Abstract
Manufacturing companies usually expect strategic improvements to focus on reducing both waste and variability in processes, whereas markets demand greater flexibility and low product costs. To deal with this issue, lean manufacturing (LM) emerged as a solution; however, it is often challenging to evaluate its true effect on corporate performance. This challenge can be overcome, nonetheless, by treating it as a multi-criteria problem using the Hesitant Fuzzy linguistic and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. In fact, the hesitant fuzzy linguistic term sets (HFLTS) is vastly employed in decision-making problems. The main contribution of this work is a method to assess the performance of LM applications in the manufacturing industry using the hesitant fuzzy set and TOPSIS to deal with criteria and attitudes from decision makers regarding such LM applications. At the end of the paper, we present a reasonable study to analyze the obtained results.
Highlights
Lean manufacturing (LM) combines a wide rage of management practices, such as just in time (JIT), quality systems, work teams, cellular manufacturing, and supply chain management (SCM) in a whole system [1]
The two main contributions of this work can be stated as follows: first, we propose an hesitant fuzzy linguistic term sets (HFLTS)-based data handling procedure to deal with lean manufacturing performance assessments
TOPSIS and HFLTS are a useful tool for managers who wish to assess the key performance indicators (KPIs)’s performance of the lean manufacturing (LM) projects
Summary
Lean manufacturing (LM) combines a wide rage of management practices, such as just in time (JIT), quality systems, work teams, cellular manufacturing, and supply chain management (SCM) in a whole system [1]. Introduced the hesitant fuzzy linguistic term sets (HFLTS), which allows decision makers to elicit several linguistic terms for the same linguistic variable [17,19]. The two main contributions of this work can be stated as follows: first, we propose an HFLTS-based data handling procedure to deal with lean manufacturing performance assessments. We propose a systematic solution to measure the LM performance with respect to a series of criteria.
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