See how Overall Equipment Effectiveness (OEE) helps food and beverage manufacturers understand production losses – from sanitation and changeovers to short stops, slower running, and rejected products – and use availability, performance, and quality data to guide improvement.
Introduction
A production line can be running and still fall short of its target. A filler that slows down, a labeler that stops repeatedly, sanitation or changeover time that extends into a scheduled run, or product sent for rework can all reduce the amount of good product a line makes. Equipment Effectiveness (OEE) helps manufacturers examine these losses through availability, performance, and quality.
The Food and Beverage industry handles several stages of time critical procedures like ingredient preparation, mixing, or cooking. Some factories run continuous processes while others produce batches. Which is why it is critical for these businesses to make sure no time is going to waste since many plants handle time or temperature sensitive ingredients and products. A production line’s results can be affected by both equipment and the product being run. A filler handling one drink or container may have a different appropriate target rate from the same machine running another. OEE helps manufacturers understand how effectively equipment is running and where these losses are occurring.
What does OEE measure?
OEE, or Overall Equipment Effectiveness, measures how much of planned production time is productive by combining equipment availability, performance, and product quality.
OEE = Availability × Performance × Quality
Availability looks at whether equipment ran during planned production time. Performance compares its actual output with its target rate. Quality measures how much of the output met requirements. Together, the three measures help teams identify where production is falling short. The OEE score points to an area to investigate; it does not explain the cause itself.
The Tennessee Manufacturing Extension Partnership, a general manufacturing benchmark, classifies scores below 45% as low, 45–65% as typical, 65–85% as good, and 85% or above as world-class. It also recommends focusing on a plant’s own OEE trend over time. For food and beverage manufacturers, compare results using consistent definitions and similar lines, products, and production conditions; use the score to identify losses and opportunities for improvement, not as a scorecard.
A quick example
A filling line is scheduled to run for 480 minutes and stops for 48 minutes, leaving 432 minutes of operating time. At a target rate of 125 bottles per minute, it could produce 54,000 bottles during that time. It produces 45,900 bottles, of which 918 are rejected because of fill-level or seal defects.
- Availability: 432 ÷ 480 = 90%
- Performance: 45,900 ÷ 54,000 = 85%
- Quality: (45,900 − 918) ÷ 45,900 = 98%
OEE = 90% × 85% × 98% = 74.97%, or about 75%.
Because performance is the lowest factor, the team could investigate slow running, short stops, or other interruptions that reduce output. The score identifies which factor to examine; recorded loss reasons can help the team investigate why.
Where food and beverage lines lose output
| Loss area | Examples on a food or beverage line | How it can affect OEE |
|---|---|---|
| Changeovers and sanitation | Cleaning tanks, pipes, fillers, or other food-contact equipment; switching recipes or package sizes; and confirming the first acceptable product. | Reduces availability if it happens during scheduled production time. Apply the plant’s planned production rules consistently. |
| Short stops | Bottle jams, label misfeeds, sensor pauses, or product backing up on a conveyor. | Repeated brief stops can reduce performance. Their classification depends on the plant’s downtime rules. |
| Speed losses | A line may run more slowly on one recipe or container size; the comparison should be against the target rate for that specific production run. | Reduces performance because the line produces less than its target output while running. |
| Quality losses | Incorrect fill levels, poor seals, damaged packaging, or products that must be rejected or reworked. | Reduces quality because less of the total output counts as a good product. |
How to interpret the results
- Compare OEE and its three factors across production lines.
- Look for products associated with longer changeovers, slower running, or more rejected units. Different recipes, ingredients, and package sizes can affect performance, so compare each product to its appropriate target rate.
- Compare the same line and product across shifts to see whether losses occur more often during a particular shift.
- Investigate patterns such as repeated stops during filling or packaging, or extended cleaning and changeovers between products. A pattern points to an area to investigate; it does not prove the cause.
Review recorded loss reasons and rank recurring issues by their frequency or by how much production time and output they affect.
Turning OEE results into action
A useful improvement effort starts with a baseline measured over a representative period. Teams will then identify a significant, recurring loss, for example repeated stops on a filling line, extended changeovers between products, or rejected packs, and review what happened with production, maintenance, and quality or food-safety staff. They should agree on a practical change, assign someone to track it, and measure the results using the same definitions. Any changes involving cleaning, sanitation, or product checks should continue to meet the plant’s requirements. If the change helps, document the updated process and keep monitoring it.
How real-time OEE visibility can help
Each loss area in the table shows up in production data. Octopus Digital’s products help teams see it sooner.
- Changeovers and sanitation: OmniConnect™ combines machine data with planned-production data from the ERP, so teams can compare how long a changeover ran against the plan and see when it extended into scheduled production time.
- Short stops: OmniOEE alerts teams when machine performance deviates, so supervisors can respond to repeated jams, or sensor pauses during the shift instead of finding them in a report the next day.
- Speed losses: Because OmniConnect™ links machine data to the product being run, teams can compare each run against the target rate for that recipe or container size.
- Quality losses: OmniOEE dashboards let teams review results by product, line, and shift, which helps them spot a recurring fill level or seal problem sooner.
Octopus Digital applied this approach in a tea-manufacturing project, combining real-time factory-floor data with planned production data from SAP. The solution automated OEE calculations and provided alerts when machine performance deviated, helping the plant monitor production more closely.