A Study on the Identification of Delayed Delivery Risk Transmission Paths in Multi-Variety and Low-Volume Enterprises Based on Bayesian Network

Abstract

Due to the prevalence of the order production mode, multi-variety, small-batch manufacturing enterprises frequently delay deliveries to downstream customers. To date, most existing studies on delayed delivery risk have focused on the response to the risk after it occurs, ignoring how the risk arises. For multi-variety, low-volume production companies, any part of the production process could lead to the ultimate risk of delayed delivery, and the risk is transmissible. Therefore, the path of risk transmission needs to be identified to effectively control the risk of late delivery at key production stages. In this paper, from the perspective of risk transmission, a recognition method based on association rules and the Bayesian network was proposed to identify the risk conduction path. This method firstly determined the strong association rules among the risk factors based on historical data stored in the ERP system and determined the Bayesian network topology structures of the risk transmission path by combining the business process and expert experience. Secondly, the prior and conditional probabilities of each node were determined using data statistics, and the risk transmission path of delayed delivery was identified using the forward and backward reasoning of the Bayesian network. Finally, this paper provided a case study to verify the method, and the following conclusions were obtained: (1) the delay in delivery to downstream customers is mainly due to the delayed delivery of upstream suppliers and the sudden change in customer demand, and (2) the adjustment of enterprise production plans is the key node of the delayed delivery risk transmission path. Through the research in this paper, production companies can identify the target of risk management more scientifically and mitigate the risk through the adjustment of key links.