Analysing Supply Chain Operation Dynamics through Logic-Based Modelling and Simulation

Supply Chain Management (SCM) is becoming increasingly important in the modern business world. In order to effectively manage and integrate a supply chain (SC), a deep understanding of overall SC operation dynamics is needed. This involves understanding how the decisions, actions and interactions between SC members affect each other, and how these relate to SC performance and SC disruptions. Achieving such an understanding is not an easy task, given the complex and dynamic nature of supply chains. Existing simulation approaches do not provide an explanation of simulation results, while related work on SC disruption analysis studies SC disruptions separately from SC operation and performance.
This thesis presents a logic-based approach for modelling, simulating and explaining SC operation that fills these gaps. SC members are modelled as logic-based intelligent agents consisting of a reasoning layer, represented through business rules, a process layer, represented through business processes and a communication layer, represented through communicative actions. The SC operation model is declaratively formalised, and a rule-based specification is provided for the execution semantics of the formal model, thus driving the simulation of SC operation. The choice of a logic-based approach enables the automated generation of explanations about simulated behaviours. SC disruptions are included in the SC operation model, and a causal model is defined, capturing relationships between different types of SC disruptions and low SC performance. This way, explanations can be generated on causal relationships between occurred SC disruptions and low SC performance.
This approach was analytically and empirically evaluated with the participation of SCM and business experts. The results indicate the following: Firstly, the approach is useful, as it allows for higher efficiency, correctness and certainty about explanations of SC operation compared to the case of no automated explanation support. Secondly, it improves the understanding of the domain for non-SCM experts with respect to their correctness and efficiency; the correctness improvement is significantly higher compared to the case of no prior explanation system use, without loss of efficiency. Thirdly, the logic-based approach allows for maintainability and reusability with respect to the specification of SC operation input models, the developed simulation system and the developed explanation system.