A business process network efficiency model for handling conflicting information

In today's big data era, where a significant volume of business data is generated daily, managing conflicting information within business process networks is crucial for maintaining operational efficiency. This paper addresses this challenge by proposing an efficiency model for business process networks tailored to handle conflict information, drawing on queuing theory and evidence theory. Firstly, we introduce a novel approach for measuring conflict information based on evidence theory and Pignistic probability transformation theory. Next, we tailor efficiency models for the four fundamental structures found in business process networks: sequential, selective, parallel, and loop structures, using queuing theory to manage conflict information effectively in each scenario. We further extend this approach by conceptualizing virtual business activities, allowing us to view the entire business process network as a sequential structure of virtual business activities, facilitating efficiency measurement across the network. Utilizing these measurements, we formulate the queuing service of the business process network as a nonlinear programming problem aimed at minimizing time, thus determining the optimal service rate for business process activities. Finally, we demonstrate the applicability and effectiveness of our proposed model through an experimental analysis focused on the railway intermodal transportation business process. The experimental results indicate that our model significantly reduces the impact of conflicting information, leading to a measurable improvement in the efficiency of the business process network. Specifically, the model achieves a notable enhancement in the coordination and execution of intermodal transportation activities, thereby streamlining operations and reducing decision-making uncertainties. This structured approach not only addresses the challenge of managing conflicting information within business process networks but also provides a clear framework for understanding and optimizing network efficiency.