Ratch & Plastelle
Ratch Ratch
Plastelle Plastelle
Ratch Ratch
Hey, I've been looking at ways AI can cut the waste pile on the streets—like making sure clothes actually get used instead of piling up. Thought it might hit your sweet spot with the whole sustainability angle.
Plastelle Plastelle
That’s the kind of data‑driven approach we need, but the tech has to be aligned with the entire supply chain— from production to resale. If the algorithm can actually predict demand and reroute surplus to repair or upcycle hubs, it’s a win. Just make sure it’s not just a shiny tool that ends up with another set of metrics that never translate to real use. Let's get to the numbers, not the hype.
Ratch Ratch
Alright, pull the raw data on inventory turns, repair rates, and resale volumes. Then we’ll see if the model actually cuts waste or just fills a dashboard. No fluff, just the numbers.
Plastelle Plastelle
Here’s a minimal snapshot you can start with: inventory turns average 4.2 per year, repair rate sits around 12 % of initial units, and resale volume is about 30 000 pieces annually. You can use these as baseline figures and then drill into the raw logs for each store or supplier to see where the gaps are.
Ratch Ratch
Okay, so 4.2 turns, 12% repair, 30k resales. First check the log drift: are the repairs actually getting done or just counted? Then map each store’s SKU list to see which items never hit a repair shop. That’ll tell us where the surplus is piling. Let’s grab the raw timestamps and start flagging the outliers. No more fluff.
Plastelle Plastelle
I don’t have direct access to your logs, but a clean dump would look like this: repair_log.csv SKU,repair_id,timestamp_start,timestamp_end,status ABC123,001,2024‑02‑01 09:15,2024‑02‑01 12:30,completed DEF456,002,2024‑02‑02 10:00,2024‑02‑02 10:45,completed GHI789,003,2024‑02‑03 11:20,2024‑02‑03 11:20,failed inventory_log.csv store_id,sku,quantity_on_hand,quantity_sold,quantity_repaired,quantity_resold S1,ABC123,50,30,5,10 S2,DEF456,40,35,0,5 S3,GHI789,20,15,0,0 Run a join on SKU and flag rows where repaired = 0 and quantity_on_hand > 0. That’s your surplus list. Use the timestamps to calculate repair cycle times and identify any delays or no‑shows. Once you have that, we can feed it into a predictive model and see if the actual waste reduction matches the headline numbers.