Evolution of Value Engineering to Automate Invention in Complex Technological Systems

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.

AI; Function; Functionality; Invention; Innovation; Value engineering

This paper combines many years of industrial experience, understandings drawn from literature reviews, and a model that is reasoned. The purpose of the paper is to mark a starting point for further research built out of practice. It does this by developing a new method that will improve dialogue between manufacturers and customers. The paper has two aims: a longer-term aim that opens the way for artificial intelligence (AI) to be used and a short-term aim to produce a way that value engineering (VE) can better utilize co-creation.

While VE practice is still firmly rooted in the idea of group decision making and workshops, its foundational ideas hold the key to coordinating many aspects of an innovation management process in large technological systems. The successes of VE stands on the articulation of functionality in multidisciplinary teams within workshops (Kaufman, 1985), and this paper offers a new methodology that can aid practice.

This paper distinguishes the act of invention from the implementation of inventions, which is viewed as innovation (Tidd and Bessant, 2015). It will focus on invention and limit its scope to the production of credible ideas from experts that have yet to stand the test of adoption. 

What characterizes the complex technological systems this paper focuses on is that they integrate many component solutions made by suppliers. Often, there are so many possible options and combinations that it can overwhelm a team of multidisciplinary decision makers, especially as workshops are time constrained. Examples of component solutions made by suppliers could be high-speed railways (Berawi et al., 2015), railway operation and maintenance (Rahman et al., 2018), a plan for a smart city (Woodhead, 2018), or an airport design (Yuliawati et al., 2015). They eventually become choices requiring expert technical selection in some methodological framework.

This paper has addressed a core challenge in VE in that its practice is founded on an idea that mixes measurable and immeasurable concepts. By overcoming this difficulty, this paper links to a longer-term ambition to use AI as a form of recommendation system.

The paper is also of immediate benefit to VE practitioners as they can now distinguish between functionality that needs cost reduction and functionality that needs value enhancement. This should help the VE community to overturn views that VE is only useful for cost reduction, as well as open doors to a stronger identification with augmented customer value.

    Today’s VE practitioners can use the value, function, and price method with subsequent incremental invention in a workshop and look to the VR to judge if the suggested improvement really does bring more value forward. This enables VE to engage in co-creation activities where designers and customers collaborate in the act of invention. Matrix 1 offers the means for these two stakeholder groups (i.e., manufacturers and customers) to reflect on what each is trying to achieve.

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