<p>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. © 2019, Emerald Publishing Limited.</p>
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Dr. Saleeshya P.G. and Binu, M., “A neuro-fuzzy hybrid model for assessing leanness of manufacturing systems”, International Journal of Lean Six Sigma, vol. 10, pp. 473-499, 2019.