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Integrated Index for assessing operational uncertainty in manufacturing for decision-making

20251 citationOpen accessRhodes University

Abstract

Orientation: Growing operational uncertainty in manufacturing industries affects decision-making processes.Research purpose: This study conceptualised and developed an integrated assessment index to measure operational uncertainty in manufacturing.Motivation for the study: There is a growing need for continuing research to develop integrated indices that fully understand and help manage uncertainty within a firm for its long-term sustainability.Research design, approach and method: The study is based on a four-step process, which involves identifying theoretical dimensions, measuring indicators, determining the level of individual factors, determining the weight estimates of the factors and composing the manufacturing operational uncertainty index (MOUI).Main findings: The illustrated index analysis was based on nine operational uncertainty at the external environmental, industrial and firm levels. The results of the present study also confirm that operational uncertainty is a norm in the manufacturing industry with a MOUI = 0.752, indicating the range of futures. This posits that it is difficult to divide these futures into a discrete and exhaustive set of possibilities due to the complexity of conditions at play.Practical/managerial implications: The study provides essential tools for decision-making, allowing stakeholders to assess performance and enhance continuous improvement efforts by providing a quantitative measure to assess operational uncertainty. It can be applied in order of rank to prioritise response, effects of operational uncertainty on performance and baseline for configuration solutions.Contribution/value-add: Developing this index is crucial in operations management, as it provides a systematic and simplified approach to assessing, comparing and managing complex data sets.

Research topics

  • Technology Assessment and Management
  • Manufacturing Process and Optimization
  • Quality and Management Systems

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DOI: 10.4102/ac.v25i1.1426

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