# Data-Based Stakeholder Identification in Technical Change Management
## 1 Introduction  
Manufacturing companies operate in an environment that is often described as increasingly 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, process, and control\" manufacturing changes (MC) as well as engineering changes (EC) [4; 5; 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 impactanalysis, or the approval of the change. An important prerequisite for the successful execution of these activities is the identification of all relevant stakeholders in order to obtain all necessary assessments of the planned changes and to take the gained knowledge into consideration in the planning process [7; 8]. In the case of this contribution, we consider stakeholders at the level of the business units and do not focus on stakeholders at the employee level. The missing or delayed involvement of relevant stakeholders may cause additional costs, unplanned impacts, or even production delays [8]. In industrial practice, stakeholder identification (SI) is currently performed primarily by change coordinators based on their subjective experience, and knowledge [4; 9]. Multiple preliminary works already demonstrate the potential of machine learning (ML) and data mining (DM)-based change management support [9; 10; 11]. However, these approaches are hardly used so far in industrial practice [12], although the widespread use of digital tools leads to a high availability of change data [12; 13]. This change data contains textual information, such as the change title or change description, and also additional information, such as the change trigger, requestor, or affected assemblies [9; 14]. Therefore, this contribution introduces a methodology for data-based SI by using text classification approaches and describes all necessary steps from data selection to evaluation and improvement of ML models by the integration of expert knowledge. The methodology focuses on the start of the reactive phase of change management, in which the initial identification of affected stakeholders takes place. The identification of all relevant stakeholders is particularly important in this step, as otherwise the risk of unconsidered change impacts increases in the subsequent step of the change impact analysis. The remainder of the work is structured as follows: Section 2 introduces the fundamental concepts of change management and describes the basics of natural language processing (NLP) and text classification. Section 3 then provides an overview of the current state of research and identifies existing research gaps. The procedure for data-based SI is then described in detail in Section 4. The industrial application and evaluation is the subject of Section 5.