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)

Efficient energy use, sometimes simply called energy efficiency, is the goal to reduce the amount of energy required to provide products and services. (from https://en.wikipedia.org/wiki/Efficient_energy_use)

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)

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

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.

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

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…

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

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

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.

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)

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…)