Process Analytical Technology (PAT)

Short definition: What is Process Analytical Technology?

Process Analytical Technology (PAT) describes an approach where manufacturing processes are designed, analysed and controlled so that critical process parameters (CPPs) are measured in (near) real time to directly infer the critical quality attributes (CQAs) of the product. The goal is to secure quality within the process, instead of mainly at the end through sample-based testing (U.S. Food and Drug Administration).

PAT was originally defined by the US FDA as a framework for pharmaceutical manufacturing, but today it is a generic concept for any production environment in which process data and quality are tightly linked – from pharma and chemicals to discrete manufacturing.

Typical characteristics of PAT

• Real-time or near-real-time measurement of process variables (e.g. temperature, pressure, humidity, concentration, force, torque)
• Derivation and monitoring of CQAs based on these process data
• Statistical and multivariate data analysis (MVDA) for pattern recognition
• Automated process control and – in the ideal case – Real-Time Release Testing (RTRT)

Longer-term PAT objectives include: shorter cycle times, less scrap, true real-time release, higher automation levels and lower energy and material consumption.

QbD, CQAs and CPPs: the conceptual foundation of PAT

PAT is not an isolated tool, but part of a Quality by Design (QbD) approach:

• Quality Target Product Profile (QTPP): What is the product supposed to deliver?

• Critical Quality Attributes (CQAs): Which measurable characteristics are decisive for product quality and safety?

• Critical Process Parameters (CPPs): Which process parameters significantly affect these CQAs?

PAT builds on this logic:

1. Understand the process
   Identify cause–effect relationships between material attributes, process parameters and quality characteristics.

2. Select sensors and analytics
   Define process analytical measurement methods (e.g. NIR, Raman, force–displacement curves, temperature and pressure profiles, machine vision).

3. Define models and limits
   Set up multivariate models, design space, alarm thresholds and intervention limits.

4. Implement a control strategy
   Configure rules, workflows and automation so that deviations are detected early and corrected – instead of just being documented afterwards.

Without clearly defined CQAs and CPPs, PAT remains a buzzword. Only the combination of process know-how, data analytics and a robust IT/OT infrastructure turns the concept into a productive, scalable practice.

PAT in practice: from pharma to discrete manufacturing

In the pharmaceutical industry, PAT is typically used to:

• Monitor granulation, drying or coating processes inline

• Measure API content, moisture or coating thickness via spectroscopic methods

• Complement or replace classical end-product testing with Real-Time Release Testing (RTRT)

The same principles transfer 1:1 to other industries – with different signals and sensors:

• Forming and molding processes:
  Pressure, temperature and cycle time profiles in injection molding, die casting or pressing

• Machining processes:
  Force, spindle load and vibration signals on milling or turning machines

• Assembly and joining processes:
  Force–displacement curves in joining operations, torque and angle signatures in tightening processes

• Test and inspection processes:
  Leak tests, force, acoustic or electrical tests, vision inspections at end-of-line (EOL)

• Material flow & intralogistics:
  Buffer levels, throughput times, AGV/AMR fleet status

Wherever these process signals are available and can be linked with quality data, PAT becomes technically and economically attractive – especially for mid-sized manufacturers that must handle high volumes with limited resources.

The role of an MES: backbone for PAT

Sensors alone do not create a PAT strategy. What really matters is that process data are:

• captured completely and with accurate timestamps,

• linked to orders, lots, products, tools and operators,

• analysed and visualised in (near) real time,

• translated into workflows (alerts, holds, escalations).

This is where a modern Manufacturing Execution System (MES) comes in:

• End-to-end collection of production KPIs (counts, cycle times, availability, OEE) and process data across all lines and shifts

• Real-time dashboards and drill-down analyses for operators, line supervisors and plant management

• Linking of process data with quality and order data to provide full traceability and objective shift comparisons

• Event- and alarm-based notifications when thresholds are violated, critical trends emerge or specific patterns appear in process data

Without a capable MES or equivalent data platform, PAT is difficult to implement at industrial scale – simply because data volume, velocity and variety can no longer be managed manually.

Process Analytical Technology with SYMESTIC

SYMESTIC is a cloud-native MES platform that provides these PAT building blocks out of the box – specifically for mid-sized companies in discrete manufacturing.

Real-time process data analytics

• Acquisition of cycle times, pressures, temperatures and other process variables directly at machine level (OPC UA, digital signals, IoT gateways)

• Contextualisation with order, quality and operator data for full traceability

• Trend analyses and limit monitoring to detect critical deviations early – with documented effects such as significantly reduced scrap

In-process quality assurance instead of end-only inspection

• Inline monitoring of critical process parameters and automatic labelling of affected lots or units

• Integration of inspection and test stations (e.g. vision systems, force/pressure tests, leak tests) into a central data foundation

• Basis for real-time release scenarios in non-validated environments (e.g. automotive, metals, plastics, FMCG) where quality is derived from process data and patterns

Operational Excellence & OEE improvements as visible outcome

• Standard analyses for OEE, downtime, loss structures and process stability

• Field experience: significant increases in asset performance, noticeable reductions in unplanned downtime and measurable productivity gains already in the first weeks after go-live

Cloud-native architecture as PAT enabler

• Scalable cloud architecture on Microsoft Azure, with edge connectivity for rapid machine integration and standardised interfaces to ERP, existing MES or energy management systems

• Multi-tenancy and centralised data storage that simplify cross-site PAT strategies (e.g. global OEE reporting, comparing design spaces across plants)

Typical PAT-related use cases with a Cloud MES

Typical PAT-like scenarios that can be implemented with a Cloud MES such as SYMESTIC:

Injection molding and pressing processes

• Monitoring of tool temperature control, injection pressure, holding time and cycle time

• Automatic tagging of affected parts when limits are violated

Assembly and joining processes

• Real-time analysis of tightening and joining curves

• Automatic NOK classification and blocking of suspicious units, with guided rework flows

CNC and machining processes

• Spindle load, vibration and temperature profiles as indicators of tool wear and quality drift

• Forecasting of maintenance needs (Predictive Maintenance) based on process data

End-of-line tests and inspection processes

• Linking EOL test data with upstream process histories to identify systematic root causes for defect patterns

• Foundation for robust improvement loops in quality and process engineering

These scenarios directly address the pain points of typical SYMESTIC target customers in discrete series and batch production: lack of transparency around downtime, scrap and quality risks, and high manual effort for data collection and analysis.

FAQ: Frequently Asked Questions about Process Analytical Technology

What is the difference between PAT and classical quality inspection?

Classical quality assurance focuses mainly on end-of-line or batch-wise lab testing. PAT shifts the focus into the process: process parameters are continuously monitored, models detect deviations early, and quality is secured “on the fly”. End tests become shorter, more targeted – or partly redundant.

Do I need pharma frameworks such as ICH Q8/Q9 for PAT?

These frameworks originate in pharma, but they provide general concepts like CQAs, CPPs, design space and risk-based control strategies. That logic is applicable in any industry – without having to adopt the full regulatory complexity of pharma.

Is PAT only worthwhile for large corporations?

No. Mid-sized plants with high automation, cost pressure and limited transparency often benefit strongly from a structured PAT approach – if implemented pragmatically: start with the most critical lines, the most relevant quality risks and the largest loss drivers.

What role does a Cloud MES play in PAT?

It forms the backbone of the data strategy: it connects sensors and controllers with order and quality data, provides analytics, dashboards, alerts and workflows, and enables real-time decisions along the entire value chain – from machine to management.