Data-Based Stakeholder Identification in Technical Change Management
The efficient and effective handling of technical changes in product and production is seen as an important factor for the long-term success of manufacturing companies. Within the associated processes, the engineering and manufacturing change management, the identification and involvement of all rel...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/16/8205 |
| _version_ | 1797432476536143872 |
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| author | Fabian Sippl Renè Magg Carla Paulina Gil Steffen Düring Gunther Reinhart |
| author_facet | Fabian Sippl Renè Magg Carla Paulina Gil Steffen Düring Gunther Reinhart |
| author_sort | Fabian Sippl |
| collection | DOAJ |
| description | The efficient and effective handling of technical changes in product and production is seen as an important factor for the long-term success of manufacturing companies. Within the associated processes, the engineering and manufacturing change management, the identification and involvement of all relevant stakeholders, i.e., departments and employees, plays an essential role. Overlooking relevant stakeholders can lead to unforeseen impacts, such as production stops or further necessary changes, and can cause unforseen increased costs. In particular, in large companies, this task is complex and error-prone due to the high number of changes and departments involved, as well as the abundant variety of changes that can take place. Therefore, this contribution introduces an approach for stakeholder identification in technical change management, which allows the automated identification of relevant stakeholders at the beginning of the reactive phases of the change management process. The approach describes all necessary steps from data preparation to the evaluation of the obtained classification models. It is based on a text-classification approach and focuses in particular on the additional integration of expert knowledge to increase model quality. The approach has been successfully applied in cooperation with a German automotive company, and the obtained model quality has been compared to an expert-based classification. |
| first_indexed | 2024-03-09T10:02:10Z |
| format | Article |
| id | doaj.art-165c17410ce54767920f3b542735e24c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T10:02:10Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-165c17410ce54767920f3b542735e24c2023-12-01T23:21:46ZengMDPI AGApplied Sciences2076-34172022-08-011216820510.3390/app12168205Data-Based Stakeholder Identification in Technical Change ManagementFabian Sippl0Renè Magg1Carla Paulina Gil2Steffen Düring3Gunther Reinhart4Institute for Machine Tools and Industrial Management, Boltzmannstraße 15, 85747 Garching, GermanyInstitute for Machine Tools and Industrial Management, Boltzmannstraße 15, 85747 Garching, GermanyBMW Group, Knorrstraße 147, 80788 München, GermanyBMW Group, Knorrstraße 147, 80788 München, GermanyInstitute for Machine Tools and Industrial Management, Boltzmannstraße 15, 85747 Garching, GermanyThe efficient and effective handling of technical changes in product and production is seen as an important factor for the long-term success of manufacturing companies. Within the associated processes, the engineering and manufacturing change management, the identification and involvement of all relevant stakeholders, i.e., departments and employees, plays an essential role. Overlooking relevant stakeholders can lead to unforeseen impacts, such as production stops or further necessary changes, and can cause unforseen increased costs. In particular, in large companies, this task is complex and error-prone due to the high number of changes and departments involved, as well as the abundant variety of changes that can take place. Therefore, this contribution introduces an approach for stakeholder identification in technical change management, which allows the automated identification of relevant stakeholders at the beginning of the reactive phases of the change management process. The approach describes all necessary steps from data preparation to the evaluation of the obtained classification models. It is based on a text-classification approach and focuses in particular on the additional integration of expert knowledge to increase model quality. The approach has been successfully applied in cooperation with a German automotive company, and the obtained model quality has been compared to an expert-based classification.https://www.mdpi.com/2076-3417/12/16/8205stakeholder identificationengineeringmanufacturingchange managementtext classification |
| spellingShingle | Fabian Sippl Renè Magg Carla Paulina Gil Steffen Düring Gunther Reinhart Data-Based Stakeholder Identification in Technical Change Management Applied Sciences stakeholder identification engineering manufacturing change management text classification |
| title | Data-Based Stakeholder Identification in Technical Change Management |
| title_full | Data-Based Stakeholder Identification in Technical Change Management |
| title_fullStr | Data-Based Stakeholder Identification in Technical Change Management |
| title_full_unstemmed | Data-Based Stakeholder Identification in Technical Change Management |
| title_short | Data-Based Stakeholder Identification in Technical Change Management |
| title_sort | data based stakeholder identification in technical change management |
| topic | stakeholder identification engineering manufacturing change management text classification |
| url | https://www.mdpi.com/2076-3417/12/16/8205 |
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