Lean implementation and its relationship with operational responsiveness, failure factors and resilience

Lean Management (LM) is a multi-faceted socio-technical approach that requires organizations to strive along several dimensions simultaneously. Initially, a conceptual model has been proposed for investigating the network of influences among lean practices, operational responsiveness and company growth performances in Italian companies. A structural equation modelling and a second order confirmatory factor analysis have been used to test the hypothesized relationships in the structural models. Secondly, including manufacturing firms belonging to 23 different countries, the factors influencing the success of LM and human, cultural, economic, strategic and supply chain integration barriers during the start-up and the sustainment phase of lean projects have been analysed. Thus, taking an organizational network view, this thesis provides a definition for Resilience and a model able to calculate the resilience of a lean organization taking into account both the organizational topology as well as the attitude and the learning curves of operators. Nodes have been modelled using the Hidden Markov Model and characterized by a learning curve. The resilience is calculated as the area included between the KPI trend during the time of the disruption event and the straight line parallel to the time axes. A case study has been analysed to explain the proposed model. Results of this thesis highlights that the operational responsiveness is only partially connected to a Lean strategy of a company and there is no direct relationship between lean bundles and firm’s performances. In addition the results revealed that some cultural aspects such as Performance Oriented and Gender Egalitarianism help to maintain a lean culture. Finally, results from this study will have a practical implication assisting human resource managers and production managers in predicting and evaluating global and local effects of the personnel relocation and possible reduction in performance of the workstations.