Plant Optimization – A Comprehensive Guide

Hidden inefficiencies lurk within every industrial plant, presenting significant opportunities for optimization. If you're part of the heavy asset industry, chances are high that your plant or fleet holds untapped potential for improvement.

At its core, plant optimization is about achieving maximum output with minimal input. It’s the strategic process of enhancing efficiency, productivity, and reliability across operations. By uncovering and addressing these inefficiencies, companies can realize significant performance improvements and cost savings.

With many options for optimization, how does a company decide where to start? Optimization can take many forms, spanning machines, people, and design. The key aspects of plant optimization depend on factors such as company objectives, plant age, operational profile, weather patterns, and regional regulations. For example, emissions reduction may be the primary focus for a plant in a region with strict environmental laws, while another plant nearing end-of-life may prioritize predictive maintenance to maximize availability. For an organization with high staff turnover, optimization could standardize processes to minimize errors and human variability.

Plant optimization is never a “one-and-done” effort. Safety improvements, for instance, require continuous attention. The same applies to cost reduction and reliability. Other goals often depend on the company’s customer base and business model. A regulated utility may prioritize customer satisfaction, while an oil company operating in a climate-conscious region may focus on reducing the environmental impact of energy production.

Common goals of plant optimization for most energy companies include:

Plant optimization starts with a clear strategy. Before selecting tools or software, companies need to define what optimization means for their organization and how it aligns with broader business goals.

The first step is to define your business objectives. Are you aiming to improve efficiency, reduce emissions, or extend asset life? Your goals will depend on factors like operational profile, equipment age, and workforce capabilities. Whenever possible, align these objectives with corporate KPIs to ensure measurable impact.

Next, conduct an initial assessment. This means evaluating data availability and quality, mapping processes, and establishing a baseline for performance. For example, an oil and gas company focused on efficiency might assess drilling, refining, and distribution processes, and a power generation company might review energy efficiency, uptime, and maintenance costs.

Once you understand your current state, look for areas of loss or inefficiency—unplanned downtime, poor heat rate, reactive maintenance practices, or lack of visibility across assets. These insights will guide your priorities.

Optimization isn’t a siloed effort. Bring in operations, maintenance, IT, and finance teams early. Understand constraints like budget, skills, and cybersecurity policies. Building consensus ensures smoother implementation and realistic ROI expectations.

Define success in clear terms: reduce forced outages by 20%, improve heat rate by 2%, or cut maintenance costs by a specific percentage. Establish timelines and accountability to keep the plan on track.

Start with low-risk, high-impact initiatives—predictive maintenance often comes before full automation. Plan for scalability and integration with existing systems, and don’t forget change management and training.

Only after you’ve mapped the strategy should you choose the technology stack—software, sensors, control systems—that will help achieve your goals.

Technology is the backbone of modern plant optimization, and success often comes from combining hardware and software upgrades. Each plays a distinct role:

When used together, hardware provides the foundation, and software unlocks its full potential. . A holistic approach ensures plants achieve optimization goals faster, more economically, and with greater resilience.

Software solutions enable plants to unlock insights from data, automate decision-making, and predict issues before they occur. For example:

These technologies don’t just optimize performance, they transform how plants operate.

Predictive maintenance is one of the most impactful strategies for plant optimization. By detecting potential failures early, teams gain valuable lead time to:

GE Vernova’s SmartSignal software is a leader in this space, using advanced analytics and domain expertise to identify anomalies before alarm thresholds are reached. For critical equipment, where failure means high repair costs and production loss, basic monitoring isn’t enough. Predictive algorithms with embedded domain expertise deliver deeper insights. Prebuilt digital twins, modelled on known failure modes, offer even greater protection by simulating real-world conditions and catching issues early.

Condition monitoring continuously measures health indicators, such as vibration, temperature, pressure, and oil quality, to detect changes signalling wear or impending failure. Moving beyond spreadsheets, modern software aggregates this data and forecasts when components will fail, enabling maintenance before breakdown occurs.

Here’s how it works:

Condition monitoring provides the raw data; predictive maintenance adds foresight. Together, they form a powerful optimization strategy.

Energy optimization is another key area where technology shines:

Prescriptive analytics goes beyond predicting what will happen, it recommends what to do next. Combined with IoT sensors and digital twins, prescriptive solutions help operators make smarter decisions faster. To bolster accuracy, the recommendations use operational data and stored knowledge to create proprietary, structured content. Security is critical when using AI, therefore guardrails are applied during prompt creation and response so that customer documents remain confidential. In addition, cross-tenancy safeguards prevent customer data from being shared between tenants.

Technology isn’t just a tool, it’s a catalyst for plant optimization. From predictive maintenance and condition monitoring to AI-driven insights and prescriptive recommendations, software solutions empower energy companies to improve reliability, reduce costs, and achieve sustainability goals.

Cosmo Oil sought to elevate operational efficiency, reliability, and risk management across its refinery assets by integrating digital twin technology with GE Vernova’s Asset Performance Management (APM) software and SmartSignal predictive analytics.

Before implementation, roughly 70% of operations time was consumed by collecting and cleaning data from disparate systems across engineering, maintenance, and inspections. Adding to these inefficiencies, a lack of proactive asset insights led to unplanned failures and reactive response strategies.

Phase 1: APM implementation
A comprehensive feasibility study paved the way for integrating APM into Cosmo’s SAP and maintenance systems. Governance was embedded into engineering practices to drive cross-functional data access and decision alignment.

Phase 2: Introducing digital twins
The strategy included building a multidimensional digital twin—covering physical, process, and maintenance layers—to model asset behavior and simulate performance under various scenarios.

Phase 3: Predictive analytics with SmartSignal
SmartSignal was deployed to monitor critical equipment (e.g., compressors), enabling early anomaly detection. Predictive alerts triggered faster remediation—uncovering an issue that conventional monitoring would not have identified.

Cosmo Oil successfully shifted from reactive to predictive operations by integrating APM, SmartSignal, and digital twins. The solution streamlined workflows, mitigated asset risk, and unlocked measurable cost savings including $1 million in avoided downtime and $700K in operational risk averted, all critical to plant optimization goals.

This case study demonstrates how digital transformation through APM and intelligent twins can operationalize reliability, drive efficiency, and support strategic decision-making for energy companies.

See the full Cosmo customer story.

Modern power plants operate in a complex environment where efficiency is not just a goal, it’s a necessity. Continuous optimization isn’t about one-time fixes; it’s about creating a strategy that adapts to changing conditions, market demands, and asset health. By leveraging advanced analytics and digital tools, operators can uncover hidden inefficiencies, reduce waste, and improve reliability without major capital investments.

The key is turning data into actionable insights. Predictive analytics, digital twins, and AI-driven monitoring enable proactive maintenance and smarter dispatch decisions helping to empower plant teams achieve more with less.

Contact a GE Vernova subject matter expert today for a free consultation on which software solutions can help you reach your plant optimization goals.