Longitudinal Insights into Intelligent Manufacturing Processes: Managerial Expectations vs. Actual Adoption

Manufacturing enterprises often plan technological development without knowing whether their expectations can realistically be achieved, which makes long-term decisions uncertain. This study is necessary because no evidence exists comparing what quality managers expected to adopt by 2025 with what was actually implemented eight years later. We conducted a two-phase empirical study using the same 42 large manufacturing enterprises first surveyed in 2017, applying an identical research matrix of 26 processes and 14 intelligent technologies to measure changes in expected and real deployment. A follow-up rapid survey expanded the sample to 136 enterprises and examined the use of ten AI-driven technologies. The results show a clear gap between expectations and reality. Real automation exceeded expectations in logistics, maintenance, and quality activities, with processes such as manipulation, warehousing, and delivering reaching more than 50 percent adoption, and collaborative robots and autonomous vehicles exceeding expectations by 8 to 25 percentage points. In contrast, forecasting, scheduling, and process planning fell 10 to 15 percentage points below expected levels. The study’s contribution is to identify which technological expectations were realistic and which were overestimated. These insights guide managers in prioritizing investments and help policymakers understand where digital transformation advances naturally and where targeted support is required.