Change management is a common theme within digital energy discussions. Although it is often suggested as a significant challenge, there are very few objective metrics for judging whether change is actually occurring. This means that judging the success of an initiative has been largely subjective.

One source of objective information is application usage. The implementation of digital energy principles brings changes to both the way applications are used and to the types of applications that are called. One component of a successful digital energy project is shifting work from manual tasks to automated work processes. In automated processes, workflows call applications to make necessary calculations instead of users checking out applications from a limited number of authorized licenses. An increase in workflow software usage can be tracked and used as one objective indicator of change. Other objective indicators are reduced application use by individuals and increased use of new and/or different applications and calculations such as analytics and statistical process control methods.

Therefore, once an organization establishes a baseline level of application and/or function usage for key workflow components then subsequent usage trends that resulted from a digital energy initiative can be monitored. For example, an individual working on an engineering design workflow may check out a license for an application for 30 minutes, acquire data, build a scenario and produce 30 calculation results in a scenario analysis. In an automated work process for the same design problem, the workflow itself calls the same application and utilizes it for 3 minutes while running 600 calculation results. By monitoring application usage, an assessment of the progress in switching to the new way of working can be monitored. In this example, the process time has decreased by a factor of 10 while the engineering rigor has increased by a factor of 20. If the statistics continue to show automated workflows in use as opposed to individual usage, it can be inferred that a change in work habits has taken place.

Although no qualitative information on decision quality is produced by application tracking, quantitatively, more wells can be analyzed and the chances of arriving at a better decision are greatly increased. Application usage data may not be a definitive indicator of change. However, when objective data is combined with qualitative feedback and other subjective data points, a more complete picture of the progress towards a new way of working can be obtained.

You can access this article if you purchase or spend a download.