Has anyone taken a course that measurably improved reorder points or safety stock settings? I finished CPIM Part 1 this spring and used a service-level model plus ABC/XYZ in Sheets to drop our holding cost 8% in Q3, and I’m debating CSCP vs a Python/SQL forecasting class for a bigger impact on stockouts and expedited freight?
“in Sheets to drop our holding cost 8% in Q3, and I’m debating CSCP” — I’d skip CSCP for now and take a Python/SQL class; use it to automate ROP/safety stock with actual lead‑time variability by SKU‑DC — CSCP is solid but, , too broad for the modeling punch you want. Do you have clean PO receipt and ship‑confirm timestamps to measure supplier lead‑time variance monthly? We pulled 18 months of order lines + receipts, recalced buffers weekly by ABC/XYZ and service targets, and cut stockouts 12% and expedites 22% in six weeks.
I’d go Python/SQL next — CSCP helps with alignment, but code will move stockouts and rush freight faster. Build a small job that pulls 18–24 months of item demand and inbound timestamps, Monte Carlo the demand‑through‑replenishment window, set ROP at your target percentile (e.g., 95%) and round to MOQ/case; use Croston/SBA for the truly intermittent stuff — it’s like teaching your spreadsheet to drive stick. For a quick primer, Hyndman’s “Forecasting: Principles and Practice” is solid: Forecasting: Principles and Practice (3rd ed); what’s your C‑class pattern like?
But nice ‘8%’. Tie service targets to margin and expedite penalty; consider CSCP only for org buy‑in…