An approach to selection of sustainable product improvement alternatives with data uncertainty

This paper presents a theoretical framework on the integration of simplified Life Cycle Assesment (LCA), Life Cycle Costing (LCC) and Quality Function Deployment (QFD) methodologies for the purpose of strategic selection of product improvement alternatives with consideration to data uncertainty. The research focus is on how manufacturing companies can be assisted in design of products so that quality, environmental and cost (QEC) requirements of stakeholders in the life cycle stages of the product system are addressed at an early stage where data imprecision is common. The consideration of these three design requirements leads to a multi-attribute decision situation with regard to the selection of an optimal product system improvement concept. The main objective is to develop a decision-oriented life cycle approach that integrates QEC parameters at an early stage of product development. A conceptual approach was developed which includes: (1) identification and documentation of QEC requirements’ information using streamlined LCA, LCC and QFD; (2) generation of alternative concepts for product improvements and construction of sustainable concept comparison matrix where fuzzy terms were adopted to describe relationships and importance; (3) fuzzy linguistic decision support system was developed and applied to evaluate and select the optimal sustainable product improvement alternative under data uncertainty; (4) sensitivity and statistical analyses. Ranking of alternative sustainable options with respect to environment, cost and quality was reported.