Using a Fuzzy-Bayesian Approach for Predictive Analysis of Delivery Delay Risk

Abstract

Although one of the major roles of delivery logistics activities is to ensure a good quality of customer service, certain risks such as damage, delay, return of transported goods occur quite often. This makes risk control and prevention one of the requirements of transport supply chain quality. The article focuses on the analysis of the risk of delay, which is often considered fundamental for the quality of service and as a center of additional costs related to the violation of time windows. Such a risk can harm the image of a supplier, which can even lead to the loss of customers in case of recurrence. The aim of the following case study is the development of a fuzzy-bayesian approach that anticipates, by predictive analysis combining Bayesian networks (BNs) and Fuzzy logic, the possible delays affecting the smooth running of a delivery operation. The results of the implementation of the late delivery risk prediction model are validated by verifying three axioms. In addition, a sensitivity and scenario analysis is performed to validate the model and identify the parameters that have the most adverse impact on the occurrence of such a risk. These results can help carriers/transport providers to minimize potential late deliveries. In addition, the developed model can be used as a basis for different types of predictions in the field of freight transport as well as in other research areas.