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.

(from https://en.wikipedia.org/wiki/Flexible_manufacturing_system)

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)

Shaping technology such as forming and machining, to address challenges related to “difficult to shape” materials and to explore new processing methods to achieve nano-sized microstructure components.

Methods for handling of parts, metrology and inspection require development also to ensure ability to manufacture at scale (volume) with high reliability.

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

Flexible Sheet-to-Sheet (S2S) and Roll-to-Roll (R2R), building in plastics electronics, large volume patterning at nanoscale (photolithography) and new materials and greater use of space on CMOS.

https://en.wikipedia.org/wiki/Control_system

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.

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.

More recent descriptions are available on https://www.effra.eu/digital-manufacturing-platforms

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.

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 “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 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 and IEC 61499 - International Electrotechnical Commission.

OPC Unified Architecture (OPC UA) is a machine to machine communication protocol for industrial automation developed by the OPC Foundation.
(see https://en.wikipedia.org/wiki/OPC_Unified_Architecture)

Time-Sensitive Networking (TSN) is a set of standards under development by the Time-Sensitive Networking task group of the IEEE 802.1 working group. The standards define mechanisms for the time-sensitive transmission of data over Ethernet networks. (See https://en.wikipedia.org/wiki/Time-Sensitive_Networking)

IEEE 802.15.4 is a technical standard which defines the operation of low-rate wireless personal area networks (LR-WPANs). (from https://en.wikipedia.org/wiki/IEEE_802.15.4)

IPC-CFX is an electronics manufacturing industry developed standard forming the foundation/backbone of Industry 4.0 Applications.  (From: http://www.ipc-cfx.org)

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 ?

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…)