SECTORAL ANALYSIS OF MANUFACTURING (NACE SECTION C), EU-28, 2016
4.3.4. Major challenge 4: Developing digital platforms, application development frameworks that integrate sensors/actuators and systemsthat integrate sensors/actuators and systems
There is undoubtedly a tendency to increase automation or the degree of digitalisation in industry, which will ultimately lead to 100% autonomous systems. Moreover, there are some outstanding flagship programmes – for autonomous driving, for example. Also, some mature manufacturing phases, or even entire production lines, are practically fully autonomous.
However, between the two extremes of entirely manual or fully autonomous there will probably always be a large area of semi-autonomous equipment, units, machines, vehicles, lines, factories and sites that are worth keeping somewhat below 100% autonomous or digitised. The reasons for this include: 1) a fully autonomous solution may simply be (technically) next to impossible to design, implement and test, 2) if fully autonomous solutions were achievable, they may be too expensive to be realised, 3) a fully
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autonomous solution may be too complex, brittle, unstable, unsafe, etc., to become accepted by industrial end-users, 4) a fully autonomous system may be too complicated and therefore unrealistic to modify or upgrade as requirements or conditions change, 5) a less-demanding semi-automatic solution may be easier to realise to a fully satisfactory level, and finally 6) there are examples where a fully autonomous solution may not represent a maximum performance, and instead an effective joint solution between human and machine would be better.
When automation and digitalisation degrees are gradually, reasonably and professionally increased, often portion by portion, they may bring proportionally significant competitive advantages and savings that strengthen the position of digital industries overall. However, since automation or digitalisation degrees remain well below 100%, the negative effects to employment are either negligible or non-existent. On the contrary, an increased market position could increase the need for more people in the respective businesses.
4.3.4.1.
Scope and ambitionOverall, human–system interaction appears in the following contexts:
Training: A higher level of formal training may be required for workers in production and maintenance due to tasks such as analytics and the use of advanced technologies. On the other hand, greater specialisation is constantly introducing product, process or company-specific further training.
Extended human capabilities enabled by big data and artificial intelligence: AI will impact almost every industry with targeted (not general purpose) AI systems. Service businesses around AI will emerge as the next big opportunity, which will also provide opportunity for SMEs.
Human operators in more autonomous plants and in remote operations: The production processes will acquire more autonomous parts as a result of development in extreme or hazardous conditions, or process industries located in remote/distant areas where much can be gained from increasing autonomy.
Human safety: With the localisation of personnel, machines and vehicles, situation-aware safety (sensing of safety issues, proximity detection, online human risk evaluation, map generation) becomes more important. This involves increased and intrinsically safe collaboration between humans, robots and any machinery.
Competence and quality of work: On a strategic level, the European automation and industrial IT industry depends on an ability to attract competent personnel to maintain their competence over time. To facilitate this, creativity and innovation processes need to be connected to the competence area, and there must be structured continuous professional education as well as transfer of knowledge from one to many.
Human–machine interfaces and machine-to-machine communications: Augmented reality (or virtual reality) will be used to support a number of tasks, such as modelling/simulation of equipment behaviour or processes before making changes or additions, pre-testing action in simulation prior to real-world introduction, enhancing remote help/support by entering into the actual help/support case environment and showing what should be done and how to do it, and also being able to run simulations prior to making changes in configurations or set-ups. Enhanced visualisation of data and analytic results will be required to support decision making.
4.3.4.2.
Competitive situation and game changersTo broaden out the scope, the following nine game changers can be listed, which are currently on different maturity levels:
Modular factory for distributed and automated production.
Live virtual twins of raw materials, process and products.
Increased information transparency between field and ERP.
Real-time data analytics.
Dynamic control and optimisation of output tolerances.
Process industry as an integrated and agile part of the energy system.
Management of critical knowledge.
Semi-autonomous automation engineering.
Integrated operational and cybersecurity management.
4.3.4.3.
High-priority R&D&I areas Human–machine relations, interaction, collaboration, complementarity.
Human-in-the-loop, human-as-part-of-the-system and HMI, including intuitive systems, wearable and implantable systems, virtual and augmented reality as well as human–machine collaboration and collaborative decision making.
New engineering tools that consider humans as part of the systems.
Human–machine/human–robot collaboration, and an enhanced role for workers and customers in manufacturing.
Manufacturing as networked, dynamic socio-technical systems, HUMANufacturing as a new era of automation and human interaction, customer-centric value creation networks.
Human-driven innovation, co-creation through manufacturing ecosystems, customer-driven manufacturing value networks, social innovation.
Hyper-personalised manufacturing, human-in-the-loop, inclusive manufacturing.
4.3.4.4.
Expected achievements Increased productivity from improved ways of working, collaboration, life-cycle management, optimisations and distributed production and services.
Decision-making based on data and facts.
User-friendly design and operation of automation systems.
Self-healing, redundant and resilient production and automation systems.
Fast and improved decision support (both for humans and machines).
Decisions based on data/big data as well as data from multiple sources.
Fewer work-related injuries and improved work safety.
Higher work performance/efficiency per unit of worked time (economic benefit).
Augmented workers, implants and robotic parts.
Structured knowledge management supported by adequate systems.
Context-driven user-centric information based on big data.
New designs of plants and production processes with higher automation levels and fewer but increasingly skilled workers.
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4.3.5.
Major Challenge 5: Sustainable manufacturing in a circular economyNearly 200 countries have committed to the Paris Agreement on climate change to limit global warming to below 2ºC. Rapid transformation of all sectors is required. Many European countries have set even more ambitious targets. Billions of mechanical devices, buildings, vehicles and industrial processes need to be changed, retrofitted or renovated. Social change, urbanisation, increasing wealth and consumerism have led to the rapid exploitation of natural resources beyond our planet’s capacity. However, challenges can be addressed through resource wisdom. Tapping into yet-unutilised reserves and closing the loops will open new potential for economy. Industries benefit from renewables and unconventional raw materials. Design will enable high-performance for-need-only consumables. Non-conventionally produced food can help to feed the growing population while saving water and the environment.
The vision of SPIRE (Sustainable Process Industry through Resource and Energy Efficiency) decomposes the above high-level goals into more concrete actions, as follows:
1. Use energy and resources more efficiently within the existing installed base of industrial processes.
Reduce or prevent waste.
2. Re-use waste streams and energy within and between different sectors, including recovery, recycling and re-use of post-consumer waste.
3. Replace current feedstock by integrating novel and renewable feedstock (such as bio-based) to reduce fossil, feedstock and mineral raw material dependency while reducing the CO2 footprint of processes or increasing the efficiency of primary feedstock. Replace current inefficient processes for more energy- and resource-efficient processes when sustainability analysis confirms the benefits.
4. Reinvent materials and products to achieve a significantly increased impact on resource and energy efficiency over the value chain.
The EFFRA (European Factories of the Future Research Association) roadmap, on the other hand, discusses environmental sustainability of manufacturing as follows:
The new possibilities offered by advanced materials, digital technologies and manufacturing technologies should be exploited; generating a considerable reduction of the ecological footprint, CO2 emissions and improvements in the recycling, use and re-use of resources on an eco-system level while still raising the performance of the manufactured products. For approaching an ultra-resource-efficient or circular approach, the understanding of impact, cooperation and resource-use must be improved along the life-cycle and across sectors. This will require the identification of appropriate metrics and parameters which allow optimisation along the life-cycle.
Finally, overall professional and experienced automation and digital technologies – which, for example, ECSEL represents – are needed. Once all environmental goals are achieved, we will have implemented an effective engineering and operation that, most obviously, will optimise performance in terms of quality, cost, flexibility, operational efficiency, safety and reliability.
4.3.5.1.
Scope and ambitionObviously, sustainable manufacturing and production need solutions and breakthroughs in many ways, but how ECSEL or electronics and software technologies can assist in this can be described as follows:
a) Life-cycle assessment
Life-cycle assessment (LCA) is a comprehensive and ISO standardised method of evaluating the
environmental aspects and potential environmental impacts of products. LCA can also be applied in evaluating the impact of technologies and processes. An LCA study covers the whole life-cycle of products, and provides information to support decision-making in product and technology development projects. LCA is a prerequisite for holistic environmental evaluation, and is a simple but systematic method, but one that requires extensive and comprehensive models and data. In practice, mixed combinations often need to be employed – for example, missing measurements must be compensated for by models or standard data. LCA software must also be better integrated into other automation systems.
b) Monitoring discharges, etc
Sustainable manufacturing needs, per se, comprehensive environmental and other measurements that may not at all be in place when particular manufacturing or production was initiated. On the other hand, this is a very typical application of many kinds of IoT sensor and system that can be informed by a careful LCA, for example.
c) Tracing material and energy streams
It is already commonplace in many industry sectors that material and energy streams need to be completely traced back to their starting point. Notable examples include food and medicine production. As more and more products, raw materials, etc, become critical, this implementation strategy must be expanded.
d) Optimisation
Discharges or losses mostly happen when production does not occur as planned, as is optimal, due to mistakes, bad condition of machinery, unskilled operation, and so on.
4.3.5.2.
Competitive situation and game changersEurope has been the most advanced and implemented the most in terms of sustainable production. This has been an issue of quality and reputation, but recent developments have also shown that it is becoming an absolute necessity to help save the planet, and that even an effective incentive to improve all engineering and operations. and all the other KPIs, may become not be enough. However, climate change seems to be hitting other continents harder than Europe, at least temporarily, meaning that the other threatened economies are starting to take these matters more seriously.
4.3.5.3.
High-priority R&D&I areas Key performance indicators (KPIs) related to the environmental performance of processes (even online).
Life-cycle analysis, simulations/optimisation and management driven by customer and societal demand.
Consideration of electrical drives/motors.
Monitoring and control (legislation, waste, energy).
Design. economic, societal and environmental aspects go hand in hand.
Complex system optimisation (energy efficiency, minimise emissions (CO2, NOx, etc.), ability to use/
cope with by-products and produce less waste).
4.3.5.4.
Expected achievements Reduced use of raw materials.
Carefully and accurately designed and operated production that minimises or avoids discharges and wastes.
New business models.
Proofs through industrial cases that the best environmental production or operation actually means the highest quality, lowest cost and other KPIs.
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4.4.
MAKE
IT HAPPEN
Some initial ideas on how to get involvement from industry to test research ideas. Participate and get in-field experience.
Gartner/ARC studies about 50 billion IoT sensors Ò communication + storage Ò applications needed that will create actual information and value from the data.
Sensor price / unit + storage capacity + application execution = investment price.
Existing standards can be used and there are a lot more applications based on standards.
Development cycle from chip provider to system designer and then to application can be shorter e.g. actual framework and faster design flow create stakeholder value
1 – DEVELOPING DIGITAL TWINS, SIMULATION MODELS FOR THE EVALUATION OF INDUSTRIAL ASSETS