It is essential for enterprises to develop lean sustainability. In this way, both the learning and understanding of the knowledge of lean tools becomes necessary. In fact, knowledge management plays a key role in the application of lean tools. In this paper, an in-depth exploration is carried out, investigating the mechanism of knowledge management which mediates between lean tools and the lean sustainability of enterprises, as well as the regulatory role of study conventions. Furthermore, a large sample from a questionnaire survey and a model based on structural equations is applied to test our theoretical hypothesis. It can be stated that lean tools display a positive effect on lean sustainability via the mediating role of knowledge management. Additionally, study conventions positively regulate the relationship among lean tools, knowledge management, and lean sustainability.

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PurposeExisting studies suggested that there is a nonlinear relationship between lean production adoption and organizational performance. Lean production adoption is a gradual process, and the application status of lean tools will affect enterprise performance. The existing literature has insufficiently explored the nonlinear relationship of the lean tools application status on operational performance and environmental performance using the same theoretical framework. A combination approach of interpretative structural modeling (ISM) and Bayesian networks was proposed in this paper, which was used to analyze the complex relationship between lean tools application status with operational and environmental performance.Design/methodology/approachISM was used to analyze the inter-relationship of 17 lean tools identified from the lean literature and construct the lean tools structure model providing reference for building Bayesian network. By calculating the prior and conditional probabilities within the lean tools and between the lean tools with the operational and environmental performance, a Bayesian simulation model was constructed and used to analyze the performance outcomes under different lean tools application status.FindingsThe performance simulation result – representing by the probability of three performance levels as good, average and poor – shows inconsistent changes with the changing of lean tools application status. By comparing the changes of operational performance and environmental performance, it can be found that environmental performance is less sensitive to the change of lean tools application status than operational performance.Originality/valueUsing the integrated ISM–Bayesian network approach, the results indicated a nonlinear relationship between lean tools with operational and environmental performance and provided a reference for the exploration of the nonlinear relationship between lean tools and performance. This research further calls for exploring the S-curve relationship between lean tools and environmental performance.

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