actually working on jobs (as opposed to being unassigned, training, or traveling). In the auto mated, optimized schedules, crews could expect to spend 65 percent of their time on jobs. Overall, the pilot achieved an approximate 20 to 30 percent increase in field productivity (Exhibit 1). - Five lessons for utility players in deploying a smart-scheduling solution Based on our experience, there are five core lessons to keep in mind during the development and deployment of smart-scheduling solutions in a utility context. 1. Data are crucial but should not be a barrier to starting Many utilities often delay analytics-based scheduling efforts due to a lack of trust in data quality. Most leaders have a misperception that data need to be rich and easy to digest to begin to get value from AI-based tools, but the opposite is true: a small amount of data can yield disproportionate insights. In fact, new data-processing methods can take existing data and make them usable for AI models. To achieve optimal results, utility players will need to map their data landscape and find resolutions to any issues that compromise the quality or usability of the data (see sidebar, “Mapping the data landscape”). This process frequently highlights the relative importance of specific data that can then be prioritized for better data governance and stewardship, which can further increase the accuracy of AI outputs. These processes can be conducted in as little as three weeks, but space must be built into any smart- scheduling rollout timetable. This time is used to prepare and process data related to timesheets, HR, and job backlogs, as well as to evaluate data quality and to run preprocessing modules to prepare data sets to be used by the AI engine. 2. Technology must work in conjunction with processes Smart scheduling will be effective only if it works for the end user. Therefore, new technologies
equivalents (FTEs). Additionally, crews may be qualified only for certain types of jobs, and some jobs (particularly those related to electrics) may also require materials that are not in stock and that have a long lead time once ordered. Most gas jobs, on the other hand, can be done with the materials readily found on trucks. Finally, jobs may require special equipment such as backhoes or diggers. One of the largest pain points for crews is job delays or “false truck rolls,” which occurs when a job cannot be started or completed on time due to the unavailability of the right crew, materials, or equipment. Smart scheduling can help ensure all job components are ready before jobs are incorporated into the schedule. The tangible benefits of smart scheduling for a US utility In our previous article, we laid out the significant, tangible benefits accrued by a US electric and gas utility after it piloted a machine learning—based schedule optimizer 2 : — Lowered HR requirements for scheduling. Scheduler productivity increased by 10 to 20 percent, which is the equivalent of freeing up one to two scheduler hours per day. — Increased automation for flexibility. AI models automate initial schedule builds and ongoing optimization and can react to changes in the system (for example, COVID–19, seasonalities, or workforce changes) within one to two days. Manual schedulers may take much longer to adjust to such shifts. — Reduced waste. Over the six-week pilot, dynamic schedule loading and a decreased number of prematurely scheduled jobs meant that break-ins were down by 75 percent, job delays by 67 percent, and false truck rolls by 80 percent. — Increased crew utilization and field productivity. Prior to the pilot, crew members at one of the utility’s sites spent 44 percent of their time
2 “Smart scheduling,” November 1, 2022.
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