Fourth Industrial Revolution
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Industry 4.0 is a collective term for technologies and concepts of value chain organization.[1] Based on the technological concepts of cyber-physical systems, the Internet of Things[2] and the Internet of Services,[3] it facilitates the vision of the 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 humans in real time. Via the Internet of Services, both internal and cross-organizational services are offered and utilized by participants of the value chain.[1]
Name
The term "Industrie 4.0" refers to the fourth industrial revolution. It originates from a project in the high-tech strategy of the German government, which promotes the computerization of manufacturing.[4] The first industrial revolution was 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.[5]
The term Industrie 4.0 was first used in 2011 at the Hanover Fair.[6] In October 2012 the Working Group on Industry 4.0 chaired by Siegfried Dais (Robert Bosch GmbH) and Kagermann (acatech) presented a set of Industry 4.0 implementation recommendations to the German federal government. On 8 April 2013 at the Hanover Fair the final report of the Working Group Industry 4.0 was presented.[7]
Design Principles
There are six design principles in Industry 4.0. These principles support companies in identifying and implementing Industry 4.0 scenarios.[1]
- Interoperability: the ability of cyber-physical systems (i.e. workpiece carriers, assembly stations and products), humans and Smart Factories to connect and communicate with each other via the Internet of Things and the Internet of Services
- Virtualization: a virtual copy of the Smart Factory which is created by linking sensor data (from monitoring physical processes) with virtual plant models and simulation models
- Decentralization: the ability of cyber-physical systems within Smart Factories to make decisions on their own
- Real-Time Capability: the capability to collect and analyse data and provide the derived insights immediately
- Service Orientation: offering of services (of cyber-physical systems, humans or Smart Factories) via the Internet of Services
- Modularity: flexible adaptation of Smart Factories to changing requirements by replacing or expanding individual modules
Meaning
Characteristic for industrial production in an Industry 4.0 environment are the strong customization of products under the conditions of high flexibilized (mass-) production. The required automation technology is improved by the introduction of methods of self-optimization, self-configuration,[8] Self-diagnosis, cognition and intelligent support of workers in their increasingly complex work.[9] The largest project in Industry 4.0 at the present time is the BMBF leading-edge cluster "Intelligent Technical Systems OstWestfalenLippe (it's OWL)". Another major project is the BMBF project RES-COM,[10] as well as the Cluster of Excellence "Integrative Production Technology for High-Wage Countries".[11]
Effects
In June 2013, consultancy firm McKinsey [12] 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 that 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, we are creating intelligent networks along the entire value chain that can control each other autonomously.
Some examples for Industry 4.0 are machines that predict failures and trigger maintenance processes autonomously or self-organized logistics that react to unexpected changes in the production.
According to Siegfried 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 factory today 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 the current condition and detecting faults and failures is an important topic to research. e. g. in production, there are various commercial tools available to provide OEE (Overall Equipment Effectiveness) information to factory management in order to highlight root cause 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.[13]
Challenges
- 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. 6C system that is consist of 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.[14][15]
Impact of the Industry 4.0
The fourth industrial revolution will affect many areas. Five key impact areas emerge:
- Machine safety
- Industry value chain
- Workers
- Socio-economic
- 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".[16]
See also
- 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
- SCADA
References
- ^ a b c Hermann, Pentek, Otto, 2015: Design Principles for Industrie 4.0 Scenarios, Last download on 03. February 2015
- ^ Jürgen Jasperneite:Was hinter Begriffen wie Industrie 4.0 steckt. In: Computer & Automation, 19. Dezember 2012; Last download on 23. December 2012
- ^ Kagermann, H., W. Wahlster and J. Helbig, eds., 2013: Recommen-dations for implementing the strategic initiative Industrie 4.0: Final report of the Industrie 4.0 Working Group.
- ^ Zukunftsprojekt Industrie 4.0
- ^ Die Evolution zur Industrie 4.0 in der Produktion Last download on 14. April 2013
- ^ Industrie 4.0: Mit dem Internet der Dinge auf dem Weg zur 4. industriellen Revolution, VDI-Nachrichten, April 2011
- ^ Industrie 4.0 Plattform Last download on 15. Juli 2013
- ^ Selbstkonfiguierende Automation für Intelligente Technische Systeme, Video, last download on 27. Dezember 2012
- ^ Jürgen Jasperneite; Oliver, Niggemann: Intelligente Assistenzsysteme zur Beherrschung der Systemkomplexität in der Automation. In: ATP edition - Automatisierungstechnische Praxis, 9/2012, Oldenbourg Verlag, München, September 2012
- ^ Projekt RES-COM
- ^ Webseite Exzellenzcluster "Integrative Produktionstechnik für Hochlohnländer", Last download on 15. July 2013
- ^ The Internet of Things and the future of manufacturing,
- ^ 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.
- ^ Lee, Jay; Lapira, Edzel; Bagheri, Behrad; Kao, Hung-an. "Recent advances and trends in predictive manufacturing systems in big data environment". Manufacturing Letters. 1 (1): 38–41. doi:10.1016/j.mfglet.2013.09.005.
- ^ http://www.imscenter.net/IMS_news/cincinnati-mayor-proclaimed-cincinnati-to-be-industry-4-0-demonstration-city
External links
- Roadmap to the Internet of Things - Its Impact, Architecture & Future Governance - Mark Fell, Carré & Strauss, 2014.
- Cloud-based design and manufacturing
- Industrie 4.0 – Hightech-Strategie der Bundesregierung
- Bundesministerium für Forschung und Entwicklung - Zukunftsprojekt Industrie 4.0
- Plattform Industrie 4.0
- Recommendations for implementing the strategic initiative INDUSTRIE 4.0 English edition - www.plattform-i40.de/
- BMBF-Spitzencluster„Intelligente technische Systeme OstwestfalenLippe it's OWL
- Exzellenzcluster Integrative Produktionstechnik für Hochlohnländer
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