Chemical manufacturing is a complex and highly regulated industry. From batch variability and unplanned downtime to energy inefficiencies and quality deviations, manufacturers face mounting pressure to increase yield, reduce waste, and maintain consistent product quality.
One of the most effective metrics for driving improvement across these dimensions is Overall Equipment Effectiveness (OEE), which is a metric to measure how your plant is performing versus theoretical perfection.
While widely used by Lean Manufacturing and Six Sigma experts, OEE is more than a continuous improvement tool. OEE adoption in chemical production environments is growing as companies recognize its value as a way to help identify hidden losses in continuous and batch processes.1
By harnessing AI-enabled digital twins, chemical manufacturers can take their OEE targets further by improving latent inefficiencies, reducing process variability, and boosting throughput beyond traditional methods.
How is OEE Calculated: Understanding OEE in the Context of Chemical Manufacturing
OEE is calculated as:
OEE = Availability × Performance × Quality
Each component reflects a different source of loss:
- Availability: Downtime losses (e.g., unplanned maintenance, cleaning cycles, material shortages)
- Performance: Speed losses (e.g., slowdowns, suboptimal batch times)
- Quality: Quality losses (e.g., off-spec batches, rework)
The Current Landscape: Challenges Limiting OEE in Chemical Plants
Frequent Downtime and Low Asset Availability in Chemical Production
In chemical production, equipment availability is frequently impacted by unplanned downtime. From lengthy changeovers and pump failures to raw material shortages, a wide array of issues can cause halts or slowdowns in production.2
Compounding this issue is a reliance on calendar-based or reactive maintenance strategies, which can result in over-maintenance and missed early signs of equipment degradation, resulting in increased costs and downtime.
A recent study found that unplanned downtime in process manufacturing can cost companies up to $20 billion annually, highlighting the urgent need for predictive maintenance strategies and real-time process monitoring to maintain operational continuity and profitability.3
Suboptimal Performance Due to Variable Process Conditions
Performance losses in chemical plants are commonly driven by inefficiencies in heating and mixing, suboptimal batch changeovers, and long equipment cleaning cycles. Even minor deviations from target process conditions like temperature ramps or flow rates can result in extended cycle times or wasted energy.4
In continuous processes, small fluctuations in raw material properties or environmental conditions (e.g., humidity, feedstock purity) can ripple through production lines, causing throughput variability and increased utility consumption.
These inefficiencies often go undetected due to the multidimensional nature of process data and the absence of real-time optimization tools.5
Off-Spec Product and Quality-Related Losses Contributing to Lower OEE in Chemical Plants
Quality deviations are another major contributor to low OEE in chemical production. Inconsistent raw materials, poorly controlled batch sequences, and inadequate inline monitoring can lead to off-spec outcomes, requiring rework or disposal.
First-pass yield can range from 60% to 85% in specialty chemical plants, highlighting significant room for improvement.6
How AI-Enabled Digital Twins Help Improve OEE
AI-enabled digital twins offer a powerful solution to the OEE challenge. By creating dynamic, data-driven models of chemical production processes, digital twins enable manufacturers to simulate, predict, and optimize plant behavior in real-time.
Here’s how they address each component of OEE:
Improving Availability: Predictive Maintenance and Anomaly Detection
Digital twins trained on historical sensor and maintenance data can identify patterns indicative of future equipment failures. This predictive insight enables a proactive maintenance approach, reducing unplanned downtime and increasing asset availability by identifying and correcting issues before they occur.
One study on predictive maintenance implementation in a process manufacturing setting highlighted the use of sensorized equipment, real-time data analytics, and forecasting models (such as ARIMA, Prophet, and Double Exponential Smoothing) to predict equipment failures and trigger timely maintenance alerts. The result:
- A significant reduction in downtime.
- An enhanced ability to make more informed decisions in real-time.
- Operational staff reported better anticipation and prevention of equipment failures, leading to smoother production and lower costs.7
Some tools, like Basetwo, leverage a Physics AI approach to predictive maintenance by combining first-principles models with machine learning. This hybrid modeling technique improves the accuracy of predictions and anomaly detection, enabling chemical manufacturers to identify potential issues early and take proactive steps to maintain process reliability.
Enhancing Performance: Cycle Time Optimization and Energy Efficiency
AI models can simulate complex process interactions to identify optimal operating conditions to maximize throughput. In batch processes, digital twins can optimize heating and mixing profiles to shorten reaction times without compromising quality.9 In continuous operations, real-time optimization (RTO) tools can adjust valve setpoints and recirculation rates to ensure consistent flow and conversion.8
In a specialty chemicals application, a global manufacturer utilized the Basetwo platform to simulate and optimize their distillation process using hybrid modeling digital twins that combined first-principles and machine learning models.
This approach enabled precise evaluation of process variables and operating conditions to determine the optimal standard operating procedure (SOP) for achieving target product concentration.9
Implementation of the optimized SOP resulted in a 40% reduction in cycle time and batch-related operating costs, along with a 20% decrease in energy consumption. For a detailed walkthrough explore the use case.
Ensuring Quality: Predictive Quality Modeling and In-Silico Testing
Digital twins can also support quality improvements by leveraging soft sensing to predict hard-to-measure key quality attributes such as pH, purity, or viscosity based on current process data. If a predicted value starts to deviate from target ranges, operators can take corrective action before a batch goes off-spec.10
These predictive quality models are particularly valuable when applied to continuous process verification (CPV) scenario, the ongoing collection of process data throughout a product's commercial lifecycle.11
As an example, a global paint and coatings manufacturer deployed an AI-based quality control system in its coatings production. The system uses spectral analysis and machine learning to monitor product characteristics in real time, allowing for immediate detection and correction of quality deviations.
The result was a 40% reduction in quality-related waste, a 25% improvement in first-pass yield, and a 50% decrease in quality testing time.12
This demonstrates how AI integrated into CPV can significantly boost both efficiency and quality outcomes.
Conclusion
OEE is no longer just a lean manufacturing metric—it’s a critical performance driver for chemical manufacturers navigating volatility, compliance, and competition. AI-enabled digital twins offer a scalable and explainable path to improving availability, performance, and quality, without requiring costly new infrastructure.
As manufacturers look to future-proof their operations, the integration of real-time simulation, predictive analytics, and low-code modeling environments is becoming essential. By taking a digital-first approach to OEE, companies can transform inefficiencies into opportunities and deliver measurable results.
Looking to improve OEE in your chemical operations? Book a time with Basetwo to see how AI can help.
References:
- ABI Research. (n.d.). Overall Equipment Effectiveness (OEE) for manufacturers. Retrieved from https://www.abiresearch.com/blog/overall-equipment-effectiveness-oee-for-manufacturers
- Aidic.it. (2024). AI applications in process industries. Retrieved from https://www.aidic.it/cisap4/webpapers/24Kidam.pdf
- ARC Advisory Group. (2023). Reducing unplanned downtime in process manufacturing. Retrieved from https://www.arcweb.com/research/reducing-unplanned-downtime-process-manufacturing
- American Chemical Society. (2021). AI applications in chemical manufacturing industry. Retrieved from https://pubs.acs.org/doi/10.1021/acs.iecr.1c00209
- Jain, A., et al. (2020). AI applications in process industries. Journal of Manufacturing Systems. Retrieved from https://www.journals.elsevier.com/journal-of-manufacturing-systems
- McKinsey & Company. (2022). Unlocking productivity in specialty chemicals through digital transformation. Retrieved from https://www.mckinsey.com/industries/chemicals/our-insights/unlocking-productivity-in-specialty-chemicals-through-digital-transformation
- Frontiers in Manufacturing Technology. (2024). Soft sensors: What are they?. Retrieved from https://www.frontiersin.org/articles/10.3389/fmtec.2024.1475078/full
- Orise. (n.d.). Real-time optimization. Retrieved from https://orise.com/process-optimization/operational-excellence-consulting/real-time-optimization
- Basetwo. (n.d.). Basetwo platform. Retrieved from Basetwo platform page
- SIMAnalytics. (n.d.). What are soft sensors?. Retrieved from https://simanalytics.com/insights/what-are-soft-sensors
- U.S. Food and Drug Administration. (n.d.). Process validation: General principles and practices. Retrieved from https://www.fda.gov/files/drugs/published/Process-Validation--General-Principles-and-Practices.pdf
- Chemical Engineering Consulting. (2025). AI applications in chemical manufacturing industry. Retrieved from https://www.chemengconsulting.com/blog/2025/02/24/ai-applications-chemical-manufacturing-industry/1106/
