Industry 4.0

Industry 4.0 is the current trend of automation and data exchange in manufacturing technologies - it includes cyber-physical systems, Internet of things & cloud computing.

The term originates from a project in the high-tech strategy of the German government, which promotes the computerization of manufacturing. & was first used in 2011 at the Hannover Fair.

Industry 4.0 creates what has been called a "smart factory". Within the modular structured smart factories, 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 used by participants of the value chain.

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.

The four industrial revolutions - "Christoph Roser at AllAboutLean.com." 

4 principles support companies in identifying and implementing Industry 4.0 scenarios.

1. 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).
2. 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.
3. 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.

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. 

In 2015, the EC 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.

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.

"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.

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.[16]

·        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, as 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, loss of many jobs to automatic processes

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:

1. Connection (sensor and networks)
2. Cloud (computing and data on demand)
3. Cyber (model & memory)
4. Content/context (meaning and correlation)
5. Community (sharing & collaboration)
6. 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.[18][19]

1. Services and Business Models
2. Reliability and continuous productivity
3. IT security
4. Machine safety
5. Product lifecycles
6. Industry value chain
7. Workers Education and skills
8. Socio-economic
9. Industry Demonstration: To help industry understand the impact of Industry 4.0, may have beneficial effects for developing countries like India

https://en.wikipedia.org/wiki/Industry_4.0