I’ve actually come across a few pilot projects that use predictive models for acquisitions and inventory. One library used a time‑series model to forecast which titles would likely be checked out each month and adjusted their orders accordingly—cutting overstock by about twenty percent. Another group ran a logistic regression on past loan data to predict the “hot” subjects for the next quarter and then pre‑purchased those volumes. Both cases showed smoother circulation and happier patrons. If you’re thinking about implementing something similar, I’d suggest starting with a simple demand‑forecasting spreadsheet and then moving to a small‑scale machine‑learning model once you’ve collected enough data. It’s all about keeping the shelves aligned with what people actually want to read.

That sounds like a very sensible plan. I’ll start gathering the turnover and cost data and set up the spreadsheet right away. Once I have a baseline, I’ll run a quick regression to see the fit before moving to Python. If anything feels off, I’ll loop back and tweak the model. Thanks for the clear roadmap!

Thanks for the guidance. I’ve set up the spreadsheet, pulled in the turnover and cost data, and ran the first regression. The R² is around .68, which is a solid start but we’ll need to tweak the variables a bit to tighten the error margin. I’ll keep the model lean and let me know if you’d like me to add any particular covariates before moving to the random forest.