While I can't speak to what traditional methods are for this type of analysis, I do have some tangentially related experience. I used to work in supply chain at a massive consumer goods company, and was part of the user rollout for software that touched on some of the topics mentioned above.
The "AI" software basically just applied common sense and basic regression modeling to the situation. Originally, SAP (used for resource planning) took input data at face value - i.e. if a carrier said they have X capacity along Y route with a 7 day lead time, that was statically entered into SAP and all dependencies on that information took it as gospel[1]. The secret sauce of the new system was that it looked at actual data in the system to calculate those variables. It'd pull raw transaction data out of SAP for stuff like when a pickup was requested/scheduled, when a pickup actually occurred, when a delivery was anticipated, when it was actually marked as delivered, etc. Run some basic SQL and regression analysis over that, then override the static values in the system so they were more realistic.
Not super sophisticated, but had a pretty substantial impact at scale. Unfortunately in the US specifically, that impact was negative, due to certain cultural traits that impacted user adoption[2]. But in most other regions globally, it was integrated into the planning process much more successfully and drastically improved forecasting accuracy.
[1] This was how it was configured where I was at, at least. I'm not familiar enough with SAP as a whole to know if this was a peculiarity of that specific SAP instance or if it was a limitation of SAP itself.
[2] I came onto the team right after initial rollout, at which point every region globally was showing positive returns from the rollout. Except the US, which had actually shown negative returns (not just neutral). My role was to basically "redo" the US rollout, and almost all of the issues were based off of cultural differences in how work is approached.
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This is so cool. What are some of the traditional methods used to analyze this kind of data?
While I can't speak to what traditional methods are for this type of analysis, I do have some tangentially related experience. I used to work in supply chain at a massive consumer goods company, and was part of the user rollout for software that touched on some of the topics mentioned above.
The "AI" software basically just applied common sense and basic regression modeling to the situation. Originally, SAP (used for resource planning) took input data at face value - i.e. if a carrier said they have X capacity along Y route with a 7 day lead time, that was statically entered into SAP and all dependencies on that information took it as gospel[1]. The secret sauce of the new system was that it looked at actual data in the system to calculate those variables. It'd pull raw transaction data out of SAP for stuff like when a pickup was requested/scheduled, when a pickup actually occurred, when a delivery was anticipated, when it was actually marked as delivered, etc. Run some basic SQL and regression analysis over that, then override the static values in the system so they were more realistic.
Not super sophisticated, but had a pretty substantial impact at scale. Unfortunately in the US specifically, that impact was negative, due to certain cultural traits that impacted user adoption[2]. But in most other regions globally, it was integrated into the planning process much more successfully and drastically improved forecasting accuracy.
[1] This was how it was configured where I was at, at least. I'm not familiar enough with SAP as a whole to know if this was a peculiarity of that specific SAP instance or if it was a limitation of SAP itself.
[2] I came onto the team right after initial rollout, at which point every region globally was showing positive returns from the rollout. Except the US, which had actually shown negative returns (not just neutral). My role was to basically "redo" the US rollout, and almost all of the issues were based off of cultural differences in how work is approached.
Any chance you can elaborate on [2]? Sounds interesting.