Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, First author, 2017. / Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, Second author, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 60-61). / Returnable Transport Items (RTI) are a critical component of domestic and international trade. The large variability in the geographic supply and demand of goods shipped using RTI impacts the items overall availability at different locations within a network. This research focuses on improving our partner firm's RTI inventory supply in the United States and Canada by developing a one-month-ahead forecasting model to predict the net monthly international flows. To develop the model, six years of historical time series data was decomposed into key elements: level, trend, and seasonality. The results of the decomposition method were used to narrow the forecasting models considered to state space seasonal exponential, SARIMA, state space Holt-Winters, and multivariate regression methods. These four methods were then used to predict the pallet flows using two different approaches. In the first approach, two separate forecasting models were developed, one for the United States-to-Canada flows and the other for the Canada-to-United States flows. The derived Canada-to-United States value was then subtracted from the corresponding United States-to-Canada forecast to calculate the predicted net international movement. In the second approach, we forecasted the net pallet flows between the two countries utilizing only historical values of net international movements. Ultimately, 36 unique models were created using both approaches. The naive forecasting method served as a performance benchmark to the developed models. The performances of the 36 models were then compared using multiplicative and mean composite scores, both of which were based on three accuracy metrics: MAPE, MASE and MAD. Our research found that out of the 36 forecasting models, only seven models outperformed the baseline naive forecasting method. These seven forecasting models were further filtered by qualitative metrics such as ease of implementation and software platform dependence. The state space seasonal exponential model was ultimately recommended due to its superior performances on both the quantitative and qualitative metrics. / by Patrick A. Jacobs and Rajdeep Singh Walia. / M. Eng. in Supply Chain Management / M. Eng. in Logistics
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/112866 |
| Date | January 2017 |
| Creators | Jacobs, Patrick A. (Patrick Allen), Walia, Rajdeep Singh |
| Contributors | Eva Ponce-Cueto., Massachusetts Institute of Technology. Supply Chain Management Program., Massachusetts Institute of Technology. Supply Chain Management Program. |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 61 pages, application/pdf |
| Rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission., http://dspace.mit.edu/handle/1721.1/7582 |
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