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Last week it routed him through a school zone during pickup time – added forty minutes to his day sitting in traffic behind minivans. The map data is apparently from 2019. Nobody bothered checking.
This kind of thing drives me crazy because the technology to do route planning well actually exists now. It's not theoretical anymore. Companies implementing artificial intelligence for logistics operations are seeing their drivers complete more stops with less mileage and fewer missed deliveries. My cousin's company just picked the wrong vendor and did zero due diligence on data freshness. The gap between what's possible and what most companies actually deploy is embarrassing. Good route planning isn't magic – it's updated maps, real-time traffic data, and algorithms that learn from actual driver behavior. But apparently buying the cheapest software and calling it innovation is still the standard approach.
Why route planning is harder than it looks
On paper it seems simple. Stops. Roads. Find shortest path. Done. Reality is messier. Shortest path might involve a left turn across six lanes during rush hour. The "fifteen minute" segment takes forty-five on Fridays because of a factory shift change. The apartment has a gate code that changes monthly. Business only accepts deliveries before 10am despite listed hours.
Human drivers accumulate this knowledge over years. They know which streets flood. Which customers hate early arrivals. The difference between the address on file and where the loading dock actually is. The challenge is capturing that knowledge and combining it with dynamic factors – weather, traffic, construction – into routes that work in the real world.
What separates good planning from bad planning
| What cheap systems do | What effective systems do |
| Use static map data that's months or years old | Continuously update road networks and restrictions |
| Optimize purely for distance | Balance time, fuel cost, driver hours, and delivery windows |
| Ignore historical patterns | Learn from actual completion times at specific locations |
| Treat all stops as equal | Prioritize based on customer requirements and penalties |
| Generate routes once at day start | Dynamically reoptimize as conditions change |
| Assume perfect information | Build in buffers for the uncertainty that always exists |
My cousin's company does basically everything in the left column. They optimized for licensing cost. Now they're paying in driver overtime, missed deliveries, and customer complaints. The math didn't work out like procurement expected.
The operations manager at a competitor – I talked to her at an industry event last year – said they track actual versus predicted arrival times for every stop. When the prediction is consistently wrong for a specific address, the system learns. Takes about six months of data before the routes start reflecting reality instead of optimistic assumptions. But once it learns, their on-time rate jumped twelve percent.
The driver factor
Here's something vendors don't emphasize. The best algorithm fails if drivers don't trust it. Company installs new system. Routes look efficient on paper. Drivers deviate because they know things the system doesn't. Management gets frustrated. Drivers get monitored. Morale drops. Turnover increases. Efficiency gains disappear into training costs.
Smart companies build feedback loops. Driver says route doesn't work? Investigate why. Driver finishes faster than predicted? Capture what they know. The system needs to learn from the people actually doing the job. My cousin’s company went the other way. Deviation triggers an alert. Supervisor calls asking why. He's explained the school zone thing five times. Nobody updates the system.
Small improvements compound
Route planning improvements don't need to be dramatic. Saving three minutes per stop across fifty stops is two and a half hours. Reducing failed deliveries from eight percent to four percent means half as many redelivery trips.
Companies doing this well obsess over details. Parking availability. Time for signature capture. Walking distance to door. Average wait at security checkpoints. Not glamorous. All affects whether routes work. One logistics manager spent a month validating address data – finding locations systematically wrong in their system. Boring project. Reduced failed deliveries by nine percent.
Getting started without chaos
Improving doesn't mean ripping out existing systems. Sometimes it's feeding better data into what you have. Updated maps. Traffic integration. Historical delivery times. Customer notes that get read. Technology gap is smaller than implementation gap. Most failures aren't software problems – they're process problems wearing technology disguises. My cousin's company could fix half their issues by spending a week validating map data. They won't because that's not a headline-worthy transformation initiative. Just work. Sometimes the unsexy solution is the right one.
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