1. Resource and skill planning

People are the most important asset in field service, yet they are becoming increasingly scarce. Therefore, accurately estimating the right capacity and the right people with the necessary skills for upcoming tasks is crucial. By employing advanced mathematical techniques, you can train your forecasting model to improve accuracy regarding the number of tasks and required skills for your field service engineers. Additionally, you can simulate various scenarios to understand the benefits of having more skilled field service engineers available, incorporating non-critical tasks into the current schedule, or outsourcing specific jobs. Optimizers create far more efficient plans compared to manual methods, typically reducing driving time by over 10% per task. In the long term, machine learning models can also assist in recruitment by determining the ideal mix of contracts and capabilities based on seasonal patterns and weekly fluctuations.

2. Steering time slot bookings of your customers

More customers desire the ability to select their own time slots rather than being directed by the field service provider. By utilizing mathematical techniques, you can guide customers to optimal time slots that maximize clustering and efficiency. This approach is already common in online grocery deliveries, where dynamic pricing or visual cues guide customer choices. The same principles can be applied in field service to cluster visits effectively. By employing simulations and machine learning models to analyze human behavior, you can assess the implications of changes before implementation and conduct A/B testing. Similarly, these techniques can propose time slots for predictive maintenance jobs, enhancing efficiency.

1. Applying Cost-to-Serve analysis to assess profitability

Machine learning allows for more accurate cost assignment to jobs than traditional Activity-Based Costing methods. In field service operations, you can define features (e.g., service level, distance, B2B/B2C) and use detailed data (e.g., job combinations in a single route) to train a model that assigns costs to each job. By comparing these costs with associated revenues, you gain a clear understanding of profit- and loss-making customers. This insight is helpful for determining where to focus your energy, whether to extend or terminate contracts, and the impact of adding new customers or activities. AI opens up a broad range of strategic opportunities.

2. Improving master data, including stop and driving times

When running an operational and execution system in field service, you can collect a large amount of data, including stop and driving times. By leveraging machine learning, you can analyze factors contributing to specific stop times, such as address details, complexity of the task, driver performance, and service levels. Additionally, you can improve the accuracy of expected driving times for each road segment throughout the day, taking traffic congestion and incidents into account. Our experience shows that this significantly improves the reliability of estimated time of arrival (ETA) and enhances overall efficiency.

These are just four practical examples of how AI can improve your field service operations. It is great to see the value in your data, learn from it, and improve your service and efficiency smoothly, while reducing the administration workload. This creates a learning loop of continuous improvement and fosters a culture of excellence within your organization. Furthermore, these strategies can support your sales and growth initiatives. A new era of opportunities has begun, harnessing the power of machine learning and mathematics in field service scheduling operations.