Leveraging Data Analytics to Evaluate Proactive Interventions to Prevent Inventory Defects
At an automated fulfillment center typically used in the retail industry, products fallen from a robot-driven shelving pod could cause inventory quality issues and obstructions on the floor, reducing throughput. Leading indicators of fallen products are limited, resulting in a lack of targeted and p...
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| Other Authors: | |
| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/139400 |
| _version_ | 1811071511122935808 |
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| author | Wu, Jieyuan |
| author2 | Zheng, Yanchong |
| author_facet | Zheng, Yanchong Wu, Jieyuan |
| author_sort | Wu, Jieyuan |
| collection | MIT |
| description | At an automated fulfillment center typically used in the retail industry, products fallen from a robot-driven shelving pod could cause inventory quality issues and obstructions on the floor, reducing throughput. Leading indicators of fallen products are limited, resulting in a lack of targeted and proactive actions. This project aims to evaluate potential interventions to reduce fallen products based on computer vision signals, accounting for the cost, complexity, and effectiveness of the interventions.
This project developed a framework to perform cost-benefit analyses for the potential interventions that could prevent inventory defects. Characteristics of multiple potential proactive interventions combined with multiple potential vision-based predictive signals form a complex solution space. We start by formulating a common basis of comparison for the options, focusing on how to measure, validate and quantify the effectiveness of the interventions. Experimental data will be derived from a hypothetical pilot that can be used to test hypotheses and evaluate intervention cost and benefit in the context of input signal characteristics and operational complexity. Quantifying the trade-offs and break-even points between use cases ultimately determines the project NPV or ROI hence helping to guide the optimal decision making.
This thesis provides insights into how to leverage analytical tools to evaluate options through the case of preventing inventory defects. This framework could be generalized and applied to any system, be it in logistics or manufacturing, where there are potentially multiple predictive signals and multiple proactive interventions to improve operations. |
| first_indexed | 2024-09-23T08:52:20Z |
| format | Thesis |
| id | mit-1721.1/139400 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T08:52:20Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1394002022-01-15T04:04:08Z Leveraging Data Analytics to Evaluate Proactive Interventions to Prevent Inventory Defects Wu, Jieyuan Zheng, Yanchong Youcef-Toumi, Kamal Massachusetts Institute of Technology. Department of Mechanical Engineering Sloan School of Management At an automated fulfillment center typically used in the retail industry, products fallen from a robot-driven shelving pod could cause inventory quality issues and obstructions on the floor, reducing throughput. Leading indicators of fallen products are limited, resulting in a lack of targeted and proactive actions. This project aims to evaluate potential interventions to reduce fallen products based on computer vision signals, accounting for the cost, complexity, and effectiveness of the interventions. This project developed a framework to perform cost-benefit analyses for the potential interventions that could prevent inventory defects. Characteristics of multiple potential proactive interventions combined with multiple potential vision-based predictive signals form a complex solution space. We start by formulating a common basis of comparison for the options, focusing on how to measure, validate and quantify the effectiveness of the interventions. Experimental data will be derived from a hypothetical pilot that can be used to test hypotheses and evaluate intervention cost and benefit in the context of input signal characteristics and operational complexity. Quantifying the trade-offs and break-even points between use cases ultimately determines the project NPV or ROI hence helping to guide the optimal decision making. This thesis provides insights into how to leverage analytical tools to evaluate options through the case of preventing inventory defects. This framework could be generalized and applied to any system, be it in logistics or manufacturing, where there are potentially multiple predictive signals and multiple proactive interventions to improve operations. M.B.A. S.M. 2022-01-14T15:09:13Z 2022-01-14T15:09:13Z 2021-06 2021-06-10T19:13:44.196Z Thesis https://hdl.handle.net/1721.1/139400 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Wu, Jieyuan Leveraging Data Analytics to Evaluate Proactive Interventions to Prevent Inventory Defects |
| title | Leveraging Data Analytics to Evaluate Proactive Interventions to Prevent Inventory Defects |
| title_full | Leveraging Data Analytics to Evaluate Proactive Interventions to Prevent Inventory Defects |
| title_fullStr | Leveraging Data Analytics to Evaluate Proactive Interventions to Prevent Inventory Defects |
| title_full_unstemmed | Leveraging Data Analytics to Evaluate Proactive Interventions to Prevent Inventory Defects |
| title_short | Leveraging Data Analytics to Evaluate Proactive Interventions to Prevent Inventory Defects |
| title_sort | leveraging data analytics to evaluate proactive interventions to prevent inventory defects |
| url | https://hdl.handle.net/1721.1/139400 |
| work_keys_str_mv | AT wujieyuan leveragingdataanalyticstoevaluateproactiveinterventionstopreventinventorydefects |