages 1027-1042, August.

    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:joinma:v:33:y:2022:i:6:d:10.1007_s10845-021-01772-5. 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.