As the chemical industry faces increasing pressure to improve safety, reliability, and efficiency - amidst workforce challenges and growing sustainability demands - this article explores how data-driven analytics can help overcome these hurdles without burdening plant resources or requiring major investment.

Based on insights from over 40 industry professionals, the article highlights a common need for better use of existing plant data. Despite widespread sensor infrastructure and digital control systems, most plants rely on manual, retrospective analysis, often limited by time and expertise. This leaves significant optimisation potential untapped.

The article outlines how Navigance helps close this gap through data-driven hybrid models that combine first-principle engineering knowledge with machine learning. These models automatically analyse and learn from real-time and historical plant data, providing prescriptive recommendations for continuous optimisation — regardless of plant size, technology, or digital maturity.

Real-world use cases, such as in formaldehyde production, show measurable benefits like improved yield within weeks of deployment. The approach also enables anomaly detection, predictive alerting, and deeper insights into performance, allowing producers to increase efficiency, reduce downtime, and stay ahead in a competitive landscape.

With low implementation effort, fast time-to-value, and minimal demand on internal resources, Navigance offers a practical pathway for producers to evolve from reactive operations to proactive, AI-assisted optimisation while tailoring the solution to their process and commercial goals.

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