ENI TECH

The First Industrial Revolution was possible by steam engine invention which used steam power to mechanize production.
The Second created mass production made possible by using electric power,
Third, was ushered in by automated production made possible by electronics and information technology.
As Klaus Schwab describes, the Fourth Industrial Revolution is nothing but building on the Third.
Industry 4.0 is based on cyber physical concept (hence the term digital twins). We are on the voyage of technical revolution with the unprecedented power of instant connectivity, data, sensors, analytics and algorithms, and what not.
The 4th Industrial Revolution is largely driven by four specific technological developments: high-speed mobile Internet, AI and automation, the use of big data analytics, and cloud technology.
It takes help and creates fusion of rapid progress achieved in Artificial Intelligence (AI), robotics, the Internet of Things (IoT), 3D printing, genetic engineering, quantum computing, and other technologies.

Industry 4.0 is the information-intensive transformation of manufacturing and related industries, (and service industries as well) in a connected and well coordinated environment of big data, people, processes, services, systems and IoT- enabled industrial assets with the generation, leverage and utilization of actionable data and information as a means to create smart industry and ecosystems of industrial innovation collaboration and convergence.
Advanced analytics is an umbrella term for a group of high-level methods and tools that can help you get more out of your data.
The unparallel predictive capabilities of advanced analytics can be used to forecast trends, events, and behaviours. This gives companies the ability to utilize advanced statistical models such as “what-if” calculations, as well as to future-proof various aspects of their operations.
Analytics is the systematic computational analysis of data or statistics.
It is helpful to discover, interpret, and communicate meaningful patterns in data. It also helps applying data patterns for ultimate effective decision-making.

A digital twin is a combination of cyber physical i.e. virtual representation that serves as the real-time digital counterpart of a physical object or process. Though the concept was originated earlier the first practical definition of digital twin was originated from NASA in an attempt to improve physical model simulation of spacecraft in 2010. - Wikipedia
A digital twin is a digital representation or digital replica of a physical object, process or service such as a jet engine or wind farms or a simple centrifugal pump etc.
The digital twin technology can be used to replicate processes in order to collect data to predict how they will perform.
Practically digital twin is a computer program that uses real world data to create simulations that can predict how a product or process will perform.
These programs can integrate the internet of things, artificial intelligence and software analytics to enhance the output.
With the advancement of AI & ML and with factors such as big data, these virtual models have become a tool in modern engineering to drive innovation and improve performance.
Creating digital twin can allow the enhancement of strategic technology trends.
It helps prevent costly failures in physical objects.
By using advanced analytical, monitoring and predictive capabilities, it help to test processes and services.

For many industries particularly continuous processing industries, one of the major costs is energy cost.
With many large companies having dozens and even hundreds of facilities around the world, the marginal gains in improving energy efficiency of each end up significantly impacting the bottom line.
Companies leveraging Energy Intelligence are better able to make decisions for energy and operations as well as for energy efficiency projects.
Industry leaders are leveraging Energy Intelligence to make progress toward energy efficiency which is translating into improved operating margins and long-term profitability.
Energy Intelligence is also helping companies simultaneously to turn overwhelming amounts of big data into operational insights like asset health monitoring and asset optimization.

By creating digital twin data is captured continuously.
By continuously monitoring the data captured and analysing it, comparing it with historic data, we can find out,
How much de rating of asset has happened i.e. how much is the downing of the performance of the asset in terms of efficiency and effectiveness.
What has gone wrong with the process being carried out and components inside of the assets?
It can give insights for corrective and predictive maintenance by extrapolating when a particular component may fail.
It obviously helps prevent unnecessary and costly breakdowns and help improve the life of assets by timely corrective and predictive maintenance.
It helps avoid accidents caused by the un monitored assets.

Asset Optimization is all about making strategic improvements to the effectiveness of your overall asset management methodology by providing more holistic adjustments.
Asset is an item of value owned and Optimization is the process or methodology for making something as efficient and effective as possible.
Creating value with asset management optimization must go beyond simple cost reduction strategies.
It ensures increase in productivity and offer an opportunity to boost long-term profitability in a way that the most aggressive cost-cutting can’t match.
Aim of ENI Analytics is that, organisations assets should run efficiently (including energy efficiency), effectively and safely means with best of availability (free of breakdowns), accident free and with longer life i.e. creating optimised state of assets.

Agility is nothing but getting to insights faster with lesser cost.
We are aware that speed of competition has increased. It is not possible or advisable for organizations to keep waiting for months to get the ‘perfect’ answer. It is now believed that a ‘good’ answer now is far more valuable or profitable than a ‘perfect’ answer some months from now.
However it does not mean that you forego quality by adapting a ‘good answer’ now. It may mean that rather than getting every question answered, you only answer a few of the most important ones for now.

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