Industrial Process Measurement and Control: The Essential Standards for Digital Manufacturing Excellence

Modern manufacturing is rapidly evolving—embracing digitalization, data-driven intelligence, and seamless integration across enterprises. As new technologies transform production, the foundation for robust, efficient, and secure operations lies in adhering to international standards for industrial process measurement and control. This guide explores four cutting-edge standards that are vital for businesses aiming to maximize productivity, security, and scalability while integrating next-generation technologies.

Overview / Introduction

The manufacturing sector is experiencing explosive digital growth. From interconnected smart factories to global supply chain integration, effective process measurement and control have become critical for success. International standards bring order, common language, and interoperability to these complex environments.

Why Standards Matter in Process Measurement and Control

• Enable seamless collaboration across platforms and organizations
• Ensure data consistency and quality for informed decision-making
• Accelerate adoption of advanced technologies (like digital twins and AI)
• Protect supply chains by enforcing security, traceability, and reliability
• Support regulatory and compliance needs in global markets

In this article, you'll learn how four recent international standards:

• ISO 21175-1:2026 – Modeling and simulation collaboration
• ISO 23247-5:2026 – Digital twin digital thread
• ISO 29002:2026 – Characteristic data exchange
• ISO/TS 8000-230:2026 – Sensor data quality/correction converge to form the backbone of scalable, secure, and future-ready industrial operations.

Detailed Standards Coverage

ISO 21175-1:2026 – Reference Model and Process for Simulation Collaboration

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

This standard defines a clear framework and reference process for establishing a Collaborative Modeling and Simulation Environment (CMSE). It's designed to address one of manufacturing's toughest challenges: how to conduct joint simulation projects across different platforms, owned by different enterprises or departments—anytime, anywhere.

Scope and Application: ISO 21175-1:2026 applies to any organization engaged in joint modeling and simulation—whether you're a major manufacturing enterprise, an SME, or even outside classic manufacturing. The focus is on supporting business planning, production control, and operations management across the functional hierarchy (referencing IEC 62264-3 levels 2 to 4).

Key Requirements and Concepts:

• General framework for CMSE: Neutral interfaces, meta-models, and service-oriented environments
• Joint simulation project guidance: Methods for analysis, realization, and implementation
• Stakeholder support: Tools to enhance collaboration and integration
• Functional hierarchy links: From business planning to production control

Practical Implications:

• Boosts agility by enabling flexible, on-demand simulations
• Removes barriers between disparate infrastructures, software tools, and operating systems
• Fosters innovation via collaborative virtual product development
• Supports digital transformation strategies based on model-based systems engineering, cyber-physical systems, and digital twins

Key highlights:

• Offers a foundation for cross-platform simulation collaboration
• Integrates business analysis, software, and infrastructure support
• Establishes neutral, reusable interfaces for rapid scalability

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

The digital twin is revolutionizing manufacturing by providing real-time, data-driven representations of observable manufacturing elements. However, this digitalization can create data silos unless all twins are linked throughout the product lifecycle. ISO 23247-5:2026 introduces the digital thread—a bi-directional, trustworthy information flow that connects digital twins across design, production, testing, and operations.

Scope and Application: This standard is indispensable for any manufacturing organization looking to deploy digital twins at scale, especially those aiming for seamless data connectivity between design, manufacturing, logistics, and service.

Key Requirements and Specifications:

• Defines the digital thread concept: Principles for linking digital twins
• Establishes a digital thread entity: Tools for managing links and metadata
• Addresses digital thread creation, management, lifecycle, and retirement
• Ensures interoperability: Works across different databases, platforms, and organizations
• Supports supply chain integration and multi-party cooperation

Practical Implications:

• Prevents data fragmentation and loss through integrated lifecycle data management
• Enables predictive maintenance, traceability, and compliance
• Improves responsiveness by reducing data isolation and delays
• Supports best-in-class manufacturing analytics and process optimization

Key highlights:

• Facilitates continuous data flow and traceability
• Builds foundations for advanced analytics and machine learning
• Makes digital twins scalable and sustainable in the extended enterprise

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

Smooth digital transformation and interoperability in manufacturing depend on reliable, unambiguous data exchange. ISO 29002:2026 provides a universal resource enabling interoperability of characteristic data across multiple industrial standards and concept dictionaries.

Scope and Application: This standard is relevant to all businesses that need to translate, share, or integrate specification data (such as properties, parameters, or tolerances) among various software tools, companies, or geographies. It's particularly applicable for supply chain integration, regulatory compliance, and digital product lifecycle management.

Key Requirements and Features:

• Resources for characteristic data exchange: Including conceptual models, identification schemes, data elements, and retrieval mechanisms
• Supports various industrial standards: ISO 13399, ISO 13584, ISO 15926, ISO 18101, ISO 22745, IEC 61360, IEC 62656, and more
• Concept dictionary access: Standardized mechanisms for resolving and fetching concept and terminological data
• Compatibility and extensibility: Formats can be profiled or used without restriction
• Web services integration: WSDL and SOAP bindings, retrieval, and search queries

Practical Implications:

• Eliminates ambiguity and redundancy in data exchange—no more mismatched specification data
• Accelerates onboarding of digital supply chains and partners
• Ensures products and processes remain traceable and compliant throughout their lifecycle
• Reduces IT integration costs by harmonizing identifiers and formats

Key highlights:

• Comprehensive support for interoperable data exchange
• Enables concept-driven, machine-readable specification transfer
• Facilitates advanced, automated search and data retrieval services

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

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

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

With the surge in IoT and sensor networks, manufacturing data is now more voluminous and critical than ever. Yet, poor data quality can cripple analytics, automation, and AI. ISO/TS 8000-230:2026 provides a systematic process for cleansing sensor data, ensuring that analytics and process controls are based on reliable, high-quality data.

Scope and Applicability: Targeted at organizations implementing large-scale sensor or IoT networks—whether in production lines, supply chains, or equipment monitoring—ISO/TS 8000-230:2026 is relevant for anyone relying on sensor-generated data for decision making or automation.

Key Guidelines and Specifications:

• Principles for sensor data cleansing: When and how to cleanse, flag, or preserve anomalies
• Robust cleansing process: Preparation, measurement, and improvement based on the plan-do-check-act cycle
• Implementation requirements: Actions, consent, and stakeholder involvement
• Catalog of anomaly detection and repair methods: Reference to best practices
• Examples and guidance: Practical walkthroughs for applied data quality improvement

Practical Implications:

• Helps ensure data integrity before advanced analytics, AI, or reporting
• Reduces errors and risks stemming from low-quality or anomalous data streams
• Supports ongoing process optimization and digital trust
• Aligns with other ISO 8000 data governance and quality frameworks

Key highlights:

• Structured, stepwise approach for sensor data correction
• Promotes stakeholder agreement and transparency
• Provides validation for digital transformation initiatives reliant on sensor networks

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

Industry Impact & Compliance

Building a world-class, digitally enabled manufacturing operation depends on rigorous adherence to international standards. By making these four standards pillars of your process measurement and control framework, organizations benefit from:

• Improved collaboration and knowledge sharing internally and across supply chains
• Consistent, high-quality data fueling accurate analytics, predictive maintenance, and decision-making
• Easy onboarding of new partners, software platforms, or technologies
• Proactive compliance and risk mitigation in regulated and safety-critical environments
• Resilience and adaptability in the face of globalization, cybersecurity threats, and emerging tech

Risks of Non-Compliance:

• Data silos, redundant work, and high integration costs
• Reduced competitiveness or agility
• Increased vulnerability to cyber threats and IP loss
• Failure to meet customer, regulatory, or sustainability requirements

Implementation Guidance

Common Implementation Approaches

1. Assess process maturity against these standards using internal audits or maturity models
2. Engage all stakeholders (IT, operations, data managers) in gap analysis and planning
3. Leverage implementation profiles or best practices from ISO/IEC guidance
4. Pilot cross-platform simulation or digital thread projects using standards-based interfaces and meta-models
5. Deploy integrated data cleansing and exchange solutions for sensor and characteristic data
6. Continuous monitoring and improvement based on Plan-Do-Check-Act cycles from ISO frameworks

Best Practices for Success

• Train staff on data quality, digital twin, and collaborative simulation concepts
• Utilize modular, scalable IT architectures that embrace service orientation and open standards
• Embed quality checks and data cleansing upstream in the data lifecycle
• Leverage concept dictionaries and meta-data repositories to centralize standards compliance
• Engage partners throughout the supply chain in standards adoption and governance

Resources for Organizations

• Standards organizations (ISO, IEC) and consortia provide implementation guides, case studies, and profiles
• Accredited auditors and certification bodies for standards compliance
• iTeh Standards' digital catalog for ongoing updates and supplementary materials

Conclusion / Next Steps

International standards for industrial process measurement and control are no longer optional—they are mission-critical for any business seeking sustainable growth in the age of digital manufacturing.

Key Takeaways:

• Implementing standards like ISO 21175-1:2026, ISO 23247-5:2026, ISO 29002:2026, and ISO/TS 8000-230:2026 injects reliability, agility, and scalability into every stage of your manufacturing process.
• These standards streamline collaboration, automate compliance, and build a foundation for advanced manufacturing technologies such as digital twins, IoT analytics, and adaptive supply chains.
• Investing in standards-based transformation future-proofs your operations against disruptions while supporting security, interoperability, and best-in-class quality outcomes.

Recommendations:

• Prioritize a standards-based approach in digital transformation roadmaps
• Engage with iTeh Standards for the latest guidance and future updates
• Champion a culture of data quality, collaboration, and continuous improvement in your organization

Take action today: Explore these and other industrial process standards on iTeh Standards and empower your business for future manufacturing success.