The O&M squeeze is real for most utilities. Leveraging existing data sources in business intelligence applications can have a massive impact on an organization looking to cut costs.
A large east coast utility was faced with a common issue seen across the industry —
A field force, with a hard job to do, and O&M reduction being a constant pressure and force weighing on them. To make matters worse, while the budget remained constant, the work volume was increasing dramatically, to the tune of 20%. While the work was ultimately “getting done” (albeit late at times) and damage rates were at a relatively low levels, achieving these goals meant utilizing extraordinary amounts of overtime, which hovered around 40%.
After an in-depth study of the organization structure, policies and procedures it was discovered that this business unit (underground location), had been relatively neglected compared to other parts of the business. There were various opportunities for improvement, and cost savings to be had.
There was a need for better oversight into how much, and how well, work was getting accomplished.
The solution required insight into what the inspectors were doing throughout the day, as well as the ability track progress over time. The management pool was thin, and supervisors needed a method of quickly seeing how their teams were performing. Although data had always been available, it was never utilized in a meaningful way, or at all. The utility decided to embrace the power of data visualization and analytics as a means to ingest data that could quickly and easily be used to answer questions and manage the business.
The OPˣ platform provided a mechanism to harness the power of this underutilized data to discover problem areas in the organization and ultimately increase productivity and reduce costs.
The measure of productivity in this case was the number of underground location tickets completed per hour.
YOY increase (post OPˣ implementation in April 2018) was 25-30%, and continues to rise.
In terms of raw savings, a reduction in total hours spent to accomplish ~20% more work was roughly 35,000 hours, with a 5% reduction in overtime compared to the same time period the previous year (April-December). This equates to savings of ~$3mm.
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