The need for small and medium size manufacturing enterprises (SMEs) to have access to unbiased advice on best practices and related improvement approaches has been well established. However, this need was not found addressed very effectively in the research literature. Current practice is consultants peddling assessment tools which have the veneer of objectivity, but in reality only highlight the need to purchase their canned solutions. In response, this research attempts to synthesize previous research results and other published assessment methodologies into a taxonomy based assessment methodology (TBAM) which targets the delivery of focused recommendations which target improved performance of the manufacturing enterprise. The assessment methodology which emerges from this research, draws upon two different taxonomies, termed the Manufacturing Enterprise Taxonomy (MET) and the Production System Taxonomy (PST). The MET was developed as one of the deliverables of this research and the PST was developed by a modest modification of previously published research. The TBAM approach was piloted using three different SMEs in order to obtain feedback from the field. As a result TBAM was enhanced using feedback obtained from these three pilot cases. In addition, a review panel process was developed so that a third party review was made of the methodology and its application within the case studies. The review panel was comprised of senior managers which have substantial experience in leading improvements across small and medium size manufacturers. Also, concerns about reliability and validity were addressed and a preliminary set of measures was obtained and evaluated. Based upon this preliminary technique, the validity and reliability results associated with the TBAM approach appear promising.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-1323 |
| Date | 15 December 2007 |
| Creators | Walden, Clayton Thomas |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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