Manufacturing & Supply Chain

The plant manager checks the morning production schedule. It was set based on a forecast from last Tuesday. Since then, a key raw material shipment slipped two days, a retailer doubled their order for next week, and the quality team flagged a batch variance that nobody logged in the ERP. The schedule runs anyway. It produces quantities nobody asked for based on assumptions nobody updated.

This is manufacturing analytics without a data foundation. Not a technology problem — a coordination problem that manifests as excess inventory, missed deliveries, and production teams operating on gut feel dressed up as planning.

The visibility gap between plan and floor. Most manufacturing operations generate plenty of data. The issue is that it lives in disconnected systems — ERP for orders and inventory, MES for production runs, spreadsheets for quality checks, email for exception handling. The result: no single view of what's happening right now, let alone what should happen next. When we built the data foundation for Carl's Jr Mexico (Grupo AFAL), their supply chain metrics were scattered across Oracle EBS, spreadsheets, and manual reports. Analysts queried production databases directly because there was no warehouse. KPI definitions varied by department. We built a complete modern data stack from scratch — Snowflake warehouse, Fivetran pipelines for automated Oracle extraction, and a dbt project with 134 staging models, 47 intermediate models, and 34 marts. The result: 231 automated data quality tests, a single source of truth across 100+ franchise locations, and reporting that moved from days of manual work to minutes with live dashboards.

Demand planning versus demand guessing. The gap between forecast accuracy and business performance is wider than most teams realize. A forecast that's 10% off at the SKU level doesn't just mean 10% too much or too little inventory. It means expedited freight for the shorts, warehousing cost for the longs, and production capacity wasted on the wrong mix. We build forecasting infrastructure that starts with clean demand signals — separating true customer demand from supply-constrained shipment history — and layers in the statistical models appropriate to your SKU complexity and lead times.

The replenishment intelligence layer. In environments where manufacturing feeds retail, the supply chain doesn't end at the dock. We developed replenishment optimization approaches at P&G that identified where automated ordering systems were actually suppressing demand through negative feedback loops. The same analytical approach — distinguishing supply-driven variance from demand-driven variance — applies to manufacturing planning. When your production schedule responds to what customers actually want rather than what a stale forecast says they wanted, the entire chain tightens.

Distribution and route optimization. For manufacturers who manage their own distribution, route-to-market analytics can surface significant hidden costs. We've worked with regional producers to digitize distribution routes, analyze delivery patterns, and identify where consolidation or resequencing reduces cost per drop while improving service levels.

What the engagement delivers. A governed data warehouse that connects your ERP, production, quality, and logistics data in one place. Automated pipelines that refresh daily without manual extraction. Quality tests that catch data errors before they reach dashboards. Dashboards your operations team actually trusts because the numbers reconcile back to source systems. And a foundation architected for AI forecasting when you're ready — not bolted on after.

Diagnostic questions. If your demand planner updates a forecast today, how long before production scheduling reflects the change? When a quality issue is identified on the floor, does that data reach your planning system automatically or through email? Can your COO see current inventory levels by location, by SKU, right now — or does someone need to pull a report first?