## 1 Introduction
Recently, the literature has suggested that resistance to change can be constructive and should not be disregarded in engineering change contexts. Instead of viewing resistance as an opponent, classical management theory now emphasizes carefully managing resistance for optimal results [13]. Another study introduced an engineering change management (ECM) decision support solution and utilized a five-step process for preprocessing, modeling, enhancing model performance, and providing interpretable results to engineers. The proposed solution offered an early assessment of change consequences for industries, aiding engineers in decision-making regarding change impact, lead time, and whether to proceed with changes that have significant effects on other systems and require extensive evaluation. The industrial case study demonstrated promising performance, and validation by domain experts confirmed the practical applicability, usefulness, and logical correctness of the approach [14]. Compared to existing research, this solution stands out for its full automation, making it convenient for engineers. Additionally, it is one of the rare data-driven ECM solutionscapable of handling a mixed format of attributes, including text, categorical, and numerical data. The inclusion of text data provides valuable insights. However, the above-mentioned work has some limitations. Data quality, particularly imbalanced datasets and issues with text data, could have affected the solution's performance. Future work should consider expanding the scope of change impacts to include system requirements, functions, change costs, and suggestions for redesign. The prediction of lead time could be enhanced by considering the critical level of changes. Moreover, it is crucial to consider current circumstances and technological advancements when applying the solution to new components and functions. In conclusion, the tested ECM decision support solution aims to enhance the quality, efficiency, and transparency of ECM processes, thus, reducing manual work in product development and optimizing time-to-market for complex products [14]. Engineering change has steadily grown in importance both as an important issue for industry and as an active area of academic research. Change is defined as a change made to parts/products, designs, or software that have already been released into production and are already in a lifecycle. A change may include any intervention in the form and/or function of the product, in whole or in part, and may alter the interactions and dependencies of the product's constituent elements. Key aspects of the engineering change process have been highlighted along with the tools and methods available to support the process. Important related areas such as organizational structure and employee skills have also been highlighted [15]. Some documents have examined the principles of change, effective implementation methods within an organization, the significance of change management, and the role of management in facilitating change [16]. Engineering change plays a pivotal role in product or process innovation and engineering change management has gained prominence in research and the automotive industry. Managing engineering change necessitates balancing the efficiency and effectiveness of the change management system, and requires guidelines to ensure accurate change requests [17]. Some studies have indicated that change demand can be detrimental, especially when products have longer lead times [18]. Another referential research introduced a methodology based on the Value Stream Mapping (VSM) 4.0 approach for continuous improvement in the downstream supply chain distribution process. The study included an analysis of the current state through VSM mapping and the design of a future state, optimizing competitive tasks, considering the country of origin and integrating Industry 4.0 technologies accepted by the company in terms of implementation time and budget.