A neuro-fuzzy hybrid model for assessing leanness of manufacturing systems

Saleeshya P.G · M. Binu

18 citations0/0 files storedInternational Journal of Lean Six Sigma

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Quality and Supply Management Management and Optimization Techniques Digital Transformation in Industry

Abstract

Purpose Lean implementation is a strategic decision. The capacity of organisation to be “Lean” can be identified before lean implementation by assessing leanness of an organisation. This study aims to attempt developing a holistic leanness assessment tool for assessing organisational leanness. Design/methodology/approach A neuro-fuzzy leanness assessment model for assessing the leanness of a manufacturing system is presented. The model is validated academically and industrially by conducting a case study. Findings Neuro-fuzzy hybridisation helped assess the leanness accurately. Fuzzy logic helped to perform the leanness assessment more realistically by accounting ambiguity and vagueness in organisational functioning and decision-making processes. Neural network increased the learning capacity of assessment model and increased the accuracy of leanness index. Research limitations/implications The industrial case study in the paper shows the results in telecom equipment manufacturing industry. This may not represent entire manufacturing sector. The generic nature of the model developed in this research ensures its wide applicability. Practical implications The neuro-fuzzy hybrid model for assessing leanness helps to identify the potential of an organisation to become “Lean”. The organisational leanness index developed by the study helps to monitor the effectiveness and impact of lean implementation programmes. Originality/value The leanness assessment models available in literature lack depth and coverage of leanness parameters. The model developed in this research assesses leanness of an organisation by accounting for leanness aspects of inventory management, industrial scheduling, organisational flexibility, ergonomics, product, process, management, workforce, supplier relationship and customer relationship with the help of neuro-fuzzy hybrid modelling.

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References

1965Fuzzy sets 2007Defining and developing measures of lean production 2010Integration of artificial neural network and MADA methods for green supplier selection 1996Assessing changes towards lean production 2005Agility evaluation using fuzzy logic 2014Exploring barriers in lean implementation 2010The impact of lean practices on inventory turnover 2008Lean manufacturing performance in China: assessment of 65 manufacturing plants

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2021Decision making process in lean assessment and implementation: a review 2021Product-service system leanness assessment model: study of a UK manufacturing company 2023Modelling the inhibitors of integrated sustainable lean manufacturing system in the South Indian SMEs using fuzzy logic 2020Improving the resolution time performance of an application support process using Six Sigma methodology

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