Understanding a process' cycle time is extremely important, especially in the context of takt time. In a mixed model environment, cycle time can be a bit less straight-forward. That's where weighted averages may make sense.

Weighted average cycle time, also known as “average weighted cycle time,” provides a representative average cycle time. Varied models or services in a given cell, line or work area often have varied work contents due to different steps, duration of steps, sequence of steps, etc. Accordingly, the cycle times vary.

Weighted average cycle times can be calculated for operator cycle times, machine cycle times and effective machine cycle times. Often weighted average cycle times are presumed to be operator related, but this is not always the case.

As we endeavor to maintain a cycle time that is less than or, at most, equal to takt time, mixed models and their varying work content will likely have cycle times for some products or services that are below takt time, while others exceed takt time. The weighted average cycle time serves as an average proxy for cycle time and is often the same as the planned cycle time.

Clearly, change in product or service mix will change the weighted average cycle time. As the demand mix shifts to one with a greater proportion of cycle time(s) that exceed the average, then the weighted average cycle time will approach and may exceed takt time. The lean practitioner must be aware of these dynamics and should proactively address the situation through reducing work content, optimizing balance between operators, adding additional operator(s) or lines, strategically applying/sizing FIFO lanes, etc.

See below for the weighted average cycle time formula and an example (click to enlarge).

Related post: Musings About FIFO Lane Sizing “Math”

## There are 3 Comments

Mark:

Here's a different approach.

We have many different skus (100s) that pass through the same basic process but they have widely different cycle times - some as much as 3 times as long as others. There is also lots of weekly and seasonal variation in demand, both by sku and total volume. A perishable product means we can't level load outside of a production week.

Our weighting process looks something like this (probably better explained in a spreadsheet, sorry!):

We schedule work on a weekly cycle so that required number of operators is constant through the week. The table that drives this figures weekly takt time for each sku and then calculates the number of operators required for each sku that week based on cycle times. This results in many "fractional operators", if you get my drift. First we load the week as a block, add up all those fractional operators to see what the total required number of operators is to get the week's work done in regular hours. Then break it down to days and push production orders around until each day's requirement for operators is the same as the weekly average. The pace of the main production area rises and falls according to the cycle time of the mix of specific products they are handling at any given time, but the operator requirement is predictable and constant through the week. Total volume of production varies by day but is specified in advance.

The payoff here is customer and employee satisfaction. Until we understood and applied the relationship of takt time, cycle time and operator loading, our daily and weekly workload was out of control, and there was no telling when the week's work would be done - 10 a.m. on Friday? Saturday overtime? Call the sitter! Now, we can make reliable commitments to employees concerning their hours and reliable commitments to customers concerning delivery. Much better for everyone!

-ALB

Andrew, my organization could really benefit using your leveling process. Would you be willing to share a worksheet or, better yet, an example of how you're balancing demand to work content across your variable demand and sku's?

Thank you!

Stephen