Logistics Exception Management: A Theory-Driven Real-Time Decision Model

With the increased complexity and uncertainty in business operations, adaptive and collaborative business processes and exception management (EM) are gaining attention. In the logistics industry now, logistics exceptions are managed using human resources together with the traditional workflow technology-based supply-chain management or other logistics tools. The traditional approach models and manages business processes and anticipated exceptions based on predefined logical procedures of activities from a centralized perspective that suffers from inadequate decision support for flexibility and adaptability in logistics EM. These procedures have limited utility when monitoring logistics activities in real-time to detect and resolve the exceptions. To mitigate these problems, a real-time logistics EM decision model is presented in this research by following a design science research approach. The proposed decision model is informed by a comprehensive logistics exception analysis, a real-time business activity monitoring architecture, Simon's decision-making process model, and the Cynefin sense-making framework. The proposed decision model is the major contribution of this research, which adds knowledge to the decision science research theory and practice. In addition, a summarized logistics exception analysis provides researchers with a comprehensive representation of the logistics exceptions and their handling method. The proposed decision model and logistics exception analysis provide researchers with a sound basis for formal study. It leads to the analysis, design and development of a logistics EM decision support system. It also provides practitioners with a means of developing logistics exception handling decision making strategies and solutions.