Hyperautomation improves productivity, reduces expenses and generates operational efficiency to help organizations achieve excellence. Hyperautomation means utilizing automated technology to streamline and smoothen every possible process in a company. It allows for repetitive tasks to run without any human guidance or intervention. Hyperautomation is a combination of robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML). Companies tend to get frustrated due to wavering automation processes implemented in their operation.

Machine Learning and Artificial Intelligence, together with other advanced modelling techniques, are continuously evolving and you can find thousands of algorithms, tools, platforms, etc., making it challenging to identify and validate the correct approach, technologies and solutions to use in the Mining industry. Furthermore, the success of a data analytics solution can only be realized if it can be readily deployed, managed and operated. We look forward to seeing you there.

Although COVID-19 has affected every industry, there are some it has changed more than others. Retail and Consumer Packaged Goods (CPG) are two such industries that have undergone irreversible transformation. Fortunately, most changes driven by the pandemic will prove to be positive in the long term. For the moment, however, CPG organizations are trying to decode the changes and examine how technological interventions can be applied for a stable and sustainable response to new consumer behavior. The experience of our global CPG clients shows that the answer lies in creating a digital, agile and real-time data-driven organization. To begin with, new consumer segments have emerged, there is a growing demand for new products, the buying behaviors have changed and the fulfillment channels have shifted from physical to digital.

Formula One is one of the most competitive and technology-intensive sports in the world. Data and analytics have become increasingly crucial for F1 teams because of how the sport has evolved over the years. Cars are supplying huge amounts of data through hundreds of channels, and it's up to the teams to make sense of the volume, variety, and velocity of their data for the ultimate competitive advantage. The Mercedes-AMG Petronas F1 Team has been able to implement an advanced digital foundation with data analytics at its core. The team uses sophisticated data collection methods and the most advanced artificial intelligence and machine learning techniques coupled with meticulous strategy, a holistic and unique approach to teamwork, and data-driven culture for a sustained competitive advantage. In 2010, after a 55-year hiatus, the Mercedes-AMG Petronas F1 Team made its return to motorsport racing and to Formula One as a formal constructor--that is, designing, building and racing its cars.

We all know that company performance depends on several factors, and that many of those factors are variable and even non deterministic. If life is prone to inconsistency, so is business. Much of this is because of the unpredictability of human behavior, which is why it is interesting to grasp it with new kind of statistical models provided by what we call today data science. In data science, building such a model is like assembling gears to create a mechanism that works on data. The only systematic and consistent approach is the scientific method – in other words, an inductive and iterative process. We make assumptions from the data to explain the fluctuations and correlations we observed, and then identify the models that could reproduce these observations.

Despite the age of big data, 71% of organisations still rely on a single data source to analyse asset performance and risk management – instead of drawing information from multiple sources for a more comprehensive view, reveals a report released by Lloyd's Register at Gastech today. The report, 'Oil & Gas: Achieving operational excellence in uncertain times' reveals the technologies US oil and gas companies currently use to manage and maintain their assets, and the methods they plan to adopt in the future. Tim Bisley, SVP, Software at Lloyd's Register commented: "Although organisations often collect vast amounts of data, they remain challenged as to how to use it. Predictive maintenance is reaching new levels thanks to AI, 3D digital twins and machine learning, which derive actionable insights from huge volumes of data. The report, however, indicates the industry has been slow to adopt this type of technology, in spite of the efficiencies it brings."

AI is mainly based on machine learning algorithms that learn from data – the underlying approach is data science. With this in mind, let's start with a crash course on data science. It is a truth universally acknowledged that company performance depends on several factors, and that many of those factors are variable. If life is prone to inconsistency, so is business. Much of this is because of the unpredictability of human behavior, which is why it is interesting to explore alternative approaches to grasp them.

Welcome to the era of robotics in manufacturing. The current shockwave of technological transformation has again reached the industrial landscape. The fourth industrial revolution is changing the way the manufacturing industry works. Notably, robots have started populating the manufacturing floors and are powering exponential growth in manufacturing productivity. However, the transition into Industry 4.0 is posing significant challenges.

Disruption is one of the main issues facing the air transport industry. We've all experienced or heard about disruption causing delays, often with huge impacts on passengers, cargo and operations throughout the rest of the day. Let me put it into perspective: recent figures show average flight delay times at 51 minutes. Delays could be costing the air transport industry as much as $25 billion a year. IATA figures also show the scale of the problem for airlines and airports alike.

Although all organizations operating in the open market strive for operational excellence, there are a select few who achieve this long-term goal. After all, continuously assessing and improving the level of performance of every wing of your business is no small task. In recent years, AI and machine learning have emerged as the go-to choices for businesses that try to achieve operational excellence. When used correctly, the two can help a business make insightful decisions that improve the profitability of the business and enhance the end-user experience. Here's how AI and Machine Learning help businesses achieve operational excellence.