3 ms·

"3.4 mistakes per million deliveries". There are lessons to be learned from these guys, but I think definition of a mistake and this stat in general is, well, f

"3.4 mistakes per million deliveries". There are lessons to be learned from these guys, but I think definition of a mistake and this stat in general is, well, flexible.

namdnay 6y ago
Like all statistics in India :)
econcon 6y ago
It would be interesting if someone makes a complaint portal for Dabba Walla and post some statistics on how many real complaints do you actually receive.
- essdas 6y ago
  It would certainly be interesting, however in the absence of hard data perhaps some anecdotal evidence is better than no information: Case 1 ------ 10 years x 280 deliveries per year x 2 deliveries per day (pick up + drop) = 5600 deliveries. Number of mistakes = 0 That puts the error rate at < 1/5600 (or approximately 0.0002). Case 2 ------ 4 years x 280 deliveries per year x 2 deliveries per day (pick up + drop) = 2240 Number of mistakes = 0 That puts the error rate at < 1/2240 (or approximately 0.0004). Both cases together ------------------- Error rate < 0.00012
wenc 6y ago
3.4 mistakes/million (also known as 3.4 DPMO = defects per million opportunities) is just a a statistic from a normal distribution. It comes from the Six-Sigma literature [1]. It's the number of rejects in a 6σ process, assuming a 1.5σ mean shift (1.5σ is an arbitrary assumption for a shift in a long-term process). You can get a reasonable estimate through random sampling -- which is the basis for most of inferential statistics -- so it's not that flexible. If an adequately powered study was conducted (with a large enough sample size and sufficiently randomized sample), I don't see why the results can't be trusted [2]. A mistake is not that hard to classify (in logistics there are failure reason codes -- wrong address, wrong order, missed delivery, etc. and typically certain categories dominate -- the others are noise). That said, 3.4 DPMO is kind of a magic number (sort of like 95% confidence intervals -- why 95%? Because Pearson or someone decided it and it stuck). [1] https://www.qualitygurus.com/why-is-6-sigma-equal-to-3-4-dpmo/ https://www.qualitygurus.com/why-is-6-sigma-equal-to-3-4-dpm... [2] Side: Systematic misreporting might be an issue of course, and there's not much that can be done there except design a system of incentives and penalties around it. Customer complaints and lost clients are often visible in such systems.

"3.4 mistakes per million deliveries". There are lessons to be learned from these guys, but I think definition of a mistake and this stat in general is, well, flexible.

namdnay 6y ago

Like all statistics in India :)

econcon 6y ago

It would be interesting if someone makes a complaint portal for Dabba Walla and post some statistics on how many real complaints do you actually receive.

essdas 6y ago
It would certainly be interesting, however in the absence of hard data perhaps some anecdotal evidence is better than no information: Case 1 ------ 10 years x 280 deliveries per year x 2 deliveries per day (pick up + drop) = 5600 deliveries. Number of mistakes = 0 That puts the error rate at < 1/5600 (or approximately 0.0002). Case 2 ------ 4 years x 280 deliveries per year x 2 deliveries per day (pick up + drop) = 2240 Number of mistakes = 0 That puts the error rate at < 1/2240 (or approximately 0.0004). Both cases together ------------------- Error rate < 0.00012

wenc 6y ago

3.4 mistakes/million (also known as 3.4 DPMO = defects per million opportunities) is just a a statistic from a normal distribution. It comes from the Six-Sigma literature [1]. It's the number of rejects in a 6σ process, assuming a 1.5σ mean shift (1.5σ is an arbitrary assumption for a shift in a long-term process). You can get a reasonable estimate through random sampling -- which is the basis for most of inferential statistics -- so it's not that flexible. If an adequately powered study was conducted (with a large enough sample size and sufficiently randomized sample), I don't see why the results can't be trusted [2]. A mistake is not that hard to classify (in logistics there are failure reason codes -- wrong address, wrong order, missed delivery, etc. and typically certain categories dominate -- the others are noise). That said, 3.4 DPMO is kind of a magic number (sort of like 95% confidence intervals -- why 95%? Because Pearson or someone decided it and it stuck). [1] https://www.qualitygurus.com/why-is-6-sigma-equal-to-3-4-dpmo/ https://www.qualitygurus.com/why-is-6-sigma-equal-to-3-4-dpm... [2] Side: Systematic misreporting might be an issue of course, and there's not much that can be done there except design a system of incentives and penalties around it. Customer complaints and lost clients are often visible in such systems.