When discussing optimization when planning routes, trips, or loads, the first thing that comes to mind is that the optimized end result reduces transportation costs by a significant amount. But there are other savings to be gained as well.
The time needed to plan those routes, trips, or loads can take quite a long time every day.
Consider a totally manual process.
The first step is to print out the service orders or delivery tickets. Unless you have a very fast printer, it will take at least a minute or two to do this.
The next step is to divide up the work into some geographical groups. Usually this is done by town name or ZIP code. And it takes some time to do this, as well.
Then, the smaller groups either need to be collected into larger groups based on geographical proximity, or groups that contain more work than will fit on a truck need to be divided into two or more smaller groups.
Once some groups are created that will pretty much fit on a truck or fill a service technician’s day, then the real work begins. Will everything fit on the truck? Is the technician’s schedule too short or too long? What needs to be moved from one group to another to make things fit?
Each one of these moves requires at least a small amount of thinking time, and this adds up quickly.
Another issue is that the human route planner can only have a good mental picture of a small to medium number of stops. Good route planners have told me that this is usually around thirty stops. And once the number of stops to be assigned exceeds this, the route planning task can only be done by breaking the larger problem into smaller problems where assignments are made separately.
After the smaller problems are solved, sometimes better assignments can be made when a stop is moved between adjacent larger groups, but, again, this takes time.
The alternative to this is a route optimization process.
The route optimizer does not care about town names or ZIP code boundaries. It only cares about the time and distance between any stop and other nearby stops. If there are two stops in different towns, but are close together, they go together. If there are two stops in the same ZIP code that are far apart, they may be assigned to different routes.
You can have routes that look like this in a fraction of time it would take to make the assignments manually.
Do you want to see how route optimization reduces your costs?
We offer a free consultation to help you determine if a route optimization process is the way to achieve significant savings in both planning and actual transportation costs.
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