End-of-Line Packaging KPIs: Benchmarks for OEE, Utilization, Changeover and Rejects
The KPIs that run end-of-line packaging, measured OEE data from packaging lines, the levers that move each number, and which figures have a cited source.
Use these benchmarks as reference points for spotting a gap, not as targets. On an end-of-line cell, the number that matters most is the one at the constraint machine, so read every range below against your own case packer or palletizer.
Every figure shows its basis. A superscript number links to a public source in the Sources list, and the sentence gives that source's scope. A figure marked no cited source is kept exactly as originally published because no public source stating it was found, so treat it as illustrative.
Words in parentheses give each figure's original framing or its source's scope. Not stated means no figure is given for that column. The packaging-line OEE and minor stop figures come from packaging line PLC data from 725 consumer packaged goods manufacturing operations, January to June 20091.
Method: How the figures in this guide are sourced
A figure with a superscript number is stated by the cited source, listed under Sources with its publisher, location in the document and access date, and the sentence gives its scope. A figure marked no cited source is kept exactly as originally published because no public source stating it was found. It is illustrative, not measured industry data. Worked examples show arithmetic only and are not benchmarks.
OEE at the constraint machine
OEE is the headline KPI for end-of-line cells, and the honest spread is wide. The Informance benchmark study captured packaging line PLC data from 725 consumer packaged goods manufacturing operations worldwide from January to June 20091. The middle 50 percent of lines were at 54.9 percent OEE and the top 25 percent at 82 percent1.
In the same data, the lowest performers were at 29 percent and the single best plant reached 90.6 percent1. World-class OEE, in Nakajima's total productive maintenance framework, sits at 85 percent and above2; across manufacturing in general, Vorne reports most companies are closer to 60 percent5.
The three components have their own targets: availability 88 to 92 percent good, performance 90 to 95 percent, quality 99 percent plus no cited source. Nakajima's world-class minimums, as tabulated by Raju et al., are availability above 90 percent, performance above 95 percent and quality above 99.9 percent2. Even 90, 95 and 99 percent multiply to only 84.6 percent OEE, as LEI's worked example shows6.
Packaging lines split the losses differently. Best-in-class lines in the Informance data were up 82.8 percent of scheduled time, but on performance they produced nearly 100 percent of the units that could have been made in that time7.
A cell reporting 90 percent OEE almost always has a soft speed loss hidden in the performance term no cited source. Treat that as a prompt to check the speed standard, not as a ceiling, since the best plant in the Informance data measured 90.6 percent from PLC counts1.
How to measure
Measure OEE at the constraint machine only, usually the case packer or palletizer, so you are not averaging away the bottleneck.
Check the ideal rate behind the performance term before you trust a high score.
Capture counts and stops from the machine controls rather than manual logs.
What moves it
Availability losses from changeovers, failures and repair time.
Performance losses from minor stops and reduced speed.
Robotic palletizing utilization separates a well fed cell from a starved one. World-class robotic utilization runs 80 to 88 percent of available time in productive motion no cited source; typical cells sit at 60 to 72 percent no cited source because the robot waits on infeed cases or pallet discharge.
Watch the idle reason codes. Infeed starvation above 8 percent of runtime means the upstream packer or conveyor is the real constraint, not the robot no cited source.
How to measure
Divide time in productive motion by available time.
Code every idle period by reason, such as infeed starvation or pallet discharge.
Report starvation as a share of runtime.
What moves it
Buffer conveyor length: adding 20 to 40 seconds of accumulation ahead of the robot often lifts utilization 6 to 10 points no cited source.
Faster pallet discharge.
Fixing the upstream packer or conveyor when it is the real constraint.
Line balance efficiency tells you how much station capacity you are wasting on the slowest step. Typical end-of-line balance efficiency is 65 to 78 percent no cited source; well tuned lines reach 85 to 92 percent no cited source.
Below 70 percent, one station is starving or blocking the rest no cited source. Find the constraint, then move work content off it or add a parallel unit.
For example, splitting a 3.1 second labeling station into two lanes at 1.55 seconds each can lift a five station line from 75 to 88 percent efficiency without touching the other four stations.
How to measure
Sum the work content of every station.
Divide it by the number of stations times the slowest station's cycle time.
Recheck the balance after every product or pattern change.
Changeover time is the KPI that decides how much of a multi-SKU line's rated capacity you actually keep. Typical case and pallet pattern changeovers run 25 to 45 minutes no cited source; good operations hit 12 to 20 minutes no cited source.
World-class SMED cells change in under 10 minutes, the defining target of SMED (single-minute exchange of die)3.
For example, on a line doing 6 changeovers a shift, cutting from 35 to 12 minutes returns 138 minutes, roughly 30 percent of a 7.5 hour shift.
How to measure
Time each changeover from the last good case of one SKU to the first good case of the next.
Count changeovers per shift alongside the average duration.
Split the time into steps done while the line is stopped and steps that could be done while it runs.
What moves it
Recipe-driven size changes.
Tool-less guide rails.
Pallet patterns staged and loaded before the line stops.
Reject and damage rates are the quality KPIs that flow straight to margin. World-class labeling no-read and misapply runs under 0.5 percent no cited source; typical lines tolerate 1.5 to 3 percent no cited source.
Case sealing defects, such as tape flags and blowouts, should stay under 0.8 percent no cited source, and palletizing damage under 0.3 percent of cases no cited source.
The dominant lever is upstream. Of end-of-line rejects, 60 to 70 percent trace to product presentation, skewed cases, damp flaps, or inconsistent bundle dimensions, not the labeler or sealer itself no cited source.
How to measure
Count rejects at each machine over the cases it handled.
Code each reject by cause, including how the product arrived.
Track palletizing damage as a share of cases palletized.
What moves it
Consistent product presentation: square cases, dry flaps and even bundle dimensions.
Labeler and sealer setup, once presentation is under control.
Mean time between failures (MTBF) and mean time to repair (MTTR) govern the availability term. Benchmark MTBF for a mature palletizing cell is 40 to 80 hours no cited source; MTTR should sit under 20 minutes with good spares and trained techs no cited source.
For example, a cell with 30 minute MTTR and 25 hour MTBF loses about 2 percent availability just to repair time, since 0.5 / 25.5 is 1.96 percent. Cutting MTTR from 30 to 12 minutes on that cell recovers roughly 1.2 points of availability.
Short stops matter as much as failures. In the Informance data, the average packaging line had 8.6 minor stops an hour, about one every 7 minutes4. Counting stops of 10 minutes or less, best-in-class lines averaged 2.2 an hour and the lowest performers 17.84.
How to measure
Divide run hours by the number of failures for MTBF.
Divide total repair time by the number of repairs for MTTR.
Count minor stops per hour separately from failures.
What moves it
Stocking the top 10 wear parts, such as gripper pads, vacuum cups, seal blades and photo eyes.
Pre-positioning those parts at the cell.
Root cause work on the jams behind repeat minor stops.
Labor productivity
Labor productivity, cases per labor hour, is the KPI executives track even after automation. Manual end-of-line lines deliver 150 to 300 cases per labor hour no cited source; automated cells with tending only reach 1,500 to 4,000 cases per labor hour depending on rate no cited source.
For example, a cell that adds a second tender to babysit a jam-prone infeed doubles its labor hours for the same output, which halves cases per labor hour.
Watch tending headcount, not just the nameplate rate. The improvement lever is reliability: fewer interventions per hour is what actually lifts cases per labor hour, not faster machine speed.
How to measure
Divide cases produced by all labor hours on the line, tenders included.
Trend it weekly per line.
Log interventions per hour alongside it.
What moves it
Fewer jams and interventions per hour.
Tending headcount matched to the cell's real reliability.
Set targets as a ladder, not a single number, and review them monthly against the constraint.
For example, a 12 month improvement path might take OEE from 62 to 78 percent, robotic utilization from 68 to 82 percent, balance efficiency from 74 to 88 percent, changeover from 35 to 15 minutes, and the reject rate from 2.4 to 0.8 percent.
On a line that makes 10 million cases a year at 62 percent OEE, each extra point of OEE is worth about 161,000 finished cases of capacity (10,000,000 / 62) with zero added labor.
Measure every KPI at the current constraint machine first.
Prioritize the KPI attached to that machine, because improving any other station only builds inventory in front of the bottleneck.
Keep each definition fixed so month-to-month changes are real.
Review the ladder monthly and re-baseline when the constraint moves.
World-Class OEE: Set Targets To Drive Improvement. Vorne Industries (OEE.com). Sections 'What Is World-Class OEE?', 'TPM and World-Class OEE', 'Some Perspective on World-Class OEE'. Accessed September 30, 2026.