An application of problem solving methodologies for small, low-technology firms
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This thesis focuses on problem solving methodologies in small, low technology firms using statistical thinking. It examines the different elements of statistical thinking and how owners and managers of small businesses can assess their performance using profit margin as a metric. The literature points to a lack of key parts of knowledge or experience on the part of the owner required to grow a business. On many levels, this is compounded by the attitudes and actions of the owner or manager. However, this research shows that with a tacit understanding of how all work is essentially a series of interconnected processes with variation within each process, one can: (1) categorically measure that variation, (2) identify areas of deficient performance, and (3) aim to improve those areas. The study uses a Split-Plot/Repeated Measures (SP/RM) design on contracted jobs of an East Coast fabrication and installation firm during the 2002 fiscal year. Data were collected on job type (fabrication/installation) and job scope (sheet metal/other). Every contract is estimated with labor and material estimates; therefore, each job submits two profit margins for evaluation: a labor profit margin and a material profit margin. Using the twenty jobs of 2002, only job type was found to be statistically significant. Statistical thinking is incorporated into this study by walking the reader through graphical analyses of the data and identifying possible sources and causes of variation. Each chapter has a section dedicated to the use or application of statistical thinking and how it is used in this study.