Chemical industry

AI-enabled production batch optimization and quality

Improve production batch quality and lower costs with tighter specifications and predictive alerts, reducing scrap and overruns.

An industrial worker holding a helmet gazes towards a large factory, symbolizing oversight or management.
nippon gases case study
Nippon Gases case study
Industrial gases company prevents unplanned downtime and extends its APM strategy with SymphonyAI’s Predictive Asset Intelligence

Manufacturing solutions for the chemical industry

Process optimization

Analyze complex chemical manufacturing processes and identify opportunities for optimization. AI algorithms determine the optimal operating conditions that lead to improved efficiency, reduced waste, and higher product quality.

Quality control

Enhance quality, reduce cost, and minimize the need for rework. AI and machine learning algorithms identify patterns associated with product defects, deviations from quality standards, or anomalies in raw materials.

Demand forecasting

Analyze market trends, historical data, and external factors to ensure products are available when needed, reduce storage costs, and minimize the risk of shortages or overproduction.


Improved quality

Detect patterns and anomalies in production data ensures consistent product quality. By identifying potential defects early in the manufacturing process, manufacturers can prevent substandard products from reaching customers.

Optimized supply chain management

Align production schedules with market demand to minimize excess inventory, reduce storage costs, and prevent shortages. By being better prepared for supply chain disruptions, such as raw material shortages or geopolitical events, manufacturers can mitigate risks and maintain a competitive edge.

Cost savings

Save costs, identify optimal operating conditions, and make real-time adjustments. Reduce energy consumption, minimize waste, and improve overall resource utilization.