Paying attention to Big Data, analytics, data management, and IIOT is becoming an ever-increasing challenge and opportunity.
Over the past few decades, mining and business processes have increasingly converged such that differentiating capabilities are reduced. New approaches and technologies are now enabling us to identify inefficiencies and optimize processes much better than before. Big data analytics is one aspect of an integrated approach that helps businesses generate more value across the full spectrum of their value streams.
Open source technologies can help us achieve enterprise analytic initiatives that optimize operational performance. By providing low cost storage and parallel processing across both OT and IT, these technologies can deliver insights that were previously hidden to industrial organizations. Large volumes of historical data can now be used to build predictive models that can then be applied to real-time data to guide short interval work execution.
To succeed, this requires companies to move away from their siloed data sets to create an end-to-end spectrum of operational efficiencies. For instance, many control loops are redundant, running on manual, or underutilized. Understanding, refining, and optimizing the control strategy can result in tremendous gains in operational efficiency. In-field gas and underground mining operations are far behind in their data collection and information integration activities, and much can be done to bring those up to a comparable level with their processing plants.
Another example is to integrate control system data with maintenance system data. This can reduce non-productive time and costs across most assets. Basically, it’s using analytics to reduce unplanned downtime. Cross-functional analytics can also pull in business system data to optimize logistics strategies. We see these designs as part of the move toward a democratization of industrial data analysis through the leveraging of new data storage technologies and an open source IOT architecture.
Building advanced analytics applications can enable industrial companies to consolidate data into an easily accessible, centralized platform. Our preference for collaboration around the information is to stand-up an integrated collaboration center where dispatch, operations, maintenance and scheduling can share their problem solving activities.
It’s important to acknowledge that mining systems, processing systems and industrial systems tend to have many different interfaces and the fundamental nature of data can be very different. Actual work flows, fortunately, are not complex. We have reached the point where data analytics projects are now something that can be formalized
The marketplace is putting intense pressure on miners and O&G companies to move away from their laggard approaches to adopt new and integrated technologies. Stock market performance, commodity prices and a reduced work force have forced us all to become digital. Capital intensive companies do not have the option to simply go out and buy new technologies. We must work at designing a new digital platform that includes existing infrastructures and work practices. For this, managing change remains a fundamental part of the complex engineering and data integration challenges of digital transformation.
VCI works with companies to assess their current digital intensity and design a road map to manage digital transformation in a logical, safe, and economically viable way.