Summarising data and evaluating suppliers
We discuss how to summarise data into two numbers that explain most of it (it's not the mean and standard deviation!) and then touch on how to evaluate suppliers.
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New articles
Wilks’ Tolerance Intervals
A nice way to summarise a distribution is through a 90 % tolerance interval, i.e. a range in which 90 % of the population falls. How large a sample do we need to estimate such an interval? And how do we do it with just 30 samples? Find out today!
Full article (8–25 minute read): Wilks’ Tolerance Intervals
Flashcard of the week
Allen Ward makes many recommendations in Lean Product and Process Development. The following has a super-obvious answer, yet it is rare that people do it right.
On what basis should you evaluate suppliers?
The most vivid recollection I have on this (perhaps because it was a relatively recent, high-stakes decision) is when we wanted to select a real estate agent for selling our previous condo. It was a high-stress situation for us, so I met with 10 or so different ones to make sure we hired one that would work well.
They all sold themselves on their potential. They spoke a lot about what they could hypothetically do for us. That's what suppliers tend to do. They promise great things.
But what we really should evaluate them on is
Their past performance. Ask to see historic data.
As forecasters know, things tend to not change too much. High-performing suppliers tend to keep performing well, and vice versa.
None of the real estate agents I met shared concrete data about past closures they'd been part of, not even when asked.
(Sure, they listed phony awards and bragged about how highly past sellers rated them, but as the most honest agent we met explained, the agents practically get to choose who rates them, and they only ask for ratings from sellers they think will rate them well. Plus I care about the buyer experience just as much as the seller experience, and nobody asks potential buyers to rate them.)
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