Investigating information and data criticality in Asset Management decision-making process

Asset Management (AM) is increasing in attention among researchers and practitioners since it aims at creating an integrated and holistic methodology to manage physical assets, as production systems or machineries. The development of such holistic methodology is founded on several AM fundamentals, which are: asset control levels (operational, tactical, and strategic), asset lifecycle stages (BoL, MoL, EoL), and AM principles (Lifecycle, System, Risk, and Value). Thus, the AM decision-making process must rely on the AM fundamentals to properly support every decision belonging to AM, e.g. capital investment, operations and maintenance and others. This being the situation, information and data become critical: every decision needs suitable information and data to support it and to respect the AM fundamentals. On one side, scientific literature is producing data models, mainly confined within the maintenance field, that schemes out the flow of information and data within the decision-making process. On the other side, the industrial world is pushing towards the creation of standards that allows formalising an information and data management strategy. However, in both cases, there is no clear way on how to improve AM decision-making through a better information and data management, while considering the AM fundamentals. Therefore, the goal of this work is to propose a framework able to support data modelling in AM. The proposed framework serves as a checklist when creating data models for AM since it provides guidelines on how to comply with AM theory.