--- title: "Production Monitoring Software: Parts Counted Against the Shift Goal | IoTFlows" description: "IoTFlows counts parts from the machine's own signal, a SenseAi on the frame or a BeamTracker across the part path, and shows each operation's good count, pace and hourly bars against a goal calculated from the ideal cycle time and the shift. Lines report first pass yield step by step; operators log scrap with a reason. Customers see 35% less unplanned downtime in the first six months (median across IoTFlows customers, internal data)." source_url: "https://www.iotflows.com/solutions/production-monitoring" --- Production monitoring # Production monitoring that counts every part against the shift goal IoTFlows counts every part from the machine itself, with a SenseAi on the frame or a BeamTracker across the line, and sets each operation’s goal from its cycle time. Everyone on the floor knows by mid-shift where the day will land. [How it counts](https://www.iotflows.com/solutions/production-monitoring#counting) Counting ## Every part counted, straight from the machine A [SenseAi](https://www.iotflows.com/products/senseai) on the frame reads every cycle; a [BeamTracker](https://www.iotflows.com/products/beamtracker) counts every unit that crosses its beam. **No PLC, no wiring, no clipboard.** Pick how each operation counts and how many parts one cycle makes, and the count follows the right part through every changeover. [Choose how an operation counts](https://www.iotflows.com/docs/production/choose-an-algorithm) Goals ## A goal for every shift, set by the cycle time Set the ideal cycle time once. IoTFlows works out what each machine should make this shift, **allowing for the downtime it usually has**, and shows the count, the pace and every hour against it. An hour that falls short turns amber while there is still time to fix it. The same counts feed [OEE](https://www.iotflows.com/solutions/oee-tracking). [Track production against shift goals](https://www.iotflows.com/docs/production/shift-production) Lines ## First pass yield at every step of the line Group the machines a part passes through into one line. Every step reports **first pass yield, parts lost and its rate against Step 1**, so the step where the gap opens is the one to fix. [Group machines into a cascade](https://www.iotflows.com/docs/monitoring/asset-groups) Scrap ## Scrap logged with a reason, at the machine Operators log scrap from the job they are running, with a **reason from your own list**. Every part lost is on record by machine, operation and cause. [Log scrap and manage reasons](https://www.iotflows.com/docs/production/log-scrap) On every screen ## The same count on the floor and in the office Put the shift on the TV by the line, the tablet at the machine and the phone in every pocket. Operators see their own count against the goal in [Operator View](https://www.iotflows.com/docs/monitoring/operator-view); the office sees every line at once. [Put a dashboard on a TV](https://www.iotflows.com/docs/monitoring/wallboards) History ## This week against a full one Compare good parts and scrap by day, week, month or quarter, by operation and machine. Every range ends at the last full period, so a week in progress never looks like a bad one. Export it, email it on a schedule, and plan the next jobs on the same counts in the [Production Scheduler](https://www.iotflows.com/platform/production-scheduler). [Compare production over time](https://www.iotflows.com/docs/production/historical-production) ## Set up for the machines you run. Each one counts its own way. Mills, molders, presses and packaging lines each make parts differently. Each has a set-up guide, and every set-up is checked by running ten parts by hand. - ### CNC mills and lathes Cycle times change with the part. Auto-Detect assigns every operation at once. CNC machine monitoring[Set up a CNC machine](https://www.iotflows.com/docs/production/cnc-machines) - ### Injection molding The shot repeats to the second, and every cavity counts as a part. Injection molding monitoring[Set up a molding machine](https://www.iotflows.com/docs/production/injection-molding) - ### Stamping and punch presses Ten to 120 strokes a minute, every one of them counted. Stamping press monitoring[Set up a press](https://www.iotflows.com/docs/production/stamping-presses) - ### Bottling and packaging lines A BeamTracker after the last reject point counts every unit that leaves. Packaging line monitoring[Set up a packaging line](https://www.iotflows.com/docs/production/packaging-lines) Customer story ## Criollos Foods raised bagging-line productivity 35% Criollos Foods packs bagged and canned foods in Medley, Florida. BeamTracker counts every bag on its lines against a target of 30 bags a minute. 35% more output on the bagging lines 92% of lines hitting their target rate 20 min faster product changeovers ### Challenge Some operators ran a line at 20 bags a minute against a target of 30, and nobody could see who, on which product, or why. Changeovers between products went untimed. ### Solution BeamTracker counts every bag and shows the live rate against target for every line, operator and product, with every stop classified. Supervisors coached the operators behind target and cut the slow changeovers. ### Products [BeamTracker](https://www.iotflows.com/products/beamtracker) and [SenseAi](https://www.iotflows.com/products/senseai). [Read the Criollos Foods story](https://www.iotflows.com/customers/criollos-foods) > “Within weeks of targeted coaching based on the IoTFlows data, our newer operators were hitting 30 bags per minute. This platform has transformed how we manage our bagging operations.” Juan Perez, Warehouse Operations Manager, Criollos Foods Altium Packaging reads first pass yield live at every step of its ten bottling lines. [Read the Altium Packaging story](https://www.iotflows.com/customers/altium-packaging). [Storm Power Components](https://www.iotflows.com/customers/storm-power-components) doubled machine utilization, and [Hilco Vision](https://www.iotflows.com/customers/hilco-vision) knows by mid-afternoon where a shift will land. ## Questions people ask ### What does IoTFlows production monitoring include? Part counts from the machine’s own signal, with four detection algorithms chosen per operation: Counter for BeamTracker, and Continuous Analysis, Discrete Analysis and Discrete without Merge for SenseAi. Quantity per cycle covers multi-cavity molds and multi-part cycles. Shift Production shows every operation’s good count, pace and hourly bars against a goal calculated from the ideal cycle time and the shift. Cascading groups report first pass yield, parts lost and rate difference against Step 1 along a line. Operators log scrap with a reason code from one organization-wide list, and Operator View carries a Part Count tab where Record Production backfills a count. Historical compares good parts and scrap by day, week, month, quarter or year, with CSV and PDF exports and Share and Schedule. Set-ups are documented for CNC mills and lathes, injection molding, stamping presses and packaging lines, and every set-up is validated by running ten parts by hand. ### How does IoTFlows count parts without a PLC? From the machine’s own signal. On a SenseAi the detection algorithm chosen for the operation turns the vibration trace into counts: Continuous Analysis when the trace never returns to the stopped level, Discrete Analysis when each part is one burst with idle time around it, Discrete without Merge for presses firing 10 to 120 times a minute. A BeamTracker counts each unit that breaks its laser beam. ### What is the shift goal based on? The ideal cycle time and quantity per cycle set on the operation, and the scheduled shift. The goal is the shift length minus the operation’s expected downtime, divided by the ideal cycle time, where expected downtime is that operation’s historical average on that machine. Shift Production shows good count, pace and hourly bars against it, and the goal follows the cycle time and the shift, so it always matches how the operation is set up. ### Can it count a line with several machines? Yes. A cascading group ties the machines a part passes through into one line on the Assets page. Step 1 is the reference, and every step after it reports first pass yield, parts lost and a rate difference against Step 1’s count. ### How do operators record scrap? From the Scrap button in the job runner’s action rail, against the work session that made it, with a reason code from one organization-wide list. The pause, complete and session editor dialogs hold that session’s scrap as editable rows. ### What if one cycle makes two parts? Set QTY/OP on the operation to 2, or to the cavity count on a mold. An exact 2x or 0.5x error in a count is a multiplier rather than a detector, and QTY/OP is the first thing to check. ### Can it monitor an injection molding machine? Yes. Mount the SenseAi on the injection unit or near the hydraulic power unit, time 10 to 20 shots by hand, create the part with one operation per mold, set the algorithm to Continuous Analysis and QTY/OP to the cavity count. Datwyler runs it across 20+ molders. ### Does it work on a bottling or packaging line? Yes. Mount a BeamTracker across the part path after the last reject point, set the operation’s algorithm to Counter, name the unit so gauges read bottles per minute, and set QTY/OP to 1 when the beam sees each unit. Criollos Foods runs it on its bagging lines and Altium Packaging on ten bottling lines. ## Documentation Want the detail? These guides walk through each feature step by step. - [Production quickstartYour first part, operation and count, validated by hand.](https://www.iotflows.com/docs/production/quickstart) - [Overview: track and schedule productionThe two halves of production and what each needs before it shows data.](https://www.iotflows.com/docs/production/overview) - [Track production against shift goalsEvery operation this shift against its calculated goal.](https://www.iotflows.com/docs/production/shift-production) - [Choose a production tracking algorithmCounter, Continuous, Discrete and Discrete without Merge.](https://www.iotflows.com/docs/production/choose-an-algorithm) - [Group machines into cascadesOne line on the Assets page, with first pass yield per step.](https://www.iotflows.com/docs/monitoring/asset-groups) - [Log scrap and manage scrap reasonsScrap per work session, with a reason from one list.](https://www.iotflows.com/docs/production/log-scrap) - [Compare production over timeGood parts and scrap by day, week, month, quarter or year.](https://www.iotflows.com/docs/production/historical-production) - [Troubleshoot inaccurate part countsQTY/OP first, then the cycle time, then the detector.](https://www.iotflows.com/docs/production/troubleshoot-part-counts) - [Configure tracking for CNC mills and lathesCycle times that change with the part.](https://www.iotflows.com/docs/production/cnc-machines) - [Configure tracking for injection moldingShots that repeat to the second; cavities as QTY/OP.](https://www.iotflows.com/docs/production/injection-molding) - [Configure tracking for stamping and punch pressesStrokes at 10 to 120 a minute.](https://www.iotflows.com/docs/production/stamping-presses) - [Configure tracking for bottling and packaging linesUnits past a point, counted by BeamTracker.](https://www.iotflows.com/docs/production/packaging-lines) ## Count your first parts this week Start with 10 to 25 machines for four weeks, with no commitment to buy. A SenseAi or BeamTracker goes on the machines you already run.