Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing
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Publication Date
2020-06-02Journal Title
International Journal of Production Research
ISSN
0020-7543
Publisher
Taylor & Francis
Volume
58
Issue
11
Pages
3330-3341
Type
Article
This Version
AM
Metadata
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Brintrup, A., Pak, J., Ratiney, D., Pearce, T., Wichmann, P., Woodall, P., & McFarlane, D. (2020). Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing. International Journal of Production Research, 58 (11), 3330-3341. https://doi.org/10.1080/00207543.2019.1685705
Abstract
Although predictive machine learning for supply chain data analytics has recently been reported as a significant area of investigation due to the rising popularity of the AI paradigm in industry, there is a distinct lack of case studies that showcase its application from a practical point of view. In this paper we discuss the application of data analytics in predicting first tier supply chain disruptions using historical data available to an OEM. Our methodology includes three phases: First, an exploratory phase is conducted to select and engineer potential features that can act as useful predictors of disruptions. This is followed by the development of a performance metric in alignment with the specific goals of the case study to rate successful methods. Third, an experimental design is created to systematically analyse the success rate of different algorithms, algorithmic parameters, on the selected feature space. Our results indicate that adding engineered features in the data, namely agility, outperforms other experiments leading to the final algorithm that can predict late orders with 80% accuracy. An additional contribution is the novel application of machine learning in predicting supply disruptions. Through the discussion and the development of the case study we hope to shed light on the development and application of data analytics techniques in the analysis of supply chain data. We conclude by highlighting the importance of domain knowledge for successfully engineering features.
Sponsorship
anonymous company
Identifiers
External DOI: https://doi.org/10.1080/00207543.2019.1685705
This record's URL: https://www.repository.cam.ac.uk/handle/1810/298194
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All rights reserved