Part 2 in a 2-part series
Part one of this series examined why edge computing is now fundamental to meeting the requirements of remote operations for the oil and gas industry and how intensive remote operations may further benefit from pairing edge computing systems with cloud computing. I now want to call out some leading and crucial industry requirements driving better performance from the Industrial Internet of Things (IIoT) and improving overall operational integrity. Both are important industry goals.
Managing assets end to end
If you claim the title and responsibility of a reliability engineer, lease operator, or field technician for the oil and gas industry, then you are likely a proponent of an IIoT data solution set capable of making sense of data in real time at the edge. You should equally be able to centralize the same data from all of your company’s edges to more robustly process and share this data in the cloud.
Decision support for remote operations means demanding evidence for how an IIoT data-driven solution is end-to-end when it comes to managing assets. In addition, the solution needs to encompass the overall work lifecycle for improving asset uptime, maintenance, asset replacement planning, and work execution. The desire to manage end-to-end processes means that edge and cloud technologies should be complementary to address a variety of challenges.
At the SAP Co-Innovation Lab, the use-case personas in our work need automation and decision support. We learned that an IIoT solution needs to work together from what is acted upon at the edge as well as what’s learned, and the business must be attuned to using cloud-based resources.
Channeling IIoT data
The personas behind the lab use cases do not get the luxury of waiting for the uplink to send something up to the cloud, which could take hours. They can’t excessively wait to learn if predictive analytics in the cloud believes there could be some imminent danger, catastrophic problem, or event at the remote site. Processing the data at the edge is what manages away the bandwidth and latency concerns that everyone has while depending on real-time analytics only from the cloud. At the edge, you act upon the data required for persistent and on-target operational integrity locally. Yet all of the data collected can and will be consumed further by the firm’s cloud-based applications and analytics. From edge to cloud, learning can be applied back as useful machine learning on the edge networks.
Scores of recent analyst reports from Gartner, IDC, and others point to the growing deluge of IIoT data. These analysts estimate that the manufacturing and process industries generate around 10% of data at the edge. They also forecast that over the next two years, the proliferation of industry sensor data will significantly grow to become 40% to 50% of all the data generated by the business across operations. This should not be a surprise when you realize that a single oil rig could mean deployment of tens of thousands of sensors.
Exploring the benefits of co-innovation with partners
Oil and gas firms zeroing in upon key enabling edge and cloud technologies should include exploring how main suppliers of edge, core, and cloud technologies can benefit from previous co-innovation with partners. This is a proven means for capturing and implementing innovation faster.
The industry is transitioning toward a scalable, real-time response at the machine level, across all operations (plants, remote sites, in-transit, and headquarters). This can come only from an edge-plus-cloud architecture that is purpose-built for the Industrial Internet. It is imperative to monitor the performance of remote assets and to use this insight to adjust equipment and process strategies. This is the way forward to continuously optimize and improve equipment performance, leading to higher production yield and ultimately, increased profitability.
For more insight on the value of collaboration, see Co-Innovation Matters To A Digital Ecosystem.