Industrial Process Measurement and Control: Key Standards for Modern Manufacturing Success

Industrial process measurement and control form the backbone of modern manufacturing, supporting real-time decision-making, automation, and continuous improvement. As companies integrate new technologies such as digital twins, advanced data analytics, and collaborative simulation platforms, international standards provide essential frameworks to ensure interoperability, reliability, and data quality. This guide explores four industry-leading standards—ISO 21175-1:2026, ISO 23247-5:2026, ISO 29002:2026, and ISO/TS 8000-230:2026—that are driving the next generation of smart manufacturing practices.

Overview / Introduction

Today’s manufacturing landscape is rapidly evolving, shaped by digital transformation, global supply chains, and the drive for improved efficiency. To manage these complexities, organizations need robust frameworks for process measurement and control—a role filled by international standards. These guidelines and specifications ensure that machinery, software, and organizational practices can communicate, interoperate, and scale seamlessly.

Adopting leading industrial process measurement and control standards is no longer optional—it’s essential. Standards like ISO 21175-1:2026 and ISO 23247-5:2026 enable collaborative simulation across platforms and support the use of digital twins, making smart factory implementations practical and reliable. Standards governing data exchange (ISO 29002:2026) and data quality (ISO/TS 8000-230:2026) ensure that information used for planning, control, and optimization remains trustworthy and actionable.

In this article, you’ll learn:

• The purpose and scope of four essential standards

• Practical ways these standards impact daily operations

• Compliance requirements and best practices

• How standards implementation can enhance productivity, security, and business scalability

Detailed Standards Coverage

ISO 21175-1:2026 – Collaborative Modeling and Simulation Environment (CMSE)

Automation systems and integration — Collaboration environment requirements of simulation on different manufacturing platforms — Part 1: Reference model and process

ISO 21175-1:2026 introduces a reference model and process for Collaborative Modeling and Simulation Environment (CMSE), creating a framework to help organizations carry out joint simulation projects across varied manufacturing platforms. It targets both manufacturing enterprises and any organizations utilizing joint simulation, whether across different companies or within departments (for example, engineering and production).

This standard places special emphasis on cross-platform simulation collaboration—crucial in today’s globalized and interconnected ecosystems. It specifies a framework for:

• Service-oriented infrastructure sharing

• Software integration and joint business process simulation

• Implementation guidance for simulation activities that support everything from business planning and logistics to production control

By focusing on collaboration among stakeholders—from suppliers to engineering to operations—ISO 21175-1 enables on-demand simulations that are essential for rapid product innovation and process optimization. The standard rests on neutral interfaces, meta-models, and interoperability principles, supporting flexible and scalable digital manufacturing.

Practical implications: Implementation ensures that joint simulation projects—whether for new equipment layouts, complex product development, or virtual commissioning—run smoothly, reduce manual integration effort, and foster stronger cross-team collaboration.

Key highlights:

• Defines the general CMSE framework and joint simulation methodologies

• Supports service-oriented infrastructure and flexible simulation access

• Enhances cross-department and cross-enterprise interoperability

Access the full standard: View ISO 21175-1:2026 on iTeh Standards

ISO 23247-5:2026 – Digital Thread for Digital Twin in Manufacturing

Automation systems and integration — Digital twin framework for manufacturing — Part 5: Digital thread for digital twin

Digital twins are transforming manufacturing by enabling real-time monitoring, predictive maintenance, and lifecycle management. ISO 23247-5:2026 provides comprehensive guidelines on how to establish and manage digital threads—the critical links that allow digital twins to span the product lifecycle, from design and engineering to production and beyond.

This standard details:

• Definitions for digital thread, digital twin entities, and metadata

• Creation, management, and maintenance of digital twins using digital threads

• Bidirectional and trustworthy linkage of data across time, processes, and organizational boundaries

• Practical methodologies and real-world use-cases

ISO 23247-5 helps eliminate data silos in manufacturing by ensuring that information is consistently available and traceable across the entire value chain. It enables advanced analytics, simulation, and improved decision-making by maintaining a continuous and contextualized digital record.

Practical implications: Manufacturers adopting digital twins for their assets can use ISO 23247-5 to unlock full value: coordinated process control, quality tracking, component genealogy, and seamless collaboration between design and operations.

Key highlights:

• Defines ‘digital thread’ and how it connects various digital twins

• Supports integration across product lifecycle: design, make, test, validate

• Improves process transparency, traceability, and cross-platform compatibility

Access the full standard: View ISO 23247-5:2026 on iTeh Standards

ISO 29002:2026 – Exchange of Characteristic Data

Industrial automation systems and integration — Exchange of characteristic data

ISO 29002:2026 specifies a versatile framework for exchanging characteristic data—a fundamental requirement for cross-system manufacturing automation and smart supply chains. The standard supports interoperability among popular industrial data systems such as ISO 13399, ISO 13584, ISO 15926, IEC 61360 (including the IEC Common Data Dictionary), and others.

It covers:

• Principles for characteristic data exchange using structured concept dictionaries

• A conceptual model for basic entities, identifiers, and exchange formats

• Methods for identifying, retrieving, and searching data related to parts, assemblies, and processes

• Interoperability mechanisms, including web services (WSDL, SOAP)

Organizations rely on unambiguous data exchange to drive procurement, quality assurance, and regulatory compliance. ISO 29002 ensures that information sent between software applications, machines, and business partners is accurate, consistent, and easily interpretable by both people and machines.

Practical implications: Enables reliable sharing of product, equipment, and process characteristics—enhancing automation, digital thread creation, and Industry 4.0 deployment.

Key highlights:

• Facilitates interoperability among major industrial data standards

• Provides robust identification, retrieval, and search capabilities

• Foundational for supplier integration and smart ordering processes

Access the full standard: View ISO 29002:2026 on iTeh Standards

ISO/TS 8000-230:2026 – Data Quality and Sensor Data Cleansing

Data quality — Part 230: Sensor data — Guidelines for data cleansing

As sensor networks, IoT, and automated measurement technologies proliferate, ensuring the quality of sensor-derived data becomes paramount. ISO/TS 8000-230:2026 provides organizations with process-based guidelines to cleanse sensor data anomalies and raise the quality of their measurement systems.

This technical specification covers:

• Principles for identifying and correcting anomalies in sensor data streams

• Structured cleansing process including preparation, measurement, and improvement cycles

• Implementation requirements for offline (post-processed) cleansing

• Lists of detection and repair methods, plus practical examples

Emphasizing consent among stakeholders and minimized modification, the standard supports both automated and manual approaches to ensure data is suitable for analysis, reporting, and automated control.

Practical implications: Critical for organizations looking to improve decision-making, model accuracy, and compliance when leveraging data from sensors across equipment and production lines.

Key highlights:

• Step-by-step guidelines for sensor data cleansing

• Supports process optimization, compliance, and data-driven management

• Boosts confidence in sensor-based monitoring, predictive analytics, and AI

Access the full standard: View ISO/TS 8000-230:2026 on iTeh Standards

Industry Impact & Compliance

Manufacturers today are under immense pressure to innovate quickly, maximize quality, and streamline costs. These standards underpin strategies for digital transformation, enabling agile process measurement, reliable automation, and stringent quality control.

Key impacts include:

• Enhanced productivity: Real-time data and simulation tools reduce downtime and improve planning

• Improved security: Common frameworks decrease integration risks, data loss, and inconsistencies

• Scalability: Seamless data and process integration ensures organizations can grow or adapt quickly

• Compliance: Regulatory bodies and quality-driven customers are increasingly demanding adherence to recognized standards

Compliance considerations:

• Conformance assessment may involve both technical evaluation and documentation of processes

• Implementing these standards often requires buy-in from cross-functional teams—IT, engineering, quality, and operations

• Failing to comply can lead to integration challenges, data quality problems, or inability to participate in modern manufacturing networks

Implementation Guidance

Common approaches for implementation:

1. Gap Analysis: Compare current capabilities and processes against the requirements set out in each standard.

2. Stakeholder Engagement: Involve technical, quality, and operational leaders from the outset.

3. Training: Equip your teams with knowledge of terminology, data models, and best practices outlined in the standards.

4. Digital Transformation Roadmap: Prioritize processes for standardization, starting with those most critical to business outcomes.

5. Tool and Platform Selection: Choose or upgrade software solutions that natively support standard-compliant interoperability and data management.

6. Continuous Improvement: Regularly review processes for ongoing compliance and performance improvements.

Best Practices:

• Document data flows and simulation processes, linking them to standard requirements

• Validate and cleanse sensor data before using it for analysis or decision-making

• Create master data management strategies for characteristic data

• Use digital twins and simulation collaboratively within and across organizational boundaries

• Leverage external consultants and official resources from standards organizations for complex integrations

Resources Available:

• Full standard documents via iTeh Standards

• Training workshops, e-learning modules, and vendor support

• Industry forums and working groups on process measurement and digital twin technologies

Conclusion / Next Steps

Adopting international standards for industrial process measurement and control is a strategic imperative in the era of smart manufacturing. ISO 21175-1:2026, ISO 23247-5:2026, ISO 29002:2026, and ISO/TS 8000-230:2026 provide the critical building blocks for scalable, secure, and data-driven production environments.

Key takeaways:

• Standards ensure interoperability, data integrity, and effective collaboration

• Implementing standards reduces risk, increases efficiency, and opens doors to advanced analytics and automation

• Regular review and alignment with evolving standards support sustained competitiveness

Recommendations:

• Conduct an internal audit or assessment against these standards

• Invest in employee training and awareness of process measurement and data management best practices

• Prioritize the integration of standards into digital transformation and Industry 4.0 projects

• Stay informed about updates and emerging best practices by following leading standards organizations

Take the next step in future-proofing your manufacturing operations by exploring the full text of each standard and leveraging the resources at iTeh Standards. Integrating these essential frameworks will position your organization for success amid ongoing technological change.