Check, check, double check!

In step 1, you figured out what you were going to do. You started planning, also known as improvising 2.0. In the next step, you started doing: you set off with a nice to-do list in your virtual hand. Now it's time for the next phase in the plan-do-check-act process: checking if things went as you had planned. But beware! Wrong conclusions are lurking.

Old-school data collection

In the past, we performed analyses based on assumptions. We decided beforehand what KPIs we wanted to collect data on. We'd assign a student with a tally sheet or collect that data in another way. Things like the route driven and delivery time were neatly recorded on paper and – since 1985 – perhaps even typed into Excel.

Take off your traditional blinkers

Traditionally, you perform analyses based on assumptions. We decided beforehand what KPIs we wanted to collect data on. We'd assign a student with a tally sheet or collect that data in another way. Today, we gather so much more data that a student with a list or other paperwork is no longer necessary. We believe this means we also need to analyze differently: look at your data and draw conclusions from it that you couldn't have imagined. For example, Google turns out to be the best predictor of epidemics based on what people type in. Of course, no one thought of that when they developed Google; it was discovered by chance. So, let your data speak and take off your traditional blinkers. Naturally, there are still standard KPIs that are useful to examine. But also, and especially, look at what you didn't know yet but will bring benefits. The problem is that you don't know what you'll find and could easily draw very wrong conclusions.

Your planning, it's completely off

Recently, students from the University of Applied Sciences in Rotterdam tested RoutiGo with electric cars. One of their conclusions was that the planned arrival times were incorrect. We, of course, immediately delved into it. What did we find? They had entered into the planning that they would start driving at 9:30 AM. In reality, they departed at 10:30 AM. Naturally, the planned arrival times would then be incorrect. With near certainty, we dare say the difference would have been approximately one hour.

Numbers don't always tell the whole story

This incident demonstrates how easy it is to draw wrong conclusions. When you check, you're checking the quality of your operation, not the quality of the system. This is often confused in practice. You've used the system to support your operation; now it's time to look at the reports and draw your conclusions from them. Be sure to involve your delivery drivers! The numbers might seem to tell you that driver X isn't performing well. For example, the reports might show that he drove an incorrect sequence. Then, engage in a conversation. It might turn out that your driver knew someone wouldn't be home at a certain time. Instead of driving to a closed door, he therefore adjusted the order of his route. Smart.

The further from the work floor, the less understanding of the business

So, look beyond the data, otherwise, wrong conclusions are lurking. You also make the system smarter by incorporating the experiences of your couriers. We believe that the further you are from the work floor, the less understanding you have of the actual operations. If you're trying to understand what all that collected data truly means, do it together with your drivers; they know. As long as we're not yet delivering with drones at least…

Want to know more?

In two weeks, we'll zoom in on the next phase: act. You've figured out what you're going to do, you've started driving and delivered packages, and in the meantime, you've collected data so you can plan even better for next time. Can't wait and want to know more now? Feel free to contact us via the contact form or call us at 030 – 7600 018.