Machine learning methods to improve the operations of 3PL logistics

Abstract

Nowadays, the variety in the product mix, unpredictable customer demand and the need for a high level of service are crucial challenges in the management of a supply chain. Flexible processes are needed to gain competitive advantage and economic edges. This paper presents a data-driven application of unsupervised machine learning clustering algorithms to a real-world case study in the automotive industry. The clustering input dataset collects the data available to a third-party logistics (3PL) provider. Clustering algorithms are used to define product families for the assignment of the workload to the processing resources. Several clustering algorithms (k-means, Gaussian mixture models and hierarchical clustering) define different product families scenarios using different tuning parameters. The impact of each clustering scenario on the operations is assessed via a dashboard of logistics KPIs to identify the best performing clustering algorithm. The performance of each clustering is, then, compared to a logistic benchmark given by a capacitated clustering to identify the best compromise between a logistic-constrained algorithm with a long runtime and fast data-driven uncapacitated algorithm.