Exhibit 1 Smart scheduling at an electric utility improved field productivity and reduced waste by 20 to 30 percent.
5–10% Improved field productivity and reduced field waste over a 6-week pilot 80% reduction in false truck rolls 75% reduction in break-ins 67% reduction in job delays
20–30%
Improvement in number of jobs worked on 1
Improvement in hours worked
1 Increase occurred during peak training time, Omicron variant outbreak, and winter weather.
McKinsey & Company
Mapping the data landscape
Utility players will need to map their data landscape—that is, understand data quality and relationships between data sets—to identify resolutions to any potential data issues. Potential data and operational issues — Changes to work orders are not tracked over time. — Schedules are not locked, making tracking adherence challenging.
— estimating travel time based on typical patterns — identifying unknown gaps in crew timesheets to improve quality of crew metrics
— Travel time may not be accurately coded. — Job duration estimates are inaccurate. — Too many data sources can be overwritten by human inputs. Potential resolutions — locking schedules to allow for better metric tracking — updating job duration estimates
Accelerating the journey to net zero
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