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IoT and Digital Engineering
Bringing Together IoT and Digital Engineering to Create Rich Experiences
Highlights
- Data collection, interpretation, and availability challenges invariably hold up digital manufacturing initiatives. An approach that involves complex engineering and multiple data processing applications may help to address these challenges.
- The unified namespace (UNS) facilitates data democratization by using standardized tag naming conventions, adding context to the data, and using a publish/subscribe data exchange method.
- Implementation of the UNS requires a careful analysis of the factory landscape and the products available in the market to identify the apt fit for the manufacturing enterprise.
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Impediments
Manufacturing companies have to overcome significant hurdles to establish a seamless data exchange between the shop floor and enterprise systems.
Data collection, interpretation, and data availability in IT systems are the key problems faced by digital manufacturing programs (see figure 1). These last-mile connectivity issues happen due to legacy protocols, multiple platforms, data availability at the operations technology (OT) level, and non-standardized data templates. They are a significant hurdle for engineers and analysts trying to make use of OT systems and data for visualization, analytics, optimization, and AI/ML-driven autonomous operations.
To overcome such challenges, organizations try various approaches with varying degrees of success. Such changes end up introducing either new points of complexity or failures in the system landscape. Some of them are as detailed below:
- Writing specific application programming interfaces (APIs) for individual applications creates a complicated landscape with operations and maintenance issues.
- Creation of OT data lakes results in large databases stored locally without context and timestamps, which adds to increased management efforts.
- Vendor-specific industrial platforms increase vendor dependencies and consequently costs.
- Ad hoc implementations where OT connectivity applications are integrated with IT-oriented middleware result in ‘force fit’ implementations, leading to loss of context to OT data, and application-specific custom coding at the middleware level to manage the OT data.
These approaches often increase the complexity of the landscape due to multiple data collection and processing applications. The diversity of data causes governance challenges and higher costs, thus requiring streamlined, scalable OT data integration that combines OT data with manufacturing operations and enterprise data to create rich information models.
Challenges in digital manufacturing initiatives
Data challenges in manufacturing
Challenges in digital manufacturing initiatives
Unified Namespace (UNS) is proposed to address the above pitfalls. Evolving as the next step in data-driven industrial automation systems, UNS forms a single source of truth for all data and information across the organization’s infrastructure.
The UNS
Bringing a ‘neural manufacturing’ enterprise through UNS.
The UNS ensures OT data availability and democratization by using standardized and easily recognizable tag naming conventions, thus adding context to the data, and using a publish/subscribe data exchange method (see figure 2). It also facilitates:
- Data availability across systems and platforms, mitigating the creation of data silos in the OT layer due to the complexity and variety of systems.
- Easily recognizable data naming conventions enable users across the spectrum to look for operations and production information that was otherwise available only through intermediaries.
- Easy integration with IT applications consuming OT data.
- Enhanced data visibility with near real-time information boosts analytics and enhances real-time decision-making, thereby increasing operational efficiency.
Figure 2: Unified Namespace
The Unified Namespace
Figure 2: Unified Namespace
Find the right mix
Navigating product diversity to implement the right fit.
A UNS implementation should address all aspects of OT data processing, including OT connectivity, data contextualization, and data democratization. Some readily available products in the market that provide UNS typically show their prowess in one of these areas and have upgraded themselves with added functionalities to provide a comprehensive UNS offering to the user.
A careful evaluation of the existing landscape and available products, along with industrial data contextualization and storage mechanisms, is required to create a comprehensive framework. The UNS products available in the market can be classified mainly as follows:
- OT connectivity products: These are products with OT maturity and prowess. They are now enhanced with data operations and data democratization components to make them suitable for UNS applications.
- Traditionally SCADA products: Many SCADA product vendors have enhanced their products to utilize their OT data collection, visualization, and scripting capabilities to cover the UNS use case.
- Data Operations products: These are products specifically designed for OT data operations, such as normalization, transformation, contextualization of OT data, and creation of information models, considering the specific characteristics of OT data.
- MQTT brokers: Many MQTT (originally Message Queuing Telemetry Transport) brokers available in the market have begun to add components that offer OT data collection and OT data operations capabilities and, offering UNS capabilities.
Often, a heterogeneous mix of products (with the appropriate data security and privacy considerations) would be the best approach to enabling a comprehensive Unified Namespace at an enterprise level. This approach also allows global organizations to achieve enterprise-level UNS implementation while keeping the landscape and vendor-specificities at a regional level.
Promising benefits
Realizing business benefits by streamlining data governance and management.
The UNS simplifies the interoperability, governance, and management of the data producer and consumer applications owned by different departments within the manufacturing enterprise. It offers the following benefits to enterprises:
- Data democratization: The publish-subscribe model facilitates data democratization. It establishes a one-to-many communication framework, thus ensuring a single source of truth. Additionally, it reduces the need for point-to-point integrations that would otherwise require the extraction of OT data for enterprise applications.
- Improved data standardization: The UNS approach can create namespaces as standardized models, like other information models based on accepted industrial standards. These naming conventions make it easier for IT teams to navigate, access, and utilize OT data without being constrained by a lack of knowledge of specific tag definitions and other industrial communication protocols. The information models that have been pre-engineered in the machine control systems lead to an additional level of complexity and variety in data.
- Managing variety in shop-floor systems: Legacy hardware coexists with modern systems, providing varying-quality data models for similar operations. The UNS helps to normalize, standardize, and contextualize data, thus enhancing the interpretation and accessibility of IT systems.
- Streamlined data exchange: It changes heterogeneous data into a consistent format for analytics and reporting platforms, thus reducing the risk of errors. Usage of common semantics further enhances interoperability by enabling integration of various systems with each other.
- Security and governance: UNS facilitates data governance policies to ensure data quality and compliance with industry regulations. Security is ensured using transport layer security (TLS) certificates, user authentication, secure data encryption methodologies, and regular updates. A comprehensive methodology is needed for IT, OT, and data security beyond UNS implementation.
Once data integration and interoperability are achieved, industries can realize benefits as given below:
- Accelerate the R&D process with data and insights shared easily through a centralized repository, enhancing collaboration. This helps accelerate the creation of new products and recipes and accelerates time to market.
- Improve manufacturing efficiency by providing contextualized data for dashboard applications and AI / ML agents. This results in better control over the production process, assurance of quality, and optimization of maintenance activities.
- Enhance traceability and causal analysis to investigate production losses and product quality issues, enabling quick remediation.
- Conduct proactive risk management by analyzing data from various sources quickly and efficiently to identify and mitigate potential risks and future production disruptions.
Getting started
UNS necessitates an approach that caters to varying levels of technological maturity and OT landscape across an enterprise.
A mature framework for data collection, operation, and democratization functionalities identified as core functional requirements is essential for embarking on the UNS journey. Existing sites with ongoing production activities require comprehensive migration strategies and integration challenges. The current application landscape and integrating these systems are key drivers for realizing the right UNS implementation. Strategies are developed for phase-wise or use-case-based migration depending upon the specific site requirements. Comprehensive project governance and change management processes need to be implemented to effectively and seamlessly execute the UNS within the enterprise. This will also avoid force-fitting costly software and hardware at sites where such products may not be economical or optimal.
A complex manufacturing enterprise can consider the UNS requirements of OT data standardization and availability as core functions, and product and hardware platforms available in the market as interchangeable tools to achieve the UNS functionality. This approach will result in a product-agnostic, federated technology architecture as required by sites of differing size, complexity, and maturity while maintaining the key tenets of UNS - data availability, contextualization, and democratization.
