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Analyzing Continuous Data with a Maximum Possible Value

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This topic contains 7 replies, has 5 voices, and was last updated by  Chris Seider 2 months, 3 weeks ago.

Viewing 8 posts - 1 through 8 (of 8 total)
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  • April 23, 2019 at 7:31 am #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%.

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    April 23, 2019 at 8:25 am #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.

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    April 23, 2019 at 3:06 pm #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.

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    April 29, 2019 at 7:19 am #238764

    James Barrett
    Participant

    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.

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    April 29, 2019 at 8:51 am #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.

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    April 29, 2019 at 9:22 am #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.).

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    April 29, 2019 at 1:05 pm #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 months, 3 weeks ago by  Chuck White.
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    April 29, 2019 at 9:49 pm #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.

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