Calculating and Improving OEE for Filling and Packaging Lines

A bagging system for bulk goods is designed to handle 300 bags per hour—on paper, that amounts to 2,400 bags during an eight-hour shift. In practice, the final count is often 1,700. The missing 700 bags haven’t vanished—they are distributed across three types of loss that seem harmless when viewed individually but, taken together, wipe out nearly a third of the theoretical capacity: unplanned downtime, speed losses, and quality losses.

OEE—Overall Equipment Effectiveness—makes all three types of loss visible in a single metric. It multiplies availability, performance, and quality into a percentage that shows how much of the planned production capacity actually results in saleable containers. Agustiady and Cudney describe OEE as a barometer for equipment health: It breaks down losses into clear categories and shows where the greatest leverage lies—in availability, performance, or quality. Kletti and Schumacher regularly document OEE values of only 30 to 40 percent in production facilities—so the theoretical doubling of plant performance is not a utopian ideal, but a mathematical consequence of systematic loss reduction.

This article performs a complete OEE calculation for a bagging system—using realistic figures from bulk material packaging. It then identifies the typical sources of loss in each of the three factors and demonstrates which measures can be taken to systematically increase the OEE of a bagging system.

What is OEE and how is it calculated?

OEE consolidates three independent loss categories into a single metric. Agustiady and Cudney identify six main sources of loss—the so-called "Six Big Losses"—which are distributed across three levels:

Availability (downtime and setup time)
Performance (short stoppages and reduced speed)
Quality (scrap and rework)

The multiplication of these three factors is no coincidence, but rather intentional: Each factor acts as a multiplier on the others—a weak individual value disproportionately drags down the overall result.

What does the formula "availability × performance × quality" mean?

OEE = Availability × Performance × Quality Rate

Each factor ranges from 0 to 100 percent. The product of all three factors yields the OEE. Agustiady and Cudney set an OEE value of 85 percent as the world-class benchmark—a figure that very few bagging systems achieve in continuous operation.

OEE-Factor	Formula	What it measures	Sources of loss at the bagging plant
Availability	Actual duration ÷ Planned production time	How much of the scheduled time the system actually runs	Unplanned downtime (faults, sensor failures), planned downtime (set-up time, cleaning, recalibration)
Performance	Actual throughput ÷ Maximum throughput	How close the plant operates to its rated capacity during its operational life	Brief stoppages (bag feed errors, post-flow corrections), reduced speed (cohesive powders, fluidisation, sub-optimal dosing parameters)
Quality	Good bags ÷ Total bags	How many of the bags produced meet the specification on the first run	Rejects (faulty sealing, contamination), rework (reweighing, repackaging), give-away exceeding tolerance

The fourth column sets this apart from any generic OEE explanation: The sources of loss are not “machine downtime” and “quality defects,” but rather bag jams at the filling spout, vacuum pump malfunctions, fluidization of ultrafine powders, and give-away due to imprecise dosing. It is precisely this specificity that makes OEE a useful diagnostic tool for bagging systems.

What does an OEE calculation for a bagging system actually look like?

The following calculation illustrates a shift at a typical bulk material bagging line—using figures that are realistic in practice:

Calculation example: OEE of a bagging line during an eight-hour shift

Step 1 – Availability
Planned production time: 8 hours = 480 minutes. Of this, the following is lost: 45 minutes of setup time (product changeover with cleaning and parameter adjustment) + 30 minutes of unplanned downtime (bag jam at the filling nozzle, loss of compressed air, sensor malfunction). Actual

Runtime: 480 − 75 = 405 minutes.

Availability = 405 ÷ 480 = 84.4%

Step 2 – Performance
Nominal capacity of the system: 300 bags per hour. Actual throughput during runtime: 260 bags per hour – reduced by brief stoppages during bag changes, residual flow corrections at the dosing station, and slowed conveying of cohesive product.

Performance = 260 ÷ 300 = 86.7%

Step 3 – Quality
Bags produced in 6.75 hours of operation at 260 bags/h: 1,755 bags. Of these, 35 were rejects (defective sealing, overfilling/underfilling outside tolerance). Acceptable bags: 1,720.

Quality = 1,720 ÷ 1,755 = 98.0%

OEE = 84.4% × 86.7% × 98.0% = 71.7%

The result: Nearly 30 percent of theoretical capacity is lost—even though no single factor is catastrophically poor. An availability of 84.4 percent sounds acceptable, a performance of 86.7 percent likewise, and a quality rate of 98 percent even sounds good. But the multiplication reveals the truth: 0.844 × 0.867 × 0.980 = 0.717. Kletti and Schumacher describe precisely this effect as typical: In industry, OEE values of 30 to 40 percent are commonly found—in our example, at 71.7 percent, we are significantly above that, but still 13 percentage points below the world-class benchmark of 85 percent.

The practical consequence is tangible: With a rated capacity of 300 bags per hour and 480 minutes of planned production time, 2,400 bags would theoretically have been possible. In reality, 1,720 good bags were produced. The difference of 680 bags—that is the OEE gap, translated into containers that were not produced.

What factors reduce the uptime of a bagging system?

For most bagging systems, availability is the weakest of the three OEE factors—and thus the one with the greatest potential for improvement. In the calculation example, 75 minutes of downtime in a single shift cost 15.6 percentage points in availability. Extrapolated over 250 working days, that amounts to over 300 hours of lost production time per year—time during which the system is idle, staff are waiting, and no bags are leaving the line.

These losses fall into two categories that require different approaches:

Unplanned downtime – the system is idle even though it should be running:

Cause of the stoppage	What happens	Typical duration
Mechanical fault in the metering mechanism	Screw conveyor blocked by product build-up, dosing valve jammed, drive overloaded	15–60 minutes, depending on accessibility
Vacuum pump fault	The vacuum breaks, and filling stops (immediately in vacuum packers; in air packers, indirectly via suction)	20–90 minutes, depending on the type of fault
Sensor failure	Load cell signal missing, level sensor on the silo faulty, bag detection sensor dirty – system goes into safety stop	5–30 mins (cleaning is often preferred to replacement)
Compressed air problems	Pressure drop in the network, valve leakage, compressor load cycle not synchronised with the system cycle	10–45 mins (the cause is often outside the bagging line)
Bag jam	Bag jammed at the filling spout, on the discharge conveyor or before the sealing station – operator must intervene manually	2–10 minutes per incident, but very frequent

These downtimes are the classic domain of TPM: how TPM eliminates unplanned downtime on bagging lines, as described in the technical article on preventive maintenance. The logic is simple—Agustiady and Cudney define the goal of TPM as the Triple Zero: zero defects, zero accidents, zero downtime. OEE is the metric, and TPM is the tool.

Scheduled downtime—the system is shut down for understandable reasons, but availability still drops:

Reason for the stoppage	What happens	Typical duration
Product changeover (set-up time)	Change of bag type, cleaning of surfaces in contact with the product, adjustment of dosing parameters, recalibration of the scales	15–60 minutes, depending on the product combination
Cleaning between varieties	Contamination-free switching between products with different specifications – particularly critical for colour pigments, foodstuffs or pharmaceutical products	20–90 minutes for hygienic-design systems, longer for conventional ones
Re-calibration of the scales	Place the test weight, adjust the calibration values, prepare the documentation – required by law, but often carried out more frequently than necessary	10–20 minutes per calibration

Scheduled downtime is often accepted as unavoidable—but its duration is not. This technical article on setup time optimization explains how SMED minimizes setup times: Simply distinguishing between internal and external setup can cut downtime during product changes in half, without a single technical modification to the equipment.

What sources of loss reduce performance during bagging?

Performance measures how closely the system operates at its rated capacity during its runtime. In the example calculation, the actual throughput was 260 bags per hour instead of 300—a loss of 13.3 percentage points. Unlike availability, where the system is visibly operational, performance losses are often invisible: The system is running and producing bags—just slower than it could.

Agustiady and Cudney distinguish between two categories of performance losses: short stoppages (the system stops briefly and restarts) and reduced speed (the system runs continuously but below rated capacity). In a bagging line for bulk materials, both categories have specific causes:

Short stoppages – the system stops briefly, usually for less than ten minutes, but the frequency adds up:

Cause	What happens at the bagging plant	Why it affects performance
Bag inlet fault	Valve not opened correctly, bag askew on the spout, wrong type of bag in the magazine – system stops, operator corrects manually	Every error costs 15–45 seconds. With two to three errors per hour, this adds up to a 5–8 per cent loss in cycle time
Reactive power correction	After the dosing valve closes, residual product continues to trickle out – the weighing control system waits for a stable reading before releasing the bag	The finer the powder, the longer the residual flow. With ultra-fine products under 50 µm, 3–5 seconds per bag can be lost
Material interruption from the silo	Bridge formation in the silicone cone, blockage at the outlet or flow disruption in the feed screw – the dosing system temporarily receives no product	10–60 seconds per event. Frequency depends on the product; for cohesive powders, several times an hour

Reduced speed – the system is running, but slower than its rated capacity:

Cause	What happens at the bagging plant	Why it affects performance
Difficult product behaviour	Cohesive powders flow more slowly than free-flowing granules. Bridging, adhesion to wall surfaces and an uneven flow of material necessitate a reduced conveying speed	The rated capacity of 300 bags per hour applies to the reference product – with cohesive powders, the achievable capacity may drop to 200–250 bags per hour without this constituting a fault
Fluidisation of ultrafine bulk materials	Powders with particle sizes of less than 100 µm absorb air during filling and behave like a liquid. The bag cannot be filled any faster than the product can release the air and settle	Physical limit – no adjustment can overcome it; only a different filling method (e.g. vacuum bagging instead of compressed air conveyance)
Suboptimal dosing parameters	Switching points between coarse and fine flow are not adjusted to the current product; delivery pressure is too low; vibration dosing is not correctly timed	The plant is running smoothly, but at 10–20 per cent below its maximum capacity – often unnoticed because no alarm is triggered

The last point—suboptimal dosing parameters—is the most insidious source of performance loss because it goes unnoticed in day-to-day operations. The system is running, the bags are filled correctly, and no error lights are on. But a comparison with the rated capacity reveals the gap. Kletti and Schumacher describe this effect as a hidden source of loss: systems often run with parameters that were set during the last product change and have not been optimized since.

Why fluidization limits filling speed will be explored in depth in the upcoming technical article on fluidization. Cycle time losses as a performance factor in filling and where the physical limit of the cycle time per bag lies are described in the technical article on cycle time.

What sources of loss reduce the yield during bottling?

In bagging systems, the quality rate is typically the best of the three OEE factors—in the example calculation, it was 98 percent. That sounds reassuring, but it hides two things: First, every single rejected bag costs not only the material but also the production time spent on its manufacture. Second, the traditional quality rate captures only the obvious rejects—not the systematic product loss due to overfilling, which often carries a greater economic burden than the 35 defective bags in the example calculation.

Agustiady and Cudney classify quality losses into two categories: defects that lead to rework or disposal, and start-up losses following setup procedures until the process runs stably. For the bagging of bulk materials, a third category emerges that is not included in the classic OEE system but can represent the largest economic item: give-away.

Rejects – bags that do not meet specifications and are sorted out or disposed of:

Reason for rejection	What happens at the bagging plant	Economic impact
Faulty weld	Weld seam incomplete, asymmetrical or not sealed – the bag may tear during storage or transport	Disposal of the bag, product loss, cleaning of the surrounding area; upon delivery: complaint
Overfilling or underfilling outside the tolerance range	The actual fill weight differs so significantly from the nominal fill weight that the bag does not comply with the Pre-packaged Goods Regulation (underfilling) or exceeds the bag’s maximum load capacity (overfilling)	Underfilling: risk of a fine, complaints. Extreme overfilling: bag rupture, safety risk
Contaminated batch	Residual dust from the previous batch ends up in the new batch – this is particularly critical for colour pigments (visually), foodstuffs (sensory) and pharmaceuticals (regulatory)	Batch suspension, recall in extreme cases, cleaning costs, loss of customer confidence

Give-away – the invisible loss of quality

Systematic overfilling produces saleable bags. No bags are rejected, no bags require rework – the quality rate in terms of OEE remains at 100 percent. But every bag contains more product than is paid for. With a give-away of 50 grams per bag, 260 bags per hour, and a 6.75-hour runtime, the system gives away 87.75 kilograms of product in a single shift—product that was produced, conveyed, dosed, and filled, but generates no revenue.

Whether give-away is included in the OEE quality rate is an operational decision. Some companies define a maximum give-away value per bag and classify anything above that as a quality loss. Others record give-away as a separate metric alongside OEE. Economically, the loss is real in both cases—and it depends directly on the calibration error limit of the scale used. How accuracy classes determine give-away is described in the technical article on calibration error limits and overfilling. How dust emissions affect the quality rate during filling—due to product leakage at the filling spout that is not recorded in either the bag or the dosing system—is explored in depth in the technical article on dust generation.

How can the OEE of a bagging system be systematically improved?

OEE shows where capacity is being lost—but it doesn’t tell you where to start. With three factors, each having multiple sources of loss, there is a risk of trying to optimize everything at once and achieving no results anywhere. Systematically improving OEE therefore requires a clear sequence: first identify the weakest factor, then tackle the biggest sources of loss within that factor, then measure and iterate.

Which OEE factor offers the greatest leverage?

The answer is provided by the example calculation—when the three factors are considered together:

Factor	Value in the example	Distance from the world-class target (95% each)	Lever
Availability	84,4 %	−10.6 percentage points	Greatest leverage
Performance	86,7 %	−8,3 percentage points	Second lever
Quality	98,0 %	+3,0 percentage points	Already past the finish line

The rule is simple: improve the factor with the lowest value first. For most bagging systems, this is availability—because setup times during product changes and unplanned downtime are the most obvious and frequent sources of loss. The second lever typically lies in performance—because product-related speed reductions and brief stoppages push throughput below the rated capacity.

Hänggi, Fimpel, and Siegenthaler recommend applying the Pareto principle within each factor: 80 percent of losses stem from 20 percent of the causes. In a typical bagging system, experience shows that these are three to four areas: setup time during product changeovers, one to two recurring mechanical defects, and cleaning time during product changes. Halving these three areas increases OEE by several percentage points—without touching the rest of the system.

What measures improve each individual OEE factor?

The following overview lists the most effective measures for each OEE factor:

Increase availability – reduce downtime:

Source of loss	Measure	Effect
Unplanned downtime (faults, sensor failures, pump problems)	How TPM reduces unplanned downtime to zero: autonomous maintenance by operators, scheduled maintenance cycles, systematic root cause analysis	Downtime per shift is falling, whilst availability is rising directly
Set-up times for product changes	How SMED minimises set-up times: separating internal and external set-up operations, bringing external steps forward into the running time	Set-up time reduced from 45 to under 15 minutes; availability increases, whilst smaller batch sizes are now possible
Wear and tear on critical components (sonotrodes, dosing drives, vacuum pumps)	Predictive maintenance based on condition data: vibration patterns, temperature profiles and pressure curves assess the wear condition before a failure occurs	Unplanned downtime becomes planned – shorter repair times, predictable spare parts procurement

Boost performance – Increase throughput

Source of loss	Measure	Effect
Suboptimal dosing parameters	Calibrate the coarse/fine flow switching points for each specific product, adjust the delivery pressure, document and compensate for post-flow behaviour	The cycle time per bag is reduced by seconds – resulting in a measurable increase in productivity when handling hundreds of bags per hour
Brief stoppages caused by bagging errors	Automatic bag positioning with position control – every bag is correctly positioned before filling begins	Fault-related brief stoppages eliminated, continuous operation ensured
Product-related speed reduction	How cycle times determine performance: Understanding the physical limits of product behaviour, adapting the filling principle to the bulk material	The plant operates at the physical limit rather than within a safety margin that nobody questions anymore

Improving Quality – Reducing Scrap and Waste

Source of loss	Measure	Effect
Faulty welding, contaminated batches	Error prevention through Poka-Yoke: position check before sealing, optical object detection at the infeed – making errors structurally impossible	The rejection rate has fallen to below 0.1 per cent
Systematische Überfüllung (Give-away)	Refining weighing control: How accuracy classes determine the give-away – the smaller the calibration error limit, the lower the required safety margin	Every gram less of waste per bag adds up to tonnes of product saved when multiplied across thousands of bags
Dust loss at the filling nozzle	How dust loss affects quality: product spillage during filling that is not accounted for either in the bag or in the dosing process – closed filling systems eliminate this source of loss	Every gram less of waste per bag adds up to tonnes of product saved when multiplied across thousands of bags

The final step brings us full circle to cost-effectiveness: Taken together—higher uptime through TPM and SMED, improved performance through optimized dosing parameters, higher quality through error prevention, and more precise dosing—these measures collectively reduce the TCO of the bagging system over its lifecycle. OEE quantifies the progress; TCO translates it into dollars.

OEE is not a goal—it is a compass

OEE does not show how good a bagging system is—it shows where it is wasting potential. In the calculation example in this article, 680 bags were lost per shift: due to setup times and malfunctions (availability), due to short stoppages and slowed conveyance (performance), and due to rejects and faulty sealing (quality). Added to this were 87.75 kilograms of wasted product due to systematic overfilling—a loss that does not appear in the OEE at all.

Anyone who calculates these three factors for their specific bagging system can identify whether the problem lies in availability, performance, or quality—and can address the issue specifically, rather than trying to adjust every parameter at once. The tools for this are described in the linked technical articles: TPM for availability, SMED and dosing optimization for performance, and weighing control and error prevention for quality. The efficiency improvement in bulk material filling starts with a number—and OEE is that number.

Sources:

Agustiady, Tina Kanti / Cudney, Elizabeth A.: Total Productive Maintenance: Strategies and Implementation Guide. CRC Press, Boca Raton 2016.

Kletti, Jürgen / Schumacher, Jochen: Die perfekte Produktion. Manufacturing Excellence durch Short Interval Technology (SIT). 2. Auflage, Springer Vieweg, Berlin Heidelberg 2014.

Hänggi, Roman / Fimpel, André / Siegenthaler, Roland: LEAN Production – einfach und umfassend. Springer Vieweg, Berlin 2021.