A Process Reference Model for Operational and Real-Time Planning in Road Freight Transport
Talk title: A Process Reference Model for Operational and Real-Time Planning in Road Freight Transport
Speaker affiliation: Sandra Lechtenberg is a research assistant and doctoral candidate at the Chair for Information Systems and Supply Chain Management at the European Research Center for Information Systems (ERCIS), WWU Münster. She also acquired her bachelor’s and master’s degree in information systems at the WWU Münster, focusing on data analysis and logistics. Combining these areas of interest in her research and doctoral thesis, she is dealing with where and how machine learning can meaningfully support operational and real-time planning in road freight transport.
Talk abstract: Operational and real-time transport planning requires decisions in a highly dynamic and complex environment, considering numerous stakeholders and regulations. Data analytics is a promising solution to support or automate decision-making. However, companies struggle to implement analytics due to a lack of means to identify suitable application cases. A requirement for use case identification is a sound process understanding. So, while there are successful applications of data analytics to operational and real-time planning, a structured overview of the processes and decision tasks is missing. Hence, this talk shows a process reference model for operational and real-time transport planning enabling companies to understand their operations and examine them to uncover application potential for data analytics. Process reference models depict standard or recommended practices and are a starting point to gaining process understanding and identifying improvement potential. The presented process reference model shows operational and real-time transport planning processes in three layers: Process overview, sub-processes, and ad-hoc processes. Each layer provides insights into typical process flows and decision tasks, forming the basis for enterprise-specific models. Hence, the developed model allows practitioners to identify application potential beyond existing lighthouse projects and can streamline research on data analytics applications.