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1) You could use ML to assess the on-time reliability of each of your delivery agents (contractors or individuals). Providing more accurate estimates of delive

1) You could use ML to assess the on-time reliability of each of your delivery agents (contractors or individuals). Providing more accurate estimates of delivery time to customers (maybe via text message) might be very desirable. Or you could notify only when expected delivery time has changed (arriving earlier or later). For Just In Time-based shops, this could be a big win. 2) You could assess different delivery routes/ regions to determine if they are more/less on-time than other routes/ regions. Are the number of delivery vehicles adequate? When should you adjust the number of vehicles or change the routes themselves (like moving some peripheral regions to another route, or adjusting the cost charged when delivery is delayed). 3) When do external factors (like weather, esp rain or snow) introduce delays? Can you predict these delays, and ideally, compensate by changing routes or adding more delivery vehicles? 4) Should you more dynamically adjust your shipping fees to reflect faster/slower delivery time targets? This way you can tune your routes and manpower to save money for those who aren't as time sensitive, and improve the response time for those who are. A lot of this is basic operations research. But you can call it AI, or use AI techniques just as well as traditional OR methods. Nobody will care what math/methods you use if you can add value.

KurtMueller 8y ago
This is so cool. What are some of the traditional methods used to analyze this kind of data?
- cosmie 8y ago
  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.
  - joshwa 8y ago
    Any chance you can elaborate on [2]? Sounds interesting.
bicubic 8y ago
> 1) You could use ML to assess the on-time reliability of each of your delivery agents (contractors or individuals) Keep in mind that there may be flow on effects and ethical considerations. Once you assign a metric to individuals, someone is going to start attaching it to KPIs, ranking individuals by it, and ultimately firing individuals by it. Ethics is an increasingly prominent aspect of ML.

KurtMueller 8y ago

This is so cool. What are some of the traditional methods used to analyze this kind of data?

cosmie 8y ago
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.
- joshwa 8y ago
  Any chance you can elaborate on [2]? Sounds interesting.

bicubic 8y ago

> 1) You could use ML to assess the on-time reliability of each of your delivery agents (contractors or individuals) Keep in mind that there may be flow on effects and ethical considerations. Once you assign a metric to individuals, someone is going to start attaching it to KPIs, ranking individuals by it, and ultimately firing individuals by it. Ethics is an increasingly prominent aspect of ML.