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About the Company

One of the largest pest control service providers in the US was in the process of rapidly expanding their operations within the country. It already had over 30 offices across 16 states, with hundreds of service delivery technicians for each region — and more to come.

The Problem

There were, however, some severe limitations in the workflow. Despite the scale of operation, the task of job scheduling (connecting technicians to thousands of customers across the US through specific routes) was done just once a day, and it took a significant amount of time for schedulers to do. This made it almost impossible to account for cancellations and new on-the-go appointments after scheduling for the day was completed. 

The fact that their scheduling software couldn’t incorporate crucial elements like technicians’ skills and on-road drive time only exacerbated the problem. Unoptimized drive time was particularly costly, since just five minutes per technician per day was equivalent to millions of dollars.

With such a high workload being handled manually with sub-par tools, the company’s routing and job sequencing functions suffered greatly. Technicians, knowing that the routes they received weren’t fully optimized, often failed to follow them, adding a further level of disruption and complexity to the operation. 

It was clear that the existing mapping and scheduling infrastructure was failing to keep up with rapid growth, and this was hurting the bottom line. The leadership wanted more and better. It was time to do job scheduling the right way — and so they turned to NextBillion.ai.

Our Solution

Owing to NextBillion.ai’s built-for-enterprise philosophy, some of the solutions in our stable were perfectly suited for exactly such a use case. For this application, our MVRP (Multiple Vehicle Routing Problem) Optimization API and Directions API would work together to meet the client’s requirements with ease.

The MVRP Optimization API is a combination of our following proprietary tools:

Distance Matrix APIComputes distances and travel times between the locations of all available technicians (origins) and all customer locations that need to be serviced (destinations). The number of origins and destinations that can be processed in an API call — and therefore the feasible scale of operation — is defined by the matrix size. The company’s existing API was limited to a 50×50 matrix, which was not nearly enough to handle over 10,000 customers in a single region. NextBillion.ai’s ability to support a far larger matrix — up to 5000×5000 — would greatly simplify operations, and was one of the major reasons the client chose our services.

Route Optimization APIThis is the component of the MVRP Optimization API that executes the core optimization function. The output of the Distance Matrix API (distances and travel times) serves as input for the Route Optimization API, which then identifies and assigns the right technicians to the right jobs for maximum efficiency, and also figures out the optimal sequence of jobs for each technician as well as the best route to follow from one stop to the next.

Geocoding APIThe Geocoding API converts geographical references like addresses, POIs, landmarks, etc. to lat-long coordinates and vice versa, and can retrieve detailed contextual information about any given location. This would be used to get the precise location coordinates to help technicians quickly navigate to and find the required customer locations.

Apart from the MVRP Optimization API, the solution would also make use of our Directions API to ultimately deliver the optimized routes to the technicians, showing them their overall route for the day and directions from one stop to the next. 

The customizability of NextBillion.ai’s solutions made it possible to incorporate every relevant operational factor into the optimization algorithm, from technicians’ skills and customer availability to expected duration of service and, most importantly, technician drive time. 

With all of these tools working in sync, the client would have everything needed to optimize and scale service delivery by connecting technicians to customers in the most effective manner possible.

The Outcome

The company’s investment in location technology and process automation began to pay off almost immediately. 

Within just a month of using NextBillion.ai’s solution, the company was able to drastically reduce the amount of man hours spent on scheduling.

Now able to easily update job schedules and routes multiple times a day, effectively incorporating additional on-the-go bookings and late cancellations was easy! This led to happier customers, and by extension, a higher Net Promoter Score (NPS) for the company. 

With the ability to account for operational nuances like technicians’ shift timings and capabilities, customer availability, service duration and more, the company was able to execute data-driven job allocation. Combined with effective route optimization, this greatly lowered drive times, and thus, enabled deeper utilization of the technicians.

Convinced that their routes were finally truly optimized, the technicians were more than happy to follow the system-generated sequences and directions, thereby increasing operational efficiency and predictability.

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