This concept is also essential in route optimization: With poor data, the calculated routes will be unusable. Thus, a mistyped address will disrupt the relevance of the route: the vehicle might park at the wrong location, the driver will take more time to find the right address and this will lead to delays. Delivery drivers often have to deal with these problems on their own, even though they could have anticipated them.

In logistics, it is very common to have missing or incorrect data: an inaccurate address, interchanged fields (e.g. phone number in the address field), wrong package weight or size, etc. This quality issue remains widespread today since operators frequently enter data by hand, increasing the risk of error. Carriers are also dependent on the data collected by their shipper customers, which is often missing or incomplete.

Data on task orders and available resources are usually collected, but not data detailing specific business constraints. These are known by operators, available in their heads only. For example: a customer has a preference for a given operator, a driver is late this morning, another is faster in an area he is more familiar with than others, etc. If the route optimization software is unaware of this essential information, it won’t be able to plan the best routes possible.

In the case of routes with technicians or experts, skill data is often not up-to-date due to the high turnover in the industry. However, this data is essential, as technicians may have different skills that are not always relevant to each customer. Depending on the complexity of the task, the time spent by a technician on a customer’s premises can be very different. Without this information in the system, poor routes are created with inaccurate times and wrong technicians.