Predicting weighing deviations in the dispatch workflow process: a case study in a cement industry

• Autor(a) principal: Barros, Júlio Dinis Lopes
• Data de Publicação: 2023
• Outros Autores: Cunha, Francisco, Martins, Cândido, Pedrosa, Paulo, Cortez, Paulo
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/1822/85647
• topic: Anomaly prediction; dispatch workflow process; Machine learning; Order fulfilment process; Weighing systems; Bridges; Supply chains; Cement industry; Transportation; Random forests; Predictive models; Anomaly detection; Science & Technology

The emergence of the Industry 4.0 concept and the profound digital transformation of the industry plays a crucial role in improving organisations' supply chain (SC) performance, consequently achieving a competitive advantage. 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. The dispatch workflow process consists of 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. We adopted a realistic and robust rolling window scheme to evaluate six classification models in a real-world case study, from which the random forest (RF) model provides the best predictive performance. 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.