TL;DR Many OT environments struggle with siloed data created by disconnected systems and technologies. Edge computing helps democratize data by making information more accessible across operations. Remote industries often cannot rely on cloud connectivity alone to...
TL;DR Mining operations generate vast amounts of asset data, but data quality and context remain major challenges. Industrial AI delivers the greatest value when supported by integrated, structured, and governed data. Modern predictive maintenance solutions combine...
TL;DR Many organizations collect large volumes of machine data but still struggle to reduce avoidable failures. Reliability teams need actionable insights, not more raw data or disconnected alerts. AI can accelerate reliability workflows, but only when it is paired...
TL;DR AI data centers are creating infrastructure challenges far beyond those of traditional enterprise facilities. Speed to market is becoming a major competitive advantage as demand for data center capacity continues to grow. Equipment shortages and long lead times...
Meeting regulatory and industry standards drives more complex plants: advanced process licenses and catalysts, heat integration, power islanding, mixed energy sources including hydrogen readiness, carbon capture, closed-loop water, and pollution abatement. More process units mean more rotating and static equipment, which means more advanced process control and far more pervasive condition monitoring.
TL;DR Continuous vibration monitoring identifies failures earlier than many traditional methods. Workforce shortages are accelerating adoption of permanent monitoring solutions. Criticality assessments help teams prioritize the right assets first. Industrial AI...
TL;DR Traditional route-based maintenance is becoming harder to sustain due to workforce shortages. Predictive maintenance requires actionable workflows, not just more data. Machinery health software helps turn condition data into operational decisions. Industrial AI...
TL;DR Operational excellence now requires continuous optimization, not just production output. Edge computing brings analytics and AI closer to the process. Local AI supports faster decision-making and safer operations. Industrial PCs provide the compute power needed...
TL;DR Industrial organizations have more data than ever, but much of it remains siloed. AI requires contextualized, accessible data to deliver meaningful value. Edge architectures provide low-latency access to high-fidelity operational data. Local LLMs improve...
TL;DR Sustainability initiatives require efficient use of limited resources. Automation enables measurable improvements in energy use and emissions. Monitoring WAGES systems is key to identifying waste. Modern control and reliability tools reduce inefficiencies and...
Why this matters now As manufacturers push toward higher levels of operational excellence, many have adopted AI and machine learning technologies to close the gap between available resources and rising performance demands. These technologies have delivered step-change...
Why this matters now As industry faces an ever-increasing number of new challenges—workforce reductions, supply chain shortages, increased global competition, and more—operational technology (OT) teams are continually looking for ways to optimize their operations....
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This blog features expert perspectives from Emerson's automation professionals on industry trends, technologies, and best practices. The information shared here is intended to inform and educate our global community of users and partners.