1. Node-Red as a local IIoT Platform for machine interoperability
Leader: INEGI
Duration: 90 minutes
Qualification Level EQF: Level 4
The Learning Path “Node-Red as a local IIoT Platform for machine interoperability” focusses on the Node-RED platform as a means to reach a higher level of connectivity and interoperability between machines. Its strengths and weaknesses will be noted, and the basic tools and knowledge needed to use Node-RED will be presented. After the basic functionalities and tools are presented, ways to use Node-RED with a variety of commonly available industrial communication protocols will be explored. Its capabilities of creating standardized databases will also be explored. Finally, Node-RED’s tools for creating dashboards for data monitoring will be presented together with an example.
Learning outcome 1: At the end of this LP the learner is able to recognize the different components and levels of a machine level IoT network as well as define their specific functions.
Learning outcome 2: At the end of this LP the learner is able to develop a communication gateway and database using an open-source tool (namely NodeRed) in order to create a local network by creating a personalized solution to a specific, one-off problem.
Learning outcome 3: At the end of this LP the learner is able to create simple remote dashboards for data visualization using the Node-RED software for backend and frontend coding as well as a managed database. The learner will be skillful enough to create not only utilitarian interfaces but intuitive and flexible dashboards capable of being adapted to a variety of situations and remote monitoring needs.
2. An Industrial Value Chain Approach
Leader: INEGI
Duration: 60 minutes
Qualification Level EQF: Level 4
In this Learning Path, the Trainee will receive an introduction to Porter’s Value Chain Concept and the influence that Industry 4.0, and its related technologies, has in its implementation and evaluation.
The first Nugget is meant to give an understanding of the concept as well as how a basic evaluation is should be done. An example is shown and each component of the Concept is described. The following Nugget focusses in describing communication channels between Value Chain Activities and how these are affected by Industry 4.0 and its associated technologies. It demonstrates why it is important to quickly adopt these technologies while not forgetting the challenges they bring. The final nugget, before the assessment quiz, gives an example of how to organize a business with geographically distant centers of activity with the Value Chain Concept as a center pillar. An assessment nugget is then provided, in addition to the optional quizzes in the previous nuggets, so that the trainee can prove that knowledge has been gained.
Learning outcome 1: At the end of the LP the learner is able of describing Porter’s Value Chain Concept in detail, knowing the difference between the Primary and Secondary Activities.
Learning outcome 2: At the end of the LP the learner is able to study a production structure and identify its Value Chain and Activity Distribution. They can detail the business model according to Porter’s definitions and how increasing its integration with digital technologies can lead to resilience and flexibility of production.
Learning outcome 3: At the end of the LP the learner is able to identify communication channels between Value Chain Levels, their importance as well as identify missing channels, or opportunities to create these channels, in a Value Chain. They are capable of defining what and how much data is shared between levels in order to optimize production without adding entropy to the system.
3. Data sharing for networked organizations using the International Data Spaces Reference Model
Leader: INESC TEC
Duration: 250 minutes
Qualification Level EQF: Level 3
The Learning Path belongs to Digital Transformation and Data topics and conducts the learner in the adoption of the International Data Spaces (IDS) Reference Model, at the theoretical and practical levels. The IDS Reference Model is an international specification developed to help share data in a coordinated and secure manner, all the while maintaining data sovereignty, meaning the owner should always have full control over the data they share.
The main objective of this LP is to equip learners with the knowledge and skills required to navigate an increasingly adopted international specification, which IDS is. By understanding IDS principles, examples, and best practices presented throughout this LP, learners are able to reflect on their organization’s data-sharing mechanisms and implement new, internationally embraced methods to securely and efficiently exchange data.
Throughout the learning process, the learner will start by identifying and understanding the foundational elements around the IDS Reference Model, followed by a practical sequence of steps that will instruct him/her to 1) build an IDS data space by deploying and customizing the central software components that will enable a trustful and secure data sharing ecosystem; 2) start publishing, searching, and consuming data in the IDS data space ecosystem and 3) know how to integrate existing business information systems into the data space ecosystem in order to allow interoperability with other information systems in a supply chain to support business transactions (e.g., production orders, products technical data, traceability of operations).
Learning outcome 1: After completing the learning module, learners are able to identify and internalize: the basic theoretical concepts defined by the IDS Reference Model to architect a dataspace ecosystem; its major software components; and their purpose and interactions.
Learning outcome 2: After completing the learning module, learners are able to build a dataspace ecosystem according to the IDS Reference Model and to deploy and configure, in a docker-based environment, the essential software components to develop an IDS ecosystem, allowing manufacturing organizations participating in a supply chain to exchange data (e.g. production orders, traceability data) in a peer to peer secure and trustful manner, whilst addressing the sovereignty of data owners by defining data usage policies and contracts.
Learning outcome 3: After completing the learning module, learners have the knowledge to manually and programmatically publish, search, and consume data in an IDS dataspace ecosystem and are able to integrate a business information system (e.g. ERP, MES) with an IDS Connector to improve digital communication with partners’ business information systems in a given supply chain environment.
4. Creating a hybrid connection between TLFs using APIs to a common value chain
Leader: Pannon Business Network
Duration: 85 minutes
Qualification Level EQF: Level 2
The Learning Path summarises the following topics:
• overview of Teaching and Learning Factory (aka: TLF) concept
• basic connections of remote processes
• example of interconnected TLFs
• description of a fictitious value chain concept between three remote TLFs
This learning path provides comprehensive information about FIWARE, as an open-source software platform, and its core components. The learning path is also describing the Interaction and Communication as well as the data workflow within FIWARE. It will be also detailed how PBN has leveraged FIWARE as a a Framework for Data Collection and Integration.
The learning path explores the process of integrating data across the value chain, highlighting the stages of modeling and the resulting optimized data model.
Learning outcome 1: After completing the learning path, the learner can analyze different open source platforms that can be used to connect Teaching and Learning Factories.
Learners will be also able to evaluate how these platforms are used in this particular implementation, which helps decreasing the production waste in connected business processes, enhancing green industrial transformation.
Learning outcome 2: Following the successful completion of the Learning Path, the learner can compare the APIs and protocols of several different TLFs.
Learning outcome 3: Following the successul completion of the learning path, the learner can recognise different possible problems (e.g: data communication, data processing) and select tools (e.g: IDS, open-source data base) to resolve them.
Leader: CIIRC, CTU in Prague
Duration: minutes
Qualification Level EQF: Level 3
Modular production is based on strong encapsulation of machine functionality and defining data model that exhibits the interface of the machine. The data model is typically composed of standard parts such as those defined by the ISA 88 standards, and machine-specific parts. An important part of the modular production is also utilization of standard communication protocols.
This learning paths leads the learners to understanding the principles of modular production on the case of a single machine. The data model, the machine control and the interfaces are explained. An important part is also monitoring of the correct machine functionality by providing IoT-based communication to get process data.
The principles will be explained on a delta robot with conveyor, which assembles parts to be transported by logistic operations to other machines within the modular production. However, the other machines are out of scope of this learning path.
The knowledge obtained in this learning path will be applied in subsequent hands-on workshop realized on site with a physical machine.
Learning outcome 1: At the end of the learning path, learners will be able to understand the relevant key concepts of modular manufacturing. Key technologies covered include robots, industrial controllers and data interfaces.
Learning outcome 2: Learners will also be able to apply the concepts from LO1 prepare data for digital twins and intelligent software services.
Learning outcome 3: After completing the learning path, learners set-up a full-blown production cell for the modular production, with data interfaces and running data streams. Moreover, the learners will be able to create a simple application analyzing the data obtained through the data streams from a running physical machine.
6. Digital Product Passport Service Toolkit
Leader: JOANNEUM RESEARCH
Duration: 120 minutes
Flagship: Digital & collaborative solutions for innovative manufacturing ecosystem Competency area
Qualification Level EQF: Level 5
The aim of digital product passports is the collection and sharing of data about a product and its supply chain throughout the value chain. With this information, the materials and products used and their environmental impact can be better understood by all stakeholders, including consumers.
This learning path includes the main technological building blocks of a digital product passport.
In the first step, the main components and the purpose of a digital product passport are presented.
Step two presents relevant background information and possible characteristics of a digital passport.
The next step is to present the most important implementing modules and implementing variants. One of the variants is a QR code with a corresponding digital software back-end service, and the second variant is an RFID/NFC technology, also with a corresponding back-end service. We will make these available in a kind of minimum value product. Furthermore, we give them a basic understanding for implementing it.
Afterwards, we go into the technical details of the technical implementation of such a digital product passport.
Finally, the learner is able to implement with the provided toolkit a digital product passport.
Learning outcome 1: At the end of the learning path, the learner is able to apply the relevant key concepts for the digital product passport. This is relevant for flexible manufacturing solutions. The marking or tagging technology used in this process can also be used for process optimisation steps in the product lifecycle.
Learning outcome 2: The learner is able to demonstrate relevant technologies such as QR codes and NFC tags for product identification on a physical product. He/she selects the appropriate assembly option for the physical product and link it to digital services (e.g. web-based REST services).
Learning outcome 3: The learners is able to configure and digital software services of the Digital Product Passport. Selecting the relevant content for the selected product and adapting the required work process is part of the configuration. The learners take into account the relevant regulations for the Digital Product Passport. In this way, they are able to guide the implementation of a Digital Product Passport as a ‘minimum value product’.
How to get started
Screen your interest here EIT (skillsmove.eu) were you can find all categories of e-lessons (free or not) and then focus your attention on CONFACTS learning paths from above.
Contact: Herwig Zeiner (Coordinator)
E-mail: herwig.zeiner@joanneum.at