An intelligent agent-assisted logistics exception management decision support system: a design-science approach

With the increased complexity and uncertainty in business operations, adaptive and collaborative business processes and exception management (EM) are gaining growing attention. In the logistics industry, the current logistics exceptions are managed using human resources together with the traditional workflow technology-based supply-chain management or other logistics tools. The traditional workflow technology models and manages business processes and anticipated exceptions based on predefined logical procedures of activities from a centralised perspective that offers inadequate decision support for flexibility and adaptability in EM. These procedures are limited when monitoring logistics activities in real time in order to detect and resolve the exceptions in a timely manner. In order to mitigate these problems, a design-science research approach-specifically an intelligent-agent decision support approach in logistics EM-has been proposed and investigated in this research. It contains three interrelated research phases. The first research phase focuses on the conceptualisation of the logistics EM. It consists of two parts. The first part is logistics exception classification, in order to enable more efficient decision support practices for logistics EM. The second part focuses on the development of the conceptual framework (an artefact) for design and development of logistics EM systems for decision making. The second research phase focuses on the formalisation of the conceptual framework. A multi-agent-based logistics EM system is designed based on the conceptual framework. The third research phase will focus on the development of the designed logistics EM artefact. It will include two stages. First, a prototype will be developed. To provide more adaptive, flexible and collaborative decision support, the intelligent agent technology will be used for implementation. Second, the prototype will be evaluated via social-science research methods: semi-structured interviews and laboratory experiment. It is proposed that this theory-driven agent-based logistics EM system will provide more efficient and timely decision-making support for managers in relation to logistics EM. The designed artefacts and the research design are the major contributions of this research, which add knowledge to design-science research theory and practice. The conceptualisation-formalisation-development research approach can be applied in other similar IS design-science research.