| Popis: |
"Operational costs are of central importance for the economic health and sustainability of any organization. There are many contributors to these costs; some are industry specific, some are not. Factors such as organizational makeup and structure or general management and/or leadership practices all play a part, albeit, challenging to measure in terms of direct dollar correlation. Others, such as payroll, capital purchases, and asset management costs, to name a few have a more direct operational cost clearly linked to dollars and become the most practical place to look when trying to minimize operational costs. One of the critical aforementioned contributors to operational costs is that of capital asset management; in particular the issue of maintenance and repair of a company’s capital assets. More specifically, one can try to determine the best maintenance practice and schedule to use on varying systems in hopes of lowering maintenance costs and ultimately operational costs. One approach is to formulate a maintenance cost equation given the specific data and constraints available to solve a probabilistic problem through simulation. This study proposes a methodology that could be used as a tool to determine what maintenance practices to use on varying systems, sub-systems, and components. The focal point of this methodology is to formulate viable simulation logic. The logic takes into account maintenance costs that must be identified and defined. Additionally, coupled with this is the need to create a Weibull Distribution, which helps predict the next failure based on historical data. By matching the maintenance cost with the Weibull Distribution of each system, sub-system or component the simulation logic or equation is created. With this model in hand, simulations are run using Monte Carlo Simulation. In the end, an optimal schedule is determined based on the input. This thesis has three main deliverables. First, a maintenance methodology which assists in determining optimal component change out schedule based on historical data is created. Secondly, from the information gathered from Industrial Support Command Alameda*, ideal systems to target are identified. Thirdly, an implementation strategy is offered. Lastly, though not a primary deliverable, this study also offers some other maintenance related miscellaneous findings and/or recommendations. " |
