Asset Administration Shell in practice

What is an Asset Administration Shell?

Industry 4.0 promises more efficient and sustainable manufacturing processes through digitalization. The foundation for this is a seamless, automatic exchange of information between systems and products. This is where the Asset Administration Shell (AAS) comes into play.

An Asset Administration Shell is a vendor-independent standard for describing digital twins. Basically, it is the digital representation of an asset; either a physical product or a virtual object (e.g., documents or software).

The AAS defines the appearance of the asset in the digital world. It describes which information of a device is relevant for communication and how this information is presented. This means the AAS can provide all important data about the asset in a standardized and automated way.

Let us take a look at a practical application to understand the benefits of an AAS:

Use case: AAS as enabler for new services

As part of the ESCOM research project, CONTACT Software collaborates with GMN Paul Müller Industrie GmbH & Co. KG to implement AAS-based component services. The family-run company manufactures motor spindles which are installed by its customers as components in metalworking machine tools and then resold.

Before the project began, GMN had already developed a new sensor technology. It enables deep insights into the behavior of a spindle and provides information on overall operation of the spindle system. The company wants to use this data to offer new, product-related services:

  • Certified commissioning: Before GMN ships its spindles, the components are put through a defined test cycle on the company’s in-house test bench. GMN uses the data from this reference cycle to ensure that motor spindles are installed and commissioned correctly at the customer’s facility.
  • Predictive services: Using the IDEA-4S sensor microelectronics, customers shall be able to continuously record and analyze operating data that provide insights into the availability and operation of the spindles. If necessary, the data can be shared with GMN, for example, for problem analysis. This saves valuable time until the machine is back up and running. In the future, GMN will be able to offer smart predictive services like predictive maintenance.

About GMN Paul Müller Industrie GmbH

GMN Paul Müller Industrie GmbH & Co. KG is a family-owned mechanical engineering company based in Nuremberg, Germany. It produces high-precision ball bearings, machine spindles, freewheel clutches, non-contact seals, and electric drives that are used in various industries. The company manufactures most of these components individually for its customers on site and sells its products via a global sales network.

How do we realize the new services?

To provide such services, companies must be able to access and analyze the sensor data of their machines. Furthermore, machines (or their components) must be enabled to communicate independently with other assets and systems on the shopfloor.

For both tasks, GMN uses CONTACT Elements for IoT. The modular software not only helps the company to record, document and evaluate the reference and usage data of their spindles. It also includes functions that enable users to create, fill and manage the AAS for an asset.

Background

During the implementation of services based on spindle operating data, GMN benefits from the cooperation with a customer. This company installs the spindles in processing machines that GMN uses to manufacture its own products. As a result, GMN can gather the operating data in-house and use it to improve the next generation of spindles.

What role does the AAS play?

For the components to exchange information in a standardized form, an AAS must be created for the spindle at item and serial number level. This is also done using CONTACT Elements for IoT. The new services are mapped in a so-called AAS metamodel. It serves as a “link” to the service offers.

AAS and submodels

The AAS of an Industry 4.0 component consists of one or more submodels that each contain a structured set of characteristics. These submodels are defined by the Industrial Digital Twin Association (IDTA), an initiative in which 113 organizations from research, industry and software (including CONTACT Software) collaborate to define AAS standards. A list of all currently published submodels is available at https://industrialdigitaltwin.org/en/content-hub/submodels.

In CONTACT Elements for IoT, GMN can populate the AAS submodels with little effort. The platform includes a widget developed as a prototype during the research project. It provides an overview of which submodels currently exist for the asset and which are available but not yet created. Through the frontend, users can jump directly to the REST node server and upload or download submodels (in AAS/JSON format).

During the implementation of data-driven service offerings, GMN focuses on the submodels

  • Time Series Data (e.g., semantic information about time series data)
  • Digital Nameplate (e. g., information about the product, the manufacturer’s name, as well as product name and family),
  • Contact Information (standardized metadata of an asset) and
  • Carbon Footprint (information about the carbon footprint of an asset)

Filling the submodels is simple. This is demonstrated by the module Time Series Data. During the reference run of a motor spindle on the in-house test bench, the time series data is recorded by CONTACT Elements for IoT. The platform automatically transfers this data to the AAS submodel of the motor spindle being tested. At the same time, the platform creates a document for the reference run. This allows GMN to track its validity at any time and make it available to external stakeholders.

New services on the horizon

Using Asset Administration Shells allows GMN to realize its service ideas. This currently concerns the commissioning service and automated quality assurance services.

By analyzing the spindle data, the company can identify outliers in the operating data and make suitable recommendations for action. For example, different vibration velocities indicate an incorrect installation of the spindle in the machine or that time-varying processes are occurring. The analysis can also be used to provide insights about anomalies in operating behavior.

Dashboards in CONTACT Elements for IoT increase transparency. They provide GMN with all relevant information about the spindles on the test bench, from 3D models to status data. This overview is extremely valuable, particularly for quality management.

An AAS in our software Elements for IoT.

Summarized

Asset Administration Shells are vendor-independent standards for describing digital twins. They are among the most important levers for implementing new Industry 4.0 business models, as they enable communication between assets, systems, and organizations. The example of GMN demonstrates the practical benefits of the AAS. The company uses it to design new, product-related services based on information from the AAS of its products. GMN can successively improve these services by continuously analyzing operating data in CONTACT Elements for IoT.

Digital authenticity: how to spot AI-generated content

In today’s digital age, we often question whether we can trust images, videos, or texts. Tracing the source of information is becoming more and more difficult. Generative AI accelerates the creation of content at an incredible pace. Images and audio files that once required a skilled artist can now be generated by AI models in a matter of seconds. Models like OpenAI’s Sora can even produce high-quality videos!

This technology offers both opportunities and risks. On the one hand, it speeds up creative processes, but on the other hand, it can be misused for malicious purposes, such as phishing attacks or creating deceptively real deepfake videos. So how can we ensure that the content shared online is genuine?

Digital watermarks: invisible protection for content

Digital watermarks are one solution that helps verify the origin of images, videos, or audio files. These patterns are invisible to the human eye but can be detected by algorithms even after minor changes, like compressing or cropping an image, and are difficult to remove. They are primarily used to protect copyright.

However, applying watermarks to text is way more difficult because text has less redundancy than pixels in images. A related method is to insert small but visible errors into the original content. Google Maps, for instance, uses this method with fictional streets – if these streets appear in a copy, it signals copyright infringement.

Digital signatures: security through cryptography

Digital signatures are based on asymmetric cryptography. This means that the content is signed with a private key that only the creator possesses. Others can verify the authenticity of the content using a public key. Even the smallest alteration to the content invalidates the signature, making it nearly impossible to forge. Digital signatures already ensure transparency in online communication, for example through the HTTPS protocol for secure browsing.

In a world where all digital content would be protected by signatures, the origin and authenticity of any piece of media could be easily verified. For example, you could confirm who took a photo, when, and where. An initiative pushing this forward is the Coalition for Content Provenance and Authenticity (C2PA), which is developing technical standards to apply digital signatures to media and document its origin. Unlike watermarks, signatures are not permanently embedded into the content itself and can be removed without altering the material. In an ideal scenario, everyone would use digital signatures – then, missing signatures would raise doubts about the trustworthiness of the content.

GenAI detectors: AI vs. AI

GenAI detectors provide another way to recognize generated content. AI models are algorithms that leave behind certain patterns, such as specific wording or sentence structures. Other AI models can detect these. Tools like GPTZero can already identify with high accuracy whether a text originates from a generative AI model like ChatGPT or Gemini. While these detectors are not perfect yet, they provide an initial indication.

What does this mean for users?

Of all the options, digital signatures offer the strongest protection because they work across all types of content and are based on cryptographic methods. It will be interesting to see if projects like C2PA can establish trusted standards. Still, different measures may be needed depending on the purpose of ensuring the trustworthiness of digital content.
In addition to technological solutions, critical thinking remains one of the best tools for navigating the information age. The amount of available information is constantly growing; therefore, it is important to critically question, verify, and be aware of the capabilities of generative AI models.

For a more comprehensive article, check out the CONTACT Research Blog.

Scope 3 emissions: A challenge for companies

Reducing greenhouse gas (GHG) emissions is crucial in the fight against climate change. Many companies face the challenge that indirect emissions in their value chain, so-called Scope 3 emissions, are often the largest contributors. Since these emissions fall outside the direct control of the company, they are usually the most difficult to determine (and optimize). How can companies address these central challenges within their value chains?

What are Scope 1, 2, and 3 emissions?

The Greenhouse Gas (GHG) Protocol classifies emissions into three categories: Scope 1 for direct emissions from company-owned sources, Scope 2 for indirect emissions from purchased energy, and Scope 3 for all other indirect emissions, including those from upstream and downstream processes within the value chain. Scope 3 is particularly important because it often accounts for the majority of GHG emissions. The GHG Protocol defines 15 categories of Scope 3 emissions that arise from both upstream and downstream activities. These include raw material extraction, production and transportation of purchased components, and the use of the manufactured products by end consumers. These emissions are difficult to capture as they are not directly under the company’s control.

Corporate Carbon Footprint (CCF) vs. Product Carbon Footprint (PCF)

There are two central approaches to calculating emissions: the Corporate Carbon Footprint (CCF), which encompasses all activities of a company, and the Product Carbon Footprint (PCF), which focuses on the lifecycle of a specific product. The PCF is particularly important when it comes to determining emissions along the value chain. Companies that aim to measure their Scope 3 emissions also need data from their suppliers regarding the PCF of the components they purchase.

Why is measuring Scope 3 emissions important?

Companies can directly influence and therefore more easily calculate Scope 1 and Scope 2 emissions. However, Scope 3 emissions should not be overlooked when aiming to assess the entire value chain. Since emissions from upstream and downstream processes often are the largest sources of GHGs, this is the only way to identify and reduce “hotspots” within the value chain.

For many SMEs, significant emissions lie in the upstream processes. However, this is also particularly relevant for industries that rely on complex and globally distributed supply chains. The automotive industry, for instance, depends heavily on purchased components and services, which significantly impact the GHG balance. According to the study “Climate-Friendly Production in the Automotive Industry” by the Öko-Institut e.V., an average of 74.8% of Scope 3 emissions occur during the usage phase, while in-house production (Scope 1 and 2 emissions) only accounts for about 1.9%, and 18.6% originate from the upstream value chain with purchased components. As the industry focuses more and more on e-mobility, the Scope 3 emissions of purchased components – and thus those from suppliers – come into sharper focus as a key lever.

Challenges in the supply chain

The pressure on suppliers to make their production more efficient and sustainable is growing, along with the need for transparency regarding the emissions of the supplied parts. Key challenges in the supply chain include data quality and availability. To tackle this and reduce greenhouse gas emissions, companies need to break new ground, ranging from material selection to production methods. A solid data foundation supports these necessary decisions, as well as the accurate documentation of emissions.
Capturing Scope 1 and Scope 2 emissions is already mandatory under the GHG Protocol Corporate Standard, while Scope 3 reporting is currently optional. However, the importance of Scope 3 reporting is increasing, as demonstrated by EU regulations like the Corporate Sustainability Reporting Directive (CSRD) and the associated European Standards (ESRS). These regulations emphasize the disclosure of emissions as a central aspect of climate action and sustainable business practices.

Three key steps to reduce Scope 3 emissions

  1. Optimize data management: Companies should collect comprehensive data on their products and their lifecycles to make design and portfolio decisions in favor of sustainability.
  2. Ensure data sovereignty and trust: Accurate calculation of Scope 3 emissions requires control over data, particularly in the context of the upstream and downstream value chains.
  3. Use open interfaces: Open data interfaces are essential for seamless integration and communication within the value chain. Approaches like the Asset Administration Shell (AAS) and concepts such as the Digital Product Passport (DPP) can provide valuable support.

Conclusion

Measuring and optimizing Scope 3 emissions is one of the greatest challenges for companies seeking to improve their GHG balance. By leveraging better data, optimizing collaboration within the supply chain, and ensuring transparent reporting, companies can meet regulatory requirements and make progress toward a more sustainable future.

Read a more detailed article on Scope 3 emissions on the CONTACT Research Blog.