Analyzing Continuous Data with a Maximum Possible Value
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- This topic has 7 replies, 5 voices, and was last updated 3 years, 2 months ago by
Chris Seider.
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April 23, 2019 at 7:31 am #238624
James BarrettParticipant@JBarrett5322Include @JBarrett5322 in your post and this person will
be notified via email.I’m looking to analyse on time delivery data. It’s continuous and Normal, but obviously it has a maximum of 100% – does this affect how to analyse? For instance, the bell curve goes beyond 100%, and I-MR charts have an UCL of 117%.
0April 23, 2019 at 8:25 am #238625
Robert ButlerParticipant@rbutlerInclude @rbutler in your post and this person will
be notified via email.Not that normality has anything to do with control charting but, in your case, 100% represents a physical upper bound. If your process is operating “close” to the upper bound then the data should be non-normal – which would be expected. If it is “far enough” away from 100% then I could see where the data might be approximately normal – again not an issue – but if your process is that removed from 100% then it would seem the first priority would be to try to find out why this is the case instead of tracking sub-optimal behavior.
To your specific question, since 100% is perfection and since you want to try tracking the process with a control chart what you want is a one sided control chart where the only interest is on falling below some limit.
1April 23, 2019 at 3:06 pm #238635
Chris SeiderParticipant@cseiderInclude @cseider in your post and this person will
be notified via email.You can set a maximum or minimum for control limits within the Minitab generated SPC charts.
Please consider SPC is not always the best tool for all situations.
0April 29, 2019 at 7:19 am #238764
James BarrettParticipant@JBarrett5322Include @JBarrett5322 in your post and this person will
be notified via email.Thanks both, really helpful.
I’m not completely married to using Control Charts, it was just an example of where the maths gives above 100%.I’m just looking for good methods of analysing on time delivery, to be honest.
0April 29, 2019 at 8:51 am #238766
Robert ButlerParticipant@rbutlerInclude @rbutler in your post and this person will
be notified via email.If that’s the case then the issue should be framed and analyzed in terms of what you mean by on-time delivery.
1. What do you mean by on-time delivery? – need an operational definition here
2. What does your customer consider to be on-time delivery? – need an operational definition here too.
3. What do you need to take into account to adjust the on-time delivery definition for different delivery scenarios?
a. If it is the physical delivery of a product to a company platform – how are you addressing things like, size of load, distance needed for travel, etc.
b. If it is a matter of delivering something electronically – how are you adjusting for things like task difficulty, resource allocation to address a problem, etc.After you have your definitions and an understanding of how your system is set up for production you should be able to start looking for ways to assess your on-time delivery and also identify ways to improve it.
0April 29, 2019 at 9:22 am #238769
grazmanParticipant@bgrazmanInclude @bgrazman in your post and this person will
be notified via email.If you use the actual times and not the %, you don’t have the 100% problem (and it’s not clear what the percentage scores are… the % OTD in that group? the % of a target time, etc.).
0April 29, 2019 at 1:05 pm #238773
Chuck WhiteParticipant@jazzchuckInclude @jazzchuck in your post and this person will
be notified via email.I agree with grazman — if you can track the actual times each shipment left your dock or arrived at your customer’s dock (depending on who is responsible for freight), you can get a lot more information with less data than %OTD. You would set each shipment’s target as zero, and record minutes before (-) or after (+) the target.
If you don’t have that data available, you can also use a P-chart for the inverse of %OTD — that is the proportion of late deliveries. (You could use a P-chart directly for proportion of on-time deliveries, but since most people associate P-chart with proportion defective, interpretation would be easier for proportion of late deliveries.)
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This reply was modified 3 years, 2 months ago by
Chuck White.
0April 29, 2019 at 9:49 pm #238797
Chris SeiderParticipant@cseiderInclude @cseider in your post and this person will
be notified via email.Consider your OTD vs family type, facility, state, etc. Things may begin to show up but I hope you have a good problem statement, team, and project champion.
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