A Lean Case Interview
Suppose you were presented with the following:
"You have been hired to help a fast food franchise, a market leader in the South West Region of the U.S. This franchise collects and reports on a metric they call "scrap rate". Scrap Rate has been increasing for the previous 3 quarters for this 60 store franchise. This problem was costing this franchise ~ $35K/month.
Using Lean and/or the DMAIC framework, please help this firm solve the "Scrap Rate" problem. Feel free to use paper, calculator, whiteboard, and to ask me questions. What’s going on with Scrap Rate?"
This was a real engagement that I was engaged in several years ago. Here are a few hints:
- The root cause is absolutely not obvious, but surprisingly trivial.
- The solution eliminated the root cause 100%.
- The solution cost less than $2.00 US Dollars.
How would you go about solving this problem?
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Comments
Hey Thomas,
You’re right, that is a good place to start. It might help to also know what exactly is meant by “Scrap Rate”.
Hey Peter,
That was going to be my first question. Sometimes how we measure a defect changes over time or changes from location to location.
So, how did they define “scrap rate”?
james
What is the ’scrap rate’, how are the metrics collected, by whom, and how does it cost the company $35K / month?
“Scrap Rate” is actually better called “Returns” — in other words, the frequency of returns by product type. So, fries, burgers, etc. would be returned by the customer, those returns were collected by the front-line associates via a checksheet.
This checksheet data showed an increase in returns for 3 specific product types. Why?
Under what circumstances would customers return products? Was it because the order was not executed properly? Was there a change in the way returns were captured over the past three months?
Using a standard checksheet, There are many fast-food-type products. But, the three that had the highest returns were Chicken Sandwich (52%), Spicy Chicken Sandwich (13%), and Chili Fries (4%). The rest of the returns are spread-out and trivial.
Question 1: What is an appropriate way to visualize the quantitative data above?
This company collected free-text responses for the returned products. The free-text responses are difficult to summarize because that is the nature of free-text responses. BUT, there seemed to be an overwhelming theme:
“I didn’t get what I wanted”
Question 2: Given the information above, what would you now do?
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Good place to start would be:
Question 1 – Why is the scrap rate increasing?
Question 2 – Why?
Question 3 – Why?
Question 4 – Why?
Question 5 – Why?