Industrial Internet of Things technology continues to mature and energy companies are contemplating how to best take advantage. Since its early days, IIoT platforms have come a long way and the feature set is challenging the traditional operational technology (OT) stack with flexible options for data acquisition, storage, and analytics, as well as simplified development and edge-computing capabilities. Today, organizations that leverage IIoT can reduce costs by increasing efficiency for existing functionality while unlocking new features and capabilities.
We worked with an energy company that recognized the potential of IIoT and wanted to initiate a pilot for compressors and flow meters in its mid-stream pipeline business. The objective was to access, store, and democratize OT data from various field assets to enable new business capabilities like predictive maintenance, optimization, productivity reporting, and complex environmental reporting. The company wanted to centralize all functionality like reporting and metering in the cloud to make the data easily accessible and usable by every part of the business. This would mean that rather than trying to manually pull together various spreadsheets to gain insights into operations, company employees would access the information instantaneously in a cloud dashboard.
We worked with the organization to leverage Microsoft Azure IoT capabilities and helped provision an edge device next to its compressors and metering stations. With this solution, the company could easily run analytical models and take rapid action as needed, which is very different – and much more cost-effective – than interpreting the data in a data center and trying to remotely take action on those insights. We also built analytical models and reports in the cloud to support predictive maintenance and operational reporting.
For this project and any IIoT initiative, a few best practices will ensure optimal results.
- Employ business-driven design: Modern IoT platforms like Microsoft Azure are highly flexible. While this is an advantage, the potential downside of a lot of options is there are also many opportunities to get distracted. For this reason, it’s critical to understand the specific business objectives at play and build an enterprise-wide reference architecture that will support it. This will help reduce the urge to deviate from the core architecture.
- Budget for process improvements: Organizations embracing these initiatives need to revamp processes to ensure they’re optimized for IIoT. For example, given the sheer volume of data produced in an IIoT model, it’s critical that the proper tagging and taxonomy be established so that the data can be read and interpreted. A change-management strategy, backed by executive leadership, is crucial to successfully implementing process improvements such as this.
- Bring the right team on board: When implementing IIoT projects, the emphasis is typically on getting skilled IIoT resources on board. While these individuals are certainly needed, companies also need to ensure they have: 1) resources with strong engineering and domain knowledge, 2) data “navigators” whose jobs are focused on data storage, governance, and adherence to industry standards, and 3) a strong program lead who can orchestrate and streamline efforts across a wide variety of partners, including edge-device manufacturers and cloud-platform providers.
- Anticipate change. Because IIoT is an emerging technology, it’s only natural that changes in functionality will happen quite frequently. It’s important to stay ahead of those by building strong relationships with partners that can keep you apprised of the product roadmap and new requirements. It’s also important to build a modular reference architecture with well-defined interface definitions so that service changes or replacements will have very little impact.
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|Chandrasekar Velayudam applies over 20 years of
rich industry experience to clients’ unique challenges.