In today's fast-paced industrial environment, operational reliability is no longer just a goal—it's a critical necessity. However, many refining and petrochemical manufacturing facilities face persistent challenges with unplanned downtime, often caused by corrosive feedstocks, erosive intermediate streams, and equipment-damaging contaminants in the production process. These issues can lead to costly equipment failures, with downtime potentially costing up to $260,000 per hour, disrupting production processes and directly impacting profitability.
Despite the continuous efforts of reliability teams to monitor these systems, proactively identifying and mitigating risks that threaten equipment health, worker safety, and production efficiency remains a significant challenge.
The Hidden Costs of Fragmented Data
Consider this scenario: A process operator spends nearly half their shift searching for and analyzing scattered information hidden across multiple systems. This isn't just an inconvenience—it's a major roadblock to timely decision-making, delaying critical actions that can impact worker safety, production targets, and turnaround times.
The root of the problem often lies in:
- Siloed manufacturing environments - Disconnected software systems, lack of cross-department collaboration, and production and maintenance teams using separate systems that use different systems of record.
- Legacy processes are still prevalent - Paper-based reporting, manual data entry, reactive (rather than predictive) maintenance, and inefficient communication methods that slow down operations.
- Data trapped in separate, uncoordinated systems - Production and maintenance data spread across systems, disconnected OT and IT data, and maintenance records stored in outdated or inconsistent formats.
Lack of easy access to data and insights creates costly inefficiencies, making it nearly impossible to predict failures and optimize processes. Relying on fragmented systems isn’t just inefficient—it’s a direct risk to profitability and safety. Manufacturers need to break free from this cycle by embracing technology and a proactive maintenance strategy to predict and prevent failures before they occur, driving operational excellence and peak performance of their assets.
From Reactive to Proactive: A data-driven path to reliability
Imagine operations free from outdated, disconnected systems. What if potential failures are detected weeks in advance, allowing for planned interventions rather than emergency shutdowns? What if complex turnarounds are guided by real-time insights? What if maintenance, operations, and reliability teams collaborate seamlessly, using a unified system where they can quickly access the information they need?
The key to achieving this vision, where manufacturing facilities break from the reactive state of asset management, lies in enabling simple access to complex industrial data and insights and establishing proactive maintenance with data-driven workflows that are powered by integrated data, predictive analytics, and AI-driven insights to make maintenance and reliability more efficient, proactive, and cost-efficient process.
This requires a holistic approach and must focus on four key areas:
- Building Your Digital Foundation: Invest in systems integrating your IT, OT, engineering, and unstructured data, providing real-time insights across production, maintenance, and asset health.
- Empowering Your Frontline: Equip frontline workers and engineers with an easy-to-use platform and tools that provide quick and seamless access to actionable data and insights, reducing inefficiencies, improving productivity, and enabling better decision-making.
- Harness the Power of AI and Advanced Analytics: Enable AI-driven predictive maintenance and advanced industrial analytics to predict failures, detect early warning signs of equipment failure, optimize maintenance schedules, and prevent costly disruptions.
- Streamline Your Workflows for Maximum Efficiency: Replace manual, paper-based processes with digital workflows that automate previously time-consuming and painful tasks such as risk assessment, work order management, and task prioritization - improving response times and reducing operational complexity.
How Cognite Powers the Shift of Proactive Operations
At Cognite, we're not just talking about predictive maintenance and improving reliability—we're already building it with many customers. Our industrial Data and AI platform, Cognite Data Fusion®, helps manufacturers shift to proactive, AI-driven maintenance strategies. It enables maintenance and reliability teams to deploy digital workflows that can help improve efficiency, reduce downtime, and drive long-term profitability.
With Cognite Data Fusion®, manufacturers:
- Unify fragmented data sources to eliminate blind spots in operations, empowering teams with real-time insights for faster, smarter decision-making. Cognite Data Fusion® automates data integration across disconnected systems, including prebuilt integrations with standard industrial systems like ERP, CMMS, and APM systems. This ensures smooth connectivity across assets and sites.
- Empower frontline workers and engineers with intuitive access to real-time data - reducing delays, improving response times, and eliminating reliance on data gatekeepers. By establishing an industrial data foundation with Cognite Data Fusion®, different teams (both in the office and the field) can quickly access relevant data such as failure reports, maintenance logs, time series, sensor readings, and more to make more informed decisions.
- AI-powered predictive analytics to detect early warning signs, preventing costly equipment failures and unplanned downtime - boosting overall asset performance and reliability. Cognite’s AI-powered data contextualization capabilities link siloed data at scale, transforming raw information into operational knowledge. Paired with advanced, AI-powered analytics and insights, it enables the early detection of anomalies and potential issues before they cause production disruptions.
- Streamline maintenance workflows with AI-driven planning tools and risk-based prioritization, ensuring teams focus on the most crucial issues first –reducing turnaround times and improving collaboration and resource efficiency. Cognite Data Fusion® provides collaborative visual planning tools, intuitive risk management features, a no-code environment for industrial data analytics, troubleshooting, and root cause analysis This enables planners to create optimal maintenance schedules, proactively address issues, and adapt to evolving field conditions.
- Extend AI capabilities beyond anomaly detection for even greater efficiency with Cognite Atlas AI™. Cognite Atlas AI™ extends Cognite Data Fusion® to help carry out more complex operations with great accuracy and help you deploy AI agents that can help automate routine troubleshooting, recommend corrective actions, and equip technicians with instant, data-driven insights to improve decision-making speed and accuracy.
Why Act Now?
The shift from reactive maintenance to proactive, AI-driven reliability is not just about incremental improvements—it's essential for survival in an increasingly digital and competitive landscape. Every unplanned outage, delayed decision, and operational inefficiency compounds the risks to sustaining profitability, safety, and long-term vitality. Manufacturers that continue to operate in siloed environments and rely on outdated processes will face escalating downtime costs, operational bottlenecks, inefficiencies, and increased safety hazards, while industry leaders are already using integrated data, predictive analytics, and AI-driven insights to stay ahead.
Don’t wait for the next equipment failure to force change. Start transforming your operations today and redefine reliability for the future.