The Design of Cost and Availability in Complex Engineering Systems

Abstract

A lot has been covered in literature about equipment/system availability, through-life engineering services (TES) and cost. However, there are still many challenges remaining in the implementation of TES solutions (e.g. maintenance, training, etc.) to achieve cost and availability targets in complex engineering systems. Industry practitioners seek to improve their ability to predict the evolution of complex engineering systems so that it can embrace proactive behaviour to reduce the through-life cost and increase the level of availability. Contracting for Availability (CfA), a commercial process which seeks to sustain a system or capability at an agreed level of availability, is a good example of how many industry organisations have been implementing TES in their business and experiencing difficulties in performing effective cost and availability estimates, in particular at the early stages of the contracts (e.g. bidding stage), where the most of the costs are committed. This chapter focuses on cost and availability trade-off analysis at the bidding stage of CfA, in the defence context. At this phase there is a need to predict the through-life behaviour of the system (e.g. in a 20/30 year life span), which will be affected by risks and uncertainties that complicate the prediction of the total cost and level of system (e.g. platforms) availability. Two case studies with the UK Ministry of Defence (MoD) enabled the identification of the attributes that impact the cost and availability targets of CfA and the key performance indicators (KPIs) to measure the availability. This led to the development of a framework that gives a major contribution to the decision makers by analysing the required investment in each attribute in order to effectively balance availability and affordability at the bidding stage of CfA. This is a novel research in this context as no other similar approach has been found in literature. It can be further implemented in practice as a simulation model using mathematical equations and appropriate software tools such as excel or matlab.