GIS Blog Series – Part 9: Inability to Optimize Maintenance Routes Due to Lack of Spatial Data

This is number nine in a series of blogs designed to address Geographic Information Systems (GIS) in conjunction with SAP. We will do this by addressing the most important customer challenges.

Inability to Optimize Maintenance Routes Due to Lack of Spatial Data


The Benefits of Route Optimization

For geographically dispersed asset maintenance, travel represents a significant portion of the overhead cost to a company. The time and cost required to travel to field locations is one area where companies could suffer, or become more profitable with superior planning and route optimization. Companies are recognizing that they can leverage Enterprise Resource Planning (ERP) and GIS systems together to create efficiencies, not just in fuel consumption savings, but other areas as well. We will identify some of these areas in this blog. 


Routing Without the Spatial Dimension

Without the spatial element, routing can be planned using other factors such as the duration of the job, criticality of the asset, or regulatory priorities. In this scenario, a better term would be “sequencing” since the elements of distance and location are not taken into account. Routing, for our purposes, occurs based on the content of the work and the location of the work. Non-spatial factors are still important, but adding in the spatial dimension unlocks significant optimization potential.


Keeping the Human Element

It should be noted that in non-spatially planned maintenance scenarios, field workers often route on-the-fly depending on the way work is dispatched, and the extent to which they can self-manage. It is often a challenge to maintain the field workers’ desired level of autonomy whilst introducing improvements to the way field jobs are dispatched and executed.

In fact, there is an advantage to keeping some of the “human element” to field routing and decision making. Field technicians gather intangible knowledge about their service areas over time that can provide invaluable input into the routing process (eg. Traffic patterns, or knowledge of a reoccurring long train crossing delay). It is also important to retain some human flexibility in field execution when dealing with customer related jobs (eg. Being able to return to the job in an hour at customer’s request).

A good solution still allows for flexibility with human input.


Address Locations and Precise Geometry

Traditionally, only addresses would be used to specify job sites. The use of only addresses for routing can be problematic. As an example, the writer was riding along with a utility crew, and after turning onto the street, over 30 minutes was spent just finding the site where a meter removal was to be performed. This was due to the inability of GIS mapping software and apps to accurately identify where the address was, or a suitable ingress point. This is the nature of addressing, and it may never improve.

Furthermore, some rural locations or new construction site may not even have precise address information. Likewise, a particular asset may not even be linked to a specific address.

Properly integrated ERP/GIS systems can eliminate the inaccurate representation of job locations. When assets are created in ERP/GIS with accurate geometry (ie. Pin-point “place on earth” coordinates), the use of addresses for routing and navigating can be relegated to a secondary option.

Now, mobile maps can generate driving directions to the asset’s actual coordinates instead of an address, which may be a large spatial area in itself. Even when addresses are accurate, the ability to pinpoint the asset location within property boundaries, is highly beneficial. The advantage becomes only more profound when dealing with underground or hidden assets.

It is also important in emergency situations to route first responders to the precise asset location (e.g. Gas valve) instead of the nearest address. Addresses can also sometimes be non-unique. A number of years ago, a mid-western utility was told of a gas leak. The supplied address turned out to be problematic as there were two identical street names within the same city. The first responders went to the unaffected house first, and unfortunately a serious incident occurred as a result. The example serves to highlight the flawed nature of the street address system and its impact on the routing process and sometimes, public safety.


Remote Asset Locations

Having precise asset geolocation is also important in situations where the asset is located in remote locations that may not even be serviced by roads. Many utilities, for example, visit job sites by helicopter or all-terrain vehicles and traditional GPS unit navigation will not necessarily be useful in these situations.

A real-life example would be that of a west-coast based Utility running a new mobile app. Within their mobile application they knew the location of where they needed to get to. It happened to be a mountainous area of the state. In addition to using driving directions on the app, they were able to switch to an aerial view showing where they were located (using GPS) and where the asset was located (fed from backend ERP/GIS). Using the satellite view they were able to find an unmarked old dirt road that a vehicle could still be driven down. In this one example, the dirt road provided a considerable short cut to the asset.


Using Shapes for More Efficient Sequencing

In cases where the work being performed covers a spatial area and not just a single location, geometry can be used to optimize the sequence of work. Consider an inspection that follows a street route, such as a gas leak survey. If the geometrical description of the work is the planned route of the surveyor, then by definition there is a start and end point. If the start and end points of the job are known and stored or accessible in ERP, then they can be efficiently sequenced with other jobs by comparing the end point of one job with the start point of other jobs.

Likewise, when work is described by a polygon (eg. Vegetation work), it’s more effective to plan spatial work with accurate geometry than an approximation represented by a point.


Routing Based on Value Return

Sometimes spatial data can be mashed up with financial “return on visit” information to impact a company’s bottom line in more ways than just travel cost. Consider a Credit and Collections department of an organization that provides cable or utility service. This department will schedule and dispatch people into the field to either try and collect overdue payments or turn off services due to non-payment.

Route optimization could use criteria such as how much a customer owes and how long they have been overdue. For example, your algorithm could be designed to go after the highest owing customers first. However, if you’re not factoring in customer locations you may be sending field workers all over the service territory in an inefficient manner.

If you do however factor in location, you can now cluster customers and their potential return value. In doing this, you can create groups of aggregated customer locations and evaluate the financial impact in visiting that area on any given day. As an example, there may be a group of customers who owe only relatively little, however, they’re all residents of the same apartment complex. Traditionally these customers may not be approached for payment (or disconnected) for a month or more. With intelligent spatial algorithms, it could actually be more cost effective to send collections workers to this apartment complex.

The image above illustrates the locations and dollar range of delinquent customers. The advantage of knowing the locations of these customers allows the collections group to prioritize clusters of accounts that have higher outstanding payments due. One such cluster is circled on the map. This ensures field collection crews are dispatched to areas where the value of their work is maximized.


Conclusion

We have listed many aspects of route optimization, from simple fuel cost savings to turning your tabular data into powerful geospatial routing parameters. It is important to understand that these concepts are only possible with a strong foundation of integrated backend systems and accurate data. When this foundation is in place, routing becomes much more effective as antiquated addressing is replaced by highly accurate asset geometry. In the end, it comes down to perfecting your organization’s knowledge of where your assets are, and reaping the benefits of efficient work routing and execution as a result.

 

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