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Supply Chain Data Analytics for Predicting Supplier Disruptions: a Case Study in Complex Asset Manufacturing by Alexandra Brintrup; Johnson Pak; David Ratiney; Tim Pearce; Pascal Wichmann; Philip Woodall; Duncan McFarlane is a Engineering article available to read on EtoBox.

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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 Original Equipment Manufacturer (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 appl

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Author

Alexandra Brintrup; Johnson Pak; David Ratiney; Tim Pearce; Pascal Wichmann; Philip Woodall; Duncan McFarlane

Published

2019

Language

EN

Field

Engineering (Physical Sciences)