A diagnostic model for green productivity assessment of manufacturing processes

• Main Authors: Pineda-Henson, Ruby, Culaba, Alvin B.
• Format: text
• Published: Animo Repository 2004
• Subjects: Semiconductor industry; Product life cycle; Green products; Multiple criteria decision making; Mechanical Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1667
• Institution: De La Salle University

Goal, Scope and Background. Green Productivity (GP) is a new paradigm in sustainable manufacturing where resource conservation and waste minimization constitute the strategy in simultaneously enhancing environmental performance and productivity. This productivity approach to the sustainability of industries requires the adoption of clean production technology and the development of appropriate indicators and instruments to measure environmental performance in a continuous improvement strategy that focuses on the manufacturing stage of the product life cycle. The analysis may be expanded to include the entire life cycle with increasing details on impacts, improvement strategies and indicators. Methods. The study proposes a methodology for GP assessment that integrates the essential components of life cycle assessment (LCA) and multicriteria decision analysis specifically the analytic hierarchy process (AHP). LCA provides a systematic and holistic perspective for GP analysis that spans inventory, impact and improvement assessment. The AHP is utilized as a decision framework and valuation tool for impact and improvement assessment to come up with priority weights. Indicators are derived and measured from a streamlined LCA focused on a number of parameters within the gate-to-gate analysis to demonstrate the GP concept in relation to resource utilization and waste minimization. An input-output approach using a suitable material balance in a scenario analysis provides the basis of GP performance measurement. Results and Conclusion. The diagnostic model is applied on a semiconductor assembly/packaging operation. From the stream-lined life cycle inventory, impact factors were derived for water resource depletion (WRD), energy resource depletion (ERD), human toxicity-air (HTA), human toxicity-land (HTL), human toxicity-water (HTW), aquatic ecotoxicity (ETA) and terrestrial ecotoxicity (ETT). Valuation of impact factors using the AHP showed the high significance of ETT, HTL, WRD and ERD. This especially reflects the impact of the industry on the solid waste problem as a result of emissions to land associated with human toxicity and ecotoxicity effects and the intensive use of water and energy resources. Using scenario analysis, the effect of implementing a process-based improvement technique on a product-specific operation was determined and the highest values in GP are for energy utilization, water utilization and terrestrial ecotoxicity. Recommendation and Perspective. Expert system technology was explored in developing a diagnostic prototype that emulates how human experts diagnose green productivity of manufacturing processes. The aim was to investigate how such a diagnosis could be performed in an intelligent fashion that it is also easily accessible as a decision support for industries. The expert system model will provide flexibility in testing the relationships of environmental performance and productivity parameters as well as in preserving and disseminating valuable human expertise in GP program implementation. This is a continuing research effort that is building the knowledge base for GP assessment. It will include case studies over a wider range or level of detail regarding the impacts and improvement techniques and the other stages of the product life cycle.