The ANP Representation of the BSC

The development and empirical verification of the balanced scorecard (BSC) model, using the multi-criteria decision-making method (MCDM) called the analytic network process (ANP), are the key issues of the presented research. The research was based on a case study of modelling the BSC for Ydria Motors LL (YM), a manufacturing company. Findings from the empirical analysis showed that the BSC and the ANP are complementary methods. Therefore, it can be asserted that introducing the ANP to implement the BSC and vice versa, improved the decision-making approach and the quality of the obtained results

The ANP Representation of the BSC

The development and empirical verification of the balanced scorecard (BSC) model, using the multi-criteria decision-making method (MCDM) called the analytic network process (ANP), are the key issues of the presented research. The research was based on a case study of modelling the BSC for Ydria Motors LL (YM), a manufacturing company. Findings from the empirical analysis showed that the BSC and the ANP are complementary methods. Therefore, it can be asserted that introducing the ANP to implement the BSC and vice versa, improved the decision-making approach and the quality of the obtained results

A Knowledge Enriched Computational Model to Support Lifecycle Activities of Computational Models in Smart Manufacturing

Due to the needs in supporting lifecycle activities of computational models in Smart Manufacturing (SM), a Knowledge Enriched Computational Model (KECM) is proposed in this dissertation to capture and integrate domain knowledge with standardized computational models. The KECM captures domain knowledge into information model(s), physics-based model(s), and rationales. To support model development in a distributed environment, the KECM can be used as the medium for formal information sharing between model developers. A case study has been developed to demonstrate the utilization of the KECM in supporting the construction of a Bayesian Network model. To support the deployment of computational models in SM systems, the KECM can be used for data integration between computational models and SM systems. A case study has been developed to show the deployment of a Constraint Programming optimization model into a Business To Manufacturing Markup Language (B2MML) -based system. In another situation where multiple computational models need to be deployed, the KECM can be used to support the combination of computational models. A case study has been developed to show the combination of an Agent-based model and a Decision Tree model using the KECM. To support model retrieval, a semantics-based method is suggested in this dissertation. As an example, a dispatching rule model retrieval problem has been addressed with a semantics-based approach. The semantics-based approach has been verified and it demonstrates good capability in using the KECM to retrieve computational models