ENISA is important to promote cyber-security in a European context -- not least to guide small and medium enterprises in manufacturing/Industry 4.0 settings.
The Industry IoT Consortium (IIC) is probably the best place to explore current efforts of advancing the knowledge and standards including:
- updated reference architectures (and patterns)
- open white papers on the most important subjects/use-cases and technologies (to be discussed)
- Foundational documents (and resources for communication)
Patterns and best practice examples, systems and models for re-use and inspiration are valuable.
Architecture Models like the OSI model for the internet and the ISA 95 model for industrial information systems are well known patterns/theory used in the industry and current "state-of-the-art", below example (figure by Badarinath Katti, thesis, 2020):
Badarinath Katti, PhD thesis (see ref. [B Katti, Thesis, Doktor-Ingenieur, Technische Universität Kaiserslautern, 2020] for context and background)Figure by
Industry 4.0 education is a key component/building block for a modern engineering education, including multi-disciplinary team-working and systems thinking. Soft skills may be just as important as "hard skills" like programming or machining. Personal and context-specific communication with active feed-back to and from other (human) actors (friends, colleagues, fellow students, supervisors) is a very important part a learning and training as an engineer - as outlined in e.g. the CDIO framework <link> and the current "CDIO Syllabus 3.0":
Education happens through systematic "Learning by doing" - and is a continuous process going well beyond a formal education- at NTNU or elsewhere. Knowing where to start is often a great challenge, and guidance may be hard to find- even though Industry 4.0 and Education 4.0 and similar concepts are everywhere on the internet and already a part of popular culture in various forms....
Experiential Learning is a foundation of modern education, not least in engineering - and "Learning Factories". The pedagogy and philosophy shares some common models with the "learning cycle" also known as the Deming cycle of Plan-Do-Study-Act -or PDCA for plan-do-check-act. In the industry this is often related to the so-called "Lean" philosophy and quality focus on continuous improvement ("Kaizen")...
Some useful video-content and more background (and quote below) on experiential learning may be found online based on the work of Kolb et al. through:
“There are two goals in the experiential learning process. One is to learn the specifics of a particular subject, and the other is to learn about one’s own learning process.”
- David A. Kolb
See info on the Cyber-Physical Learning Factory at NTNU via link to examples above (title) or the link to the department website below:
"It all depends on your point of view". Figure caption/text also includes a short intro on "viewpoints", in text below. In the figure above, a conceptual model (figure above) of a learning factory is central for discussing points of view in a learning context. Together with key enabling technology such as AR, "Augmented Reality" and AI, "Assisted/Artificial Intelligence", Learning Factories are used in the context of the vision (and implementation) of Industry 4.0 (top). The figure was first presented in a paper from 2020 (Tvenge et al.), available at NTNU Open:
So-called "Learning factories (book)" have been developed over the years, and may be viewed as best-practice and "The future of Industry 4.0 Education". A recent textbook on Learning factories by Abele et al. (Springer) is available through the link below, and e.g. the NTNU library - also as a pdf-version/eBook:
The background, definitions and many examples are presented in the textbook, and related literature. Together with end-users and partners, Festo Didactic has implemented a large number of Cyber-Physical Learning Factories at NTNU and elsewhere.
A recent paper describes "A Learning Approach for Future Competencies in Manufacturing using a Learning Factory", available through CIRP and Science Direct, also in link(title): https://www.sciencedirect.com/science/article/pii/S2212827123004055?via%3Dihub
In any approach to understanding complex systems, the viewpoint will determine how the system looks to an actor, or user- or learner. A view may highlight different system components, or aspects and details of any particular system function. A simple scale (bottom-up) mapping of "Human centric" vs "Machine centric" points of view may be helpful- not least to discuss the functions of the Human-Machine-Interfaces (HMI) of the system and its components.
Recent paper from Thomas Riemann et al. (TU Darmstadt, see other LinkedIn page for background):
Hybrid Learning Factories for Lean Education: Approach and Morphology for Competency-Oriented Design of Suitable Virtual Reality Learning Environments
Learning in the Digital Era : 7th European Lean Educator Conference, ELEC 2021 · Dec 31, 2021Learning in the Digital Era : 7th European Lean Educator Conference, ELEC 2021 · Dec 31, 2021
In recent years, learning factories have proven to be an effective instrument for developing competencies, especially in lean production and digitization. The concept of learning factories has been enriched in the recent past by elements and training units in virtual reality (VR). This enrichment allows an expansion of the mapping abilities of different training environments and value streams in the context of lean education. Nevertheless, learning factory developers are faced with the challenge of selecting suitable scenarios in terms of content and scope. An approach for the competency-oriented and structured design of such scenarios will be presented in this publication and illustrated by means of an application example of the research project PortaL (Virtual action tasks for personalized, adaptive learning).
More examples and context to follow:
This wiki-page will form a joint study of this wide topic (Industry 4.0) and education to support the smart factories of the future.
The famous image above, from December 1968- taken on the Apollo 8 mission marks a paradigm-shift in our thinking about our place in the universe- in space and time. As of December 2022, it's been 50 years since a human walked on the moon- but the Artemis missions now clearly marks the return- soon. As engineers and scientists we find inspiration in finding the sustainable solutions we need to survive on "spaceship earth".
We start through a project and aim to continue after the project - through ongoing support at our places of work and life (support) at home.
More Stories and Context will be added as we continue our studies and cooperation.
Industry 4.0 and smart manufacturing. What do these terms mean? Can they be used interchangeably or not?
Jeff Winter (follow on LinkedIn if you can)
The blog-text presented on ISA's web-page (above quote) is probably the best definition of Industry 4.0 available, but has to be pointed to by users in manufacturing and related fields. So, please help share this when you can - and contribute to discussions where you are. For students at NTNU, and connected to our 2 year Master program (MSc) in Sustainable Manufacturing you can access other text and clippings also "offline", not least on the Blackboard Learning Management System (course work).
We start our wiki-page with a similar question, just as relevant for us, and the "Industrial Internet", and the Cyber-Physical Production Systems of the (near) future:
What is smart, sustainable manufacturing?
In 2016, the NIST provided the following introduction and a good overview of state-of-the-art that applies to planet-wide initiatives also, not just the USA (link below):
A manufacturer’s sustainable competitiveness depends on its capabilities with respect to cost, delivery, flexibility, and quality . Smart Manufacturing Systems (SMS) attempt to maximize those capabilities by using advanced technologies that promote rapid flow and widespread use of digital information within and between manufacturing systems. SMS are driving unprecedented gains in production agility, quality, and efficiency across U.S. manufacturers, improving long-term competitiveness. Specifically, SMS use information and communication technologies along with intelligent software applications to
1. Optimize the use of labor, material, and energy to produce customized, high-quality products for ontime delivery.
2. Quickly respond to changes in market demands and supply chains.
NIST (2016): http://dx.doi.org/10.6028/NIST.IR.8107
And, in particular for Sustainable Manufacturing:
Sustainability: While time and cost as measures of productivity have been the traditional drivers for manufacturing, sustainability has taken on more importance. Measurement science for manufacturing sustainability is not as mature as for time and cost and is an active area of research  . As productivity and agility of manufacturing systems increases, the necessity for better understanding and controlling the sustainability-related impacts of those systems increases. Manufacturing sustainability is defined in terms of environmental impact (such as energy and natural resources), safety and well-being of employees, and economic viability .