Do you like grocery shopping at the supermarket?
Me neither.
We do it because we have to. Most of us have a system that’s designed to get us in and out as fast as possible. We start at one end of the store and move to the other, checking off items on a list as we go.
That approach works – if you’re picking up 20 items at Kroger’s. But it quickly falls apart when you apply that same manual approach to designing routes for a truck delivery fleet.
Here’s what I mean.
Let’s say that, instead of having to get 20 items in 60 minutes during your own weekly shopping trip, you have to pick up 400 items and do it in 10 minutes? You’ll obviously need some help to meet that objective, so we’ll give you 20 shopping helpers – and a new job. Yes, you’re no longer a shopper; you’re a shopping planner, responsible for organizing your 20 helpers to complete the shopping task in the allotted time.
The good news is you know the supermarket pretty well, having shopped there for years. It would be nice to have some route optimization software to negotiate the supermarket aisles, but you think you can do a good job of divvying up the list by item location and assigning each shopper his own store section. So that’s what you do. Each shopper ends up with a portion of the list and they’re off…
Twelve minutes later, the first shopper arrives back, but a full 20 minutes elapses before all of the shoppers finally straggle in – 10 minutes later than the time objective.
What the heck happened?
You did the math in your head and thought that 10 minutes was plenty of time for each shopper to grab 20 items in a concentrated area of the store.
Well, what happened was that you did the math in your head!
400 items, 400 locations, 20 different shoppers. It’s more permutations than a human brain can calculate – particularly when there is pressure to get the shopping plan done quickly. As a result, some shoppers collided while in the same aisle and others unnecessarily criss-crossed into each other’s store sections.
Your delivery route planners are like our shopping planner – extremely knowledgeable, but unequipped to do tasks meant for a computer. UNLIKE our hypothetical shopping exercise, truck routing inefficiencies happen in the real world and have serious bottom line consequences.
Let’s say by automating your delivery planning with route optimization software you could become 15% more efficient. If you run a 20-truck fleet, with each truck travelling 50,000 miles per year, the potential savings might look like this:
When you consider the additional expense reductions from tires, tolls, insurance and other overheads, you could easily generate savings of $250,000 annually in this one example, delivering an ROI within just a few months for your investment in top-quality route optimization software. And that doesn’t even include the indirect savings from reducing planning time from hours to minutes.
Obviously, your savings will vary based on fleet size, equipment type and, primarily, your current level of fleet efficiency. If you are still manually planning delivery routes, our experience suggests that a 10%–30% reduction is possible in total fleet-related expenses.
Our hypothetical shopping task could easily be automated, since the exact location of each store item is already known. All that’s needed is the right application to mine this data to create the perfect route through the supermarket for all of the shoppers. They would go to each item on their shopping list in a perfectly timed sequence, without taking one unnecessary step.
Like items in a supermarket, your exact delivery stop locations are already known, along with dozens of other variables like historic traffic conditions, average road speeds and driver hours of service limits. Luckily, Paragon has an application that can help you mine this existing data. It’s called route optimization software and its algorithms translate drop locations and relevant variables into a perfect sequence of routes for your delivery trucks – making your fleet as efficient as our time-constrained shopper.
