Industry 4 and IIot are largely about connectivity of equipment and devices to improve the way we work. In manufacturing the goals include improving productivity, product quality reduced cost and better plant utilisation. The currency for these improvements is data, and the value is what we gain from its use. This transformative data brings benefits to its users from better deployment of OT systems and improved IT processing. Traditionally separate disciplines, there is a growing convergence of OT and IT within manufacturing to gain competitive advantage.
Collecting data is not a problem, and researchers predict exponential data growth from both existing sources and the growing introduction of intelligent devices and sensors. The challenge comes when trying to make sense of the data by converting it to real world information.
As a result, several terms are becoming synonymous with the development and management IIoT of data. They include: Big Data, cloud computing, edge processing, data mining and artificial intelligence. Huge volumes of structured and unstructured data are collected and used by every industrial, commercial, and manufacturing organisations. They include healthcare, governments, media, telecommunications, social media, insurance, education and science. Each has its own requirement and requirements.
Big Data is the name given to a collection of data from various sources. The term has been in use for over thirty years and refers to data sets with sizes or complexity exceeding the capacity of traditional desktop software to process within an acceptable time. The data sets grow rapidly in characteristics driven by the 4 Vs:
Volume – The quantity of generated and store data
Variety – The type and nature of data
Velocity – The speed of data generation and processing
Veracity – The quality, value and structure of the data created
Analysis of Big Data
Data extracted from IoT devices provides a mapping of transactions that allow companies and governments to accurately target their audience. Analysis of Big Data sets provides business Intelligence for measuring trends.
As industry/manufacturing installs more intelligent field devices, the volumes of data expand exponentially. It becomes impossible to derive understanding simply from observing what is happening on a timely basis.
Data Analytics helps organisations make sense of the large data volumes and also derive easy to understand benefits for its stakeholders. Analysis of data using artificial intelligence algorithms helps identifying patterns from information that companies can use to improve their performance. The value of this customer data is significant. Using the data held on the habits of their users, it enhances the value of companies like Google, Facebook and Amazon as a resource and a currency.
Data analysis in manufacturing
The AI algorithms used are often adaptive, meaning they can change themselves each time they run. Also known as machine learning (ML), they offer enormous potential in applications like predictive maintenance. Adaptive algorithms also help users find new ways to extract other value from the data to optimise their production networks.
Many manufacturing companies are identifying AI as an opportunity to increasing their Overall Equipment Effectiveness (OEE) and combining reduced costs with higher productivity. It also helps with developing smart factories of the future.
Cloud computing is another important factor in the development in the storage and processing of Big Data. It can deliver more reliable, more scalable and more affordable data collection and distribution than on-site IT platforms. It also offers increased transparency and consistency throughout the value chain.
Issues about ownership and data security are addressable using reputable supplier partners such as Mitsubishi Electric and Schneider Electric. Their proven is at the forefront of technologies for the digital transformation of manufacturing processes provide innovative IIoT solutions. They address cloud computing concerns by providing easy and safe access to different kinds of cloud services.
Importantly, Big Data is not the answer for all companies. For example, it is probable that most SMEs are better served by edge computing and cloud storage. Edge computing and cloud computing are complementary, enabling manufacturers to integrate both to benefit from the Industrial Internet of Things.
Many different cloud solutions are available, from global suppliers to smaller niche companies. Many specialist solution providers also offer dedicated cloud-based analytics services. With its e-F@ctory approach, Mitsubishi Electric supports connection to these different cloud services.
For many smaller users cloud based processing is unsuitable for supporting applications that demand a real-time response. For these edge computing is often more appropriate. Here, aggregated data from intelligent devices is, filtered, pre-processed and analysed directly from within the automation platform. This eliminates the lag and latency of the cloud.
Using Edge computing, analysing information collected from shop floor devices and sensors take place within a dedicated C Controller and MES Interface. Data is immediately available to the processes that need it. This includes forwarding it to higher level enterprise systems for supply chain optimisation, improved production control and plant operation simulations.
Keeping time-critical information within the automation environment and pre-processing information needed by higher-level or cloud systems also reduces bandwidth requirements for the IT infrastructure. It gives greater assurances of security, and removes issues of regional data handling and storage compliance regulations.