Manufacturing change management – an AI- and data-enhanced Delphi study and algorithm to support change process tailoring and the identification of suitable methods and digital tools

Today’s manufacturing industry is exposed to increasing external and internal disturbing issues, including supply chain disruptions, geopolitical uncertainties, and workforce shortages. These dynamics require companies to frequently implement Manufacturing Changes (MCs), coordinated through structured Manufacturing Change Management (MCM) processes. However, existing MCM approaches often fall short in adapting process steps and selecting suitable support tools to the specific characteristics of a given change. This results in inefficiencies and limited responsiveness. To address this gap, this paper develops the core logic for a change-specific and company-individual MCM methodology. The focus lies on establishing and operationalizing correlations between change characteristics, process logic, and supporting methods and digital tools (M&DTs). A three-phase approach was applied: a hybrid Delphi study combined expert input and artificial-intelligence-supported assessments to derive initial correlation matrices, then selected dependencies were verified with real-world change data, and a configurable algorithm was developed to transform structured input into tailored change processes and M&DTs recommendations. The resulting framework and application methodology enables manufacturing companies to assess MCs based on structured attributes and to derive adapted process models, including suitable M&DTs. This contributes to a more efficient and context-specific handling of changes in dynamic industrial environments.