The impact of installed base and machine failure prediction on spare parts forecasting and inventory planning
Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2016.
| Main Authors: | , |
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2017
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| Online Access: | http://hdl.handle.net/1721.1/107524 |
| _version_ | 1826198106026278912 |
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| author | Brocks, Michael Patrick Trujillo Castañeda, Renzo Eliseo |
| author2 | Daniel W. Steeneck. |
| author_facet | Daniel W. Steeneck. Brocks, Michael Patrick Trujillo Castañeda, Renzo Eliseo |
| author_sort | Brocks, Michael Patrick |
| collection | MIT |
| description | Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2016. |
| first_indexed | 2024-09-23T10:58:56Z |
| format | Thesis |
| id | mit-1721.1/107524 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T10:58:56Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1075242019-04-12T13:50:17Z The impact of installed base and machine failure prediction on spare parts forecasting and inventory planning Brocks, Michael Patrick Trujillo Castañeda, Renzo Eliseo Daniel W. Steeneck. Massachusetts Institute of Technology. Engineering Systems Division. Massachusetts Institute of Technology. Supply Chain Management Program. Massachusetts Institute of Technology. Engineering Systems Division. Supply Chain Management Program. Engineering Systems Division. Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2016. Cataloged from PDF version of thesis. Includes bibliographical references (pages 66-70). Recent advances in technological capability and economics have opened up a new world of capability known as the Internet of Things (IoT). The Internet of Things is the concept that all machines can be connected to the internet, and be remotely monitored through an infrastructure of interconnected software and hardware. Many companies are just beginning to explore the economic value that the Internet of Things can unlock, with much of the initial focus on remote diagnostics and predictive maintenance, particularly in application to industrial machines. This research tests various scenarios of predictive failure accuracy, creating spare parts forecasts based off of varying predictive forecast parameters. We compare these scenarios and their respective outputs to a regular time-series forecasting scenario, inserting each type of forecast into a periodic review (R, S) inventory system. We measure the output of each forecast put into the system in terms of spare parts inventory levels and in-stock service performance. We find that as long as the true positive rate (TPR) and false positive rate (FPR) have different values, our model is able to hold a lower average inventory while providing a higher level of service. Additionally, as the difference between the two values increases, the average amount of inventory held decreases, while the level of service provided increases. A more detailed summary of the results found and the implications on service supply chain were developed, and further areas of research are discussed. by Michael Patrick Brocks and Renzo Eliseo Trujillo Castaneda. M. Eng. in Logistics 2017-03-20T19:38:20Z 2017-03-20T19:38:20Z 2016 2016 Thesis http://hdl.handle.net/1721.1/107524 962919174 eng 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 70 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Supply Chain Management Program. Engineering Systems Division. Brocks, Michael Patrick Trujillo Castañeda, Renzo Eliseo The impact of installed base and machine failure prediction on spare parts forecasting and inventory planning |
| title | The impact of installed base and machine failure prediction on spare parts forecasting and inventory planning |
| title_full | The impact of installed base and machine failure prediction on spare parts forecasting and inventory planning |
| title_fullStr | The impact of installed base and machine failure prediction on spare parts forecasting and inventory planning |
| title_full_unstemmed | The impact of installed base and machine failure prediction on spare parts forecasting and inventory planning |
| title_short | The impact of installed base and machine failure prediction on spare parts forecasting and inventory planning |
| title_sort | impact of installed base and machine failure prediction on spare parts forecasting and inventory planning |
| topic | Supply Chain Management Program. Engineering Systems Division. |
| url | http://hdl.handle.net/1721.1/107524 |
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