Abstract: |
The supply chain (SC) consists of a complex and unique network, which includes several entities, processes and resources. Logistics constitutes one of the crucial factors in the success of the SC, which consists of planning and coordinating the movement of products in a timely, safe and effective way. Logistics management activities comprise inbound and outbound transportation management, fleet management, warehousing, materials handling, order fulfilment, logistics network design, and inventory management, among others. The order fulfilment process (OFP) consists of one of the key business processes for the organization SC and represents a core process for the operational logistics flow. Indeed, the problems associated with this process can result in high losses, such as losses of customer’s confidence, reputation, and revenues. The assessment of the OFP problems and the creation of digital solutions, according to the Industry 4.0 concept, to support this process allow the improvement of the efficiency of the supply chain and consequently improves the organizational performance and achieve a competitive advantage. The dispatch workflow process is an integral part of the OFP and is also a crucial process in the SC of cement industry organizations. In this work, we focus on enhancing the order fulfilment process by improving the dispatch workflow process, specifically with respect to the cement loading process. Thus, we proposed a machine learning (ML) approach to predict weighing deviations in the cement loading process motivated mainly by (i) the nonexistence of scientific works that focus on improving OFP, more specifically regarding the problem of deviation in weighings during the dispatch workflow process, (ii) the lack of studies regarding the cement industry supply chain, although the SCs problems are an attractive topic and, (iii) the scarce consideration of ML techniques in SC management scientific studies. Hence, the occurrence of weight deviations, which represents an anomaly in the loading of cement bags, poses a complex problem that directly impacts the OFP and consequently the SC performance, resulting in several losses, including monetary and service level losses. Indeed, in this work, we adopted a realistic and robust rolling window (RW) scheme to evaluate six classification models, namely decision tree (DT), random forest (RF), support vector machines (SVM), gradient-boosted tree (GBT), extreme gradient-boosting tree (XGBT), and multilayer perceptron (MLP) in a real-world case study in the company Cachapuz - Weighing & Logistics Systems, Lda, Portugal, from which the random forest (RF) model provides the best predictive performance with a median AUC of 0.937 over the twenty iterations of the RW, followed by the GBT, XGBT, SVM, MLP, and DT models. We also extracted explainable knowledge from the RF classifier by using the Shapley additive explanations (SHAP) method, demonstrating the influence of each input data attribute used in the prediction process. Hence, we provide the top ranking of features and demonstrate that eight of the eleven features considered were the result of feature engineering. The selected model (RF model) is deployed on a technological architecture that we propose, which enables gathering data from the existing Cachapuz database, training the model and making predictions from that data, and presenting the model outcomes on a PowerBI dashboard, together with useful explanations of the predictions. |