Enhancing Manufacturing Performance to Plan with Predictive Analytics
Modern manufacturing requires meticulous planning to coordinate tightly wound supply chain activities in the face of disruption. This is especially true for automotive companies, which produce complex products at high rates. Their production planning process involves estimating the demand for variou...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/153332 |
| _version_ | 1826211657705062400 |
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| author | Weisberg, Joshua |
| author2 | Perakis, Georgia |
| author_facet | Perakis, Georgia Weisberg, Joshua |
| author_sort | Weisberg, Joshua |
| collection | MIT |
| description | Modern manufacturing requires meticulous planning to coordinate tightly wound supply chain activities in the face of disruption. This is especially true for automotive companies, which produce complex products at high rates. Their production planning process involves estimating the demand for various vehicles, determining the most profitable mix of products to meet that demand, and then selecting the production parameters which provide maximum efficiency. All this is done while balancing the the short term demands of a volatile market with the long term implications of capital equipment purchases, staffing changes, and supplier management. From the time of each decision to the day of production, demand may change, supply may be disrupted, and manufacturing performance may fall short of expectations. These uncertainties lead to high error in production plans, which propagates to suppliers, other areas of the business, and future periods. Changes harm stability, efficiency, and thus profitability for all stakeholders. This study shows how predictions of performance can be used to revise a plan, using predictive analytics models trained on the characteristics of the plan. To this end, 480+ features are developed to describe plan characteristics and recent manufacturing performance. Several algorithms are utilized to evaluate the relationship between these features and manufacturing performance to plan, measured by ratios of actual production rate to that planned, and hours actually worked to planned. Results of the best performing features, algorithms, and modeling architectures on out-of-sample manufacturing days in the Post-Covid Era showed Median Absolute Error improvements of 40%-60% over a 3-month lead time and 10%-40% over a 1-month lead time across several production lines. These reductions in error can improve stability such that better decisions can be made. Interpretation of the predictive models can lead to improvements in the factory’s ability to meet demand. Benefactors include customers looking to purchase and receive their desired products, employees needing more day-to-day consistency, and suppliers aiming to maintain a healthy business. The only certainty in operations is uncertainty, making it critical for operations companies to improve their understanding and estimation of their performance to plan. |
| first_indexed | 2024-09-23T15:09:28Z |
| format | Thesis |
| id | mit-1721.1/153332 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:09:28Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1533322024-01-17T03:11:53Z Enhancing Manufacturing Performance to Plan with Predictive Analytics Weisberg, Joshua Perakis, Georgia Amin, Saurabh Sloan School of Management Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Modern manufacturing requires meticulous planning to coordinate tightly wound supply chain activities in the face of disruption. This is especially true for automotive companies, which produce complex products at high rates. Their production planning process involves estimating the demand for various vehicles, determining the most profitable mix of products to meet that demand, and then selecting the production parameters which provide maximum efficiency. All this is done while balancing the the short term demands of a volatile market with the long term implications of capital equipment purchases, staffing changes, and supplier management. From the time of each decision to the day of production, demand may change, supply may be disrupted, and manufacturing performance may fall short of expectations. These uncertainties lead to high error in production plans, which propagates to suppliers, other areas of the business, and future periods. Changes harm stability, efficiency, and thus profitability for all stakeholders. This study shows how predictions of performance can be used to revise a plan, using predictive analytics models trained on the characteristics of the plan. To this end, 480+ features are developed to describe plan characteristics and recent manufacturing performance. Several algorithms are utilized to evaluate the relationship between these features and manufacturing performance to plan, measured by ratios of actual production rate to that planned, and hours actually worked to planned. Results of the best performing features, algorithms, and modeling architectures on out-of-sample manufacturing days in the Post-Covid Era showed Median Absolute Error improvements of 40%-60% over a 3-month lead time and 10%-40% over a 1-month lead time across several production lines. These reductions in error can improve stability such that better decisions can be made. Interpretation of the predictive models can lead to improvements in the factory’s ability to meet demand. Benefactors include customers looking to purchase and receive their desired products, employees needing more day-to-day consistency, and suppliers aiming to maintain a healthy business. The only certainty in operations is uncertainty, making it critical for operations companies to improve their understanding and estimation of their performance to plan. S.M. M.B.A. 2024-01-16T21:51:46Z 2024-01-16T21:51:46Z 2023-06 2023-12-11T21:01:45.533Z Thesis https://hdl.handle.net/1721.1/153332 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 | Weisberg, Joshua Enhancing Manufacturing Performance to Plan with Predictive Analytics |
| title | Enhancing Manufacturing Performance to Plan with Predictive Analytics |
| title_full | Enhancing Manufacturing Performance to Plan with Predictive Analytics |
| title_fullStr | Enhancing Manufacturing Performance to Plan with Predictive Analytics |
| title_full_unstemmed | Enhancing Manufacturing Performance to Plan with Predictive Analytics |
| title_short | Enhancing Manufacturing Performance to Plan with Predictive Analytics |
| title_sort | enhancing manufacturing performance to plan with predictive analytics |
| url | https://hdl.handle.net/1721.1/153332 |
| work_keys_str_mv | AT weisbergjoshua enhancingmanufacturingperformancetoplanwithpredictiveanalytics |