Cycle Time

Cycle time in Overall Equipment Effectiveness (OEE) measures the total time taken to produce one unit of product. 

It includes time spent on setup and the actual production process.

This measure not only considers the processing time but also accounts for any downtime during the production process. 

Effective management of cycle time helps in understanding the efficiency of manufacturing operations.

Cycle Time in OEE

Cycle time plays a crucial role in evaluating OEE. 

By analysing this metric, you can pinpoint inefficiencies that lead to increased production time. 

A shorter cycle time indicates a more effective manufacturing process, contributing to higher OEE scores.

Also, improvements in cycle time can lead to increased productivity and profitability, as resources are used more efficiently. 

Optimising cycle time eventually enhances overall operational performance.

Cycle Time vs Actual Cycle Time

Cycle time provides an ideal benchmark for performance efficiency.  

By contrast actual cycle time reflects the real-world time it takes to complete a cycle, accounting for inefficiencies such as delays, machine wear, or operator variability.

The difference between ideal and actual cycle time is a key indicator of performance losses. 

If actual cycle time significantly exceeds the ideal, it signals issues such as reduced equipment speed or process interruptions. 

Closing this gap is essential to improving OEE, often requiring actions like equipment maintenance, operator training, or process optimisations.

By aligning actual cycle time more closely with the ideal, manufacturers can boost efficiency, reduce waste, and enhance overall equipment effectiveness.

 

How to Measure Cycle Time

Measuring cycle time involves assessing the total time required to produce a single unit of product. 

This measurement includes both setup and production phases. 

Accurate tracking helps identify inefficiencies and improves overall equipment effectiveness (OEE).

 

Methods for Measurement

Time Studies

Time studies are a low-cost and straightforward method for measuring cycle time, requiring only basic tools like a stopwatch and a notepad. 

There are several steps involved in conducting a time study as follow: 

  1. Get a stopwatch and recording sheet.
  2. Identify start and end points for one production cycle.
  3. Time multiple cycles, recording each duration.
  4. Calculate the average cycle time across observations.
  5. Note results and any inefficiencies observed.

Time studies provide immediate, detailed insights into specific cycles and are particularly useful for identifying inefficiencies or understanding operator behaviour. 

However, this method can be time-consuming and labor-intensive, especially when observing multiple cycles to ensure accuracy. 

Additionally, the presence of an observer may influence operator performance, known as the Hawthorne effect, and the limited data sample size can reduce the reliability of the measurements.

Systematic Logging

Systematic logging offers a more detailed approach by breaking the cycle into phases and tracking the duration of each. 

There are 5 key steps to follow to accurately log cycle time:

  1. Define phases within the cycle (e.g., setup, processing).
  2. Use a template to record start, end, and duration of each phase.
  3. Record data for multiple cycles manually or semi-automatically.
  4. Calculate phase durations for total cycle time and average across cycles.
  5. Identify patterns and bottlenecks from logged information.

This method allows for a deeper understanding of inefficiencies and is ideal for analysing complex processes. 

Semi-automated tools can also reduce human error during data collection. 

However, it requires disciplined and consistent logging, which can still be prone to mistakes if done manually. 

Additionally, the process may slow down production, and it is not as fast or comprehensive as automated systems for large-scale operations.

Automated production data systems

Automated real-time systems are highly accurate and efficient, providing consistent data collection with minimal manual intervention. 

They offer real-time insights, enabling faster decision-making and scalability for continuous monitoring in large or complex operations. 

Automated systems require different steps to setup than those for the previously mentioned methods: 

  1. Install sensors or IoT devices to track cycle start and end points.
  2. Configure software to log relevant data automatically.
  3. Allow the system to monitor cycles during normal operations.
  4. Review cycle time reports to identify averages and trends.
  5. Occasionally cross-check automated data with manual observations.

Despite their advantages, automated systems require a significant initial investment in hardware and software and may demand technical expertise for installation and maintenance. 

While highly precise, automated systems may overlook nuances such as human inefficiencies or environmental factors, which could impact cycle time.

 

How does Cycle Time impact OEE?

A shorter cycle time enhances production efficiency, leading to higher OEE scores. 

When cycle time decreases, production increases, allowing for more units produced within the same timeframe.

Cycle time directly correlates with performance metrics in manufacturing. 

Effective management of cycle time contributes to reduced operational costs, improved throughput, and better resource utilisation. 

When you analyse cycle time alongside OEE, it becomes evident that performance improves as cycle time shortens. 

Inadequate cycle times often signal underlying issues such as equipment malfunctions or inefficient workflows, negatively affecting overall performance.

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