Blog
Apr. 28, 2026
A food packaging machines factory can improve output without simply adding more operators. When automation is correctly designed, an automated food packaging line can coordinate filling, sealing, labeling, and inspection while reducing changeover waste. The main technologies are packaging automation, machine vision, and predictive maintenance, supported by a PLC, servo motors, and OEE reporting. This article explains where performance improves, what preparation is required, how to measure the result, and how to solve common problems before investing in equipment from a food packaging machines manufacturer.
Packaging managers usually do not start with a request for “more automation.” They start with practical problems: a sealing machine stops during the night shift, operators record different production numbers, products leave the line with missing labels, or a changeover takes 90 minutes for a product that is sold in several package sizes.
Automation addresses these problems by converting manual decisions into repeatable control rules. A photoelectric sensor can detect product position, a servo motor can control film length, a vision system can reject an incorrect label, and a PLC can stop the line when a safety or quality limit is exceeded. The benefit is not an attractive dashboard by itself; it is measurable control over speed, quality, labor, and downtime.
For example, if a line is scheduled for 8 hours at 60 packs per minute, its theoretical capacity is 28,800 packs. If it produces 23,000 good packs, its effective performance is approximately:
OEE = Availability × Performance × Quality
Availability: actual running time divided by planned production time.
Performance: actual speed compared with the ideal machine speed.
Quality: good packs divided by total packs produced.
If availability is 85%, performance is 88%, and quality is 98%, the OEE is 73.3%. Automation can improve each factor, but only when sensors, recipes, operator procedures, and maintenance data are connected correctly.
Manual feeding often creates a speed ceiling. Operators may feed products too slowly when they are tired or too quickly when upstream equipment releases a batch. An automated infeed conveyor, servo-controlled timing screw, or robotic pick-and-place unit maintains a defined product pitch.
In practical terms, a machine set to 60 packs per minute should receive one product every second. If the spacing changes from 0.8 seconds to 1.4 seconds, the packaging machine must either accumulate products, run empty, or stop. Automation reduces this variation by controlling the gap with an encoder and feedback loop.
The result should be measured over a complete shift, not during a five-minute demonstration. Useful indicators include:
Average packs per minute.
Speed variation between shifts.
Number of no-product and no-film stops.
Percentage of time operating at the approved recipe speed.
Seal defects are often caused by a combination of temperature, dwell time, pressure, film alignment, and contamination. Increasing sealing temperature alone can damage heat-sensitive film or deform the package.
A controlled sealing station uses temperature sensors, proportional-integral-derivative control, pneumatic pressure regulation, and servo timing. For a multilayer polyethylene film, a plant may validate a sealing window such as 145–175°C, but the correct range must come from the film supplier and package testing. The machine should not be adjusted from a general internet value.
Automation improves consistency by:
Maintaining a stable jaw temperature through closed-loop control.
Holding dwell time within a defined tolerance.
Detecting film tracking errors before a long roll is wasted.
Stopping when a heater, thermocouple, or pneumatic circuit is outside its limit.
Seal performance should be verified with burst testing, peel-strength testing, dye penetration testing, or another method approved for the product. A lower reject count is not enough if the remaining defects create leakage or shelf-life risk.
Human inspection becomes difficult when operators must check thousands of packs per hour. Machine vision can inspect label position, print presence, expiry date, barcode readability, cap placement, and package orientation.
A typical vision system includes a camera, lens, lighting, trigger sensor, image-processing software, and a reject actuator. Lighting is as important as camera resolution. Glossy film may require diffuse lighting, while embossed codes may need angled lighting to create contrast.
For reliable operation, the inspection recipe should define:
Minimum barcode grade or readability requirement.
Allowed label-position tolerance in millimeters.
Print contrast or optical character recognition rules.
Reject confirmation time.
What happens when the camera cannot make a valid decision.
The safest default is usually “fail safe”: an uncertain image causes a controlled reject or line stop rather than allowing an unverified pack to continue.
Automation does not eliminate every labor requirement. It changes the work from repetitive feeding and visual checking to setup, material replenishment, sanitation, troubleshooting, and quality verification.
A realistic labor calculation should include:
Operators required during normal running.
People required during film or material changes.
Sanitation and allergen-changeover labor.
Maintenance and quality support.
Training time for new recipes and fault recovery.
For instance, reducing a line from four operators to two may not produce a net saving if the line requires a maintenance technician on every shift. The correct comparison is total cost per good pack, not headcount alone.
The following anonymized case is based on a production log supplied for an equipment improvement review. The company packed snack products in pillow bags and experienced frequent film tracking stops, inconsistent date-code placement, and long changeovers between three bag sizes.
Before the improvement, the line operated at a nominal 55 packs per minute. During a 10-hour planned shift, the recorded figures were:
Planned production time: 600 minutes.
Unplanned and adjustment downtime: 96 minutes.
Average running speed: 48 packs per minute.
Total packs produced: 24,192.
Rejected packs: 1,065.
Changeover time: 74 minutes per product-size change.
The factory added an automatic film-centering system, encoder feedback, a recipe-controlled servo drive, a date-code vision check, and a changeover checklist linked to the HMI. After a 12-week stabilization period, the recorded average results were:
Unplanned and adjustment downtime: 51 minutes per shift.
Average running speed: 53 packs per minute.
Rejected packs: 438 per shift.
Changeover time: 41 minutes per product-size change.
Good-pack output increased from 23,127 to 28,609 packs per comparable shift.
That is a 23.7% increase in good-pack output under the stated operating conditions. The improvement did not come from running the machine at an unsafe speed. It came from reducing stops, controlling film position, and preventing incorrect date-coded packs from reaching final inspection.
The maintenance supervisor also reported an important limitation: the vision system initially produced false rejects because the lighting changed when the film roll diameter became smaller. The team corrected this by installing a fixed light shield, locking camera exposure, and adding a daily reference-image check. This illustrates a common lesson: automation amplifies the quality of the surrounding process. Poor lighting, unstable film, or inconsistent product presentation will create automated errors faster than a human operator.
Collect at least two to four weeks of production data before specifying automation. A short demonstration may hide the problems that occur during cleaning, material changes, low-volume products, and night shifts.
Record the following information:
Product details: dimensions, weight range, temperature, moisture, stickiness, fragility, and orientation.
Packaging material: film structure, thickness, coefficient of friction, roll width, sealant layer, and print registration marks.
Output target: packs per minute, packs per hour, shift length, and seasonal demand.
Quality limits: seal strength, leak rate, code position, label tolerance, and acceptable reject percentage.
Changeover requirements: number of products, bag sizes, tooling changes, cleaning method, and allergen controls.
Utilities: electrical voltage, compressed-air pressure, air consumption, drainage, ventilation, and network requirements.
Compliance needs: food-contact materials, guarding, emergency stops, sanitation access, traceability, and local machine-safety rules.
A useful factory acceptance test requires more than the machine itself. Prepare the exact materials that will be used in production:
Production-grade product samples.
At least one full packaging-film roll from the approved supplier.
Correct labels, cartons, trays, or containers.
Approved date-code format and sample artwork.
Calibrated weighing scales.
Thermometer or temperature data logger.
Seal-strength or leak-testing equipment.
Barcode verifier, if barcode inspection is required.
Stopwatch or digital downtime-tracking system.
Changeover checklist and cleaning instructions.
Ask the food packaging machines manufacturer to test the smallest and largest product sizes, the lowest and highest film-roll diameters, and a realistic production speed. If the trial uses only one perfect product and one new film roll, it does not represent the actual operating risk.
Do not begin by changing settings. First measure the current state for several normal production shifts.
Record planned production minutes.
Record every stop longer than an agreed threshold, such as 30 seconds.
Classify stops as material, mechanical, electrical, quality, cleaning, or operator-related.
Record actual running speed, not the speed shown on the recipe screen.
Count total packs and rejected packs separately.
Calculate availability, performance, quality, and OEE.
A simple spreadsheet is sufficient at the beginning. The purpose is to identify the largest loss category. If film changes account for 40% of lost time, installing a camera may not be the first priority.
Draw the material and information flow from product release to finished-case palletizing. Include the feeder, weighing system, filler, wrapper, sealing jaws, cutter, date coder, vision inspection, checkweigher, metal detector, conveyor, case packer, and palletizer where applicable.
Mark each control point and answer three questions:
What signal tells the machine that the product is ready?
What condition causes a reject or stop?
What evidence proves that the product was accepted?
This process map prevents an isolated automation purchase. A high-speed wrapper cannot deliver high output if the upstream filler produces irregular product spacing.
Use the simplest technology that solves the measured problem. Typical equipment includes:
Photoelectric sensors: product detection, film registration, and position confirmation.
Encoders: speed and distance feedback for film and conveyor movement.
Load cells: weight measurement in fillers and checkweighers.
Temperature sensors: sealing-jaw and heater control.
Servo motors: synchronized cutting, feeding, and film transport.
PLCs: sequence control, interlocks, alarm logic, and recipe management.
Machine vision: label, code, seal, and package-presence inspection.
Specify sensor response time, ingress-protection rating, food-zone suitability, washdown resistance, and replacement availability. A sensor that works in a dry room may fail under daily high-pressure sanitation.
A recipe should contain more than a target speed. Include film length, jaw temperature, dwell time, conveyor timing, cutter position, coder offset, vision tolerances, checkweigher limits, and reject delay.
Create a master recipe using approved engineering values.
Restrict operator access to critical parameters.
Set upper and lower limits for adjustable values.
Require a confirmation step after a recipe is loaded.
Store the recipe version with the product code and packaging-material code.
Test that the machine rejects an incorrect or incomplete recipe.
Recipe control reduces setup variation. It does not remove the need for trained operators to verify the first production samples.
Place inspection devices where they can detect a defect before the next process makes it difficult to isolate. For example, a label camera should inspect after the label is applied but before cases are sealed.
Trigger the camera or sensor at a repeatable product position.
Validate the image using approved good and bad samples.
Set the reject delay according to conveyor speed and product distance.
Install a reject-bin-full sensor.
Add reject confirmation so a failed product cannot pass without an alarm.
Record the reason for each reject instead of using one general “reject” category.
When a vision system rejects more than expected, compare false rejects with genuine defects. A 4% reject rate may represent either poor packaging quality or an inspection recipe that is too strict.
Agree on acceptance criteria before the test begins. A useful protocol may specify output, quality, changeover, and uptime targets.
| Test category | Example acceptance measure |
|---|---|
| Output | At least 95% of the agreed sustainable speed for 60 minutes |
| Quality | Seal and product tests meet the approved specification |
| Inspection | All prepared defect samples are detected and rejected |
| Changeover | Completed within the agreed time using trained operators |
| Data | Counts, alarms, rejects, and downtime are correctly recorded |
Use production material and operate the machine long enough to expose heat drift, film-roll changes, and operator interaction. Photograph the setup and save the final parameter file.
Training should be task-based rather than limited to a general explanation of the HMI.
Show how to start, stop, and safely isolate the equipment.
Demonstrate film loading, threading, and registration-mark alignment.
Explain how to identify a mechanical jam, sensor fault, and quality fault.
Practice recipe selection and first-pack approval.
Practice cleaning without damaging sensors or electrical components.
Require each operator to recover from prepared fault scenarios.
Record training completion and authorization level.
Keep a one-page fault-recovery guide beside the HMI. It should tell the operator what to check, what not to adjust, and when to call maintenance.
After commissioning, review the data weekly. The most useful signals often include motor current, servo following error, bearing temperature, air pressure, heater output, sensor switching frequency, and alarm recurrence.
Predictive maintenance is valuable when a signal is connected to an action. For example:
A rising servo following error triggers belt inspection.
Repeated low-air alarms trigger a leak test and regulator inspection.
Increasing heater output at the same setpoint triggers thermocouple and insulation checks.
More frequent film-registration corrections trigger roller-cleaning and alignment checks.
Do not replace parts only because a dashboard changes color. Establish a trend, confirm the physical condition, and document the corrective action.
Symptom: The machine misses products, creates double counts, or stops with a “no product” alarm.
Cause: The sensor detects reflective film, product edges, or vibration instead of the intended target.
Solution: Change the sensor angle, use polarized or background-suppression sensing, stabilize the product guide, and test detection at minimum and maximum line speeds. Record the sensor distance and alignment in the maintenance standard.
Symptom: Seals move sideways, registration marks are missed, or the cutter damages the package.
Cause: Roll width variation, incorrect film threading, roller contamination, insufficient tension control, or a registration sensor with poor contrast.
Solution: Check roll specifications, clean rollers, verify dancer-arm movement, calibrate the registration sensor, and include a first-pack film-alignment check in the changeover procedure.
Symptom: The line rejects good packs at a rate higher than the approved limit.
Cause: Changing light, condensation, vibration, incorrect exposure, product-position variation, or an image recipe that is too narrow.
Solution: Lock the camera settings, improve lighting, isolate vibration, stabilize product presentation, and validate the inspection with a statistically meaningful sample. Do not widen tolerances until quality personnel confirm that the defect risk remains acceptable.
Symptom: The machine runs but produces the wrong bag length, code position, or sealing temperature.
Cause: An old recipe is selected, a parameter is manually changed, or the recipe is not linked to the material or product code.
Solution: Use user permissions, barcode-based recipe selection, parameter limits, and a first-pack approval process. Keep a revision history so the team can identify when a setting changed.
Symptom: Pneumatic cylinders move slowly, actuators fail to complete their stroke, or the PLC generates intermittent faults.
Cause: Low air pressure, water in the air line, undersized piping, voltage fluctuations, loose terminals, or inadequate grounding.
Solution: Measure pressure at the machine during peak demand, inspect filters and drains, verify electrical supply under load, tighten connections according to the maintenance procedure, and ask the food packaging machines manufacturer for utility tolerances.
Symptom: Cleaning takes longer after automation, or residue accumulates around sensors, belts, and guards.
Cause: Components were selected for control performance but not for food-zone cleaning and access.
Solution: Specify stainless-steel construction where required, suitable ingress protection, sloped surfaces, removable guards, protected cable routing, and tool-free access where appropriate. Validate the cleaning method with quality and sanitation teams.
Estimate the return using good-pack output rather than machine speed alone.
Annual benefit = additional good packs × contribution margin per pack + labor savings + avoided waste − additional operating cost
Include film waste, rejected product, overtime, maintenance contracts, software support, spare parts, energy, compressed air, and training. A machine that increases nominal speed by 15% but raises film waste by 3% may produce less financial value than a slower machine that improves quality and changeover time.
For a more complete comparison, calculate:
Cost per good pack.
OEE before and after automation.
Changeover minutes per SKU.
Unplanned downtime per shift.
Rejects per million packs.
Mean time between failures.
Mean time to repair.
Operator hours per 10,000 good packs.
Use at least one full production season when demand, product temperature, and packaging materials vary. A short payback calculation based on the best shift can produce an unrealistic investment decision.
Evaluate the supplier on the complete operating system, not only the stainless-steel frame or catalog speed. Ask Yijianuo or any prospective supplier to provide evidence for the following points:
Performance using your actual product and packaging film.
Documented sustainable speed rather than peak empty-machine speed.
Changeover procedure and recorded changeover time.
Electrical drawings, pneumatic diagrams, software backups, and spare-parts lists.
Remote-support method and response time.
Training plan for operators, maintenance, and quality personnel.
Sanitation access and food-zone material specifications.
Data export options for OEE, alarms, recipes, and rejects.
Availability of local service and commonly replaced components.
Request a risk review before purchase. The supplier should identify limitations such as product variation, film curl, sticky ingredients, condensation, fragile items, or restricted factory utilities. A supplier that clearly explains these conditions is generally more useful than one that promises a fixed speed for every product.
Automation improves food packaging machines performance when it controls the causes of loss: unstable product spacing, inconsistent film movement, incorrect seals, manual inspection gaps, slow changeovers, and reactive maintenance. The measurable results should appear in availability, performance, quality, OEE, good-pack output, reject rate, changeover time, and cost per pack.
Start with production data, define the largest loss, test the complete process with real materials, and specify acceptance criteria before installation. Then connect the PLC, servo system, sensors, vision inspection, and maintenance data to clear operating procedures. Packaging automation, machine vision, and predictive maintenance only deliver durable results when operators can understand and recover from faults. These controls, together with OEE measurement, PLC recipe management, and servo motion control, should remain central when comparing a food packaging machines manufacturer or a food packaging machines factory.
Not always. Automation may first reduce speed while recipes, sensors, and inspection limits are validated. A sustainable improvement is the speed the line can maintain while meeting quality requirements for a complete shift. For example, increasing from 48 to 53 packs per minute while reducing stops and rejects can create more good output than briefly running at 65 packs per minute.
There is no universal target because products, sanitation, batch sizes, and changeover frequency differ. A line with many short runs may have lower availability than a long-run line. Establish a baseline, remove the largest loss, and set staged targets. An improvement from 62% to 72% OEE may be more valuable and realistic than an immediate promise of 85%.
No. Vision systems perform repeatable checks at high speed, but quality personnel must define specifications, validate samples, investigate trends, and manage unusual conditions. The camera detects according to its programmed rule; it does not independently decide whether a new product defect is acceptable.
Two to four weeks is a useful starting point, provided the data includes normal shifts, material changes, cleaning, and common product sizes. For seasonal products or highly variable operations, collect data across the relevant production period.
Include product and material specifications, sustainable output, acceptable reject rate, seal and inspection criteria, changeover method, utility requirements, safety documentation, software ownership or backup access, training, spare parts, warranty response, and factory/site acceptance procedures.
Begin with the bottleneck that has the clearest financial effect. Common first steps include automatic film registration, checkweighing, date-code verification, conveyor synchronization, or recipe management. A modular upgrade can be easier to justify than replacing the entire line, provided the existing equipment has compatible controls and sufficient mechanical condition.
Clean and verify sensors, inspect belts and rollers, check sealing temperature calibration, drain compressed-air filters, back up PLC and HMI programs, review recurring alarms, and compare current OEE with the commissioning baseline. Preventive maintenance should be based on operating hours and manufacturer recommendations, while predictive maintenance should use trends such as temperature, vibration, current, and servo error.
When these practices are followed, automation becomes a production-control system rather than a collection of electronic components. The objective is measurable: more good packs, fewer stops, shorter changeovers, controlled food safety risks, and stable performance from the first shift to the last.
