Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing. Issue 11 (2nd June 2020)

• Record Type: Journal Article
• Authors: Brintrup, Alexandra; Pak, Johnson; Ratiney, David; Pearce, Tim; Wichmann, Pascal; Woodall, Philip; McFarlane, Duncan
• Is Part Of: International journal of production research. Volume 58:Issue 11(2020)
• Page Start: 3330
• Page End: 3341
• Publication Date: 2020-06-02
• Subjects: supply chain analytics -- data -- machine learning -- feature engineering -- risk -- disruption; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2019.1685705
• Languages: English
• ISSNs: 0020-7543

Abstract

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 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 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.