Decision Support With Robust Analytics
Successful Life Cycle Management requires an ability to make well-informed decisions even in situations with high uncertainty and lack of real data. In this context, it is important to have an ability to identify, understand and influence the key parameters that impacts operational performance and life cycle cost. This can be accomplished through the analytical approach provided in this paper, which is based on modelling and simulation of the operations and the logistics support scenarios. The approach makes it possible to balance different qualities against each other from a cost effectiveness standpoint, compare different solutions, understand the consequences of decisions and navigate towards the best possible solution with a life cycle perspective.
Procurement and ownership of advanced technical systems such as aircraft, ships, trains and power plants are associated with huge investment costs, high complexity and substantial costs for operations and maintenance over the life cycle of the system.
Early decisions regarding concepts, requirements and choice of supplier will impact the Total Ownership Cost (TOC) more than anything else. Unfortunately, these decisions need to be made without exact knowledge about all influencing parameters. To make these kinds of decisions under major uncertainties calls for an efficient and systematic decision-making process, using modeling and simulation tools to analyze the consequences of the decisions.
Another obvious conclusion is the need to continuously monitor and control the systems over their life cycle to gain as much benefit from each system as possible. At the same time the costs associated with developing, owning and using the systems also needs to be monitored and if possible minimized.
We call this continuous process Life Cycle Management (LCM) as we look upon it as a management process or a tool to monitor the system towards fulfilling the operational needs at the lowest possible TOC, thus creating more affordable systems for the users.
Get access to full white paper:
