Overcoming these challenges successfully calls for the combination of deep niche software (such as the control system of an individual oil or gas pump) with high-level software that orchestrates and governs enterprise-wide processes (such as an enterprise asset management system).
One example would be developing a model to predict the optimal maintenance for an individual pump, where its output should trigger the automatic creation of relevant tasks in the enterprise asset management system (EAM), considering supply chain, resource, and risk interdependencies. Industry is full of such potential improvements that can be made by linking relevant, contextualized data with real-time operations.
Taken in aggregate, such fusions of technologies, data, and automated processes are central to the concept of Industry 4.0, or the Fourth Industrial Revolution. At the core of the necessary infrastructure to handle these improvements at scale lies industrial DataOps.
Effective Industrial DataOps is imperative to ensure the development, scaling, and managing of data-driven improvements across different applications.
Opportunities for Industrial DataOps
Let us first briefly discuss where the opportunities are. Industry’s core driver is production, be that the extraction and processing of oil and gas, the generation and delivery of power, or the manufacturing of goods.
Optimization of throughput is crucial, both in terms of production volume and, when the output is not a raw material, production quality. Your assets and equipment are vital in this, so naturally the maintenance of those assets is one of the main levers to increase throughput and reduce costs. The chief objective of a maintenance program is therefore to maximize uptime and minimize cost in an asset life cycle perspective. Production optimization and maintenance are both key areas ripe for data-driven improvement with an Industrial DataOps approach.
Managing field-worker efficiency is another key area of opportunity for improvement with Industrial DataOps. Often, practitioners separate equipment maintenance and asset integrity maintenance, where there will be different experts assuring the proper prioritization, risk assessment, and planning of the two.
The objective remains the same: to minimize negative production impact at lowest lifetime cost. The ultimate manifestation of the process is that some task is performed in the field by personnel, whether that task is a change of equipment oil, or the corrosion treatment of a handrail.
As well as production optimization, maintenance, and field worker efficiency, supply chain is also on the list of areas where Industrial DataOps can be transformative. We can add capital project planning and execution (in certain industries), as well as subsurface oil and gas with its exploration, drilling and wells, and reservoir domains.
The promise of Industrial DataOps is to increase the pace of deployment of data-driven improvements, including the scaling of any individual improvement across the fleet of assets, whether that asset represents equipment or larger facilities. Now let’s take a closer look at some real-world examples of data-enabled use cases and the data operations necessary to support them.
Extending the lifetime of critical assets and reducing service time