Texas Instruments Turns 1.5 Million Daily Data Points into Real-Time Manufacturing Visibility with InfluxDB

Overview

A real-time view of a complex, 24/7 manufacturing operations

Texas Instruments (TI) wanted greater visibility across its manufacturing operations to understand where inefficiencies were occurring and why. With equipment running around the clock, the team needed to track everything from utilization and failures to downtime and repair times, then make that information useful to people across the business. Using InfluxDB, TI analyzes 1.5 million data points per day from industrial sensors, turning continuously streaming equipment data into dashboards and alerts that help teams understand manufacturing performance in real time.

Challenge

24/7 manufacturing leaves little room for hidden inefficiencies

The scale and complexity of TI’s manufacturing environment make visibility difficult. Roughly 1,000 different types of machines run different software packages across distributed factories. Equipment, factory layouts, staffing arrangements, and shift patterns all introduce variables that can affect production. In an operation expected to run continuously, even a small problem can become expensive through lost production, missed deadlines, and lost business.

Understanding those inefficiencies is also critical to catching problems before they escalate. A diffusion furnace, for example, can begin misbehaving for weeks before failing completely, then torch 50,000 to 100,000 wafers in a matter of minutes. TI’s longer-term goal is to automate operations to the point where equipment can identify a problem and shut itself down before that happens.

Finding those signals in the data was difficult. Sensor data streams in at different rates and in different formats, and machines can operate differently from one factory to another. TI needed high-level indicators that could show the health of the overall operation while still allowing teams to drill into individual machines and identify local anomalies.

Previous efforts relied on automated longitudinal reports that showed how equipment performed over time. But those reports were static. Teams couldn’t easily manipulate the data into dashboards for different business needs, explore it as it streamed in, or use it as a foundation for prediction and forecasting.

ENTER INFLUXDB

From static reports to streaming manufacturing data

Probe Engineering and Manufacturing Supervisor Michael Hinkle spun up an instance of InfluxDB and began streaming testing equipment data into the platform.

He was surprised by how quickly he could start working with the data. Using InfluxDB with Grafana, Hinkle built dashboards that show metrics including equipment utilization, performance anomalies, downtime, and time to repair across different locations. Teams can move from a high-level view of manufacturing health to the individual equipment behind a change.

The system was also easy to iterate on. Hinkle comes from an electrical engineering background rather than software development, but he was able to use the InfluxDB Python client and InfluxQL to build new analyses in minutes.

Hinkle also set up email alerts for real-time issues and reports that track rates of change in equipment use. That context matters because the same signal can mean very different things. If a group of machines begins slowing down, it might simply reflect a shift change, or it could be an early indication of a power problem.

By analyzing detailed streaming data, TI can distinguish between normal operating patterns and changes that warrant closer attention, then get the right people and systems involved sooner.

Result

From factory-wide visibility to individual machine performance

Texas Instruments now uses InfluxDB to identify changes in performance across its production and testing lines. Leaders can start with dashboards showing the statistical distribution and overall health of equipment, then drill into the information they need to make a decision.

Teams can answer questions such as which testers are idle, running, or down; where incident response is taking longer; and how individual pieces of equipment are performing against their goals. Instead of relying on static reports, they can explore the underlying time series data to understand what changed and where.

The result is a more flexible view of manufacturing performance, one that gives different teams the level of detail they need while preserving the underlying data required for deeper investigation.

That foundation also gives Hinkle room to keep expanding what the system can do. He plans to build more sophisticated dashboards, simplify internal reporting, and explore forecasting techniques such as ARIMA and Holt-Winters to identify potential problems earlier.