Comparative analysis of traditional and innovative approaches to modeling enterprise business processes as a reengineering tool

摘要

Considering the unique characteristics of Ukrainian enterprises, their resource base, and industry differences, a scientifically based analysis of the advantages and disadvantages of each approach is required. Adaptive modeling of enterprise business processes facilitates the identification of relevant tools for post-war economic transformation. The purpose of the study is to conduct a comparative analysis of traditional and innovative approaches to modeling enterprise business processes as a reengineering tool in the context of post-war development. The article substantiates the differences between traditional and innovative models of business process reengineering, which are primarily determined by the level of flexibility, speed of transformation, and implementation costs. The strengths and weaknesses of structural-functional approaches, which are characterized by high costs and significant risks, are examined. These approaches remain effective in large enterprises with stable financing. It has also been proven that innovative models based on digital technologies, BPMN modeling, robotic process automation, and artificial intelligence provide greater adaptability and sustainability for businesses. In this regard, attention is focused on the fact that modern models can meet the needs of Ukrainian enterprises in the post-war period, when the speed of decision-making and risk minimization are critically important. It is determined that their application contributes to increasing the efficiency of management processes, restoring production potential, and forming new competitive advantages. It is summarized that the combination of innovative models with adaptive development strategies enables the creation of a foundation for sustainable enterprise growth in both post-war and post-crisis periods. Keywords: business process modeling, reengineering, innovation, digitalization, change, risk, efficiency, costs.