Simulation Model for Cost and Availability Estimation at the Bidding Stage in the Presence of Limited Data

Abstract

Contracting for Availability (CfA) is an increasingly adopted commercial process which consists of a partnership between customer and solution provider(s) towards supporting system availability for long periods with the target of better value for money. In a competitive bidding process, contractors commit an effective bid price to win contracts, which the process heavily depends on existing data from similar projects. Within the CfA context, based on industrial engagement and literature review, it has been realised that one of the biggest challenges is to deal with a lack of available data to aid the cost and asset availability estimation process. In order to fill this gap, this paper presents an innovative simulation model for Cost and Availability Trade-Off and Estimation in CfA Bids (CATECAB), which uses multiple regression analysis and a mixed Monte-Carlo and bootstrapping re-sampling technique. Its main innovation is the ability to produce estimates based on a comprehensive analysis across the different attributes that impact system availability, and in scenarios where data availability is limited. A case study is presented with four CfA scenarios, provided by a major defence contractor in the UK, which showed that additional investment could improve availability. Experienced cost engineers validated the results and acknowledged that it is a valuable contribution to improve cost and availability estimation during bidding.