Forecasting supply chain disruptions in the textile industry using machine learning: A case study

Abstract

The disruption of the material supply chain may impact planned production schedules with both, financial and non-financial implications. It has always been difficult to make the Supply Chain (SC) more resilient. There is a lack in the literature to examine how logistics processes operate at the firm level and the ways that can mitigate Supply Chain Disruptions (SCD). This work is focused on the textile industry as a case to explain the application of data analytics such as the ML model for predicting SCD. To conclude, the performance of each classifier is analyzed, to understand whether or not this approach applies to the selected problem. Creating effectiveness of the methods, a performance metric that correlates with the set objectives of the case study is developed. The work adopts an investigational design to identify the FS space and selectively review the most successful algorithms. This case study is important to a paper in the sense that it avails and demonstrates the use and development of data analytics techniques to work with SC data. The work is centered around stressing the importance of the notion of a domain when engineering features. Altogether, the paper contributes to expounding the possibility of employing different ML techniques for the estimation of SCD in the textile industry and other sectors.