The Assessment of Car Making Plants with an Integrated Stochastic Frontier Analysis Model
As global competition has intensified in the automotive industry, there is a strong need for management teams to develop methods that allow accurate and objective assessments of plant productivity and to identify productivity improvement opportunities for the best manufacturing practices. Stochastic...
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| Format: | Article |
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MDPI AG
2021-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/11/1296 |
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| author | Seog-Chan Oh Jaemin Shin |
| author_facet | Seog-Chan Oh Jaemin Shin |
| author_sort | Seog-Chan Oh |
| collection | DOAJ |
| description | As global competition has intensified in the automotive industry, there is a strong need for management teams to develop methods that allow accurate and objective assessments of plant productivity and to identify productivity improvement opportunities for the best manufacturing practices. Stochastic frontier analysis (SFA) models have been used as a statistical benchmarking tool to provide a bird’s-eye view of an industrial sector. SFA models can also be adapted for plant productivity assessment. However, owing to the problem of multicollinearity, the general form of SFA is difficult to apply to the assessment of complex manufacturing systems in the automotive industry, which is characterized by many control and external factors that are intercorrelated to each other. This study proposes a method for applying SFA to vehicle manufacturing plants with a focus on gaining high accuracy in model parameter estimation, by decomposing a plant into components (i.e., shops), building an SFA model for each shop, and reintegrating the general plant system through the appropriate combination of shop-level inefficiency distributions. In particular, this study focuses on documenting the derivation of a new probability density function that integrates three different inefficiency distributions. For illustration of the proposed approach, hypothetical vehicle assembly plants are assessed as examples, where the total labor hours are split into Bodyshop, Paintshop, and General Assembly, exclusively and collectively. Finally, this study offers a solution process to clarify the reasons for underperforming plants in terms of labor productivity and identify the course of actions to cure the issues with some managerial insights emphasizing the balanced approach, incorporating people, process and technology. |
| first_indexed | 2024-03-10T10:42:12Z |
| format | Article |
| id | doaj.art-9fb985521e204a298200c0f497983874 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T10:42:12Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj.art-9fb985521e204a298200c0f4979838742023-11-21T22:54:22ZengMDPI AGMathematics2227-73902021-06-01911129610.3390/math9111296The Assessment of Car Making Plants with an Integrated Stochastic Frontier Analysis ModelSeog-Chan Oh0Jaemin Shin1Manufacturing Systems Research Laboratory, General Motors R&D, Warren, MI 48090, USADepartment of Mathematical Sciences, Hanbat National University, Daejeon 34158, KoreaAs global competition has intensified in the automotive industry, there is a strong need for management teams to develop methods that allow accurate and objective assessments of plant productivity and to identify productivity improvement opportunities for the best manufacturing practices. Stochastic frontier analysis (SFA) models have been used as a statistical benchmarking tool to provide a bird’s-eye view of an industrial sector. SFA models can also be adapted for plant productivity assessment. However, owing to the problem of multicollinearity, the general form of SFA is difficult to apply to the assessment of complex manufacturing systems in the automotive industry, which is characterized by many control and external factors that are intercorrelated to each other. This study proposes a method for applying SFA to vehicle manufacturing plants with a focus on gaining high accuracy in model parameter estimation, by decomposing a plant into components (i.e., shops), building an SFA model for each shop, and reintegrating the general plant system through the appropriate combination of shop-level inefficiency distributions. In particular, this study focuses on documenting the derivation of a new probability density function that integrates three different inefficiency distributions. For illustration of the proposed approach, hypothetical vehicle assembly plants are assessed as examples, where the total labor hours are split into Bodyshop, Paintshop, and General Assembly, exclusively and collectively. Finally, this study offers a solution process to clarify the reasons for underperforming plants in terms of labor productivity and identify the course of actions to cure the issues with some managerial insights emphasizing the balanced approach, incorporating people, process and technology.https://www.mdpi.com/2227-7390/9/11/1296productivity assessmentbenchmarkingstochastic frontier analysis |
| spellingShingle | Seog-Chan Oh Jaemin Shin The Assessment of Car Making Plants with an Integrated Stochastic Frontier Analysis Model Mathematics productivity assessment benchmarking stochastic frontier analysis |
| title | The Assessment of Car Making Plants with an Integrated Stochastic Frontier Analysis Model |
| title_full | The Assessment of Car Making Plants with an Integrated Stochastic Frontier Analysis Model |
| title_fullStr | The Assessment of Car Making Plants with an Integrated Stochastic Frontier Analysis Model |
| title_full_unstemmed | The Assessment of Car Making Plants with an Integrated Stochastic Frontier Analysis Model |
| title_short | The Assessment of Car Making Plants with an Integrated Stochastic Frontier Analysis Model |
| title_sort | assessment of car making plants with an integrated stochastic frontier analysis model |
| topic | productivity assessment benchmarking stochastic frontier analysis |
| url | https://www.mdpi.com/2227-7390/9/11/1296 |
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