What is Route Optimisation?

In simple terms, route optimisation is the process of planning the fastest and most cost-effective way for your mobile Field Service workers to get from one appointment to another. Route optimisation means your mobile workers spend less time driving, which boosts efficiency and supports sustainability.

Inefficient routing is a costly challenge for Field Service organisations. It impacts schedule accuracy, increases fuel costs, and can lead to missed service level agreement (SLA) requirements. Worst of all, it frustrates customers.

Automation and real-time traffic information deliver significant, measurable benefits to Field Service organisations. While historical traffic patterns help with overall route planning, real-time data takes service to another level by ensuring the most precise travel estimates at all times. That means more time spent with customers rather than behind the wheel, improving efficiency and productivity, SLA compliance, and customer satisfaction.

We created this guide to help service organisations achieve real results with route optimisation. If you’re ready to exceed customer expectations, let’s get started.

Contents

• Optimise routes with precision
• Measure results
• Start real-time field scheduling

Field Service businesses are challenged to meet customers’ rising expectations while balancing costs and other important business considerations, including sustainability. Route optimisation identifies the most efficient way possible, decreases travel time, and improves productivity while enabling businesses to make more precise commitments to customers.

By using automation and live traffic information, Field Service organisations improve efficiency, reduce travel costs, and increase schedule accuracy. Let’s compare point-to-point options.

Many scheduling applications still dispatch mobile workers as though they can travel from job to job in a straight line. These solutions don’t account for turns, traffic, construction, bodies of water, or other roadblocks that are likely to influence actual travel time.

This method doesn’t yield accurate results. Here’s a hypothetical example: There’s an emergency and an automated system searches for the nearest mobile worker. The options are Leo, who is the closest, and Sabrina, who is one mile farther but has the right skills and the right inventory for the job. The system selects Leo because he’s closer, but it doesn’t consider his skills, inventory, or that he has to cross a river with a drawbridge. Sabrina may be farther away, but she’s on the same side of the river and has the right skills and inventory to complete the job.

Street-level routing takes into account turn-by-turn directions. It’s a much more accurate way of calculating travel time because it considers real environmental factors that will affect travel.

In the example scenario, Leo would quickly be eliminated as the best choice because the system would factor in the river and the extra turns that he would need to take to cross the bridge.

But this approach, just like the previous one, makes no explicit consideration for traffic — or his skill sets and inventory. While some models may include this — for example, looking at total travel time based on previous data points — it’s important to include traffic as a critical and explicit part of the route optimisation algorithm.

Real-time automation responds to any schedule change, such as a new job request, late arrival, or delayed job completion, and optimises the schedule to ensure proper usage and service.

This is the foundation for forward planning. If there is an accident on the day of service or a last-minute cancellation, schedules adjust accordingly. Jobs that are in jeopardy of being missed due to job delays are automatically rescheduled to another mobile worker with a clearer route to the job and the right skill set and parts available.

What would saving 10 minutes per job mean over the course of a year? Nissan and the University of California, Riverside, jointly evaluated the use of traffic information as a way to decrease journey time versus systems with no traffic data. With route optimisation, the outcome was striking. Average travel time was reduced by 16.2%, with 10 minutes shaved off every hour of travel, and average fuel economy was improved by 7.8%.

Accuracy and routing efficiency affect everything from scheduling and resource utilisation to employee satisfaction and user adoption of Field Service management solutions. Route optimisation reduces staffing costs, especially around overtime and the use of contractors. Meanwhile, more predictable work hours for mobile workers reduces turnover rates.

Customer satisfaction grows, too, as Field Service organisations uphold their commitments and adhere to SLAs. This is about the predictability of service — predictability enables firm commitments to customers.

Routing is a complex challenge for field service organisations. When travel times are too optimistic, unanticipated traffic delays have severe impacts: Appointments are pushed to the next day, which can create additional overtime costs, unhappy customers and employees, and SLA noncompliance penalties.

Field service route optimisation combines turn-by-turn directions with predictive and real-time traffic. This saves time and unnecessary costs while boosting employee and customer satisfaction.

Get the right field service technician to the right place at the right time with route optimisation.