Industry 4.0 Glossary

Glossary of Terms for Technologies facilitating Industry 4.0

The new age of manufacturing has brought with it its own range of terms and acronyms. Here are some of the most common buzzwords you will encounter in relation to Smart Factory implementation and operation.

3D printing
Allowing one-off or small batch production, digital 3D printers can be leveraged into the manufacturing process of Smart Factories as the ultimate in flexible production equipment.

Additive Manufacturing
Technology that manufactures objects from Computer Aided Design (CAD) sketches by adding a layer of material at a time, falls into the category of Additive Manufacturing. This includes 3D Printing, layered manufacturing and additive fabrication as well as Rapid Prototyping (RP) and Direct Digital Manufacturing (DDM).
Additive Manufacturing is used across all industries for very small production runs, notably in the medical industry for fully customized implants.

Advanced Manufacturing
Industry 4.0 leverages the latest available technology to carry out the most efficient production processes, maximizing productivity.  This is called Advanced Manufacturing.

Artificial Intelligence (AI)
Artificial Intelligence is what is exhibited by computers that have 'learned' based on processing large amounts of data, including historical facts and also information on their environment. In the Industry 4.0 context, environmental information is provided by sensors and pattern recognition applied to historical data by AI to make predictions and optimize scenarios in order to maximize results.

Augmented Reality (AR)
This new technology adds digital information to existing environmental information, displaying them together to provide a composite picture that is much more informative. Unlike Virtual Reality, Augmented Reality does not create a totally artificial environment, but overlays the new information onto the real one.
In the Smart Factory, Augmented Reality is used to assist in assembly and machine operation and training.
Big Data Analytics
Industry 4.0 operation relies on digitally connected devices, with a large volume of high quality data being recorded, processed and stored at any point in time. Big Data Analytics has enabled the Smart Factory to examine these huge data sets to make associations and spot patterns and trends. These can be used for purposes such as production scheduling, planning predictive maintenance and the prevention of bottlenecks.

Cloud Computing
Cloud Computing has enabled the Industry 4.0 environment to be fully connected, using the internet, with data security and large storage facilities. This remote centralization of business information provides the ideal platform for Digital Manufacturing.

Cyber-Physical Production Systems (CPPS)
This is the name for the connected machines in a Smart Factory, where they are centrally controlled and where the status and actions of one piece of equipment affects the others. Sensors are used for data collection on each machine that is analyzed and used to provide information on the performance and condition of the overall production system.

The move to Digitization has allowed Industry 4.0 and the connected factory to develop, facilitating a fully integrated supply chain from a product's development right through to its final distribution. With all information being in a format that can be understood by a computer, systems and machines can interact in a way that provides highly efficient operation.
Human-Machine Interface (HMI)
Allowing a person to interact with a machine, the HMI consists of hardware and software for control and communication. Extensive use is made of this interface in the Smart Factory, where there is increased demand for discrete HMI applications across many industries.

Industry 4.0
Industry 4.0 is the common name used to describe the current trend towards a fully connected and automated manufacturing system, or Smart Factory. Conceived in Germany, so also written as Industrie 4.0, it is being internationally hailed as the latest, or Fourth, Industrial Revolution. All production decisions are optimized based on real time information from a fully integrated and linked set of equipment and people.

The Internet of Things (IoT)
This term encompasses the connection of smart devices that can communicate and exchange information. Applied in the manufacturing context, this intelligent connectivity is used to collect data and optimize the production line in terms of quality, cost and throughput.
Machine 2 Machine (M2M)
Also called Interoperability, this labels the ability of digital equipment to communicate with each other without requiring any manual assistance from humans. This is particularly useful in applications of remote monitoring and forms the basis for the IoT. Historically first used in telemetry, M2M is reliant upon sensors and software as well as a communication network and identification protocol and is now finding application in everyday household products and appliances. Equipment like this, with M2M capability, is referred to as "Smart".

Machine Learning
The process by which intelligent devices gain their 'knowledge' is known as Machine Learning. Large amounts of data are processed to recognize patterns, identify correlations and apply rules. They can then detect anomalies and react accordingly. The learning process may occur within an individual device or as a result of connection to an intelligent network. In Industry 4.0, Machine Learning is effective in optimizing production processes according to real-time information,  within rules or constraints.

Mixed-Model Production
Mixed-Model Production occurs in the Smart Factory environment when several distinct models of a product are manufactured on the same production line without changeovers. This is carried out in order to provide the optimal input to later workstations for improved overall productivity or to smooth demand on suppliers and reduce inventory.
Predictive Maintenance
With connected machines in the Digital Manufacturing environment providing real-time status information, Machine Learning can be applied to the data to spot trends and patterns in timing specifically for maintenance requirements. This results in the avoidance of unscheduled downtime and maximizes overall machine availability. Predictive Maintenance is also being extended into the field after product delivery to facilitate Servitization.
Risk-based Planning & Scheduling (RPS)
Risk-based Planning and Scheduling extends traditional planning and scheduling techniques to fully account for the variation that is present in nearly any production system. Using a simulation model, RPS generates both a detailed resource-constrained deterministic schedule and a probability-based risk analysis of that schedule to account for variation in the system.  In this way, RPS is used to generate schedules that minimize risks and reduce costs in the presence of uncertainty.

SMAC is an acronym for "Social, Mobile, Analytics, Cloud" and is sometimes referred to as 'Third Platform'. Working together, these technologies combine in a way to make a potentially very intelligent, successful system. In the manufacturing context, these factors link products to their end users, providing instant feedback into market trends and preferences which drives innovation and growth.

Smart Factory
A Smart Factory is the implementation of Industry 4.0 technology, where large volumes of data, at a very detailed level, can be analyzed and modeled to produce plans and schedules that provide immense competitive advantage.


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