Date of Conferral
2019
Degree
Doctor of Information Technology (D.I.T.)
School
Information Systems and Technology
Advisor
Steven Case
Abstract
There is a significant technology deficiency within the U.S. manufacturing industry compared to other countries. To adequately compete in the global market, lean manufacturing organizations in the United States need to look beyond their traditional methods of evaluating their processes to optimize their assembly cells for efficiency. Utilizing the task-technology fit theory this quantitative correlational study examined the relationships among software using probabilistic algorithms, lean methodology techniques, and manufacturer cell optimization results. Participants consisted of individuals performing the role of the systems analyst within a manufacturing organization using lean methodologies in the Southwestern United States. Data were collected from 118 responses from systems analysts through a survey instrument, which was an integration of two instruments with proven reliability. Multiple regression analysis revealed significant positive relationships among software using probabilistic algorithms, lean methodology, and cell optimization results. These findings may provide management with information regarding the skillsets required for systems analysts to implement software using probabilistic algorithms and lean manufacturing techniques to improve cell optimization results. The findings of this study may contribute to society through the potential to bring sustainable economic improvement to impoverished communities through the implementation of efficient manufacturing solutions with lower capital expenditures.
Recommended Citation
McCurrey, Michael, "Probabilistic Algorithms, Lean Methodology Techniques, and Cell Optimization Results" (2019). Walden Dissertations and Doctoral Studies. 7939.
https://scholarworks.waldenu.edu/dissertations/7939
