Better data, better plans: why input matters more than you think.

When a route plan doesn't perform well, the instinct is to adjust the plan.

Usually, the better question is: what's wrong with the inputs?

Plans are only as good as the data that shapes them. Poor job durations, incomplete capacity information, and constraints that don't reflect reality don't produce slightly worse plans — they produce plans that are fundamentally misaligned with the actual day. That misalignment shows up where it hurts: driver stress, customer complaints, and the constant manual intervention required to keep things moving.

The three input problems that cause the most damage

Duration estimates that don't reflect reality are the most common culprit. Job times are often set once and rarely revisited. If the initial estimates were optimistic — or if the nature of work has changed over time — every route built on those estimates will be systematically too tight. A 10-minute underestimate on each of 8 jobs produces an 80-minute overrun before any actual delay occurs.

Capacity that's only partially captured is the second. If your vehicles can be constrained by both weight and volume, but your planning data only captures one, your plans will produce vehicles that arrive overfull — or routes that leave vehicles underused because the recorded dimension suggests they're full.

Constraints that are stricter than necessary are the third. Time windows are the most common version: what started as a customer preference — 'we'd prefer morning deliveries' — becomes a hard rule in the system. Over time, accumulated pseudo-constraints can quietly account for a significant portion of your planning inefficiency, making routes harder to build without providing real operational benefit.

The data improvement loop

Planning data shouldn't be static. Every time a plan runs and execution happens, there's information available that could improve the next plan. This doesn't require sophisticated systems — it requires a consistent habit.

After each planning period, review what didn't work as expected:

• Which routes ran consistently long or short — and by how much?

• Were certain job types, customers, or areas consistently causing problems?

• Were vehicles consistently loaded differently than planned?

Then adjust the inputs, not just the plan. Update duration estimates based on actual data. Refine capacity measures. Review constraints that caused repeated issues and confirm they're genuinely necessary.

Over time, this creates plans that require progressively less manual intervention — not because the planning tool improved, but because the data it's working from more accurately reflects what actually happens.

The difference between clean data and perfect data

You don't need perfect data to plan well. You need data that's close enough to reality to support good decisions.

An address that's slightly off is fine. An address that puts a job in a completely different suburb is not. A duration estimate that's 5 minutes out is fine. An estimate that's consistently 20 minutes short on every job is not.

One useful test: look at your last week of executed routes. How much did actual job times differ from planned times? How often were vehicles loaded differently than expected? The gap between plan and reality is a direct measurement of input quality — and it tells you where to focus.

Where to start

You don't need to fix everything at once. Focus on the areas with the highest frequency and impact:

• Identify which job types have the least reliable duration estimates and recalibrate

• Confirm that all binding capacity dimensions are captured in your planning data

• Audit constraints for ones that are preferences rather than real operational requirements

• Start reviewing execution data against plan data, even informally

When your data reflects how work actually gets done, plans become more stable, require less manual correction, and build more trust with the people who execute them. That's when planning stops being something you fix every day — and starts being something you rely on.