How Reliable is the Data in Last-Mile Delivery? Uncovering the Truth Behind the Metrics

When it comes to last-mile delivery, data plays a pivotal role in evaluating performance and guiding decision-making. A recent conversation with an IT expert left me thinking:

How reliable is the data that’s being used to assess delivery operations?

In the example I heard, the expert mentioned that a team of data specialists spent several days combing through delivery data to uncover the cause of a day-long delay. The team worked hard to pinpoint what went wrong in the sequence, but what struck me as even more important was the question: Can we fully trust the data they were analyzing?

The reality is that a significant portion of the delivery industry relies on “indications” to track performance, evaluate efficiency, and measure improvements. These indicators, such as time spent on tasks or delivery completion rates, are often used to set goals and drive success.

However, there’s a catch. These performance indicators can lead to a conflict of interest within the delivery chain.

While the data shows the desired results, the reality on the ground, where drivers and other workers are actively involved, may not always align with these expectations. To meet performance targets, many workers manipulate the data in subtle ways. And unfortunately, this manipulation often goes unnoticed by those interpreting the numbers.

One area where this is particularly evident is in loading times. Delivery drivers are expected to load their vehicles within a certain time frame. However, the time required to load can vary significantly depending on the quantity of packages, yet the planning process doesn’t always account for these variations. To ensure that delivery schedules stay on track, loading managers may instruct the loadres to begin the loading process with a few packages, ensuring that the system registers a start time. However, it’s common for it to take an additional 10-15 minutes before the remaining packages begin to be loaded.

The key takeaway here is that while performance indicators may give the impression of efficiency, the reality can be quite different. Data manipulation, although subtle, is common in efforts to meet these indicators. However, these artificial adjustments can distort the true picture of performance and may not actually reflect improvements in the system.

As last-mile delivery operations evolve, it’s crucial to examine not only the data we rely on but also how it’s generated.

Only by ensuring transparency and honesty in the data collection process can we achieve genuine improvements in the delivery experience.

Have you experienced challenges with data accuracy in your operations?