We are entering a new era of human history. An unprecedented amount of data is generated every second, and companies are now treating data as an important resource. Businesses are now able to track important measures, such as sales volume or viewership, simply by analyzing existing data. With data so easily available, adopting smart enterprise practices in the industrial sector is easier than ever. Smart enterprise uses data not only to reinforce existing practices, but to drive a company to new practices and modes of operation.
For an industrial enterprise, data needs to come from sensors installed on its equipment. This has proven to be a large and expensive challenge. After all, during a retrofit, the equipment still needs to be productive while the sensors are being installed. For new equipment, the capital cost of the asset needs to be taken into account.
It is no wonder then that smart enterprise, which relies on data to drive decision-making on every level of a business, has taken so long to take hold in the industrial sector. Increasingly, however, industrial organizations are realizing the value of the capturing and analyzing data to drive better business decisions. The industrial internet of things, industrial digitalization and advances in cloud data storage have ushered in the era of smart enterprise in industry.
It is important to keep in mind that smart enterprise isn’t a destination; it is a practice and process that seeks to continuously improve an organization based on the best data available. It can be thought of like refinery optimization, in which the cut point of petroleum distillation is adjusted according to the market conditions, with the understanding that it might be moved multiple times a day to remain profitable. This type of optimization process helps a company stay nimble and profitable throughout its lifecycle.
The most important component for smart enterprise is good data. The installation of networked sensors in industrial operations, whether undertaken for the purposes of plant digitalization or installed as part of a push towards smart enterprise, is an important starting point. Many companies already track their assets in fine detail, so the change to smart enterprise requires going from using the data to reinforce existing goals to being driven by the data towards new opportunities.
Companies also need a way to make sense of all the information coming in. Data analytics tools, run locally or in the cloud, can perform sophisticated statistical analyses to find signals in noisy information. Many data analytics tools include visualizations to help translate the numbers into something a human can intuitively grasp.
In smart enterprise, data not only support strategic goals, but help set the direction of the company. For example, a company that is attempting to increase margins through production optimization will outline goals through their traditional decision-making structure, then look for data that can provide insight into whether those goals are being met. In smart enterprise, the first question might be: what does the data show we’re doing well, and how can we do it better?
This can be a challenge to implement, as there is no guarantee that the data will show the company leadership what they want to see. In addition, the role data plays in smart enterprise has traditionally been the reserve of the C-suite and upper management. In a company hierarchy where who makes the decision is as important as the decision itself, a data scientist or algorithm might step on some toes.
Companies can struggle to identify what they do best. It is easy to mistake focus for competence; with so much energy devoted to one directive, it is easy to conclude that those efforts must be important. Just because something has been named a goal doesn’t mean that goal is being met, or that it makes business sense to pursue that goal.
Smart enterprise helps companies get out of their own way by sidestepping traditional management and strategy structures and replacing them with hard data. A company that functions as a smart enterprise is nimble and responsive to changing conditions and doesn’t waste resources chasing false leads or unwise decisions.
With increases in source material costs, additional environmental compliance requirements, and an unpredictable world in which to operate, companies need any advantage they can get. For a company that is already generating and analyzing a lot of data, making the transition to smart enterprise can provide a critical edge.
How can industrial AI benefit smart enterprise?
Making sense of all the information required to be a data-driven enterprise is a daunting task. Industrial AI can sift through huge volumes of information and find patterns and linkages between data points.
For example, AI for oil and gas can help set the swing cut for a refinery based on patterns of past productivity. By analyzing the relationship between the types of distillate the company produces and the profitability of those products, the range of cut points can be set intelligently. This kind of data-driven refinery optimization is typical of smart enterprise.
Is smart enterprise just about profits?
Smart enterprise empowers companies to be better corporate citizens by shining light on the entire operation. For example, a great deal of methane, a potent greenhouse gas, is released through oversight or equipment malfunction every year. Tracking those methane losses with the tools of smart enterprise gives a company an idea about the scale of the problem, and the data may give clues to how to fix it.
How is a smart refinery related to smart enterprise?
A smart refinery is a refinery that uses smart enterprise practices for optimization and planning. Petroleum refineries are well suited to adopt smart enterprise practices, as the price of oil can vary dramatically, meaning the refinery operations must change to remain profitable so a data-intensive approach is highly beneficial.
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