Evaluation of the impact of Lean Manufacturing implementation on operational performance in Zimbabwean industries

Abstract

The rapid rate of increase in competition among the manufacturing industries has caused many organizations to continuously seek improvement in the quality of the products they manufacture to meet and exceed customer expectations. Organizations are under pressure to minimize the production costs to offer competitive prices for their products. The success story of Toyota Motor Company in implementing Lean Manufacturing (LM) has inspired many organizations around the world to adopt LM in order to improve their operational performance. There are, however, mixed results on the impact of LM on operational performance. Some studies have shown that its implementation increases operational performance while others have shown little to no improvement or even negative results. Institutional and contingency theories may provide insight into some of these contradictions and give a perception of why the implementation of LM has yielded different results on operational performance. The institutional theory states that organizations mimic the actions and practices of other organizations because of the pressure to remain competitive. Organizations in the developing countries also seem to have been imitating the Toyota Motor company that has been successful in implementing LM. On the other hand, the contingency theory states that corporations are organized according to external situations. Related to the contingency theory is the effect of Industry Clockspeed (IC). Some industries are transforming at a high speed while others are transforming at a low speed. The high IC industries are characterized by the quick development and release of new products, shorter development time and frequent changes in organizational structures. Low IC industries, however, manufacture products with a long life cycle, thus the products, processes and organizational structures for these industries change only after a long period. This study opines that the environment under which an organisation operates may affect the results of LM implementation process. The research was conducted in three parts and each of these parts is presented as chapters in this thesis. The first part (Chapter 4) gives a review and classifies the impact measurement models that have been used by various researchers to measure the success of implementing LM. These models can be classified as quantitative, qualitative, simulation-based and graphical measurement models. Pareto analysis is used to select the type of measurement model and Lean practices that are frequently used by researchers to develop Lean measurement models. The qualitative measurement model was preferred for evaluating the effect of implementing LM on operational performance because of its ability to use question structures that allow qualitative data collection for a rich analysis of opinion. With a proper structure, the questionnaire items can also be parsed and analyzed quantitatively with modern statistical techniques like Structural Equation Modelling. The Lean practices selected were Just In Time (JIT), Jidoka, People integration and Stability and standardization for building the model. This part concludes by developing a structural model that can be used to measure the impact of Lean implementation in industry, using Zimbabwean industry data. The second part (Chapter 5) evaluates the effect of implementing LM tools on operational performance across various industries in Zimbabwe. The major goal of this chapter was to develop an operational model (based on the lead from chapter 4) and test it in manufacturing organizations across various industries. A structured survey questionnaire was used for the collection of data in identified companies and 214 useful responses were obtained. The results of the study indicated that operational performance was improved by implementing the selected LM tools. The performance improvement variables that were significantly influenced were speed, flexibility and dependability. The third part (Chapter 6) analyzed the moderation effect of IC on the relationship between LM tools and operational performance. The industries grouped under low IC were pharmaceutical, agrochemicals, steel, automobile, timber production, battery, chemical and plastics. The high IC industries were food, beverage, electronics and garment. A structural equation model was proposed and investigated across the two groups. A structured survey questionnaire was used to collect empirical data from manufacturing companies. The data obtained from the responses was analysed using Smart PLS 3 and SPSS version 25. The results of the study showed that IC had a moderating effect on the relationship between LM practices and operational performance for both low and high IC industries. The last chapter summarises the findings, made recommendations and proposes directions for further research.