Manufacturing
Industrial analytics for modern manufacturing operations
In the manufacturing industry, manufacturing process optimization has become critical, asevery day brings the need to make decisions in highly volatile conditions: increasing quality requirements, cost and social pressure, and the need to maintain high machine availability. Plants operate on the basis of hundreds of devices and thousands of process variables, and a single malfunction can quickly translate into material losses, reduced OEE, or unplanned downtime.
In such an environment, data becomes a key resource – it connects the automation layer, production systems, and business information, creating a common source of knowledge about the process. Only their integration and consistency allow not only to respond to problems, but also to anticipate them in advance and optimize factory operations in real time.
Manufacturing process optimization with Smart RDM
Today’s factories are not looking for solutions based on Excel,
another report, or an MES module.
They need a decision-making system that can:
- understand data from across the factory,
- predict deviations and failures,
- recommend actions to operators and engineers,
- automatically create analyses and visualizations,
- operate in real time and without coding.
Smart RDM is a modern platform that combines OT, process, and business data in one place,
allowing companies to start their journey toward operational excellence.
See how leaders in their industries achieve manufacturing excellence
What is manufacturing process optimization?
Manufacturing process optimization in Smart RDM means transforming raw operational data into concrete, measurable improvements in how production systems work. It goes beyond monitoring and reporting to actively support better decisions across machines, lines, and entire plants. Smart RDM connects process data, quality metrics, energy consumption, and maintenance signals into one coherent operational context – enabling organizations to optimize performance continuously, not only after problems occur.
Beyond spreadsheets: real-time manufacturing optimization
Traditional approaches to process optimization in manufacturing rely on spreadsheets, lean initiatives, or isolated MES functions. While these tools provide visibility, they are largely retrospective and manual. Optimization happens after deviations are detected, often too late to prevent losses. Smart RDM introduces an AI-driven approach where optimization is embedded directly into daily operations and supported by real-time analytics and predictive models.
AI-driven insight for continuous process optimization
Process optimization manufacturing in Smart RDM is driven by advanced analytics and AI models that understand how processes behave over time. The platform analyzes thousands of parameters simultaneously, detects early signs of instability, predicts future outcomes, and recommends the best possible actions within real operational constraints. Instead of asking what went wrong, teams can focus on what should be done next – with clear guidance grounded in data.
Optimization that directly impacts OEE, quality, and cost
In Smart RDM, it is a continuous, data-driven cycle that directly impacts key operational KPIs:
- OEE improvement through stabilization of process parameters and reduction of micro-stoppages
- Quality optimization by identifying root causes of deviations and preventing defects before they occur
- Downtime reduction through early detection of process drift and predictive maintenance insights
- Energy and resource efficiency by aligning process behavior with cost and sustainability objectives
Manufacturing process optimization becomes an ongoing capability, not a one-time initiative – fully integrated with production, quality, energy, and maintenance in a single intelligent platform.
Smart RDM contributes to enhancing Twinings’ Overall Equipment Effectiveness (OEE)
What makes Smart RDM stand out in manufacturing?
AI without programming
Engineers describe problems in natural language, and the system generates analyses, recommendations, and dashboards.
Process and KPI prediction
Automatic anomaly detection models improve OEE, reduce losses, improve quality, and reduce downtime.
Built-in best practices
An intelligent knowledge base and procedures that AI draws on to advise operators.
Real-time OT/IT integrations
Data from machines, sensors, SCADA, and Historian in seconds, not hours.
Energy & ESG in one
Calculations of energy, losses, emissions, and efficiency at the process and line level.
Scalable architecture
From a single line to a global portfolio of factories without compromising performance or security.
From reactive control to predictive quality
Machine learning for manufacturing process optimization in Smart RDM enables manufacturers to move beyond fixed thresholds and manual rules toward adaptive, data-driven control of production processes. Instead of reacting to quality losses after they occur, AI models continuously analyze process behavior and detect early signals of instability or deviation.
Key capabilities include:
Predictive quality modeling
AI models forecast quality outcomes by correlating process parameters, machine states, environmental conditions, and historical quality data. This allows teams to intervene before defects occur, reducing scrap, rework, and yield losses.
Advanced anomaly detection
Machine learning identifies abnormal patterns across machines, lines, and operating modes – including subtle process drifts and hidden correlations that traditional rule-based systems cannot detect.
Quality control without manual rules
Smart RDM replaces static thresholds and hard-coded logic with adaptive models that learn normal process behavior and automatically adjust to changing conditions.
Context-aware quality insights
Quality deviations are analyzed in the full operational context, including production state, maintenance events, energy conditions, and environmental factors.
Unlike traditional AI for manufacturing quality control, Smart RDM does not depend on predefined limits or isolated quality checks. Models continuously learn from new data and evolve together with the process, ensuring long-term robustness and scalability.
By combining predictive quality, anomaly detection, and adaptive analytics, Smart RDM turns quality control into an intelligent, self-learning capability that directly supports continuous manufacturing process optimization.
Read about how Danone used Smart RDM to improve productive efficiency and be more sustainable.
