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Tech Front: Fuzzy Logic Assesses Lean Manufacturing


 Senior Editor Michael Anderson

 

 

 

 

 

  

 

 
 By Senior Editor Michael Anderson

Just how lean are you? Lean, the experts remind us, is more than a set of tools: it’s an empowering culture of continuous improvement. But improvement needs to be recognized and charted in order to be learned from. If lean is a journey, what’s your odometer and speedometer?
 
That’s a challenge, particularly if you want to measure the degree of lean implementation across a larger enterprise. With all of the variables that go into maintaining and growing a lean manufacturing environment, is there really a way to benchmark progress across a large facility? How about an entire enterprise? Sounds implausible but how about entire country’s industry?

Anita Susilawati, John Tan, David Bell, and Mohammed Sarwar, all of the Department of Mechanical & Construction Engineering, Faculty of Engineering and Environment, Northumbria University (Newcastle Upon Tyne, UK), say that yes, there is—and they’ve used a novel method to measure the leanness of Indonesia’s manufacturing industry.
Astra Daihatsu Motor celebrates a milestone at its Jakarta, Indonesia factory in May 2015. Photo courtesy Astra Daihatsu Motor
Susilawati et al. describe their approach in a Journal of Manufacturing Systems feature, “Fuzzy Logic Based Method to Measure Degree of Lean Activity in Manufacturing Industry.” The authors recognize the complexity of their task, which they say arises due to “the inherent multidimensional concept of leanness,” lack of a manufacturing-practice database that can be used to benchmark, and the fact that subjective human opinion—with its variances in bias and knowledge—are an unavoidable part of the evaluators’ baggage.

In their paper, they identify 66 lean parameters and base a degree-of-lean-implementation score on value stream mapping, with the length of the implementation of lean practices considered in the scoring. They also model the vagueness of subjective human judgment with a “fuzzy number.” Results from “an initial survey from a sample of respondents from the manufacturing industry in Indonesia” illustrate the potential strength of their method. Read their entire paper at no charge at http://tinyurl.com/JMS-lean.

 

This article was first published in the October 2015 edition of Manufacturing Engineering magazine.  


Published Date : 10/2/2015

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