Many global companies specializing in complex technological systems use forms of group decision making to select a combination of solutions from suppliers. This requires technical expertise and up-to-date awareness of what is available within and outside the company. The use of artificial intelligence seems like an obvious progression but is fraught with difficulties. As a step in this longer-term direction, this paper looks to a methodology that uses the idea of functionality to first list abstract requirements before finding potential solutions with appropriate performance characteristics. This paper re-examines a methodology called value engineering, which mixes measurable and immeasurable concepts in its foundational idea. This paper reasons and deduces a new way to conceive this foundational idea so that it can be modelled mathematically and provide a useful step toward a database ontology and schema that would suit artificial intelligence. It also provides an immediate benefit to value engineering practitioners in workshops.
- Value engineering