If you ever had to take the SAT or the ACT, you’ll remember those pesky analogy questions. Let me invite you to recall those – at least for today’s article. The topic for today is how Cycle Time is the top of the surface metric that tells you A LOT about an operation; similar to how high levels of finished goods inventory tells you A LOT about the nature of a manufacturing operation. Let me explain.
First off, we know there are many differences between Service and Manufacturing. But one this they definitely have in common is one metric that can – very quickly – tell you the health of the operation.
Cycle Time is to Service as Inventory is to Manufacturing
I’m sure you’ve seen this chart – it’s a common one used to explain Lean:
In Lean, we use the analogy of a river and flow. When the river is high, it masks the rocks below. Similarly, when the finished goods inventory levels of a company are high, then it masks many issues in a company. These issues are often categorized in one of the 7 Wastes as is seen in the image above.
Now, let’s switch gears a little bit.
Replace “Finished Goods Inventory” with Cycle Time.
In a service context, we get the same effect.
When cycle time is high (high is defined by the customer and the operation), then it masks many inefficiencies in the service operation. Some examples are:
- Order Entry Accuracy
- Unavailability of Equipment
- Unavailability of Personnel
On and on and on. And, if you think about it, this all makes sense.
Have you ever stood in line to buy something? Did your order take longer than expected? Was the reason because the clerk had to fix an order? Or the espresso machine wasn’t working the way it was supposed to? Or, was it because the cash register didn’t work as expected or it ran out of paper?
All of these reasons can cause cycle time to complete an order to increase.
And, when cycle time is high for a service operation, we know it is masking many issues beneath the surface.
Reduce Cycle Time to Make Step Change Improvement in Service Processes
If you work in a service operation, you should test out my claim. Measure a service operation end-to-end. You’ll see that if there’s variability in Cycle Time, you will see that beneath the surface there are issues that can be reduce or eliminated.
Now, my claim is true for almost every service operation – even healthcare. Though in healthcare, I would first focus on the patient safety and reducing medical errors – but even with these, Cycle Time will be one of those metrics that will be a very reliable metric that can describe the state of patient safety and medical errors.
But focusing on just Cycle Time in a service process isn’t enough. A balanced approach will be to look at other dimensions such as Cost, Errors, and Cycle Time simultaneously.
Let me summarize the chart above:
- Any service process can be measures end-to-end along the dimensions of Cycle Time, Errors, and Cost.
- Simultaneous improvement along all three dimensions is achievable – rather than a requirement that trade-offs must be made between the dimensions (think medical errors versus cycle time)
- Redesign efforts that focus only on costs usually achieve only temporary savings – reduced headcount assigned to the same workload results in an increased error rate, overburden on the current employees, and likely excessive delays in time
- Redesign efforts that focus only on error reduction will probably increase cycle time if the focus is on extra or redundant checks – or quality that is not built-in. Often times, Poka Yoke isn’t considered. If this is the case, then Cycle Time will definitely increase.
- But, efforts that focus on Cycle Time will address issues related to errors that cause delays and also eliminate redundant steps that cause delays. This approach will enable you to address all 3 dimensions of my model above.
I challenge you to test my assumptions. If you’re in a service operation, go ahead and see if what I’m claiming rings true in your operation. If so, see what you can apply today to improve your service process.
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