Optimal staffing recommendation for inbound operations
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2017.
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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/1721.1/111938 |
| _version_ | 1826193503554633728 |
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| author | Li-Carrillo, Carla (Li-Carrillo Paredes) |
| author2 | Stanley Gershwin and Stephen Graves. |
| author_facet | Stanley Gershwin and Stephen Graves. Li-Carrillo, Carla (Li-Carrillo Paredes) |
| author_sort | Li-Carrillo, Carla (Li-Carrillo Paredes) |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2017. |
| first_indexed | 2024-09-23T09:40:12Z |
| format | Thesis |
| id | mit-1721.1/111938 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T09:40:12Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1119382022-01-28T15:41:15Z Optimal staffing recommendation for inbound operations Li-Carrillo, Carla (Li-Carrillo Paredes) Stanley Gershwin and Stephen Graves. Leaders for Global Operations Program. Leaders for Global Operations Program at MIT Massachusetts Institute of Technology. Department of Mechanical Engineering Sloan School of Management Mechanical Engineering. Sloan School of Management. Leaders for Global Operations Program. Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2017. Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (page 72). Amazon inbound operations are staffed following a 'staffing-to-charge' model in which labor is planned to match the incoming volume capacity required by the weekly Sales Operations Planning (S&OP) forecast. Staffing-to-charge is a lean model of staffing that attempts to maximize labor utilization by minimizing the possibility of a labor surplus or deficit. However, due to inaccuracies in the S&OP freight forecast, poor visibility into incoming inventory, and last minute staffing changes, it is often the case that labor capacity is not adequately aligned with the actual unit receipts. This leads to additional labor costs and network inefficiencies. This project explored the current staffing policies and current system constraints such as forecast accuracy, backlog management, and hiring schedules to understand the scope of the problem. From these findings, an alternate method for staffing, known as 'Level loading,' was proposed. Level loading consists of staffing to a known and consistent headcount every day of the week with the intent to reduce staffing costs and labor capacity variability. Level loading was found to improve the efficiency of inbound operations, leading to considerable costs savings for the distribution center. The project also created an optimization model that allows Fulfillment Center managers to plan the transition from their current shifts to level loading; Amazon's Production Planning Team will implement this model by mid-2017. To fully achieve the benefits from level loading, the system requires a change in the planning of incoming freight. In particular, the incoming freight should be scheduled and planned according to a known labor capacity, as set by the level loading policy. This change to freight planning is currently being investigated. The study found that delayed restocking of the network is a costly inefficiency, similar in magnitude to the cost from excess labor capacity. To mitigate this, a labor plan that allows for greater capacity is necessary. The cost savings of more effective inbound operations offsets the additional labor costs of such a plan. The findings of this study are based on an Amazon warehouse, but a staffing model with greater labor capacity can be applied to inbound operations at any distribution center. by Carla Li-Carrillo. S.M. M.B.A. 2017-10-18T15:10:50Z 2017-10-18T15:10:50Z 2017 2017 Thesis http://hdl.handle.net/1721.1/111938 1005922900 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 72 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Mechanical Engineering. Sloan School of Management. Leaders for Global Operations Program. Li-Carrillo, Carla (Li-Carrillo Paredes) Optimal staffing recommendation for inbound operations |
| title | Optimal staffing recommendation for inbound operations |
| title_full | Optimal staffing recommendation for inbound operations |
| title_fullStr | Optimal staffing recommendation for inbound operations |
| title_full_unstemmed | Optimal staffing recommendation for inbound operations |
| title_short | Optimal staffing recommendation for inbound operations |
| title_sort | optimal staffing recommendation for inbound operations |
| topic | Mechanical Engineering. Sloan School of Management. Leaders for Global Operations Program. |
| url | http://hdl.handle.net/1721.1/111938 |
| work_keys_str_mv | AT licarrillocarlalicarrilloparedes optimalstaffingrecommendationforinboundoperations |