Creating Improved Models of Business Processes Through Statistical Analysis of Customer Orders with Multiple Types of Complexity

Abstract

To gain a better understanding of the internal work processes in service-oriented supply chains, it is very important to design models that are able to realistically describe the components of the supply chain. To meet this goal, it is necessary to find suitable statistical distributions of the processing times for the orders passing the chain. In this article we examine sample data sets with more than 2,000 individual work times from four steps in the work processes of a time-based aeronautical supply chain and derive the best possible distributions fitting the sample data sets. To increase the realism of the model, both the data sets and the resulting statistical distributions were subdivided into several categories of order complexities, a task made more challenging by the limited amount of data available for the rarer high-complexity orders.