Industry 5.0 has its roots in the concept of “Industry 4.0,” which has been coined in Germany in 2011 as a future project and part of the country’s high-tech strategy to be commonly adopted by business, science and decision-makers. It was originally linked to how and to what extent the country had succeeded during the first decade of the 21st century and how it could be more effective in the coming decades in order to keep the number of employees in production largely stable. It was focused not only to better meet the economic but also the special ecological requirements of “green production” for a carbon-neutral, energy-efficient industry.

Author: Ing. Jan Stoker MSc. MEng.

In 2013, Acatech (the German Academy of Engineering Sciences) presented a research agenda and implementation recommendations, which were developed at the instigation of the Federal Ministry of Research (BMBF) and based on the “National Roadmap Embedded Systems”. It described the impact that the Internet of Things (IoT) was going to have on the organisation of production thanks to a new interplay between humans and machines and a new wave of digital application to manufacturing. Deutsche Bank (2014) suggested that adoption of Industry 4.0 was to become the “factory outfitter of the world”. Professor Klaus Schwab, founder and executive chair of the World Economic Forum, has published two books in which he describes how Industry 4.0 is fundamentally different from previous industrial concepts, which were characterized mainly by advances in technology.

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1. Defining Industry 5.0

Even though Industry 5.0 is a relatively new concept, some early academic writing describing the main features of this notion exists. The analysis of the Industry 5.0 literature (see Annex II) shows a lot of uncertainty about what it will bring and how it will disrupt business in detail, as well as about its potential to break down barriers between the real world and the virtual one.

Based on the literature review and our forward-looking analysis, we believe Industry 5.0 will be defined by a re-found and widened purposefulness, going beyond producing goods and services for profit. This wider purpose constitutes three core elements: human-centricity, sustainability and resilience.

A purely profit-driven approach has become increasingly untenable. In a globalised world, a narrow focus on profit fails to account correctly for environmental and societal costs and benefits. For industry to become the provider of true prosperity, the definition of its true purpose must include social, environmental and societal considerations. This includes responsible innovation, not only or primarily aimed at increasing cost-efficiency or maximising profit, but also at increasing prosperity for all involved: investors, workers, consumers, society, and the environment.

1.1 Human-Centric

Rather than taking emergent technology as a starting point and examining its potential for increasing efficiency, a human-centric approach in industry puts core human needs and interests at the heart of the production process. Rather than asking what we can do with new technology, we ask what the technology can do for us. Rather than asking the industry worker to adapt his or her skills to the needs of rapidly evolving technology, we want to use technology to adapt the production process to the needs of the worker, e.g. to guide and train him/her. It also means making sure the use of new technologies does not impinge on workers’ fundamental rights, such as the right to privacy, autonomy and human dignity.

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1.2 Sustainability 

For industry to respect planetary boundaries, it needs to be sustainable. It needs to develop circular processes that re-use, re-purpose and recycle natural resources, reduce waste and environmental impact. Sustainability means reducing energy consumption and greenhouse emissions, to avoid depletion and degradation of natural resources, to ensure the needs of today’s generations without jeopardising the needs of future generations. Technologies like AI and additive manufacturing can play a large role here, by optimising resource-efficiency and minimising waste.

1.3 Resilliance

Resilience refers to the need to develop a higher degree of robustness in industrial production, arming it better against disruptions and making sure it can provide and support critical infrastructure in times of crisis. Geopolitical shifts and natural crises, such as the Covid-19 pandemic, highlight the fragility of our current approach to globalised production. It should be balanced by developing sufficiently resilient strategic value chains, adaptable production capacity and flexible business processes, especially where value chains serve basic human needs, such as healthcare or security.
As indicated earlier, our concept of Industry 5.0 is an open and evolving concept, providing a basis for further development of a collaborative and co-creative vision of the European industry of the future. Nonetheless, we believe the core of Industry 5.0 can be defined as follows:

Industry 5.0 recognises the power of industry to achieve societal goals beyond jobs and growth to become a resilient provider of prosperity, by making production respect the boundaries of our planet and placing the wellbeing of the industry worker at the centre of the production process.

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Source: 

European Commission
Directorate-General for Research and Innovation
Directorate F — Prosperity
Unit F.5 — Industry 5.0

2. Reference Articles 

2.1 Industry 5.0 Related

1. Maturity assessment for Industry 5.0: A review of existing maturity models
2. Industry 5.0: Past, Present and Near Future
3. IR5.0 Human-Centric underpinned with 2022 Industrial Maintenance study
4. Human in the loop: Industry 4.0 vs. Industry 5.0: Co-existence, Transition, or a Hybrid
5. Industry 5.0 further explained
6. Industry 5.0 and Society 5.0: Comparison, complementation and co-evolution
7. Outlook on human-centric manufacturing towards Industry 5.0
8. Maintenance 5.0: Towards a Worker-in-the-Loop Framework for Resilient Smart Manufacturing
9. Industry 5.0: Prospect and retrospect
10. Industry 5.0 definitions
11. Be informed…. we are already in the Industry 5.0 timeframe

2.2 IR4.0 & IR4.0 Readiness

1. An Industry 4.0 readiness Assessment tool
2. Intelligent warehouse in Industry 4.0
3. Maintenance Performance in the Age of Industry 4.0
4. Simulating dynamic RUL based CBM scheduling
5. Maintenance Analytics – The New Know in Maintenance
6. Rethinking Maintenance Terminology for an Industry 4.0 Future
7. Maintenance optimization in industry 4.0; Strategies, Information and the Reversed Data Pyramid
8. An RUL-informed approach for Life Extension of high-value assets: Overview of LE practice
9. Lean Maintenance 4.0: implementation for aviation industry
10. Developing prescriptive maintenance strategies in the aviation industry
11. Development of flexible Predictive Maintenance systems in the context of industry 4.0: the implementation framework

2.3 Digital Twin Related

1. Reflection: Disruptive Innovation Asset & Maintenance Management
2. Collecting Real-Time Data for Predictive Maintenance
3. Lean Maintenance 4.0: implementation for aviation industry
4. A digital twin-based decision analysis framework for operation and maintenance of tunnels
5. Digital building twins and blockchain for performance-based (smart) contracts
6. IoT for predictive assets monitoring and maintenance: An implementation strategy
7. About auditing in the field of Asset Management
8. A Digital Twin Design for Maintenance Optimization
9. The difference between Machine Learning(ML) and Deep Learning (DP)
10. Digital Twin Definitions: a time perspective
11. Definition Digital Twin
12. Approach for a Holistic Predictive Maintenance Strategy by Incorporating a Digital Twin
13. Data-driven failure mode and effect analysis (FMEA) to enhance maintenance planning
14. Advances of Digital Twins for Predictive Maintenance
15. The 250 classifications of Digital Twin technology

2.4 Maintenance Management

1. Decision-based framework for Predictive Maintenance Technique selection in Industry 4.0
2. Data-driven failure mode and effect analysis (FMEA) to enhance maintenance planning
3. Recent advances and trends of predictive maintenance from data driven machine prognostics perspective
4. Data-driven decision-making for equipment maintenance: Data-driven RCM
5. Toward cognitive predictive maintenance: A survey of graph-based approaches
6. A deep learning predictive model for selective maintenance optimization
7. Inspection schedule for prognostics with uncertainty management
8. Development of Digital Twin for Intelligent Maintenance of Civil Infrastructure
9. Risk Based Inspection Framework part of evolutions in Maintenance Management; Framework and Process
10. KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0
11. Sharping the mind: Find The Sweet spot
12. The Bathtub Curve Fallacy
13. The framework for data-driven maintenance planning and problem-solving in maintenance communities
14. The Maintenance Body of Knowledge
15. Decision Framework for Predictive Maintenance Method Selection
16. Maintenance Engineering defined
17. The Maintenance 5.0 Framework
18. The Maintenance 5.0 Cycle

2.5 Interpretation Article’s

1. Line of Sight: Asset Management in the aligned timeframe
2. Interpretation Figure 1 ISO55000
3. The elephant in the room
4. Asset & Maintenance Management amidst the Industry 5.0 timeframe
5. Revised A&MM The Big Picture
6. Article IR5.0 Human-Centric
7. Explaining Predictive Maintenance using the KISS-Principle
8. Asset Management 5.0: Balancing Risk, Performance and Value with IR5.0
9. Food for Thoughts: ChatGPT in the field of Asset & Maintenance Management.

2.6 Additional Pages To Consult

1. Sustainable Asset Management 
2. The Maintenance Engineer
3. The Maintenance Manager
4. The Asset Manager
5. Industry 5.0
6. Circular Asset Management

Tags: Assetmanagement, Industry4.0