Artificial intelligence is playing a vital role in processing the big data sets of industrial companies.

Artificial intelligence is being used by large industrial companies to analyze their long array of unstructured datasets and put them into smart use. AI is creating analytics models that are creating accurate operating strategies based on variables like pump speed or weather. To be successful in this process, the big industries must know how to create an amenable environment for AI to work properly with their big datasets.

The Making of Smart Data

There is a five-step approach that can be adapted to process the big datasets into smart data. First of all, the steps of the process must be outlined, along with addressing the physical and chemical changes like grinding, heating, oxidation, and polymerization. The process flow of the operation should be labeled using paint schematics or engineering drawing. In the next step, the non-standard operating regimes should be removed. A common data science approach should be used to engineer input combinations to produce new features. When combined with the sheer number of sensors available in modern plants, this demands a massive number of observations. Instead, teams should prepare the features list to include only those inputs that describe the physical process, and then they should apply deterministic equations to create features that intelligently combine sensor information.

The sensor calibrations should be addressed and a high-quality dataset should be built. The next phase of the process would be to leverage the engineering formulas to combine the sensor data in an intelligent manner.

In the next step, advanced analytic models should be overlaid on engineered data for capturing the stochastic variability. Teams should evaluate features by inspecting their importance and therefore their explanatory power. Ideally, expert-engineered features that capture, for example, the physics of the process should rank among the most important. Overall, the focus should be on creating models that drive plant improvement, as opposed to tuning a model to achieve the highest predictive accuracy. Teams should bear in mind that process data naturally exhibit high correlations. In some cases, model performance can appear excellent, but it is more important to isolate the causal components and controllable variables than to solely rely on correlations. The last step includes checking casualties and ensuring the facts that the results are physical.

The Making of Analytics Team

The team responsible for the implementation of AI must have a variety of members from operators to data scientists, automation engineers, and process experts. Companies that are looking to implement AI generally need to rebuild their expert pipeline initially. Knowing the skills is the most important factor when it comes to choosing the perfect process expert. Planning out the model development can be a good exercise to solidify a way of working and avoid linear approaches that include exhaustively completing one stage before proceeding to the next. Later the team can decide what to invest in for the next stage.

Industrial companies are looking to AI to boost their plant operations, reduce downtime, proactively schedule maintenance, improve product quality, and so on. However, achieving operational impact from AI is not easy. To be successful, these companies will need to engineer their big data to include knowledge of the operations. The cross-functional data science teams should include employees who are capable of bridging the gap between machine learning approaches and process knowledge. Once these elements are combined with an agile way of working that advocates iterative improvement and a bias to implement findings, a true transformation can be achieved.

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Insolvencies loom large, according to the International Monetary Fund’s (IMF) recent report into the global economy. As governments ease their financial support for businesses, we are already seeing redundancies across a number of sectors, including retail, travel, hospitality, entertainment and manufacturing. With so many businesses grinding to a halt over the past few months and now looking to re-ignite interest in a difficult economy, they could be forgiven for trying to re-imagine how they operate and how, in the immediate future, they will survive, let alone grow.

Understandably, interest in robotics is growing, as advances in robotics and robotic process automation (RPA) technologies accelerate. Manufacturing, in particular the motor industry, has traditionally been a leading sector for robotics but now interest is spreading across industries and that includes some unusual applications. The ICE+FRIES bar in Iceland’s capital Reykjavik, for example, with its robot cocktail maker Toni, the robotic restaurant start-up Karakuri, or Spot the robot dog from Boston Dynamics, that patrolled Singapore’s Bishan-Ang Moh Kio Park back in May, reminding people to maintain social distancing. All illustrate the increasing breadth of automated technology but for most organisations, robotics will drive the more mundane.

According to Dr. Leslie Willcocks, Professor of Work, Technology, and Globalisation at the London School of Economics Department of Management, while most businesses want to harness automation for resilience, speed, lower costs and keeping up with market demands, the reality is somewhat different.

“In our latest research, some 65 percent are focusing on making outlays only on lower end Robotic Process Automation to get cheap quick wins necessary to either sweat the assets, or underpin today’s business performance,” he says. “Some 20 percent are continuing to make investments following a long-term strategy but at a slower rate, while only 15 percent are fully on board with investing in advanced automation as part of a larger digital transformation.”

Unsurprisingly perhaps, Willcocks adds that “health sectors everywhere are finding quite a lot of use for automation because of increased pressure on resources.” The same of course could be said for logistics businesses. It’s the repetitive, everyday tasks that now have to be performed at a safe distance that are starting to be partially automated, primarily through the use of teleoperation, at least according to Dr Antonio Espingardeiro, senior member of the IEEE. He suggests that these tasks are ranging from “collecting and transporting hospital garbage or even constantly patrolling corridors for spraying disinfection materials.”

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