Obsolescence is an unavoidable reality in manufacturing systems and supply chain environments as systems are needed to be sustained for longer and longer periods of time. These extended life cycle products include airplanes, ships, industrial equipment, medical equipment, and military systems. The United States military has coined this issue as Diminishing Manufacturing Sources and Material Shortages (DMSMS). Research shows that the main areas of concern for obsolescence are cost optimization, obsolescence management, system life cycle, design/system refresh planning, architecture/open systems, and end-of-life (EOL) predictions. This effort suggests a need for a more effective management approach to tackling obsolescence with an emphasis on proactive management. The goal of this research was to create an obsolescence management framework for the purpose of managing obsolescence issues with military based systems. This research shows the potential for using machine learning as a life cycle forecasting tool over traditional data mining tools. The results for this small-scale case study show promising results for a larger scale experiment. Another powerful proactive strategy using machine learning is building technology refresh cycles into a system based on obsolescence risk levels. Some key areas of focus for a strong framework are funding for a robust DMSMS team, a robust supply chain, system design that factors in obsolescence risk, and consistent communication with all parties involved. It is imperative to develop an effective and data-driven approach to communicating obsolescence impacts to leadership to ensure successful mitigation of obsolescence issues. Some post-case tools and strategies include utilizing sustainment, production, and technology refresh roadmaps, along with employing data driven metrics to provide key information to leadership and demonstrate value to the customer. This study demonstrates opportunities and challenges for entities dealing with component obsolescence, methods for minimizing the issues that go along with it, and identifies best practices for obsolescence management.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1755 |
| Date | 01 January 2021 |
| Creators | Rust, Ryan |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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