A two-level hierarchical framework for early-stage sustainability assessment (FESSA) amongst a set of alternatives applicable from the earliest stages of process or product development is introduced, and its use in combination with an improved method weighted-sum method multi-criteria decision analysis (WSM-MCDA) in the presence of uncertainty is presented through application to a case study based upon a real-world decision scenario from speciality polymer manufacture. The approach taken addresses the challenge faced by those responsible for innovation management in the manufacturing process industries to make simultaneously timely and rational decisions early in the innovation cycle when knowledge gaps and uncertainty about the options tend to be at their highest. The Computed Uncertainty Range Evaluations (CURE) WSM-MCDA method provides better discrimination than the existing Multiple Attribute Range Evaluations (MARE) method without the computational burden of generating heuristic outcome distributions via Monte-Carlo simulation.
Keywords: Decision analysis; Decision making under uncertainty; Decision processes; Manufacturing process scale-up risk management; Multiple criteria analysis; Sustainable process design.
This paper introduces a framework that teams can use to think systematically about the wide range of criteria which go into deciding whether a proposed innovation enhances sustainability or not and shows how an improved method for multiple-criteria decision analysis can be used to put it into practice with an example drawn from the speciality chemicals industry. Innovation in the manufacturing process industries requires decisions to be made. In individual projects, scientists and technical managers must decide which technology, materials, and equipment to use. Equally, those responsible for directing a portfolio of projects must choose which projects to prioritise. In either case, early decision making is desirable to avoid sinking time and money into dead-end projects, and to identify what further work is needed for projects with a future. The earlier you decide however, the harder it can be to obtain firm evidence (e.g. conclusive experimental data, fully validated costings, or life cycle impacts) upon which to base your decision. The growing societal expectation that sustainability criteria are factored into such decisions merely adds to the challenges faced by the decision maker. Decisions must be made upon the evidence that is available combined with the informed judgement of those with knowledge of the system under consideration. This is best approached as a facilitated, team-based activity where assertions, assumptions and interpolations or extrapolations from the limited data can be tested and challenged. A sound decision-making process needs a suitable computational method for turning this complex qualitative and semi-quantitative assessment into a clear output indicator of potential success or failure for the options under consideration. The method described in this paper addresses this need but, just as importantly, the methodology ensures that the thought process behind whatever decision is indicated is clearly and transparently documented for future reference.
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