Intelligent automation is reshaping production lines, quality control, and supply chain operations. Here's what manufacturing leaders need to know about RPA in 2026.
The State of Manufacturing Automation
Manufacturing has always been at the forefront of automation. But traditional automation — rigid PLCs, fixed sequences, manual reprogramming — is giving way to intelligent RPA that adapts in real time. AI-driven process mining, throughput analysis, and quality control are no longer futuristic concepts; they're production-ready tools.
PLC Integration: Bridging Old and New
Most factories run on PLCs that are years — sometimes decades — old. The challenge isn't replacing them; it's connecting them to modern systems. OnWebApp's industrial automation module bridges this gap with protocol adapters that translate PLC signals into API calls, enabling real-time monitoring and control from a central dashboard.
AI-Driven Process Mining
Process mining analyzes your production data to identify bottlenecks, waste, and optimization opportunities. OnWebApp's AI engine processes millions of data points from sensors, cameras, and ERP systems to recommend actionable improvements — like adjusting cycle times, reallocating resources, or predicting quality issues before they occur.
Quality Control at Scale
Computer vision powered by machine learning inspects products at line speed. Defects that human inspectors miss are caught automatically. And every inspection result feeds back into the process mining engine, creating a continuous improvement loop.
Measurable Impact
Manufacturers using OnWebApp's industrial automation report:
- 35% increase in Overall Equipment Effectiveness (OEE)
- 60% reduction in unplanned downtime
- 25% improvement in first-pass yield
- 40% reduction in quality-related costs
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