The Unified Namespace (UNS) in Manufacturing

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The digitalization of industrial production has been driven forward for years with ever-increasing investment. However, comprehensive factory-wide or even company-wide digitalization is still the exception. According to McKinsey, there is a value creation potential of USD 3.7 trillion, but more than 70% of companies are unable to exploit this potential (source). Even on factory tours through so-called lighthouse factories, the production lines presented are often lighthouses themselves. So what is stopping the industry from making the breakthrough and can the application of the Unified Namespace (UNS) concept help?

 

The data problem in the manufacturing industry

To answer the question of the decisive obstacle, we need to understand where the potential of USD 3.7 trillion actually comes from. The simple answer: data. Industry 4.0 strives to use all available data to gain comprehensive insights into the past, current and future business situation. This includes:

  1. analyzing past business processes to create transparency for key performance indicators such as OEE
  2. Monitoring and analyzing current processes (e.g. through the implementation of condition monitoring)
  3. as well as the prediction of future business processes (e.g. by predicting machine failures with the help of predictive maintenance).

It does not matter whether the resulting decisions are made by company managers or by automated decision-making processes. If data is now unlocking potential, what is holding the industry back?

Although the effective use of data is crucial to remaining competitive, the industry has not yet managed to achieve a breakthrough. In addition to general challenges such as data silos, data quality and data security, the following two specific problems exist within the manufacturing industry.

 

1. Extreme system heterogeneity in OT and IT

The integration of OT/IT (e.g. machines, Historian, MES) usually takes place in the brownfield, with considerable differences between production lines and factories. Numerous generations of systems exist side by side, some of which may have been in operation for decades. The heterogeneity of the systems generally increases with the size of the company. The lack of standards in the systems, their interfaces and their data semantics makes data connection and system integration considerably more difficult.

 

2. Application-centric architecture

Today’s system architectures are very complex. Every system provider, whether in the OT or IT sector, sees its own application as the central pivotal point. The applications are integrated with all other applications on the neighboring levels of the automation pyramid. Each integration is created individually. This essentially means that protocols, data types and semantics have to be mapped, copied back and forth and integrated each time. Each new integration increases the overall complexity of the system architecture. And with increasing complexity, data acquisition and the system integration is becoming more and more complicated.

Complex system architectures in the manufacturing industryIn addition, the dependency on an OT or IT system increases with every integration. The dependency does not usually arise from the application itself, but from the non-transparent network of integrations that are built around it. This is exactly what makes it difficult to switch providers. While systems should actually be interchangeable, they are at the center of the architecture (application-centric architecture).

 

The Unified Namespace (UNS) – Overview

In contrast to an application-centric architecture, a data-centric architecture focuses on the data. The idea is that systems and applications should be built around the data and be interchangeable. This is precisely where the concept of the Unified Namespace (UNS) comes in. A Unified Namespace (UNS) represents a non-hierarchical system architecture in which all data is accessible via a standardized naming convention and data structure in a central message broker. Systems that are connected to the US act as both data producers and consumers. You publish and subscribe to data via the central message broker and adhere to plant or company-wide data standards.

The Unified Namespace (UNS) in Manufacturing

This enables simple organization and access to data as well as the shared use and linking of resources across different systems and locations. In the manufacturing industry, MQTT and/or Kafka are often used successfully as Unified Namespace (UNS) brokers.

 

OT/IT integration and standardization in the UNS

To integrate heterogeneous OT and IT systems (e.g. PLC, SAP) into the broker, an additional component in the data infrastructure is recommended. This provides the corresponding OT/IT connectors and structures all data according to the defined naming conventions and data structures. This ensures that the connected data sources publish the data to the broker in accordance with a defined standard so that every system or user can retrieve and use this data directly without any additional effort. Standardization, especially of the heterogeneous machine landscape, is the key to a scalable application of the Unified Namespace (UNS) in the manufacturing industry.

 

Advantages of the UNS in the manufacturing industry

A UNS ensures fast data availability, scalability and dynamic exchangeability of the systems involved. Therefore, the use of the Unified Namespace (UNS) in the manufacturing industry has the following advantages:

  1. A flexible architecture that can adapt to changing business requirements (e.g. scale-up or scale-down scenarios)
  2. A central location for sharing and managing factory data (single source of truth), regardless of source, protocol or format
  3. Improved data integration and interoperability across departments, processes, systems and technologies
  4. Improved data accessibility and transparency for all users
  5. Independence from OT/IT system providers

 

The Unified Namespace (UNS) in Manufacturing

While the concept of a UNS can be used in various industries, there are some specific questions that need to be answered in the manufacturing industry. Examples of this include the question of the appropriate architecture level for the UNS implementation (e.g. factory edge, cloud) or the question of integrating OT systems (e.g. machines) into the UNS. Often dozens or even hundreds of factories and thousands of plants, systems and technologies have to be integrated. It should also be borne in mind that although a UNS in production can bring the aforementioned benefits, the costs of implementing and maintaining such an architecture must also be taken into account.

To meet these challenges successfully, careful evaluation in advance is crucial. The following steps provide guidance.

 

1. Define your goals in the area of data management

Identify the most important requirements of your business units (e.g. increasing transparency in operational processes) in order to derive objectives for data management at company, business unit and operational level (e.g. increasing data accessibility). Define priorities and set measurable goals (e.g. make data from system X accessible to department Y). This is important in order to monitor and check your UNS Pilot use case at a later stage.

2. Define your data architecture

A well-defined data architecture includes a blueprint for how your factory data is organized and managed. This includes how data is published, transformed, stored and accessed by different users in the UNS. The blueprint also includes a definition of standardized data models that can be applied to plants and factories. A data model is an abstract archetype for describing the properties of real objects (e.g. machines, test benches) and their relationship to each other. It essentially defines the structure of the data (e.g. how data is received or displayed in an application). Defining a common data model can be challenging because you may not yet have a complete picture of all the use cases in your factories. Therefore:

  1. Use general standards as a basis (e.g. ISA95, OPC UA Companions, specifications from consortia)
  2. Start with your first pilot use case (e.g. company-wide OEE analysis)
  3. Develop your data models further if there are extensions or changes in the use cases.

 

3. Identify the most important OT/IT systems and stakeholders

Identify the most important OT/IT systems that need to be integrated into the UNS. Examples include PLCs, sensors, SCADA, historians, databases, MES and ERP systems. Also determine the key stakeholders who need to access these resources (e.g. business users, data analysts).

 

4. Define your infrastructure

The data infrastructure should be derived from your data management objectives and ensure that the defined data architecture can be rolled out across all factories. Remember that you may have real-time or closed-loop requirements in your factories that need to be considered when defining the server infrastructure and networks for your UNS. As a rule of thumb, pre-processing and harmonization of factory data should be carried out as close to the source as possible. In this way, you can make factory data available in your network as early as possible and minimize cloud costs at the same time. They also enable closed-loop use cases with lower latencies and higher security requirements. In most cases, the implementation of a well-coordinated edge-to-cloud UNS architecture is most effective (see the example with i-flow as a factory UNS combined with Azure IoT).

Industrial Unified Namespace (UNS) ArchitectureIn addition to the definition of servers, networks, etc., this step also includes the selection of suitable middleware for your UNS. The middleware takes care of the integration of the various systems and provides a message broker as a central location for your factory data. At best, a search mechanism makes finding your factory resources as easy as possible. The more intuitive the UNS middleware is designed, the less training is required for your employees (e.g. for accessing resources within the UNS).

 

5. Start with your Unified Namespace (UNS) Pilot Use-case

Prioritize potential use cases based on their cost-benefit ratio to define a pilot use case. Monitor and review your pilot use case with regard to your data management goals.

 

Summary

Data is crucial for the competitiveness of the manufacturing industry. The effective use of data in different factories enables efficiency and quality improvements as well as cost reductions on a global level. However, one key mistake is holding the industry back: application-centric architectures. Why do many OT and IT system providers believe that their application is the center of all things? Why is data often the property of the system provider or the application (e.g. because data is stored in individual data structures in inaccessible application databases)? This approach is fundamentally wrong!

The concept of the Unified Namespace (UNS) corrects this error. A UNS places the data at the center of the system architecture and manufacturing companies become the real owners of their data. Applications are then integrated around this data. The result is greater flexibility, interoperability and data availability.

When implementing a UNS in the manufacturing industry, there are some challenges that should be considered in the evaluation process. By defining clear steps in the evaluation (such as defining data management goals), you can minimize the risk of not simply putting another application (instead of your data) at the center of your architecture with the UNS.

 

i-flow – Unified Namespace (UNS) for the Industrial IoT

The i-flow software offers an Industrial Unified Namespace (UNS) – tailor-made for use in factories. It provides the key components for implementing a UNS architecture.

i-flow Industrial Unified Namespace (UNS)

Connectivity Layer: With over 200 connectors, you can integrate common OT and IT systems into your UNS with just a few clicks – whether in the shopfloor or in IT.

Harmonization Layer: The i-flow software harmonizes and standardizes system interfaces and source data before the data is made available to other systems in the Message Broker.

Message-Broker: i-flow provides a fully integrated and powerful MQTT broker. If the infrastructure already exists, the software supports existing message brokers such as MQTT and Kafka.

Microservices: Combine, aggregate and transform OT and IT data via i-flow microservices. Example: Instead of a cycle time, a machine only provides a time stamp at the start and end of the process. Using an i-flow microservice, the cycle time can be calculated and published in standardized form in the UNS.

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