Harnessing Process Models and Standards for Change Management Boosted by Industry 4.0

Abstract

Digitalization and regulatory compliance pose substantial challenges to companies, requiring adjustments to operations and business processes. Smooth transitions can be facilitated by analyzing discrepancies between current and target processes, enabling the identification of necessary organizational changes. Based on these insights, change managers can develop action plans to support effective implementation and ensure return on investment. Although scholars emphasize the importance of data-driven evaluation in change management (CM) and recognize the value of information embedded in business process models, the literature lacks systematic methods for extracting and integrating such information, particularly from text-based sources. In collaboration with industrial partners, we developed a method to address this gap. Our approach integrates semantic business process management, text analytics, and CM to compare process models with industrial standards, align process ontologies, and translate detected deviations into actionable recommendations. The method also resolves terminological inconsistencies across heterogeneous sources. This paper presents an analytics-based framework that delivers practical, context-specific guidance to change managers. To demonstrate applicability, we implemented a proof of concept in an industrial environment to validate process adherence against natural language documents such as industry standards