EXTON, PA — Actemium Avanceon is introducing a DataOps framework aimed at helping manufacturers extract more value from existing operational data, reflecting a broader industry push to improve production performance and prepare facilities for advanced analytics and artificial intelligence applications.
The industrial automation and digital transformation firm said its approach focuses on organizing, standardizing, and contextualizing plant-floor data that is often collected but underutilized due to fragmented systems, inconsistent reporting structures, and disconnected operational technologies.
For manufacturers, the challenge is increasingly shifting from data collection to data usability as companies seek to improve efficiency, reduce waste, and support data-driven decision-making across multiple facilities.
“The issue is usually not a lack of data,” Chief Executive Officer Matt Ruth said. “The challenge is making that data consistent, contextualized, and usable within the realities of day-to-day production operations.”
The company’s methodology emphasizes improving existing operational data before expanding into broader analytics, visualization, optimization, Unified Namespace (UNS) initiatives, and AI-driven applications.
As part of the offering, Actemium Avanceon has launched ImpactNOW™, a fixed-scope assessment designed to identify operational inefficiencies and quantify potential returns on investment using existing historian, manufacturing execution system (MES), and control system data. The company said the process can be completed in three to five weeks.
The initiative targets manufacturers seeking to improve operational performance without undertaking large-scale technology transformation projects.
According to the company, DataOps programs often require integration across industrial control systems, SCADA platforms, manufacturing execution systems, production workflows, and industrial networks to generate actionable operational insights.
Actemium Avanceon said it has applied similar approaches in projects involving startup-time reductions, scrap reduction analysis, clean-in-place optimization, energy consumption analysis, and production performance reporting.
In one implementation, the company reported helping a manufacturer reduce production startup times by approximately 50%, resulting in annual operational savings.
“Manufacturers do not always need to start by collecting more data,” Ruth said. “In many cases, the bigger opportunity is making the data they already have more useful, more consistent, and more connected to the decisions people need to make on the plant floor.”
The launch comes as manufacturers increasingly evaluate how operational data can support productivity improvements, standardization efforts, and future AI deployments without requiring major new investments in data collection infrastructure.
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