Just wrapped a 6-week ABC/XYZ pilot to rebalance safety stock ahead of Q4 and cut stockouts 14%; now I’m deciding what to study next. For those who’ve done both, did CPIM deepen day-to-day inventory efficiency faster than a Python/Power BI forecasting track, or was the forecasting route the quicker win for S&OP and buy plans?
Given your 6-week “ABC/XYZ” win, Python+Power BI yields faster S&OP lift; CPIM later. Curious what your data coverage looks.
I’ve found that diving into Python and Power BI can really streamline your forecasting process, especially for those quick wins you’re after. The visualizations make it easier to present data to stakeholders, which can provide immediate insights to improve S&OP. Have you considered looking at how historical data influences your safety stock levels?
When I transitioned from inventory management to forecasting, I found that using historical sales data significantly improved my accuracy. It’s amazing how combining seasonal trends with basic regression analysis can inform your buy plans much faster than expected. Have you considered any specific tools to analyze your past data?