Expert Judgement in Cost Estimating: Modelling the Reasoning Process.

Abstract

Expert Judgement (EJ) is used extensively during the generation of cost estimates. Cost estimators have to make numerous assumptions and judgements about what they think a new product will cost. However, the use of EJ is often frowned upon, not well accepted or understood by non-cost estimators within a concurrent engineering environment. Computerised cost models, in many ways, have reduced the need for EJ but by no means have they, or can they, replace it. The cost estimates produced from both algorithmic and non-algorithmic cost models can be widely inaccurate; and, as the work of this paper highlights, require extensive use of judgement in order to produce a meaningful result. Very little research tackles the issues of capturing and integrating EJ and rationale into the cost estimating process. Therefore, this paper presents a case with respect to the wide use of EJ within cost estimating. EJ is examined in terms of what thought processes are used when a judgement is made. This paper highlights that most judgements are based on the results of referring to historical costs data, and then adjusting up or down accordingly in order to predict the cost of a new project. This is often referred to as analogy. The reasoning processes of EJ are identified and an inference structure has been developed, which represents an abstraction of the reasoning steps used by an expert as they generate an estimate. This model has been validated through both literature and interviews with cost estimating experts across various industry sectors. Furthermore, the key inferences of the experts are identified. These inferences are considered as those where many of the assumptions and expert judgements are made. The thesis of this paper is that through modelling the reasoning processes of EJ, it becomes possible to capture, structure, and integrate EJ and rationale into the cost estimating process as estimates are being generated. Consequently, the rationale capture will both improve the understanding of estimates throughout a product life cycle, and improve management decisions based upon these cost estimates.