In recent years businesses around the world have been facing the challenges of a rapidly changing business and technology environment. As a result, organisations are paying more attention to supporting business process management by adapting to the dynamic environment. With the increased complexity and uncertainty in business operations, adaptive and collaborative business process and exception management (EM) are gaining attention. In the logistics industry, the growing importance of logistics worldwide as well as the increasing complexity of logistics networks and the service requirement of customers has become a challenge. The current logistics exceptions are managed using human brain power 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. This situation offers inadequate decision support for flexibility and adaptability in logistics EM. The traditional workflow technology is also limited to monitoring the logistics activities in real-time to detect and resolve exceptions in a timely manner. To mitigate these problems, an intelligent agent incorporating business activity monitoring (BAM) decision support approach in logistics EM has been proposed and investigated in this research. This research creates and evaluates two IT designed artefacts (conceptual framework and prototype) intended to efficiently and automatically monitor and handle logistics exceptions. It follows a design science research strategy. The design, development, and evaluation adhere to the principles enunciated in the design science literature. The aim of this research is to solve the important logistics EM problem in a more effective and efficient manner. Two designed artefacts were strictly informed by, and incorporated with, three different theories. An exploratory case study and a later confirmatory case study assisted in the rigorous derivation of the design and framework. The results of the confirmatory case study were used in particular to refine the designed artefacts. Such a build-and-evaluate loop iterated several times before the final designed artefacts were generated. The designed artefacts were then evaluated empirically via a field experiment. The research included both a technical presentation and a practical framing in terms of application in the logistics exception monitoring and handling domain. In this study, there were three interrelated research phases. In the first research phase, a decision-making conceptual framework (an artefact) for design and development of real-time logistics EM system was developed. To enable more efficient decision support practices for logistics EM, the characteristics of logistics exceptions were first examined and identified. The logistics exception analysis was conducted through a comprehensive literature review and an exploratory case study conducted in a major logistics company in Australia. The logistics exceptions were then classified into known and knowable categories, based on the Cynefin sense-making framework (Snowden, 2002). On the basis of the logistics analysis, informed by Gartner’s three-layer BAM architecture (Dresner, 2003), the Cynefin sense-making framework decision models (Snowden, 2002), and Simon’s (1977) decision-making/problem-solving process, the real-time logistics EM conceptual framework was depicted. The BAM architecture provided the real-time decision support. Based on Cynefin’s decision model, adaptive business process flow was chosen for known and knowable logistics exceptions to speed up the decision-making process. In addition, Simon’s process theory was deployed to model the diagnosing process for known and knowable logistics exceptions. This conceptual model guided the analysis, design, and development for real-time logistics EM systems. In the second research phase, based on the logistics EM conceptual framework, a Web-service-multi-agent-based real-time logistics EM system (an artefact) was designed and developed. Intelligent agent technology was applied to deal with the complex, dynamic, and distributed logistics EM processes. Web-services techniques were proposed for more interoperability and scalability in network-based business environment. By integrating agent technology with Web-services to make use of the advantages from both, this approach provided a more intelligent, flexible, autonomous, and comprehensive solution to real-time logistics EM. In the third research phase, two designed artefacts were evaluated via a confirmatory case study and a field experiment. The confirmatory case study was conducted to collect feedback on the two designed artefacts (i.e., conceptual framework and prototype system) to refine them. The field experiment was then conducted to investigate the proposed logistics EM prototype system decision support effectiveness and efficiency by comparing the human decision-making performance with/without the logistics EM decision support facility. The evaluation results indicated that the proposed logistics EM prototype outperformed the one without logistics EM decision support in terms of more efficient decision process, higher decision outcome quality, and better user perception. The two designed artefacts were the major contributions of this research. They add knowledge to decision theory and practice. The artefacts are the real-time extension for Simon’s (1977) classic decision-making/problem-solving process model in logistics EM by incorporating BAM (Dresner, 2003). In addition, by adding the Cynefin sense-making framework (Snowden, 2002), the artefacts provide a more efficient decision-making routine for logistics EM. This research provides the first attempt (to the best of the researcher’s knowledge) to design a real-time logistics EM decision support mechanism based on decision science theories. To demonstrate the usability of the proposed conceptual framework, a logistics EM decision support prototype was designed, developed, and evaluated. For practice, the logistics exceptions classification, logistics EM conceptual framework, and incorporating agent technologies into logistics EM all will assist logistics companies to develop their logistics exception handling decision-making strategies and solutions.

In recent years businesses around the world have been facing the challenges of a rapidly changing business and technology environment. As a result, organisations are paying more attention to supporting business process management by adapting to the dynamic environment. With the increased complexity and uncertainty in business operations, adaptive and collaborative business process and exception management (EM) are gaining attention. In the logistics industry, the growing importance of logistics worldwide as well as the increasing complexity of logistics networks and the service requirement of customers has become a challenge. The current logistics exceptions are managed using human brain power 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. This situation offers inadequate decision support for flexibility and adaptability in logistics EM. The traditional workflow technology is also limited to monitoring the logistics activities in real-time to detect and resolve exceptions in a timely manner. To mitigate these problems, an intelligent agent incorporating business activity monitoring (BAM) decision support approach in logistics EM has been proposed and investigated in this research. This research creates and evaluates two IT designed artefacts (conceptual framework and prototype) intended to efficiently and automatically monitor and handle logistics exceptions. It follows a design science research strategy. The design, development, and evaluation adhere to the principles enunciated in the design science literature. The aim of this research is to solve the important logistics EM problem in a more effective and efficient manner. Two designed artefacts were strictly informed by, and incorporated with, three different theories. An exploratory case study and a later confirmatory case study assisted in the rigorous derivation of the design and framework. The results of the confirmatory case study were used in particular to refine the designed artefacts. Such a build-and-evaluate loop iterated several times before the final designed artefacts were generated. The designed artefacts were then evaluated empirically via a field experiment. The research included both a technical presentation and a practical framing in terms of application in the logistics exception monitoring and handling domain. In this study, there were three interrelated research phases. In the first research phase, a decision-making conceptual framework (an artefact) for design and development of real-time logistics EM system was developed. To enable more efficient decision support practices for logistics EM, the characteristics of logistics exceptions were first examined and identified. The logistics exception analysis was conducted through a comprehensive literature review and an exploratory case study conducted in a major logistics company in Australia. The logistics exceptions were then classified into known and knowable categories, based on the Cynefin sense-making framework (Snowden, 2002). On the basis of the logistics analysis, informed by Gartner’s three-layer BAM architecture (Dresner, 2003), the Cynefin sense-making framework decision models (Snowden, 2002), and Simon’s (1977) decision-making/problem-solving process, the real-time logistics EM conceptual framework was depicted. The BAM architecture provided the real-time decision support. Based on Cynefin’s decision model, adaptive business process flow was chosen for known and knowable logistics exceptions to speed up the decision-making process. In addition, Simon’s process theory was deployed to model the diagnosing process for known and knowable logistics exceptions. This conceptual model guided the analysis, design, and development for real-time logistics EM systems. In the second research phase, based on the logistics EM conceptual framework, a Web-service-multi-agent-based real-time logistics EM system (an artefact) was designed and developed. Intelligent agent technology was applied to deal with the complex, dynamic, and distributed logistics EM processes. Web-services techniques were proposed for more interoperability and scalability in network-based business environment. By integrating agent technology with Web-services to make use of the advantages from both, this approach provided a more intelligent, flexible, autonomous, and comprehensive solution to real-time logistics EM. In the third research phase, two designed artefacts were evaluated via a confirmatory case study and a field experiment. The confirmatory case study was conducted to collect feedback on the two designed artefacts (i.e., conceptual framework and prototype system) to refine them. The field experiment was then conducted to investigate the proposed logistics EM prototype system decision support effectiveness and efficiency by comparing the human decision-making performance with/without the logistics EM decision support facility. The evaluation results indicated that the proposed logistics EM prototype outperformed the one without logistics EM decision support in terms of more efficient decision process, higher decision outcome quality, and better user perception. The two designed artefacts were the major contributions of this research. They add knowledge to decision theory and practice. The artefacts are the real-time extension for Simon’s (1977) classic decision-making/problem-solving process model in logistics EM by incorporating BAM (Dresner, 2003). In addition, by adding the Cynefin sense-making framework (Snowden, 2002), the artefacts provide a more efficient decision-making routine for logistics EM. This research provides the first attempt (to the best of the researcher’s knowledge) to design a real-time logistics EM decision support mechanism based on decision science theories. To demonstrate the usability of the proposed conceptual framework, a logistics EM decision support prototype was designed, developed, and evaluated. For practice, the logistics exceptions classification, logistics EM conceptual framework, and incorporating agent technologies into logistics EM all will assist logistics companies to develop their logistics exception handling decision-making strategies and solutions.