Sure thing, here’s a quick playbook: 1. collect the right metrics, 2. build a simple predictive model, 3. set thresholds to flag red‑flags early, 4. act on the insights before the hiccup hits. If you need help setting it up, just let me know.

Let’s cut to the chase: **Step 1 – Pick the KPIs** - On‑time delivery % - Inventory turns - Supplier lead‑time variance - Order fill rate These are the ones that usually give the biggest impact. **Step 2 – Source the data** - ERP tables for orders and shipments - MES or warehouse WMS for inventory levels - Supplier portal feeds (if available) - External weather or logistics APIs for risk factors **Step 3 – Build the pipeline** - Use a simple ETL tool (Alteryx, Talend, or a quick Python script with Pandas) - Clean and aggregate the data at a daily cadence - Store it in a relational DB or a data lake; keep a versioned copy for the model **Step 4 – Model** - Start with a logistic regression or a decision tree to predict “disruption likely” - Train on the last 12 months, test on the most recent quarter - Validate with ROC‑AUC > 0.8 before you trust it **Step 5 – Pilot** - Run the model in parallel with the current process, log predictions and outcomes - Adjust the probability threshold until you hit a tolerable false‑positive rate **Step 6 – Scale** - Automate the pipeline with Airflow or Prefect, schedule the model daily, push alerts to Slack or Teams - Add a dashboard in Power BI or Tableau for executives to see the risk heatmap If you need the exact SQL for pulling the ERP data or a skeleton Python script, just shout. That’s the most efficient way to get it running.

Nice idea, the error‑rate vs. ROC plot will make the trade‑offs crystal clear. I can whip up a quick SQL template for the ERP join and a lightweight Airflow DAG. Just ping me with the table names and column list, and we’ll have it staged for sprint one.