The Grand Societal Challenges generate the need for a wide range of products that should be manufactured at an affordable price. Manufacturing is therefore a key enabler technology to realise these products and solutions.
A lead time is the latency between the initiation and execution of a process. For example, the lead time between the placement of an order and delivery of a new car from a manufacturer (from https://en.wikipedia.org/wiki/Lead_time)
Flexibility in manufacturing means the ability to deal with slightly or greatly mixed parts, to allow variation in parts assembly and variations in process sequence, change the production volume and change the design of certain product being manufactured.
In business, engineering, and manufacturing, quality has a pragmatic interpretation as the non-inferiority or superiority of something; it's also defined as being suitable for its intended purpose (fitness for purpose) while satisfying customer expectations. (from https://en.wikipedia.org/wiki/Quality_(business))
Quality assurance (QA) is a way of preventing mistakes and defects in manufactured products and avoiding problems when delivering solutions or services to customers; which ISO 9000 defines as "part of quality management focused on providing confidence that quality requirements will be fulfilled". This defect prevention in quality assurance differs subtly from defect detection and rejection in quality control, and has been referred to as a shift left as it focuses on quality earlier in the process i.e. to the left of a linear process diagram reading left to right. (from https://en.wikipedia.org/wiki/Quality_control)
Over the next decade, for a wide range of complex products, the holistic optimization of performance will push towards new multi-material and multi-functional solutions. This will result in a change in the manufacturing paradigm by introducing new methods and process technologies within the factory in order to ensure both the required quality and sufficiently high productivity to guarantee cost-efficient manufacturing. Economic sustainability will require a re-design of products and production processes respecting the manufacturing conditions and strengths of Europe. In turn this will imply maximising manufacturing efficiency by implementing, where adequate, automated, complex and precise manufacturing steps, which can be supported by advanced technologies and knowledge available in Europe. From the perspective of mass production, economic viability is also of fundamental importance. Solutions like the adoption of lighter and higher resistant materials such as titanium, carbon composite remain critical from a cost perspective, while material availability and new regulations concerning End of Life (EoL) already constitute significant challenges for Industry. To achieve solutions which are truly viable, the ratio of cost to performance must be reduced to improve global competitiveness. The assessment of manufacturing related cost and investment factors will be strategic for the selection and optimization of innovative product/process/system solutions. New appropriate cost modelling techniques are needed to evaluate the future cost of products manufactured either by existing or new technologies, considering future scenarios where market needs, production volumes and technology maturation cause the continuous evolution of product/process/system solutions. These challenges must be faced along the entire supply chain involving OEMs, components suppliers and SMEs due to the typical supply chain of a complex product.
Productivity describes various measures of the efficiency of production. A productivity measure is expressed as the ratio of output to inputs used in a production process, i.e. output per unit of input. Productivity is a crucial factor in production performance of firms and nations. (from https://en.wikipedia.org/wiki/Productivity)
In systems engineering, dependability is a measure of a system's availability, reliability, and its maintainability, and maintenance support performance, and, in some cases, other characteristics such as durability, safety and security. In software engineering, dependability is the ability to provide services that can defensibly be trusted within a time-period. This may also encompass mechanisms designed to increase and maintain the dependability of a system or software. (from https://en.wikipedia.org/wiki/Dependability)
Business development entails tasks and processes to develop and implement growth opportunities within and between organizations. It is a subset of the fields of business, commerce and organizational theory. Business development is the creation of long-term value for an organization from customers, markets, and relationships. (from https://en.wikipedia.org/wiki/Business_development)
Occupational safety and health (OSH), also commonly referred to as occupational health and safety (OHS), occupational health or workplace health and safety (WHS), is a multidisciplinary field concerned with the safety, health, and welfare of people at work. (from https://en.wikipedia.org/wiki/Occupational_safety_and_health)
Material efficiency is a description or metric which expresses the degree in which raw materials are consumed, incorporated, or wasted, as compared to previous measures in construction / manufacturing projects or physical processes. Making a usable item out of thinner stock than a prior version increases the material efficiency of the manufacturing process. (from https://en.wikipedia.org/wiki/Material_efficiency)
Waste minimisation is a set of processes and practices intended to reduce the amount of waste produced. By reducing or eliminating the generation of harmful and persistent wastes, waste minimisation supports efforts to promote a more sustainable society. Waste minimisation involves redesigning products and processes and/or changing societal patterns of consumption and production. (from https://en.wikipedia.org/wiki/Waste_minimisation)
The efficiency and sustainability of both the manufacturing of actual and future products is still very much determined by the processes that shape and assemble the components of these products.
Innovative products and advanced materials (including nano-materials) are emerging but are not yet developing to their full advantage since robust manufacturing methods to deliver these products and materials are not developed for large scale. Research is needed to ensure that novel manufacturing processes can efficiently exploit the potential of novel products for a wide range of applications.
Integration of non-conventional technologies (e.g. laser, ultrasonic) towards the development of new multifunctional manufacturing processes (including in process concept: inspection, thermal treatment, stress relieving, machining, joining
Manufacturing systems include machines, modules and components that integrate mechanics, materials processing technologies, electronics, and computing capabilities (ICT technologies) to perform desired tasks according to expectations. Mechatronic systems do not only interface with materials, parts and products, they also co-operate safely with factory workers and communicate with other systems in the factory.
Also they connect to manufacturing execution and monitoring systems on a higher factory and management level.
Hence manufacturing systems are becoming smarter in order to generate high value (quality, productivity) while consuming less energy and generating less waste. They feature high levels of autonomy and cognitive capabilities, largely inspired by and making use of robotic technologies.
The needs for reconfigurability and the ability to produce smaller lot sizes of personalized products require not only smart mechatronics but also for higher efficiency and effectiveness in the planning and engineering of such manufacturing systems.
Control technologies will be further exploiting the increasing computational power and intelligence in order to come forward to the demands of increased speed and precision in manufacturing. Advanced control strategies will allow the use of lighter actuators and structural elements for obtaining very rigid and accurate solutions, replacing slower and more energy-intensive approaches. Learning controllers adapt the behaviour of systems to changing environments or system degradation, taking into account constraints and considering alternatives, hereby relying on robust industrial real-time communication technologies, system modelling approaches and distributed intelligence architectures.
Continuous monitoring of the condition and performance of the manufacturing system on component and machine level, enables sustainable and competive manufacturing, also by introducing autonomous diagnosis capabilities and context-awareness. Detecting, measuring and monitoring the variables, events and situations will increase the performance and reliability of manufacturing systems. This involves advanced metrology, calibration and sensing, signal processing and model-based virtual sensing for a wide range of applications, e.g. event pattern detection, diagnostics, anomaly detection, prognostics and predictive maintenance.
Intelligent machinery components will enable the deployment of safe, energy-efficient, accurate and flexible or reconfigurable production systems. This includes the introduction of smart actuators and the use of advanced end-effectors composed of passive and active materials for complex part manipulation or assembly. Energy technologies are gaining importance, such as (super)capacitors, pneumatic storage devices, batteries and energy harvesting technologies.
Production equipment does not yet take full advantage of the benefits that new and advanced materials offer, and factories of the future will need more advanced equipment to meet the requirements for energy efficiency and environmental targets and to meet new demands for a connected world. The future will therefore see modern, lightweight, long-lasting/flexible and smart equipment able to produce current and future products for existing and new markets. There will be a step change in the construction of such equipment, leading to a sustainable manufacturing base able to deliver high added value products and customised production. Increased smartness in the manufacturing equipment also enables a systems approach with machines able to learn from each other and impacting on the human-machine interface.
Smarter equipment and manufacturing systems with self-diagnosis (temperature, vibrations, noise) and embedded sensing, memory or active architecture, with functional materials allowing them to adjust work processes and operations to variances in structure, shape and material composition (right first time manufacture).. Capture of machine data through this inherent ‘smartness’ for communication between machines (for M2M), at factory level and through supply chains for a systems approach to manufacturing and meeting customer demand.
New equipment components taking advantage of new designs and advanced materials (e.g. gears and transmissions providing longer lifetime of equipment, active surfaces that can embed and release lubricant when needed (higher pressures or temperatures))
According to the Factories of the Future 2020 roadmap issued in 2013 (see https://www.effra.eu/factories-future-roadmap), digital manufacturing platforms are described as distributed and collaborative applications, implemented through mash-ups of services implemented by different small and large ICT and manufacturing vendors. The cloud will be the “agora” for provisioning customised functionalities through services that are reliable, secure, and guarantee performance. Open standards will ensure the full inter-operability in terms of data and applications.
Real-world resources such as machinery, robots, lines, items and operators are an integral part of the information structure of production processes. All of them need to be connected to each other and to back-end systems and at the same time to be self-aware of the surrounding environment.
Advanced machine interaction with humans through ubiquity of mobile devices will enable users to receive relevant production and enterprise-specific information regardless of their geographical location and tailored to the context and the skills/responsibilities they own. Interactions with ICT infrastructures and equipment will be natural language-like
Data storage is the recording (storing) of information (data) in a storage medium. DNA and RNA, handwriting, phonographic recording, magnetic tape, and optical discs are all examples of storage media. (from https://en.wikipedia.org/wiki/Database)
Data acquisition is the process of sampling signals that measure real world physical conditions and converting the resulting samples into digital numeric values that can be manipulated by a computer. Data acquisition systems, abbreviated by the acronyms DAS or DAQ, typically convert analog waveforms into digital values for processing. The components of data acquisition systems include:
Sensors, to convert physical parameters to electrical signals.
Signal conditioning circuitry, to convert sensor signals into a form that can be converted to digital values.
Analog-to-digital converters, to convert conditioned sensor signals to digital values.
Data acquisition applications are usually controlled by software programs developed using various general purpose programming languages
So, as a summary, Data acquisition is in itself a vast group of protocols, technologies, sensors, hardware and software…
In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is used to describe machines that mimic "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving" (from https://en.wikipedia.org/wiki/Artificial_intelligence)
Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 inclusive. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false (from https://en.wikipedia.org/wiki/Fuzzy_logic)
Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks and astrocytes that constitute animal brains. The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. (from https://en.wikipedia.org/wiki/Artificial_neural_network)
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection. (https://en.wikipedia.org/wiki/Genetic_algorithm)
Simulation (often referred to as digital twins) is the imitation of the operation of a real-world process or system. The act of simulating something first requires that a model be developed; this model represents the key characteristics, behaviors and functions of the selected physical or abstract system or process. The model represents the system itself, whereas the simulation represents the operation of the system over time. (from https://en.wikipedia.org/wiki/Simulation)
The Orion Context Broker Generic Enabler is the core and mandatory component of any “Powered by FIWARE” platform or solution. It enables to manage context information in a highly decentralized and large-scale manner. It provides the FIWARE NGSIv2 API which is a simple yet powerful Restful API enabling to perform updates, queries or subscribe to changes on context information.
The Cygnus Generic Enabler brings the means for managing the history of context that is created as a stream of data which can be injected into multiple data sinks, including some popular databases like PostgreSQL, MySQL, MongoDB or AWS DynamoDB as well as BigData platforms like Hadoop, Storm, Spark or Flink.
In extended enterprises and globalized markets, applications (e.g. Life Cycle Management, Supply Chain Management, Monitoring & Control, and Customer Relationship Management) will no longer operate in closed monolithic structures. Stakeholders and customers collaborating on a common application platform implemented with the cloud approach will bank on new software development and testing environments more oriented towards non-technical users and support development of business processes. Distributed applications with low footprints targeting large user base would be supported by enhanced Business Process Re-engineering tools for rapid development and deployment.
The “servitization wave” of manufacturing has already spread out to the advanced countries and many leading high-capital investment sectors (e.g. aerospace and automotive) are already competing in the international markets providing to their customers a composition of services for product operation (e.g. maintenance, reliability, upgrades), and end-of-life use (e.g. re-manufacturing, recycling, disposal). Especially SMEs are trying to compete in the international markets with their niche solutions, adding innovative services to their value propositions. Such innovative business models are based on a dynamic network of companies, continuously moving and changing in order to afford more and more complex compositions of services. In such a context, there is a strong need to create distributed, adaptive, and interoperable virtual enterprise environments supporting these undergoing processes. In order to do so, new tools must be provided for enabling and fostering the dynamic composition of enterprise networks. In particular, SMEs call for tools and instruments which follow them in their continuously re-shaping process, enabling collaboration and communication among the different actors of the product-service value chains. New IPR methods are also needed.
The rise of the transport cost, the need for higher efficiency and productivity, the customer demand for greener product, the higher instability of raw material and energy prices and the shortening of the lead-time production will push for a more critical assessment of the delocalisation strategy towards low cost countries. Service-led personalised products will require a new paradigm for western countries re-industrialisation (Globalisation 2.0), moving back manufacturing of selected products.
Finally, innovation should become a business model in itself and a continuously run business process (the factory innovation): increasing the competitiveness through the design of a new product requires the development of a company strategy where product and process innovation is seen as a permanent, widely distributed, multi level, social oriented and user centric activity. Collaboration among companies of different sectors to exploit multi-disciplinary cross fertilisation is also envisaged. New tools, methodology and approaches for the user experience intelligence (i.e. social networks, crowd sourcing, social science methods, qualitative and quantitative, to generate insights, models and demonstrations, etc.) need to be addressed and explored.
According to the new paradigm of sustainability, the importance of the user is increasing. The user is at the same time a customer, a citizen and a worker. The well-being of the user could therefore become a winning strategy both for B2B as well as B2C companies. More detailed modelling behaviour can help the development of innovative solutions, aiming at user comfort, safety, performance, style; this requires new competitive focus for the development of these innovative solutions and new business models to support a quick and dynamic response to market changes.
As products are today virtually designed and tested before being engineered for production, new business models need also to have tools to support the company to design and test them before they are implemented through products, services and manufacturing processes. The complexity of these tools is higher than that of tools for product development, due the need for holistic modelling of product and processes.
The European Factories of the Future are expected to provide global manufacturing competitiveness, but also to create a large amount of work opportunities for the European population. Future factory workers are therefore key resources for industrial competitiveness as well as important consumers. However, as previously stated, the changing demographics and high skill requirements faced by European industry pose new challenges. Workers with high knowledge and skills (“knowledge workers”) will be scarce resources. Research efforts within Horizon 2020 must address ways to increase the number of people available for, and interested in, manufacturing tasks. This includes the following important aspects of the human resources: - New technology-based approaches to accommodate age-related limitations, through ICT and automation - New technical, educational, and organisational ways to increase the attractiveness of factory work to the young potential workforce, the existing workforce, the potential immigrant workforce, and the older workforce - New approaches to skill- and competence development, as well as skill and knowledge management, to increase competitiveness and be part of the global knowledge society - New ways to organise and compensate factory knowledge workers - New factory human-centric work-environments based on safety and comfort - Ways to integrate future factory work in global and local societal agendas and social patterns
The international standard IEC 61499, addressing the topic of function blocks for industrial process measurement and control systems, was initially published in 2005. The specification of IEC 61499 defines a generic model for distributed control systems and is based on the IEC 61131 standard. (see https://en.wikipedia.org/wiki/IEC_61499)
IEC 61499 at International Electrotechnical Commission.
In general, compliance means conforming to a rule, such as a specification, policy, standard or law. Regulatory compliance describes the goal that organizations aspire to achieve in their efforts to ensure that they are aware of and take steps to comply with relevant laws, policies, and regulations. (from https://en.wikipedia.org/wiki/Regulatory_compliance)
Here the term “business models” is used in a wide sense, complementing the technological and organisation aspects of digital platforms.
One proven tool for analysing and shaping business model is the “Business Model Canvas”. When trying to apply this tool to platforms, it appears that some elements apply to platform-based business models (e.g. the “value proposition”) and that tools as the ”canvas” can provide a first inspiration.
However, for digital platforms the traditional business models view in the narrow sense falls short of describing the business and relationship aspects of plattforms. In particular, the strict “partner” and “customer”- view has to be replaced by an ecosystem-perspective. In addition, this ecosystem can be higly dynamic, which means that platfoms can move into new user groups, change their features and might have the typical effects. Another dfference is the central role of data for platforms, meaning that data governance is one of the essential elements of the value proposition of platforms.
By definition, by bringing together actors from different sides, platforms are defined by their stakeholders. There are core stakeholders (target customers, core suppliers, value chain partners), but it should not be forgotten that there are also actors with an indirect or external interest in the activities in the platform (competitors, existing customers not addressed through the platform). A platform also defines the relationship with and the channels with the different user groups.
Digital platforms will be successful if they provide a clear value proposition to the user groups involved. In general, digital platforms offer added-value basd upon three main mechanisms:
Reduction of transaction costs
Use of data integration for new services (mainly optimisation) and business models
Based upon these mechanisms, added-value can be created in a variety of perspectives, such as the process perspective (what process or activity is optimised?) or the KPI perspective (what KPI is the focus of the optimisation). This added value enables the financing of the digital processes through e.g.increased price margins, market shares or reduced costs.
In order to be sustainable, the value proposition must be mirrored by a revenue stream, which is orchestrated by the platform. This value streams can be direct (pay-per-use, subscription, sales etc.), but could also be indirect (increasing price of products, increasing market share).
Platform as a Service (PaaS) or application platform as a Service (aPaaS) or platform base service is a category of cloud computing services that provides a platform allowing customers to develop, run, and manage applications without the complexity of building and maintaining the infrastructure typically associated with developing and launching an app. (from https://en.wikipedia.org/wiki/Platform_as_a_service)
Software as a service (SaaS /sæs/) is a software licensing and delivery model in which software is licensed on a subscription basis and is centrally hosted. It is sometimes referred to as "on-demand software". (from https://en.wikipedia.org/wiki/Software_as_a_service)
Pay-per-use or pay-per-duration-of-use implies that users are charged pro-rata of how much they used the service (in terms of consumed resources, computing power,... or in terms of the duration of the use of the service)
At the core of all potential industrial use case scenarios of platforms are data. When formerly isolated data are shared, suddenly a new set of factors arises, both in terms of new external factors, but also in terms of business/microeconomic implications. Therefore, at the core of every digital platform must be a legally, organizationally and commercially viable concept for data sharing/trading/exchange.
When shaping this model, the following questions must be answered:
What is the legal arrangement for data “ownership”? Can users classify their data, is staggered approach possible (closed, traded or open data)? What are legal means that the platform uses to ensure the confidentiality of data ? (Trade Secrets, data base directive)
Transparency: Can users monitor/control the sharing of data with third parties? Are there “expiration dates” for data use?
Is the legal setting a fixed standards (“general conditions”) or is it a flexible, individual approach? Are model contracts available?
Are there sectorial regulatory requirements concerning data?
How far is portability and change of platform possible?
Who is responsible in the case of breaches of confidentiality?
How is fairness/ a level playing field between the platform and smaller players ensured ?
Proprietary software is non-free computer software for which the software's publisher or another person retains intellectual property rights—usually copyright of the source code, but sometimes patent rights. (from https://en.wikipedia.org/wiki/Proprietary_software)
Open-source software (OSS) is a type of computer software whose source code is released under a license in which the copyright holder grants users the rights to study, change, and distribute the software to anyone and for any purpose. Open-source software may be developed in a collaborative public manner. According to scientists who studied it, open-source software is a prominent example of open collaboration. (from https://en.wikipedia.org/wiki/Open-source_software)
In the same way that software can be developed and commercialized using different business models according to the software ownership, digital platforms could be developed and commercialized using different business models according to the infrastructure ownership. Different infrastructure ownerships can be identified in this chapter and also their business models (like renting, pay per use…)