Speed Losses, or cadence loss, is one of the most recurrent forms of productive capacity loss within the industry, as it occurs without the line coming to a complete halt. Even though the process continues and the machinery remains in operation, the expected output volume for that shift is not achieved. Within the OEE framework, this condition manifests primarily in the Performance component.
Performance is responsible for measuring the gap between the expectation for that line, at its ideal run rate, and the reality of what was effectively produced during uptime. In practice, this means that an operation may present few relevant stoppages and still conclude the shift with a significant capacity loss.
The core issue with this type of loss lies in the fact that it rarely manifests as an isolated or evident event. Instead, it emerges as an accumulation of minor speed reductions distributed throughout the operation. In many cases, cadence loss becomes adopted as an informal process stabilization mechanism, particularly when certain conditions increase the frequency of interventions.
This dynamic tends to become even more complex because speed losses remain within a shared interpretation zone across different plant departments, for example:
- The production department understands it is preserving operational stability;
- The maintenance department considers that the equipment presents no critical failures;
- The quality department understands that operating at higher run rates may increase rejection indexes.
For example, in a condiments and sauces filling line, the operation may reduce its run rate by approximately 10% to decrease rejects related to fill weight variation. The line continues producing normally and registers virtually no formal downtime. However, when analyzing the actual speed distribution throughout the shift, it becomes evident that the line remained for hours operating below its nominal throughput condition.
How to identify throughput losses in the industrial routine
To identify speed losses in a structured manner, it is essential to understand how losses are distributed throughout a shift. In many cases, the operational perception on the plant floor remains focused solely on major stoppages, while prolonged speed reductions remain diluted within time that is considered productive.
Taking a condiments and sauces filling line as a reference, the nominal speed may be set at 300 units per minute during an 8-hour shift. At the end of production, the team identifies approximately 60 minutes of visible downtime related to product changeovers, major adjustments, and extended interventions.
The initial operational reading suggests that the shift performed relatively stable, given that most of the time was considered productive. However, when comparing the line’s theoretical throughput against the volume actually produced, the gap becomes evident. Considering 420 minutes of line runtime, the theoretical capacity would be 126,000 units. Yet, the shift closeout reports only 108,000 units produced.
In this scenario, cadence loss typically manifests as a consequence of extended periods operating at intermediate speeds, without the line actually stopping. In viscous product filling operations, minor variations in process conditions can impact stability, fill repeatability, and product behavior. In response, the operation tends to reduce speed to minimize interventions and preserve quality.
The greatest challenge is that, without adequate operational context, all these conditions end up being interpreted simply as “instability,” making it difficult to identify the actual root cause of the loss. At this point, the challenge shifts beyond data collection and begins to involve transforming scattered signals into contextualized operational intelligence.
TPM, OEE and speed losses as a strategy in the industrial routine
Within industrial operations, speed losses are often difficult to perceive, as a significant portion tends to be absorbed as normal operating behavior. Within the TPM and “Six Big Losses” framework, Seiichi Nakajima (1970) describes performance losses as a combination of minor stoppages and reduced operating speed. In these situations, the line remains available but fails to operate under its ideal conditions.
This approach reshapes how operations interpret industrial efficiency. Rather than analyzing only equipment availability, the assessment begins to consider how much of the nominal capacity was effectively converted into sustained output throughout the shift. This is particularly relevant because a production line may present high operational availability and still experience recurring volume losses due to continuous speed reduction.
In practice, this means OEE ceases to function solely as a consolidated performance indicator and begins to operate as a tool for operational understanding. Performance analysis starts to require correlation between:
- Actual running speed;
- Line behavior;
- Operational interventions;
- Process conditions, enabling the identification of patterns that typically remain invisible within traditional production reporting.
In this context, operational data gains relevance not only for its ability to log events, but for its potential to contextualize productive behavior over time, through:
- Instantaneous speed;
- Machine states;
- Minor stoppage frequency;
- Alarms;
- Rejections.
The value of this analysis lies in its ability to differentiate situations where the line presents structural failures from those where the operation deliberately reduces cadence as a stabilization mechanism. This distinction becomes critical because losses related to minor stoppages and losses related to reduced speed typically require different continuous improvement approaches.
In the condiments and sauces sector, when operations can correlate actual running speed, process conditions, and impact on productive capacity, speed losses cease to be merely an operational perception and begin to function as a concrete reference for improvement prioritization and industrial efficiency gains.
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