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Please use this identifier to cite or link to this item: http://dspace.lib.cranfield.ac.uk/handle/1826/3521

Document Type: Thesis or dissertation
Title: Development of an impact assessment framework for lean manufacturing within SMEs
Authors: Achanga, Pius Coxwell
Supervisors: Shehab, Essam
Roy, Rajkumar
Issue Date: Oct-2007
Abstract: The main aim of the research work presented in this thesis, is the development of a novel framework with the capability of assessing the impact of implementing lean manufacturing within small-to-medium sized manufacturing firms (SMEs). By assessing the impact of lean implementation, SMEs can make informed decisions on the viability of lean adoption at the conceptual implementation stage. Companies are also able determine their status in terms of lean manufacturing affordability. Thus, in order to achieve the above-stated aim, the following were the main set research objectives; (1) identifying the key drivers for implementing lean manufacturing within SMEs, (2) investigating the operational activities of SMEs in order to understand their manufacturing issues, (3) exploring the current level of lean manufacturing usage within SMEs so as to categorise users based on their levels of involvement, (4) identifying factors that determine the assessment of lean manufacturing, (5) developing an impact assessment framework for justifying lean manufacturing within SMEs, (6) developing a knowledge based advisory system and (7) validating the impact assessment framework and the developed knowledge based advisory system through real-life case studies, workshops, and expert opinions. A combination of research methodology approaches have been employed in this research study. This comprises literature review, observation of companies' practices and personal interview. The data collection process involved ten SMEs that provided consistent information throughout the research project life. Additionally, visitations to three large size manufacturing firms were also conducted. Hence, the framework and system development process passed through several stages. Firstly, the data were collected from companies who had successfully implemented lean manufacturing within their premise. The second development stage included the analysis and validation of the dataset through company practitioners. An impact assessment framework was thus developed with the aid of regression analysis as a predictive model. However, it was realised that there were few correlations between the dataset generated and analysis. The reasons for this were unclear. ,a knowledge based advisory system was adopted to conceptualise, enhance the robustness of the impact assessment framework and address the problem of the imprecise data in the impact assessment process. Three major factors of impact assessment were considered in the framework and the system development process, namely relative cost of lean implementation, a company lean readiness status and the level of value-added to be achieved (impact/benefits). Three knowledge based advisory sub-systems that consisted of the abovementioned factors were built. Results obtained from them were then fed into the final system. The three sub-systems were validated with the original set of data from companies. This enabled the assignment of a number of input variables whose membership functions aided the definition of the fuzzy expert system language (linguistic variables) used. The final system yielded heuristic rules that enable the postulation of scenarios of lean implementation. Results were sought and tested on a number of firms based within the UK, for the purposes validation. These also included expert opinions both in academic and industrial settings. A major contribution of the developed system is its ability to aid decision-making processes for lean implementation at the early implementation stage. The visualisation facility of the developed system is also useful in enabling potential lean users to make forecasts on the relative cost of lean projects upfront, anticipate lean benefits, and realise one' degree of lean readiness.
URI: http://hdl.handle.net/1826/3521
Appears in Collections:PhD, EngD and MSc by research theses (School of Applied Sciences)

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