Purpose: This paper aims to describe a structured review framework for managing business performance. The framework entails the review of business performance, including the strategic relevance of the measures, as well as the efficiency and effectiveness of the performance measurement system itself.Design/methodology/approach: A range of approaches and tools are employed in the framework, which features a review card providing a high‐level view of the review process, showing the different types of review perspectives and their interactions.Findings: The performance measurement system of an organisation is a mechanism to manage and control the organisation. Maintaining the effectiveness of the organisation and the measurement systems requires a systematic review process. The process of reviewing performance is a complex task that spans the whole organisation. A good review process seeks the correct balance between organisational benefits and the effort required.Originality/value: Overall, the framework presented provides a structured path of performance review in a methodical and effective manner. This is useful for practitioners to apply and adapt in their situations.

This paper aims to look at the value and the necessity of XAI (Explainable Artificial Intelligence) when using DNNs (Deep Neural Networks) in PM (Predictive Maintenance). The context will be the field of Aerospace IVHM (Integrated Vehicle Health Management) when using DNNs. An XAI (Explainable Artificial Intelligence) system is necessary so that the result of an AI (Artificial Intelligence) solution is clearly explained and understood by a human expert. This would allow the IVHM system to use XAI based PM to improve effectiveness of predictive model. An IVHM system would be able to utilize the information to assess the health of the subsystems, and their effect on the aircraft. Even if the underlying mathematical principles are understood, they lack an understandable insight, hence have difficulty in generating the underlying explanatory structures (i.e. black box). This calls for a process, or system, that enables decisions to be explainable, transparent, and understandable. It is argued that research in XAI would generally help to accelerate the implementation of AI/ML (Machine Learning) in the aerospace domain, and specifically help to facilitate compliance, transparency, and trust. This paper explains the following areas: Challenges & benefits of AI based PM in aerospace Why XAI is required for DNNs in aerospace PM? Evolution of XAI models and industry adoption Framework for XAI using XPA (Explainability Parameters) Discussion about future research in adopting XAI & DNNs in improving IVHM.

The effective management of knowledge as capital is considered essential to the success of engineering product/service systems. As Knowledge Management (KM) and Product Lifecycle Management (PLM) practice gain industrial adoption, the question of functional overlaps between both the approaches becomes evident. This article explores the interoperability between PLM and Knowledge-Based Engineering (KBE) as a strategy for engineering KM. The opinion of key KBE/PLM practitioners are systematically captured and analysed. A set of ranked business functionalities to be fulfiled by the KBE/PLM systems integration is elicited. The article provides insights for the researchers and the practitioners playing both the user and development roles on the future needs for knowledge systems based on PLM.

A simulation model of an in-line Flexible Manufacturing System has been built using the declarative computer programming lan guage PROLOG. The model is a vehicle for the study of dynamic responses of Flexible Manufacturing System control. Animation of system status and powerful user interaction are essential for this application. A simple but effective structure has been formulated in PROLOG as a core kernel of the simulator. The decision making process is modelled as a set of functional queues. Partitioning of the de cision functions permits modeling of complex peer-to-peer co ordinated decision making in non-trivial control environments. This offers many advantages over traditional manufacturing simu lation systems. The authors describe appropriate strategies for the realization of acceptable computational performance while retaining the powerful benefits of the PROLOG environment. The result is a practical operational system which features an incremental de velopment upgrade path, quick prototyping, fast and clearly understood code changes.