Drivers, Barriers, and Paradoxical Tensions of Data Analytics Use in German Large Manufacturing Companies

Digitization has transformed manufacturing by enabling real-time data collection through advanced sensor technologies. To remain competitive, manufacturers have increasingly relied on data analytics to enhance efficiency and decision-making. This study assessed how descriptive and advanced analytics, comprising predictive and prescriptive approaches, have been adopted by large manufacturing companies in Germany. A three-step mixed-methods design was employed: first, a structured literature review identified 5 overarching themes encompassing 20 topics; second, a Delphi study with industry experts evaluated these topics to determine the current strengths of data analytics; third, qualitative interviews with 15 practitioners provided deeper insights into related drivers, barriers, and paradoxical tensions. The analysis revealed 6 major strengths centered on predictive maintenance, prescriptive maintenance, real-time data utilization, simulation-based forecasting, production flow optimization, and process synchronization. Drivers included growing employee acceptance and an urgency to build analytical know-how for competitive differentiation. Barriers comprised security concerns, limited trust in machine-generated results, and insufficient training initiatives among others. Paradoxical tensions emerged between regulatory compliance versus operational agility, high initial investments versus long-term savings potential, and heterogeneous software-hardware standards versus the need for integration. The study contributed transparency regarding the status quo of data analytics in German manufacturing and proposed managerial implications: strategic change management to foster acceptance and skill development; adoption of scalable as-a-service technologies; and integrative frameworks supporting assessment and planning for future advancements.