Find out exactly why your machines are stopping
Connect directly to the equipment on your floor and combine automated machine states with operator reason codes. Track availability, performance, and quality in real time so you can act before the shift is lost.
When equipment data lives on paper, whiteboards, or in spreadsheets, you only find out you missed your target after the line has shut down. Without real-time visibility, supervisors spend their time reacting to yesterday's problems instead of resolving today's bottlenecks.
A blinking red light tells you a machine stopped. It doesn't tell you if it stopped for a material shortage, a broken tool, or an operator waiting on quality approval. Without human context attached to machine states, the actual root cause of downtime remains invisible.
Lean initiatives rely on accurate data to prove ROI. When OEE is calculated manually based on guesstimates and standard times, you risk optimizing the wrong step. Teams need a true baseline to run experiments, deploy changes, and measure the actual impact on cycle time and availability.
Tulip is a composable platform that connects your equipment, enterprise systems, and frontline workforce to create a single source of truth for production performance. Machine telemetry flows in automatically while operators provide the context, giving you a complete picture of your line's true capacity.
Automatic OEE Calculation
Availability and performance come from machine and PLC data over native OPC UA or MQTT, and combine with the reason code or part number an operator enters to produce one OEE number, broken out by line and by shift. Nobody rebuilds it in a spreadsheet after the shift, because it traces back to the same data that produced it.
Downtime Reason Codes
An operator picks a reason code the moment a line stops, and that entry writes into the same table structure that holds every other record on the floor. Roll enough of those entries up by line, shift, or date range, and the Pareto view shows which causes are actually costing the most time.
Real-Time Andon Alerts
When a line stops or OEE drifts out of target, an automation can trigger an Andon-style alert that notifies the right person immediately, and tracks the issue through to resolution instead of letting it sit in a log until the next meeting.
Machine & Device Connectivity
OPC UA and MQTT connections are native, so most modern machines and PLCs connect directly. Older equipment that was never built to talk to software connects through edge hardware instead, so run state, cycle counts, and stop events still land in the same tables that feed OEE and downtime.
Plain-Language OEE Insights
Ask the OEE and downtime tables a plain-language question, like which line lost the most availability this week, and get an answer grounded in your own data, with a person still in the loop before anything gets acted on. The answer draws from the same OEE and downtime tables already feeding the dashboard.
Customer stories
See how manufacturers use Tulip to track OEE, eliminate downtime, and scale their capacity.
Outset Medical introduced AI-driven troubleshooting apps to support their operators on the floor. By connecting their systems and analyzing fault data, they dramatically cut the time it takes to resolve machine issues, increasing equipment efficiency.
"We introduced an AI-driven troubleshooting app that scans over 2,500 alarm codes and suggests the best corrective action—reducing machine downtime and repair time significantly."
Zaleco needed better visibility into their production performance to maximize output. By using Tulip to connect their operations and track performance metrics, they achieved a 30% increase in operational availability.
"Tulip has been a game-changer for us because it provides us with a platform to create custom apps tailored to our specific needs."
OEE (Overall Equipment Effectiveness) tracking software measures the availability, performance, and quality of your manufacturing equipment. It captures data from machines and operators to calculate how efficiently a production line is running compared to its theoretical maximum capacity. In Tulip, you track OEE by connecting apps to your equipment and having operators log downtime reasons, giving you real-time visibility into where time and yield are lost.
Tulip connects to shop floor equipment through Edge Devices and native protocols. You can pull data from PLCs, sensors, and legacy machines using industry standards like OPC UA, MQTT, and Modbus. This telemetry flows directly into the Tulip platform, triggering app logic, updating dashboards, and recording machine states without requiring custom middleware.
Tulip tracks production across entirely manual assembly lines. Operators interact with guided workflows on a screen, and Tulip automatically captures step-level timing, cycle times, and completion rates in the background. This provides the same granular visibility and performance tracking you would expect from automated equipment.
When a machine faults or a line stops, Tulip can automatically display a downtime categorization screen at the operator's terminal. The operator selects the reason for the stop from a pre-configured list (e.g., waiting on materials, tool change, quality hold). This attaches human context to the machine event, turning a generic fault code into an actionable data point.
Initial tracking applications can be running at a station in a matter of weeks. The typical deployment starts by connecting a single line or work cell to establish a baseline, gathering data, and refining the operator interfaces. Once the data model and dashboards are validated on the first line, the apps are scaled across the facility.
Tulip uses triggers to run logic based on machine states or operator inputs. If a machine goes down or an operator flags an issue, a trigger can automatically send an SMS, email, or a push notification to maintenance or supervisors. You define the escalation rules, ensuring the right personnel are notified immediately to minimize downtime.
OEE is calculated using three metrics: Availability (operating time divided by planned production time), Performance (ideal cycle time multiplied by total parts, divided by operating time), and Quality (good parts divided by total parts). Tulip captures machine uptime, cycle durations, and defect logs to calculate these metrics dynamically.
See how manufacturers connect their machines and their people to capture downtime, increase throughput, and run more efficient shifts.