Browsing by Author "Dr. Jeff Joines, Committee Co-Chair"

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    Algorithm to Systematically Reduce Human Errors in Healthcare
    (2006-03-02) Seastrunk, Chad Stephen; Dr. Jeff Thompson, Committee Member; Dr. Jeff Joines, Committee Co-Chair; Dr. Timothy Clapp, Committee Chair
    The purpose of the research was to develop an algorithm to permanently reduce human errors in the healthcare industry. The algorithm will be able to be applied to all healthcare organizations and provide a preventative approach to errors. The research involved looking at past methods of error reduction/prevention. Certain methods proved to be useful in generating the algorithm like the Healthcare Failure Modes and Effects Analysis while others like Root Cause Analysis proved to only have limited success. The algorithm takes a three phase approach to reducing errors. Phase One identifies the potential error producing situations. Phase Two uses error proofing principles and known solution directions to generate solutions while Phase Three uses a new method developed called Solution Priority Number to rank and evaluate the solutions. Throughout the algorithm many worksheets have been developed to aid in a team's progression through the process. Two case studies were performed. The first case study followed a traditional team through the error prevention process while the second case used the algorithm. When comparing the two cases the team using the algorithm finished the process in shorter time, produced more effective failure modes, and generated a richer set of solutions to error proof the process.
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    Application of Data Mining Tools for Exploring Data: Yarn Quality Case Study
    (2008-11-24) Daley, Caitlin Marie; Dr. Timothy Clapp, Committee Chair; Dr. Jeff Joines, Committee Co-Chair; Dr. Jeff Thompson, Committee Member
    Businesses are constantly striving for a competitive edge in the economy, and data-driven decision making is crucial to achieve this goal. Four data mining tools, principal component analysis, cluster analysis, recursive partitioning, and discriminant analysis, were used to explore the major factors that contribute to ends down in a rotor spinning manufacturing process. Principal component analysis was used to explore the research question about whether the large number of cotton properties used to classify cotton could be reduced to a significant few. Cluster analysis was used to gain insight about whether there were groups of gins, counties, or classing offices that produced better raw material than others and led to less ends down. The important research question of what raw material properties were affecting ends down was explored with both recursive partitioning and discriminant analysis. Additional research investigated the effect of cotton variety and atmospheric conditions on spinning productivity. Each of the four data mining tools used was informative and offered a different perspective to the overall research question. Several significant factors emerged including humidity, temperature, %DP 555, and uniformity in addition to micronaire and the color properties (+b and Rd). With these results the researcher developed an improvement plan for better control and increased spinning productivity in future operations. A designed experiment is necessary to thoroughly investigate the impact of certain factors beyond the exploratory conclusions obtained from this study.