A computer simulation experiment was conducted to evaluate and compare five individual forecasting models across nine different demand patterns. The models were based on the Medical Materiel Management System used by the US Air Force hospitals. Results indicated the best model varied depending on the demand pattern, the safety stock level, the noise level of the demand pattern, and the measure of forecast error. Across all demand patterns, exponential smoothing and 12-month moving average were best for the short term forecast used by the system, regardless of noise level in the demand patterns. Analysis of models within a single demand pattern showed, in most cases, several models as ranking equally well. When overall system requirements were considered, the exponential smoothing method was by far the best choice.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:rtd-1264 |
| Date | 01 January 1976 |
| Creators | Van Ess, Phillip John |
| Publisher | Florida Technological University |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Retrospective Theses and Dissertations |
| Rights | Public Domain |
Page generated in 0.0021 seconds