In many factories, machinery continues to operate without major breakdowns, yet actual output falls short of planned targets. A frequently overlooked cause is unstable machine speed or operation consistently below the designed speed.

Unlike downtime, this issue is harder to detect because the equipment remains running. However, if speed reductions persist over an extended period or recur frequently during a shift, the resulting loss in production volume can be substantial.

In the context of OEE, this is known as “Reduced Speed ​​Loss“—a direct factor that lowers the Performance metric. What causes this instability in production machine speeds, and how can businesses detect and rectify the situation?

I. What is “Reduced Speed ​​Loss” in OEE?

Under ideal conditions, a machine is designed to operate at a specific speed or cycle time. However, actual production speeds often fail to reach this level. Reduced Speed ​​Loss occurs when the machine is running, but its actual speed is lower than the established standard or ideal speed.

Example:

A packaging machine is designed to process 120 products per minute, yet during actual operation, it only maintains a rate of approximately 90 products per minute. The machine does not stop, but it loses 30 units of capacity per minute compared to the target speed. If this situation persists for 60 minutes, the theoretical shortfall could amount to: 30 × 60 = 1,800 products. This illustrates why reduced speed can be a form of “hidden loss” in manufacturing.

1. How does Reduced Speed ​​Loss affect OEE?

OEE is composed of three main factors: OEE = Availability × Performance × Quality.

Performance reflects the machine’s operating rate relative to its ideal speed. Consequently, a machine might experience no significant downtime, maintaining high availability and good product quality, yet still yield a low OEE if it frequently runs below standard speed. This explains why simply tracking whether a machine is “Running” or “Stopped” is insufficient for accurately assessing production efficiency.

II. Technical causes of unstable machine speed

Reduced production speed can stem from various technical factors. Notably, these issues do not always cause the machine to stop completely; instead, they often result in the equipment slowing down or experiencing speed fluctuations.

1. Equipment wear causing unstable machine speed

After prolonged operation, mechanical components—such as motors, gearboxes, bearings, belts, chains, or drive mechanisms—may experience wear and tear. While the equipment remains functional, it no longer operates at peak efficiency. Symptoms may include:

• Gradual decrease in speed
• Increased vibration or noise
• Unstable machine cycles
• Frequent need for machine adjustments
• Increased actual cycle time

If left undetected, this condition not only impacts performance but can also escalate into a breakdown, resulting in unplanned downtime.

2. Suboptimal maintenance

Some facilities perform maintenance only after a failure occurs or according to a fixed schedule, rather than basing it on actual operating conditions. This approach allows minor issues to accumulate over time; the machine may not have failed yet, but its performance has already begun to decline.

Monitoring trends in speed, cycle time, downtime, and performance provides valuable data, enabling maintenance teams to identify equipment showing signs of abnormality.

3. Improper parameter settings

Machines may operate below their design speed due to suboptimal parameter settings.

Examples include:

• Motor or conveyor speeds set lower than required
• Inappropriate VFD (Variable Frequency Drive) settings
• Excessive delay times between cycles
• Recipe parameters not optimized for specific products
• Speed ​​limits set lower than necessary

This is particularly relevant for production lines handling multiple SKUs, where operating speeds can vary significantly after each changeover if parameters are not consistently set and controlled.

III. Operational causes of reduced production speed

Not all speed-related issues stem from the equipment itself. People, processes, and input materials can also prevent a machine from achieving its design capacity.

1. Operator skills and handling

In stages requiring significant operator intervention, production speed can depend heavily on how the machine is operated.

Examples include:

• Incorrect machine adjustments
• Prolonged setup times
• Slow response to minor faults
• Frequent speed reductions to prevent product jams
• Use of different operating parameters across shifts

If performance varies significantly on the same machine across different shifts, the business should analyze operational factors rather than focusing solely on the equipment.

2. Inconsistent input materials

The quality and characteristics of input materials can directly affect machine speed. In bottling or packaging lines, issues such as uneven packaging dimensions, variations in packaging materials, or inconsistent input products can increase the risk of machine jams.

To minimize errors, operators might proactively slow down the equipment. Consequently, while the machine continues to run, actual output falls significantly short of the plan.

3. Impact of upstream and downstream processes

Sometimes, a machine itself is functioning correctly but must slow down due to imbalances between process stages. If a downstream stage cannot keep up, products accumulate in the buffer, forcing the upstream machine to slow down or stop and wait.

Conversely, if the upstream stage fails to supply sufficient product, the downstream machine cannot operate continuously at its design speed. In such cases, the issue requires analysis at the entire line level rather than an assessment of individual machines in isolation.

IV. Why is it difficult to detect real-time speed fluctuations through manual monitoring?

In many factories, production data is still tracked via:

• Excel
• End-of-shift reports
• Manual logs
• Direct operator observation
• End-of-day output checks

While these methods can indicate total output or total operating time, they struggle to accurately reflect fluctuations in machine speed throughout the production shift.

For example, a machine might:

• 08:00–09:00: run at 120 units/minute
• 09:00–10:00: slow down to 105 units/minute
• 10:00–10:30: drop to 85 units/minute
• After adjustment: return to 120 units/minute

If an end-of-shift report only records total output, such fluctuations are easily overlooked. A business might realize that output fell short of the plan but struggle to answer critical questions: When did the machine start slowing down? How long did the slowdown last? How often did this occur? Which machine or process step was most affected? This highlights a major limitation of relying solely on aggregated data to manage production performance.

V. Methods for continuous machine speed monitoring using sensors and software

To detect speed reductions early, businesses need to shift from merely reviewing end-of-shift results to monitoring operational data in real-time. Depending on the existing system, data can be collected from PLCs, sensors, product counters, or line control devices and transmitted to a centralized monitoring software platform.

1. Monitoring actual Cycle Time

Cycle Time indicates the time a machine requires to complete one cycle or produce a single unit. When the actual Cycle Time begins to exceed the standard value, it may signal that the machine is slowing down. Continuously monitoring cycle time allows businesses to detect changes early, rather than waiting until the end of a shift to discover that output targets were not met.

2. Comparing actual speed against standard speed

Monitoring systems can continuously compare a machine’s actual speed with its target speed.

Example:

• Standard speed: 120 products/minute
• Actual speed: 92 products/minute

When the discrepancy exceeds a permissible threshold for a specified duration, the system can flag the anomaly for the operations team to investigate.

3. Monitoring performance by machine and time interval

Instead of relying solely on an average end-of-day performance figure, real-time dashboards allow businesses to observe performance trends based on:

• Machine
• Production line
• Shift
• SKU
• Time interval

This enables businesses to identify which machines frequently run below standard speed and when slowdowns occur most often.

4. Correlating speed data with downtime and operational factors

Knowing a machine is running slowly is not enough; the real value lies in combining multiple data sources to identify the root cause.

For instance, when performance drops, businesses can simultaneously examine:

• Cycle time
• Micro-downtime
• Machine stoppage reasons
• Output
• Changeover timing
• Operational shift
• SKU currently in production

Analyzing data along a unified timeline helps the production team narrow down potential causes and prioritize the issues that need improvement.

VI. How does i-OEE help detect speed reduction?

Instead of waiting until the end of a shift to aggregate figures, i-OEE enables businesses to collect and visualize operational data, allowing for real-time monitoring of machine and production line performance.

Through a centralized dashboard, businesses can track key metrics such as:

• OEE
• Availability
• Performance
• Quality
• Cycle Time
• Downtime
• Output
• Performance by individual machine and production line

When actual speed deviates from the standard speed, Performance and Cycle Time data allow the operations team to quickly detect the change, pinpoint exactly when it occurred, and analyze the root cause. This is particularly critical for high-speed production lines, where even a minor but prolonged reduction in speed can result in significant production losses.

VII. Conclusion

Unstable machine speed is a type of Performance loss that is difficult to detect, as the equipment remains operational without necessarily triggering obvious downtime. Causes can range from equipment wear and tear and suboptimal maintenance to parameter settings, operational practices, input materials, or imbalances between production stages.

Relying solely on Excel or end-of-shift reports often reveals only the consequence—reduced output—without pinpointing exactly when the slowdown occurred or identifying the underlying cause.

Real-time monitoring of speed, Cycle Time, and Performance enables factories to shift from reactive problem detection to proactively identifying and addressing the losses that silently erode OEE.

VIII. Discover the i-OEE real-time machine speed monitoring solution

Is your machinery running, yet output consistently falls short of the plan? Are you seeing Performance fluctuations without knowing the cause? i-OEE helps businesses monitor machine and line performance in real-time, detecting speed reductions, micro-downtime, and specific areas where production efficiency is being lost. Contact i-Soft to learn about the real-time speed monitoring module of i-OEE and sign up for a live demo using actual production line data.

i-Soft Joint Stock Company – specializing in providing factory digitalization software solutions

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