Your production floor runs on data. Your decisions run on last month's report.

Manufacturers generate more operational data than almost any other industry. Every machine cycle, every sensor reading, every quality check, every batch record creates data. The problem is not generation. It is access.

Your machines produce gigabytes of telemetry every day, but that data sits in controllers, historians, and log files that only automation engineers can touch. Your ERP holds years of production records, but getting a yield report means filing an IT ticket or waiting for a legacy report to run. Your quality team tracks inspections in spreadsheets that nobody connects back to the line. The data exists. The infrastructure to use it does not.

Real-time production monitoring. IoT data pipelines that ingest sensor data at millisecond intervals — fast enough to capture individual machine cycles — store it in time series databases, and render it in dashboards your engineering team checks daily. At one biotech manufacturer, these pipelines process over a billion sensor readings per day across 60+ machines. The same architecture scales to any production environment where machines generate data faster than your team can consume it.

Operational dashboards from legacy ERP data. We connect to your ERP — whether it is SAP, Oracle, JD Edwards, or another system — and build transformation layers that surface the metrics plant managers actually need. At an industrial distributor, we built automated reporting on warehouse productivity, sales orders, and fulfillment efficiency. At a commercial laundry service, we assessed production output tracking, inventory management, route optimization, and chemical usage across two plant facilities.

Quality analytics and traceability. Batch-level data pipelines that connect raw machine output to quality outcomes. When a failure pattern emerges in sensor data, your team sees it in the dashboard, not in a monthly report. We have built these systems for robotic arm analysis, vision system analytics, and statistical process control tracking.

ML infrastructure for manufacturing. For manufacturers ready to move beyond dashboards, we build the data preparation layer that makes machine learning possible: feature engineering pipelines, labeled training datasets, and model deployment infrastructure for predictive maintenance, defect detection, and process optimization.

From our podcasts: [Developing Sustainable Materials Using AI with Cambrium with Pierre Salvy](/podcasts/data-in-biotech/ep-16-cambrium-sustainable-materials), and [Biotech Manufacturing Quality with Stewart Fossceco with Stewart Fossceco](/podcasts/data-in-biotech/ep-18-stewart-fossceco-manufacturing-quality).

Manufacturing data is CorrDyn's deepest area of experience. We understand the gap between what IoT vendors promise and what production teams actually need. We have worked with PLC data, SCADA historians, legacy ERPs, and the organizational dynamics of plant teams that are skeptical of new tools until they see them produce useful numbers. We start with a working prototype on real production data, not a slide deck about what is possible.