# Analyzing Continuous Data with a Maximum Possible Value

Six Sigma – iSixSigma Forums General Forums Tools & Templates Analyzing Continuous Data with a Maximum Possible Value

Viewing 8 posts - 1 through 8 (of 8 total)
• Author
Posts
• #238624

James Barrett
Participant

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%.

0
#238625

Robert Butler
Participant

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.

1
#238635

Chris Seider
Participant

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.

0
#238764

James Barrett
Participant

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.

0
#238766

Robert Butler
Participant

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.

0
#238769

grazman
Participant

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.).

0
#238773

Chuck White
Participant

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.)

• This reply was modified 2 years, 8 months ago by Chuck White.
0
#238797

Chris Seider
Participant

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.

0
Viewing 8 posts - 1 through 8 (of 8 total)

You must be logged in to reply to this topic.