Differentiating monitoring approaches for engineered-to-order (EtO) and configure-to-order (CtO) assets

Industrial asset monitoring depends on AI (Artificial Intelligence) driven analytics to enhance performance; however, approaches differ according to asset complexity, data availability, and operational context. This paper explores decision-support strategies for monitoring engineered-to-order (EtO) and configure-to-order (CtO) assets, comparing predictive analytics strategies, insight generation, and return on investment (ROI). Analysing two real-world cases, we illustrate how AI-powered diagnostics, operational variability, and system integration impact decision-making and action-taking. The findings emphasise AI’s potential role in improving asset resilience, optimising maintenance strategies, and supporting data-driven decision-making within industrial ecosystems, with ramifications for interoperability, efficiency, and business model innovation.