Myth: The cycle times in your ERP system are correct.
Fact: Unless you are updating the cycle time in your system on a regular basis based on your factory’s performance, it is not correct.
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.
“If it ain’t broke, don’t fix it,” is a phrase that is often attributed to Thomas Bertram Lance, a close adviser to Jimmy Carter. In the manufacturing world, this means that if a factory is functional and orders are being processed in a reasonable timeframe, then you shouldn’t make any significant changes to the system.
There is some validity to that reasoning. Updating cycle times can wreak havoc to the formulas within an ERP system. The standardized cycle time values defined in an ERP or scheduling system are often based on estimates or historical data. Manufacturers will only update cycle times annually, or when their “gut” tells them that it is off.
Calculating your real cycle time
Calculating a current cycle time can be difficult without a production monitoring system like Mingo Smart Factory. Without an automatic data collection process, manufacturers have to calculate cycle times based on three methodologies:
- Gut feeling
- One-day snapshot
- Manual reporting of a one week average
| Methodology | Pros | Cons |
| Gut Feeling | Based on Individual Industry Experience | Not Data-Driven |
| One-Day Snapshot | Consistent Data Set for One Day | No Way to Know If This Was a Good Day or Bad Day |
| Manual Reporting for One Week Average | Larger Data Set | Consistency Can Be Difficult to Maintain/Track Across Multiple Days/Shifts |
| Mingo Smart Factory | Accurate Data is Constantly Collated with Historical Performance | You Didn’t Get It Sooner |
Instead or relying on a small data set, Mingo Smart Factory customers can average their data over a year. Many of Mingo’s customers discover that their cycle time is off by 20%. That 20% can help manufacturers determine if a line is producing slower or faster than anticipated. There is a joke that manufacturers know whether they made money or not. What they don’t always know is which products contributed to their profit. Knowing your true cycle time can help you determine the winners and losers.
The losers with a slower than expected cycle time are being produced slower than you think. You’re probably running more overtime than planned to hit your scheduled delivery dates. It’s effectively costing you more to efficiently produce that line.
The winners with the faster than expected cycle time can also be an issue. If you’re always finishing early, then you’re probably ending up with excess inventory. If this line hasn’t been identified as a money maker by now, it should become a priority. Knowing which lines need attention and which are performing efficiently is important to know when understanding your exact margins.
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.
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
Check out Mingo Smart Factory’s Cycle Time Calculator to get an idea of what your cycle time actually is.
