Citation: Sippl, F.; Magg, R.; Gil, C.P.;
Duering, S.; Reinhart, G. Data-Based
Stakeholder Identification in
Technical Change Management. Appl.
Sci. 2022, 12, 8205. https://doi.org/
10.3390/app12168205
Academic Editors: Wenjun (Chris)
Zhang, Dhanjoo N. Ghista, Kelvin
K.L. Wong and Andrew W.H. Ip
Received: 13 June 2022
Accepted: 3 August 2022
Published: 17 August 2022
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Article
Data-Based Stakeholder Identification in Technical
Change Management
Fabian Sippl
1,
*, Renè Magg
1
, Carla Paulina Gil
2
, Steffen Düring
2
and Gunther Reinhart
1
1
Institute for Machine Tools and Industrial Management, Boltzmannstraße 15, 85747 Garching, Germany
2
BMW Group, Knorrstraße 147, 80788 München, Germany
* Correspondence: fabian.sippl@iwb.tum.de
Abstract:
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 com-
panies, 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.
Keywords:
stakeholder identification; engineering; manufacturing; change management; text
classification
1. Introduction
Manufacturing companies operate in an environment that is often described as in-
creasingly volatile, uncertain, complex, and ambiguous [
1
]. In recent years, this VUCA
world has led to intensive discussions of various paradigms to tackle this complexity in
manufacturing, such as flexibility, agility, reconfigurability, and the introduction of concrete
approaches such as continuous factory planning or change management. These approaches
are intended to enable an efficient and effective handling of the high and permanent
need for changes in the product (engineering change) and production (manufacturing
change), which are caused, e.g., by rapidly changing customer requirements and legislation
or shortened technology life-cycles [
2
]. Technical change management that is efficient
and effective is seen as a critical key factor for the long-term success of manufacturing
companies [
3
]. The associated network of activities refers “to organizing and controlling
the process of making alterations to a factory or a product”. This includes “the totality
of measures to avoid and specifically front-load as well as to efficiently plan, select, pro-
cess, and control” manufacturing changes (MC) as well as engineering changes (EC) [
4
–
6
].
The handling of EC is therefore referred to as engineering change management (ECM)
and the handling of MC as manufacturing change management (MCM). Both domains
are summarized in the remainder of this paper as technical change management (TCM).
Within these processes, numerous activities have to be completed before a change can be
finally implemented, such as change description, solution development, change impact
Appl. Sci. 2022, 12, 8205. https://doi.org/10.3390/app12168205 https://www.mdpi.com/journal/applsci
