Industry 4.0, Industrie 4.0 or the fourth industrial revolution, is a collective term embracing a number of contemporary automation, data exchange and manufacturing technologies. It had been defined as 'a collective term for technologies and concepts of value chain organization' which draws together Cyber-Physical Systems, the Internet of Things and the Internet of Services.
Industry 4.0 facilitates the vision and execution of a "Smart Factory". Within the modular structured Smart Factories of Industry 4.0, cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the Internet of Things, cyber-physical systems communicate and cooperate with each other and with humans in real time, and via the Internet of Services, both internal and cross-organizational services are offered and utilized by participants of the value chain.
The term "Industrie 4.0" originates from a project in the high-tech strategy of the German government, which promotes the computerization of manufacturing. The first industrial revolution mobilised the mechanization of production using water and steam power. The second industrial revolution then introduced mass production with the help of electric power, followed by the digital revolution and the use of electronics and IT to further automate production.
The term was first used in 2011 at the Hannover Fair. In October 2012 the Working Group on Industry 4.0 chaired by Siegfried Dais (Robert Bosch GmbH) and Henning Kagermann (acatech) presented a set of Industry 4.0 implementation recommendations to the German federal government. On 8 April 2013 at the Hannover Fair, the final report of the Working Group Industry 4.0 was presented.
Other rationales exist for the meaning of "4.0". For example, McKinsey cite: "the fourth major upheaval in modern manufacturing, following the lean revolution of the 1970s, the outsourcing phenomenon of the 1990s, and the automation that took off in the 2000s. 
There are four design principles in Industry 4.0. These principles support companies in identifying and implementing Industry 4.0 scenarios.
- Interoperability: The ability of machines, devices, sensors, and people to connect and communicate with each other via the Internet of Things (IoT) or the Internet of People (IoP).
- Information transparency: The ability of information systems to create a virtual copy of the physical world by enriching digital plant models with sensor data. This requires the aggregation of raw sensor data to higher-value context information.
- Technical assistance: First, the ability of assistance systems to support humans by aggregating and visualizing information comprehensibly for making informed decisions and solving urgent problems on short notice. Second, the ability of cyber physical systems to physically support humans by conducting a range of tasks that are unpleasant, too exhausting, or unsafe for their human co-workers.
- Decentralized decisions: The ability of cyber physical systems to make decisions on their own and to perform their tasks as autonomous as possible. Only in case of exceptions, interferences, or conflicting goals, tasks are delegated to a higher level.
Characteristic for industrial production in an Industry 4.0 environment are the strong customization of products under the conditions of highly flexibilized (mass-) production. The required automation technology is improved by the introduction of methods of self-optimization, self-configuration, Self-diagnosis, cognition and intelligent support of workers in their increasingly complex work. The largest project in Industry 4.0 at the present time (July 2013) is the BMBF leading-edge cluster "Intelligent Technical Systems OstWestfalenLippe (it's OWL)". Another major project is the BMBF project RES-COM, as well as the Cluster of Excellence "Integrative Production Technology for High-Wage Countries". In 2015, the European Commission started the international Horizon 2020 research project CREMA (Providing Cloud-based Rapid Elastic Manufacturing based on the XaaS and Cloud model) as a major initiative to foster the Industry 4.0 topic.
In June 2013, consultancy firm McKinsey  released an interview featuring an expert discussion between executives at Robert Bosch - Siegfried Dais (Partner of the Robert Bosch Industrietreuhand KG) and Heinz Derenbach (CEO of Bosch Software Innovations GmbH) - and McKinsey experts. This interview addressed the prevalence of the Internet of Things in manufacturing and the consequent technology-driven changes which promise to trigger a new industrial revolution. At Bosch, and generally in Germany, this phenomenon is referred to as Industry 4.0. The basic principle of Industry 4.0 is that by connecting machines, work pieces and systems, businesses are creating intelligent networks along the entire value chain that can control each other autonomously.
Some examples for Industry 4.0 are machines which can predict failures and trigger maintenance processes autonomously or self-organized logistics which react to unexpected changes in production.
According to Dais, “it is highly likely that the world of production will become more and more networked until everything is interlinked with everything else”. While this sounds like a fair assumption and the driving force behind the Internet of Things, it also means that the complexity of production and supplier networks will grow enormously. Networks and processes have so far been limited to one factory. But in an Industry 4.0 scenario, these boundaries of individual factories will most likely no longer exist. Instead, they will be lifted in order to interconnect multiple factories or even geographical regions.
There are differences between a typical traditional factory and an Industry 4.0 factory. In the current industry environment, providing high-end quality service or product with the least cost is the key to success and industrial factories are trying to achieve as much performance as possible to increase their profit as well as their reputation. In this way, various data sources are available to provide worthwhile information about different aspects of the factory. In this stage, the utilization of data for understanding current operating conditions and detecting faults and failures is an important topic to research. e.g. in production, there are various commercial tools available to provide Overall Equipment Effectiveness (OEE) information to factory management in order to highlight the root causes of problems and possible faults in the system. In contrast, in an Industry 4.0 factory, in addition to condition monitoring and fault diagnosis, components and systems are able to gain self-awareness and self-predictiveness, which will provide management with more insight on the status of the factory. Furthermore, peer-to-peer comparison and fusion of health information from various components provides a precise health prediction in component and system levels and force factory management to trigger required maintenance at the best possible time to reach just-in time maintenance and gain near zero downtime.
Challenges which have been identified include
- IT security issues, which are greatly aggravated by the inherent need to open up those previously closed production shops
- Reliability and stability needed for critical machine-to-machine communication (M2M), including very short and stable latency times
- Need to maintain the integrity of production processes
- Need to avoid any IT snags, those would cause expensive production outages
- Need to protect industrial knowhow (contained also in the control files for the industrial automation gear)
- Lack of adequate skill-sets to expedite the march towards fourth industrial revolution
- Threat of redundancy of the corporate IT department
- General reluctance to change by stakeholders
Role of big data and analytics
Modern information and communication technologies like Cyber-Physical Systems, Big Data or Cloud Computing will help predict the possibility to increase productivity, quality and flexibility within the manufacturing industry and thus to understand advantages within the competition.
Big Data Analytics consists of 6Cs in the integrated Industry 4.0 and Cyber Physical Systems environment. The 6C system comprises Connection (sensor and networks), Cloud (computing and data on demand), Cyber (model & memory), Content/context (meaning and correlation), Community (sharing & collaboration), and Customization (personalization and value). In this scenario and in order to provide useful insight to the factory management and gain correct content, data has to be processed with advanced tools (analytics and algorithms) to generate meaningful information. Considering the presence of visible and invisible issues in an industrial factory, the information generation algorithm has to be capable of detecting and addressing invisible issues such as machine degradation, component wear, etc. in the factory floor.
Impact of Industry 4.0
The fourth industrial revolution will affect many areas. A number of key impact areas emerge:
- Services and Business Models
- Reliability and continuous productivity
- IT security
- Machine safety
- Product lifecycles
- Industry value chain
- Industry Demonstration: To help industry understand the impact of Industry 4.0, Cincinnati Mayor, John Cranley, signed a proclamation to state "Cincinnati to be Industry 4.0 Demonstration City".
- A recent article suggests that Industry 4.0 may have a beneficial effects for developing countries like India.
- Big data
- Computer-integrated manufacturing
- Digital modeling and fabrication
- Industrial control system
- Industrial Internet
- Intelligent Maintenance Systems
- Internet of Things
- Machine to machine
- Predictive manufacturing system
- World Economic Forum 2016
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- Projekt RES-COM
- Webseite Exzellenzcluster "Integrative Produktionstechnik für Hochlohnländer", Last download on 15. July 2013
- Projekt CREMA
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- Lee, Jay, Industry 4.0 in Big Data Environment, Harting Tech News 26, 2013, http://www.harting.com/fileadmin/harting/documents/lg/hartingtechnologygroup/news/tec-news/tec-news26/EN_tecNews26.pdf
- Lee, Jay; Bagheri, Behrad; Kao, Hung-An (2014). "Recent Advances and Trends of Cyber-Physical Systems and Big Data Analytics in Industrial Informatics". IEEE Int. Conference on Industrial Informatics (INDIN) 2014.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
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- Industry 4.0 - Only One-Tenth of Germany's High-Tech Strategy
- Cloud-based design and manufacturing
- Industrie 4.0 – Hightech-Strategie der Bundesregierung
- Bundesministerium für Forschung und Entwicklung - Zukunftsprojekt Industrie 4.0
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- BMBF-Spitzencluster„Intelligente technische Systeme OstwestfalenLippe it's OWL
- Exzellenzcluster Integrative Produktionstechnik für Hochlohnländer