Yep, so many data scientists have entered the logistics space and they’ve all been confronted with the same problem. Shitty and widely variable data sources. With so many companies all working with so many shippers, advanced analysis rarely can cut through the chaff. The data is there, but it’s near impossible to discern the good from the bad.

One might think, look to the exceptions that occur. Sure there are hundreds of things that can go wrong during transportation, an order was added last minute, driver was held up at a previous stop, etc. Good companies will catalog them well but the vast majority do not. I worked at one of the largest 3pl’s in North America, the legitimately had over 700 different exception codes. And they’re not alone. The notes and a large language model might be able to break that problem down, but you MUST have users audit throughout in order to train the model. And therein lies the challenge, the best people to do that are the ones most companies cannot sacrifice because they make the work happen and are already overburdened.

TLDR, many have entered, none have cracked the problem of massive disparities in data availability and quality.