Robotic process automation (RPA) continues to gain traction in many industries as a way to perform multiple digital tasks in an automated, less costly way. Oil and gas companies are among those embracing the technology.
With RPA, a software "robot" replicates actions such as entering data into an enterprise software platform. Once the software has been trained to grasp certain processes, it can automatically manipulate data, communicate with other systems, and process transactions as needed.
RPA "has the potential to make a significant impact in the oil and gas industry," said Tony Mataya, director of the Energy Practice at technology consulting firm Information Services Group (ISG).
Many organizations in the industry are implementing RPA, from small, proof-of-concept projects to large-scale initiatives, Mataya said. "RPA automates repetitive tasks and manual integration of disparate data sets, improving efficiency, reducing or virtually eliminating error, and reducing cycle times," he said.
Cognitive computing takes over from RPA when the processes are not as repetitive and there is intuition and thinking required, and a large amount of sometimes seemingly unrelated factors are considered.
"For example, when using cognitive systems in making decisions on drilling, engineers are able to identify patterns, read drilling data and sensor information, compare that data to previous experience with similar formations, and make significantly better decisions concerning a larger number of wells--with significantly fewer people," Mataya said.
Very seldom are all of the factors the same in such decisions, "so RPA would not address all the potential combinations that need to be considered," Mataya said.
In a recent post, Peggy Krendl, global finance management consulting lead for Accenture's Resources operating group, noted five points companies need to consider before automating oil and gas processes.
One is to determine if there is a compelling business case for deploying RPA. For example, Krendl noted, a company might want to reduce an accountant's tedious workload with a bot, but it needs to think about the total costs of the technology, including software licensing fees and updates that will need to be programmed in as system changes occur, as well as organizational issues that need resolution.
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Another consideration is whether the company is perpetuating cumbersome procedures. Similar activities conducted across geographies can have different procedures, some of which are unnecessarily complex. Businesses need to look to best practices and redesign to standardize and streamline procedures with RPA.
A third factor concerns how bots will be governed. The majority of bots used in RPA systems today aren't deep thinkers and can't anticipate conflicts with an ERP system, for example, that could result in slowdown. Companies need managers who know how to design new operating models, implement governance structures, assess performance, and update bots.
Fourth, companies need to examine how human roles and responsibilities will change with the use of RPA.
The automation of tasks might provide human workers with more time for analysis of data. Before moving ahead with RPA, companies should think of ways to redirect current resources to create a more intelligent workforce.
Finally, individual departments should question whether they can deploy and use RPA on their own, without help from IT. They need to gain input from key stakeholders, including IT, security, and internal auditing teams.
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