Smart scheduling for Utilities: A Fast Solution for Today’s Priorities

Lessons Learned from Deploying Smart Scheduling Tools

When implementing AI-driven smart scheduling systems, utility companies glean numerous valuable lessons that shape the trajectory and success of these technological endeavors.

1. Data Is Key but Not a Barrier: One of the fundamental lessons is that while comprehensive, clean data enhances system capabilities, starting with limited or imperfect data should not deter efforts. Early deployment can yield critical insights and highlight specific areas where data improvements are needed. Initial small datasets can be enriched and expanded as the system processes more operational cycles, enhancing accuracy and efficiency.
2. Integrate With Existing Processes: Technology should not be an isolated tool but part of an integrated solution that complements and enhances existing processes. This integration requires a thorough understanding of current workflows to ensure that the smart scheduling tools enhance these processes rather than creating new complexities. Engaging with frontline employees during the design and implementation phases ensures that the technology supports actual operational needs and is embraced rather than resisted.
3. Specify Clear Optimization Criteria: Clear, measurable objectives are crucial for the success of any AI-driven scheduling system. Utilities must define what they aim to achieve, whether it’s minimizing downtime, maximizing resource utilization, or reducing operational costs. These criteria should directly reflect strategic business goals and be clearly communicated to the developers of the scheduling algorithms to ensure alignment.
4. Importance of Piloting and Scaling: Piloting allows for testing how the scheduling tool fits within the existing ecosystem on a small scale, adjusting it before broader deployment. This stage is crucial for training the AI models specific to the utility’s unique demands and operational challenges. Scaling should follow a successful pilot, expanding the solution in a controlled manner to avoid widespread disruption and to learn from each phase of the rollout.
5. User-Friendly and Holistic Solutions: The end solution must be easy to use and understand. Complex systems can lead to poor adoption and underutilization. A holistic approach ensures the tool considers all aspects of the scheduling process, from initial data input and processing to crew feedback and schedule adjustments. The interface should be intuitive, allowing quick adaptations and minimal training.

Case Study: Tangible Benefits for a US Utility

A practical example of these principles in action can be seen in a case study involving a major US electric and gas utility.

1. Tangible Improvements: After implementing a smart scheduling tool, the utility observed a 20 to 30 percent increase in field productivity. This improvement was attributed to more efficient job scheduling and better resource allocation, which reduced the time crews spent waiting for assignments or traveling inefficiently between sites.
2. Increased Scheduler Productivity: Scheduler efficiency improved significantly, with productivity increases of 10 to 20 percent. This efficiency gain freed up schedulers to focus on more complex tasks and strategic planning rather than daily firefighting.
3. Reduced Waste and Enhanced Field Productivity: The pilot program at the utility reduced unnecessary job delays and resource waste. Specifically, there was a 75 percent decrease in job break-ins, a 67 percent reduction in job delays, and an 80 percent decrease in false truck rolls, which are dispatches that cannot be completed due to scheduling errors or lack of resources.

The Technical Side of Smart Scheduling

Smart scheduling is not just about implementing new software; it’s about harnessing advanced AI models and algorithms that are capable of managing the complexities of utility operations. These technologies need to handle dynamic, multifactorial scenarios that change in real-time, ensuring optimal deployment of resources.

AI Models and Algorithms

Foundational Technologies

The core of smart scheduling lies in its use of predictive analytics and machine learning. These technologies process vast amounts of historical data to identify patterns and predict future needs. By understanding past trends, the AI can forecast future demands with a high degree of accuracy.

Variables Considered

The algorithms take into account a myriad of factors:

• Crew Availability: Who is available, their skills, and their locations.
• Job Type: The specifics of the jobs, including duration, complexity, and necessary skills.
• Emergency Incidents: Unplanned events that require immediate attention.
• Weather Conditions: Weather can significantly impact scheduling, especially for jobs that are sensitive to environmental conditions.

Learning and Adaptation

Machine learning models are not static. They learn from every job scheduled and completed, refining their predictions and recommendations based on new data. This continuous learning cycle allows the system to become increasingly effective over time.

Real-Time Operation of Optimization Models

Need for Speed

Utility operations can change in an instant—a power line goes down, a water main bursts, or a storm hits unexpectedly. Smart scheduling tools must process information and adapt schedules in near real-time to effectively handle such emergencies.

Dynamic Adjustment Capabilities

These models can adjust to changes at a moment’s notice. For example:

• If a crew member calls in sick, the system can immediately adjust the schedule, reassigning tasks without disrupting the entire workflow.
• In the case of a sudden service demand spike, the system can redistribute resources to ensure that critical jobs are prioritized.

Technological Infrastructure

Implementing real-time operational capabilities requires robust technological infrastructure. This includes powerful computing hardware and software that can quickly process large datasets, as well as reliable data connectivity to ensure that updates to the scheduling system are received and processed without delay.

Integration with Other Systems

For maximum effectiveness, smart scheduling tools must be integrated with other operational systems within the utility, such as customer management systems, workforce management tools, and emergency response platforms. This integration allows for seamless communication and data sharing, making it possible to respond dynamically to operational demands.

Challenges in Implementing Smart Scheduling

While smart scheduling offers significant benefits, several challenges need addressing during implementation.

1. Complexity of Scheduling Optimization: The complexity of creating a schedule that accommodates various job types, crew qualifications, and emergency responses is a significant technical and operational challenge. Each variable can significantly impact the efficiency of the schedule, requiring sophisticated algorithms to manage effectively.
2. Variations in Work Types and Operational Challenges: Utilities deal with a broad range of work types—from emergency repairs to routine maintenance. Each type has unique requirements and constraints, which can vary widely even within the same utility depending on regional and local regulations, geography, and infrastructure conditions. These variations necessitate flexible and adaptable scheduling solutions that can cater to diverse needs.

These lessons and case study highlights underscore the transformative potential of smart scheduling in utilities but also the meticulous planning and adaptation required to realize these benefits fully.