A Continuous Monitoring and Reallocation Method for Successful Decisions in Change Management

Businesses and organizations often struggle to cope in a rapidly changing and competitive environment, requiring changes with increasing frequency and complexity. The evolution of information systems has enabled direct access to information and data, which can be utilized for the effective implementation of changes. This usually requires managing resistance to change, which is often presented by employees. The aim of this study is to address the problem of change management in order to limit the decline in overall performance during the critical period of change and to accelerate the implementation of change. This study provides a new innovative approach to managing employee resistance during the change process based on a continuous monitoring and reallocation algorithm. The proposed method introduces the idea of solving the human resource allocation problem by continuously readjusting staff allocation during the change process. The Hungarian algorithm is used to optimize assignments. In this way, the resistance of each employee is taken into account regularly, and the assignment can be altered in the early stages of change. The new method is mathematically formulated and described in detail through an algorithm, which is then used in experiments. The proposed method of frequently reallocating human resources to tasks leads to better overall performance and improves decision making during change processes. The experimental results show that the new approach significantly increases the total performance by up to 124% when compared to existing change management approaches and reduces the time required to achieve the desired state by up to 20%. Thus, the enhanced management of human resistance to change provides distinct advantages over traditional methods by ensuring more dynamic, timely, and adaptive resource allocation during the change period, ultimately leading to successful decision making and sustainable change management.