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Debunking the Myth of Standardized Cycle Times

Standardized cycle times are often perceived as a benchmark for efficiency. Cycle times represent the rate at which a product runs on a specific production line or piece of equipment. If a CNC machine processes 120 automotive components in an hour, then the cycle time for that component is 30 seconds per unit. When correctly calculated, the performance component of OEE shows how well your factory is performing.

However, assuming that a standardized cycle time remains consistent across different facilities, overlooks the inherent complexities of manufacturing operations. If you are not actively tracking cycle time or doing very frequent time studies, your cycle time data is almost definitely out of date.

OEE Formula

Common Misconceptions When Evaluating Cycle Times

One of the most common misconceptions that manufacturers run into when measuring productivity is that they assume cycle times are standardized. The truth is the cycle time that you have in your ERP system or scheduling system is probably wrong. The standardized cycle time values defined in an ERP or scheduling system are often based on estimates or historical data. Manufacturing facilities differ in terms of equipment capabilities, workforce skill levels, maintenance practices, and environmental factors. Attempting to impose a standard cycle time across these diverse environments fails to account for these variations. This assumption leads to inefficiencies and suboptimal performance.

How fast can you run a mile?

Applying standardized cycle times is like predicting how fast someone will run based on their age and gender. A 40 year old man can run one mile in 9:55 minutes according to Running Level. Does that mean you can run a mile in under 10 minutes the first time you lace up a pair of running shoes? Probably not. It took me two years to get my mile average consistently under 10 minutes and another year before I broke the 30 minute mark for my 5K (3.1 miles). The Marathon Handbook has 10:18 min/mile as their average pace for a 40-50 year old man. A study of 10,000 5K runners by Pace Calculator found that the average pace of men 40-44 running a 5K was 10:28 min/mile.

Which standard is correct for all runners? There will be natural variation across the board based on the genetics and shoes (equipment), current level of fitness (experience of the staff) and number of walking breaks that someone takes (downtime).

Average 1 mile run time by age and ability. Original Source: https://runninglevel.com/running-times/1-mile-times
Using standardized cycle times to predict how a line will perform is an estimate at best, and a lie at the worst.

Helping manufacturers discover their true cycle time was one of the reasons I founded Mingo Smart Factory. Working with the best ERP systems in the world doesn’t matter if the metric used to measure if a factory is having a good day or bad day is based on a flawed cycle time.

Bryan Sapot

Real-Time Visibility on the Factory Floor

Manufacturing companies operate in a variety of production environments, each with its own set of challenges and constraints. Factors such as equipment capabilities, technology levels, and automation vary significantly. What works seamlessly in a highly automated production line might not be feasible in a more manual or labor-intensive setting. Factory floor monitoring with real-time data collection is the only way to get an accurate read on how your plant is performing.

Calculating the ideal cycle time for your factory floor

Not sure how to get started? Let’s go back to the running analogy for a moment: how long does it take you to run a mile? To determine that, you need two units of measurement: a set distance and a way to measure time. Assuming that you will run a mile in the exact same time, every time, is a recipe for disappointment. Measuring metrics on the factory floor works the same.

cycle time formula

The Importance of Real-Time Monitoring and Adjustment

Manufacturers should embrace real-time monitoring and adjustment of cycle times based on actual production data. By leveraging advanced data analytics and monitoring technologies, manufacturers can identify inefficiencies, optimize production processes, and adapt cycle times to dynamic operational conditions.

Tailoring Cycle Times for Optimal Efficiency

To achieve optimal efficiency, manufacturers must adopt a flexible approach to cycle time management. This involves tailoring cycle times to specific production environments, continuously monitoring performance metrics, and making data-driven adjustments as needed. When tracking manually, many manufacturers do not remove downtimes from the cycle time calculation. This results in incorrect cycle times and confusion between performance issues and uptime issues.

The first two steps most often taken by customers after going live with Mingo Smart Factory are reducing downtimes and adjusting cycle times for accuracy. They do this by making data-driven decision based on the information they gain from our production monitoring system. Mingo is always tracking the actual cycle time of production runs. Time studies are constantly being performed whether you are looking at the data or not. This saves many hours of performing time studies to determine issues or validate performance improvements.

Cycle Time Trends: Increase Throughput

Manufacturers That Have Found Cycle Time Clarity with Mingo Smart Factory

Lincoln Plastics discovered that the cycle times they were using to plan, cost, and quote their parts were off by as much as 40%. They were able to adjust their numbers to gain an understanding of their true capacity, actual cost, and which parts were actually profitable.

Lyons Blow Molding also discovered that their cycle times were not accurately reflecting what they were actually doing. They revised their times to improve cost and scheduling. Check out the case study.

Operational Flexibility

The myth of standardized cycle times overlooks the inherent variability and complexity of manufacturing operations. Instead of adhering to a one-size-fits-all approach, manufacturers should prioritize flexibility, real-time monitoring, and data-driven decision-making to optimize cycle times and enhance overall efficiency.

To thrive in today’s diverse industrial landscape, companies must recognize and embrace the uniqueness of their respective factory set-up. A manufacturing dashboard and production monitoring platform like Mingo Smart Factory is crucial to tailoring cycle times. Manufacturing analytics are the key to unlocking true efficiency and competitiveness in manufacturing. By debunking this myth, manufacturers can unlock the full potential of their production processes and drive sustainable growth in the ever-evolving manufacturing landscape.

Learn More

Check out Mingo Smart Factory’s Cycle Time Calculator to get an idea of what your cycle time actually is.

Alyxandra Sherwood
Alyxandra Sherwood
Digital Marketing Manager @ Mingo Smart Factory I Adjunct Professor @ SUNY Geneseo I Boston Marathoner I Second Street Award Winner I Media Professional with 15 Years Experience