A marginal analysis guided technology evaluation and selection

Making decisions on strategic investments, such as early stage manufacturing technology (MT), is a complicated task. Early stage technologies are usually costly, and surrounded by uncertainty. The potential benefits are often hard to quantify prior to implementation. Thus, how could managers make good decisions in a high-risk, technically complex business when the information they need to make those decisions comes largely from the project champions who are competing against one another for resources? Traditionally, in this problem domain, decisions are made based upon gut-feeling and past experience, sometimes with the support of some multi-criteria decision-support tools. The criteria evaluation process is very subjective and relies heavily on managers' experience, knowledge, as well as intuition. Thus, the evaluation approach is often not effectively carried out as there is lack of visibility and traceability in the decision making process. The impact of this scenario is that managers are not confident that resources are being optimised and applied to a mixed portfolio of projects to maximise benefits. This paper proposes a marginal analysis directed branch and bound approach for evaluating and selecting early stage manufacturing technology (MT) projects. A case study is used to demonstrate the application of the proposed approach. Implications of the proposed approach to practitioners and academia are discussed and future research outlined.