A Systematic Framework for Quantifying Production System-Specific Challenges in Life Cycle Inventory Data Collection

Marija Glisic, Shoaib Sarfraz, Badrinath (Badri) Veluri, Devarajan Ramanujan*

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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Understanding the environmental impacts of production setups and process parameters is a necessity for process optimization and new process development within sustainable manufacturing. Previous research studies have focused on developing standard methodologies and frameworks for parametrically modelling the life cycle inventories of unit manufacturing processes. However, these approaches do not fully account for the challenges associated with implementing life cycle inventory models in real-world production setups. Therefore, the time- and cost-intensiveness associated with constructing such models limit their use for identifying sustainability-focused process improvements in complex, real-world production processes. To address the above challenges, this paper proposes a framework to identify process inventory data that have a significant influence on process resource consumption, taking into consideration the difficulties and variabilities in measuring these data. The overarching goal is to identify feasible process improvements from the perspective of process monitoring for sustainable manufacturing. The application of the proposed framework is presented using a case study on a real-world through-feed centerless grinding production process for rotor manufacturing. This study reveals grinding time is the most sensitive process parameter among the other time-related parameters. The manual nature of the process, lack of a data acquisition system, non-standardized sequence of operation, and inability to capture in-process measurements without disrupting real-time production significantly contributes to the difficulty and variability of measuring grinding time.

Original languageEnglish
JournalProcedia CIRP
Pages (from-to)210-218
Number of pages9
Publication statusPublished - 8 Mar 2022


  • Life Cycle Assessment
  • Process modelling
  • Sustainable Manufacturing


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