Industry 4.0, also known as the Fourth Industrial Revolution, refers to the current trend of automation and data exchange in manufacturing technologies, including the Internet of Things (IoT), artificial intelligence, and cloud computing. This trend is expected to lead to a more integrated and flexible manufacturing process, as well as increased efficiency and productivity. In terms of maintenance strategy, Industry 4.0 is likely to lead to a shift towards predictive maintenance, in which maintenance is performed based on data and analytics rather than on a fixed schedule. This can involve the use of sensors and IoT devices to monitor the condition of equipment in real-time, and the use of data analysis and machine learning algorithms to predict when maintenance will be needed. Predictive maintenance can help to reduce downtime and improve equipment reliability, as well as potentially reducing maintenance costs.

From chemicals to energy, artificial intelligence (AI) is already showing just how far it can help achieve global sustainability targets across different industrial sectors. One example is Petroliam Nasional Berhad (PETRONAS), has committed to achieving net-zero carbon emissions by 2050. For the Malaysian oil and gas multinational, plant reliability is key to achieving its sustainability goals. PETRONAS identified that early insight into impending equipment failure would enable plant operators to fix equipment proactively before small issues become bigger problems. Proof of concept came via a pilot project in their corporate cloud on Microsoft Azure at four upstream and two downstream units.

Machines play a huge role in our lives, including the machines we use every day, but without maintenance, every machine will eventually break down. Companies follow various maintenance programs to increase operational reliability and reduce costs. Maintenance is the set of operations necessary to preserve the functionality and efficiency of an asset and can take place in response to a failure or as a previously planned action. According to research conducted by Deloitte, a non-optimized maintenance strategy can reduce the production capacity of an industrial plant by 5 to 20%. Recent studies also show that downtime costs industrial manufacturers about 45 billion euros a year.

Every industry faces operational challenges on an everyday basis, whether it is due to machine downtime or equipment failure. However, the latest advents in technology like artificial intelligence; IoT, etc. help industries to tackle such challenges efficiently. After witnessing this, the oil and gas industry has finally started the integration of these technologies in its operations. There are various applications of artificial intelligence for different industries. Out of which, the main applications of AI for the oil and gas industry are machine learning (ML) and data science.

Any unnecessary downtime can be expensive for pharmaceutical manufacturing operations. What's more, unplanned stoppages can delay the delivery of much-needed product, potentially causing damage to a company's reputation. David Leitham, senior vice president and general manager at industrial artificial intelligence (AI) technology firm AspenTech, recently spoke with Outsourcing-Pharma (OSP) about how AI can be put to use to help predict when maintenance is needed, and avoid unplanned or over-maintenance. OSP: Could you please share an'elevator presentation' description of AspenTech? DL: AspenTech develops software to help customers in capital-intensive industries (such as energy, chemicals, and pharmaceuticals), address their biggest challenges: delivering increased value to stakeholders, responding to an evolving global population, and reducing environmental impact and waste.

In today's tech-savvy world, it is really fascinating to see millions of devices talking, exchanging data and transforming valuable insights into vital courses of action. Thanks to the Internet-of-Things (IoT) and Artificial intelligence (AI) which has transformed the business world by storm. Indeed, they have made businesses less dependent on humans and more dependent on machines. The result is ultimately auspicious! Now, it is easier for companies to aggregate a tremendous amount of data, analyze and make fearless decisions to take a big leap in business using smart technologies.

How can manufacturers put artificial intelligence to work in the industry? In this article, you will find five possible applications of Machine learning and Deep learning to industrial processes optimization. Successful manufacturers prevent equipment failures before they come up. Rather than relying on routine inspections, the ML approach uses time-series data to detect failure patterns and predict future issues. Equipment failure can be caused by various factors.

According to the World Bank, in 2017 (the latest year data are available) the worldwide manufacturing economy added $13.17T in value to the global GDP. Various sources estimate that anywhere from 4 percent to 20 percent of manufacturing capacity is lost to unplanned downtime (depending on the particular company and industry). Choosing a conservative 5 percent number averaged across all companies and industries means that the $13T number is 5 percent lower than it otherwise might be - an astonishing $693B in global productivity lost to unexpected maintenance issues for manufacturers. Reducing that number even slightly has huge potential benefits to the world's economy. Of course, manufacturers have always worked to minimize equipment failures resulting in unplanned downtime and have developed multiple techniques and processes along the way to mitigate the impact.

The fourth industrial revolution is giving rise to a data culture for accelerating digital transformation. Organizations are developing data-driven business models for utilizing data to its maximum potential. As a result, data has become an asset and a significant part of almost every business operation. Practically every organization has started implementing aggressive data collection and analysis for various applications. For this purpose, organizations deploy large data centers to store and process data.

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