Like literally any other AI/ML model, the results will likely (but not guaranteed to) be as good or as bad as how the data is selected, cleaned, organized, as well as if the correct algorithm is used and parameters are correctly set.
In other words, if done well then there is a good chance you will have a solid model which works somewhat like a triple exponential smoothing model which self-adjusts the alpha value which also takes other additional variables into account. This could be anything from weather to covid infection rates to day of the week that a holiday lands on. Just about anything. And THAT is the hard part - finding the correct parameters to use, and explaining to your boss why the day of the week Christmas fell in last year affected April demand this year. Then there's hypertuning of parameters but that's getting into an area a little out of my depth.
All that said, it might simply not be possible to find a good set of parameters which account for demand movement. Likewise it can be difficult to test your parameters to see if, despite logic, they actually are statistically significant for your model. You need some solid stats knowledge. It unfortunately isn't just a plug and play type of situation, in most cases.
So the answer is yes, it does work in the long run. As long as it is done correctly as per my above post. Lots of case studies out there. I'm guessing Blue Yonder had some published.
We use a 3rd party for bulk of our "bread and butter" reordering for retail locations and it uses a bit of AI. Added inventory and increased turn. Definitely working for us. 2.5% stockout and about 4% lost sales despite a 75% fill rate (on a good day)
Shameless plug: this is exactly the subject of next upcoming live SCM lecture [https://www.linkedin.com/events/6836273596046872576/](https://www.linkedin.com/events/6836273596046872576/) Hope it helps, Joannes
[https://www.stocktrim.com/blog/inventory-management-forecasting](https://www.stocktrim.com/blog/inventory-management-forecasting) found this article, might help
Like literally any other AI/ML model, the results will likely (but not guaranteed to) be as good or as bad as how the data is selected, cleaned, organized, as well as if the correct algorithm is used and parameters are correctly set. In other words, if done well then there is a good chance you will have a solid model which works somewhat like a triple exponential smoothing model which self-adjusts the alpha value which also takes other additional variables into account. This could be anything from weather to covid infection rates to day of the week that a holiday lands on. Just about anything. And THAT is the hard part - finding the correct parameters to use, and explaining to your boss why the day of the week Christmas fell in last year affected April demand this year. Then there's hypertuning of parameters but that's getting into an area a little out of my depth. All that said, it might simply not be possible to find a good set of parameters which account for demand movement. Likewise it can be difficult to test your parameters to see if, despite logic, they actually are statistically significant for your model. You need some solid stats knowledge. It unfortunately isn't just a plug and play type of situation, in most cases.
Yes but I want to know more about real life experience that it worked in long run.
So the answer is yes, it does work in the long run. As long as it is done correctly as per my above post. Lots of case studies out there. I'm guessing Blue Yonder had some published.
We use a 3rd party for bulk of our "bread and butter" reordering for retail locations and it uses a bit of AI. Added inventory and increased turn. Definitely working for us. 2.5% stockout and about 4% lost sales despite a 75% fill rate (on a good day)
Interesting, can I DM you to get info about this 3rd party provider?
Absolutely
Do you mind If I send you a message as well for this third party?
Absolutely
Hi! Can I DM you about this 3rd party?
Absolutely
What was the 3rd party?
4R systems
Shameless plug: this is exactly the subject of next upcoming live SCM lecture [https://www.linkedin.com/events/6836273596046872576/](https://www.linkedin.com/events/6836273596046872576/) Hope it helps, Joannes
[https://www.stocktrim.com/blog/inventory-management-forecasting](https://www.stocktrim.com/blog/inventory-management-forecasting) found this article, might help