S&OP: Why Your Excel Is Lying to You (and How to Interrogate It with Python)

In S&OP (Sales & Operations Planning) meetings, opinions are often discussed instead of facts. "I think we'll sell more" , "Last month was weird" . The root problem is not the lack of business vision, it's the lack of signal integrity . Most supply chains are managed on spreadsheets that accept anything: dates as text, blank spaces, and typos that turn a 100-unit order into 100,000. When you feed your prediction algorithm with that "garbage," you get amplified garbage (the financial Bullwhip effect). Today we kick off the S&OP Engineering series. We're not going to talk theory; we're going to build a data architecture that audits your business automatically. The Problem: Signal-to-Noise Ratio In telecommunications (my original background), noise is any interference that corrupts the signal. In Supply Chain, "noise" is dirty data. If you don't filter the noise before planning demand, you're immobilizing capital . An undetected outlier is money on fire. If your algorithm sees a false spi