Lean manufacturing has been the most deliberated concept ever since its introduction. Many organization across the world implemented lean concept and witnessed dramatic improvements in all contemporary performance parameters. Lean manufacturing has been a sort of mirage for the Indian automotive industry. The present research investigated the key lean barriers to lean implementation through literature survey, confirmatory factor analysis, multiple regression, and analytic network process. The general factors to lean implementation were inadequate lean planning, resource constraints, half-hearted commitment from management, and behavioral issues. The most important factor in the context of lean implementation in Indian automotive industry was inadequate lean planning found with the help of confirmatory factor analysis and multiple regression analysis. Further analysis of these extracted factors through analytic network process suggested the key lean barriers in Indian automotive industry, starting from the most important were absence of proper lean implementation methodology, lack of customer focus, absence of proper lean measurement system, inadequate capital, improper selection of lean tools & practices, leadership issues, resistance to change, and poorly defined roles & responsibilities. Though literature identifying various lean barriers are available. The novelty of current research emerges from the identification and subsequent prioritization of key lean barriers within Indian automotive SMEs environment. The research assists in smooth transition from traditional to lean system by identifying key barriers and developing customized framework of lean implementation for Indian automotive SMEs.

In recent years, we have been able to observe a dynamic development of MCDA (multi-criteria decision analysis) methods, which have become widely used in various sectors, including construction. These methods are characterised by simplicity and one of their key advantages is their simple modelling of non-linear dependencies within decision problems and their analysis under the conditions of incomplete, uncertain and hard-to-measure information. The universality and simple use of these methods does not, however, free the decision-maker from the necessity to adopt the proper approach to modelling and analysing specific decision problems. To highlight the fact that it is the character of the problem that should determine the selection of the method of analysing it and not the other way around, the authors assessed the AHP (Analytic Hierarchy Process) and the ANP (Analytic Network Process) method in terms of verifying the impact of the different decision model structures on analysis outcomes and analysed their sensitivity to input data changes. This analysis was based on the example of selecting a telecommunications tower footing reinforcement alternative. The findings confirmed the significant impact of decision model structure on the ranking of the analysed alternatives.