Hi everyone, my name is James and I'm new here. This is my first post, so I figure there's no better subject than the one I spend day and night thinking about anyway...

SHAMELESS PLUG: You know that feeling you get when you receive a huge package in the mail and all it has in it is a tiny item like a pen or a bar of soap? Paccurate is an algorithm engine that helps shippers ensure that doesn't ever happen. Picture 3D Tetris, but weaponized to fight wasted shipping material in the supply chain and logistics space.

In some ways, we're just beginning our journey with Paccurate, but we've found some exciting trends among our users, including a 16-20% reduction of corrugated and plastic fill. Perhaps more encouraging than the material saved is the truck capacity impact. Imagine 20% fewer trucks on the road!

Feel free to grab an API key and play with it, if you are technical and so inclined. Would love to hear what you think. We've given free access to tinkerers in the past, so if you can think of an environmental application for Paccurate that you want to experiment on, let me know. Thanks!

Thanks @Cali-Johnston! Paccurate is actually a class of software called "Narrow AI," which basically just means a set of algorithms oriented toward a specific goal or set of goals. We tend to avoid calling it AI because that conjures images of Skynet for some people, or just a meaningless buzzword for others. For reference, IBM Watson is Narrow AI- Watson is just a bunch of data scientists feeding data into algorithms.

In practice, the material savings are almost always sporadic- many shipments we don't find savings, but then a minority we find huge material savings. For one of our biggest customers who already had some cartonization logic in place, we save ~13%, or one square foot, of just cardboard per order (averaged out across all orders). And we probably actually only find those savings in 1/10 of their orders (EDIT: multi-item orders).

It only seems like magic because there's a vast amount of data the engine is crunching through to find packing solutions, including material and labor costs, type of packing fill, fragility constraints, negotiated rate tables, etc. All those factors make it too difficult to "eyeball" the optimal packing solution, for even an experienced warehouse worker.

Thanks for the encouragement @olliej. We're trying our hardest to get this out there. If you want to help with the name-and-shame tactic, next time you get an embarrassingly bad packing job in the mail consider sending the offending company a note along the lines of:

"I'm sorry to report my recent purchase was packed so carelessly that it made me feel guilty for being a customer of yours. Please consider implementing Paccurate or another solution that reduces excess packaging waste."

Amazon might not listen, but other companies might. It's worth a shot anyway!

Thanks for the encouragement @olliej. We're trying our hardest to get this out there. If you want to help with the name-and-shame tactic, next time you get an embarrassingly bad packing job in the mail consider sending the offending company a note along the lines of:

"I'm sorry to report my recent purchase was packed so carelessly that it made me feel guilty for being a customer of yours. Please consider implementing Paccurate or another solution that reduces excess packaging waste."

Amazon might not listen, but other companies might. It's worth a shot anyway!

Thanks @Cali-Johnston! Paccurate is actually a class of software called "Narrow AI," which basically just means a set of algorithms oriented toward a specific goal or set of goals. We tend to avoid calling it AI because that conjures images of Skynet for some people, or just a meaningless buzzword for others. For reference, IBM Watson is Narrow AI- Watson is just a bunch of data scientists feeding data into algorithms.

In practice, the material savings are almost always sporadic- many shipments we don't find savings, but then a minority we find huge material savings. For one of our biggest customers who already had some cartonization logic in place, we save ~13%, or one square foot, of just cardboard per order (averaged out across all orders). And we probably actually only find those savings in 1/10 of their orders (EDIT: multi-item orders).

It only seems like magic because there's a vast amount of data the engine is crunching through to find packing solutions, including material and labor costs, type of packing fill, fragility constraints, negotiated rate tables, etc. All those factors make it too difficult to "eyeball" the optimal packing solution, for even an experienced warehouse worker.

Thanks @Sam-Hughes. Our most effective method of lead generation is taking pictures of bad packages we get at home, and then sending them to the offending company. Shaming a company into being a customer maybe flirts with unethical maybe a tad, but receiving a box like that in 2020 genuinely makes me mad.

Hi everyone, my name is James and I'm new here. This is my first post, so I figure there's no better subject than the one I spend day and night thinking about anyway...

SHAMELESS PLUG: You know that feeling you get when you receive a huge package in the mail and all it has in it is a tiny item like a pen or a bar of soap? Paccurate is an algorithm engine that helps shippers ensure that doesn't ever happen. Picture 3D Tetris, but weaponized to fight wasted shipping material in the supply chain and logistics space.

In some ways, we're just beginning our journey with Paccurate, but we've found some exciting trends among our users, including a 16-20% reduction of corrugated and plastic fill. Perhaps more encouraging than the material saved is the truck capacity impact. Imagine 20% fewer trucks on the road!

Feel free to grab an API key and play with it, if you are technical and so inclined. Would love to hear what you think. We've given free access to tinkerers in the past, so if you can think of an environmental application for Paccurate that you want to experiment on, let me know. Thanks!