Much has been written of late of the Internet of Things (IoT) and its impact on many industries. Oil and gas, despite the ongoing price decline, is no exception. For those of you not familiar with the concepts of IoT, the idea is that moving forward we’ll have a myriad of tiny Internet connected devices embedded in all of our field equipment. These devices will be focused on monitoring and sending data back to the mothership for review.
IoT always reminds me of a science fair project I did with my daughter a couple of years ago where we had to measure the vibration caused by a solar powered toy when subjected to light of different colors. To measure the vibration, we simply downloaded a seismograph app onto an old smart phone, then configured it to transmit the data back to a PC for collection and analysis. In effect, we turned an old junk drawer smart phone into a remote earthquake monitor.
The potential applications in the oil and gas and process space are limitless. Imagine equipment that knows when it is breached, or functioning sub optimally….or have been subjected to stresses outside of approved operating limits. A ticket can be issued and a maintenance team dispatched. That’s at the tactical side of things. (And, on that topic, here’s a post on scheduling maintenance tickets.)
What about the broader perspective? What happens when, in monitoring the data, we identify that equipment manufactured by a specific supplier is prone to failure or that we’re statistically having more incidents in a specific location than we should? Such data prompts an investigation, and more often than not, that results in a project request: a process improvement exercise, a supplier evaluation, an investment in new materials.
This is where we come in with project and portfolio selection. How does a company implement a structured methodology for assessing the business case of these projects? How are the investment requirements to support one project balanced against the needs of the overall organization?
After all, what is the point of implementing a sensing mechanism to feed data back to us when there’s nothing in place to structure decisions based on that feedback?
(For more on sensing mechanisms, please check out this post.)