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How to improve forecast accuracy in a call centre with six sigma?

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Viewing 7 posts - 1 through 7 (of 7 total)
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  • #53430

    Tsavdaridou
    Participant

    Hello.
    I’m currently working for a telecommunication company, and we’re forecasting call volume and staff requirements….at least we try to. However, when measuring forecast accuracy resaults don’t exceed 80-85%.
    How could I use six sigma in order to improve this kind of situation? I was thinking about DMAIC approach, what is your opinion to that?
    Currently, we use calculations in Excel. We measure daily and weekly distribution of call volume, using historical data, in some cases apply seasonality factor, exclude special days (bank holidays), and finally use monthly and annual growth rate in order to apply relevant increase or decrease in next months traffic.
    Kindly let me know if you have any ideas on this matter.
    And how could I possibly use six sigma method to improve this situation?
    Any suggestions will more than welcome!

    Thank you in advance for any reply!

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    #190071

    Severino
    Participant

    Why don’t you start by defining what you would consider to be acceptable forecasting accuracy? Keep in mind also that rather than improving the accuracy of your forecasting you would most likely be better served in looking at your entire value stream and eliminating non-value added activities so that you can handle variation in call volume without the need to adjust staffing levels.

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    #190072

    Tsavdaridou
    Participant

    Hi!
    If I understand correct, you mean starting first with presenting our goals, such as forecast accuracy >92%, in monthly base.
    However, I cannot overcome staffing requirements since they’re fully connected with call volumes, correct?
    Should I use maybe another forecasting method? In my company we use currently a WFM Tool, but we cannot evaluate well forecast figures at the end.

    I’m a little bit confused with this. What’s your opinion to that?

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    #190074

    Hody
    Member

    Is “forecast accuracy” the goal, or isn’t the “customer wait time” and/or “dropped call rates” really the goal?

    Once you determine your Big-Y, set goals, normality, stability, capability, etc. you will probably eventually get to either General Linear Models (which can handle both discrete or continuous Xs), or Multiple Regression (for continuous Xs) to determine which measured Xs (if any) impact your Big-Y…

    Good Luck!

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    #190076

    Tsavdaridou
    Participant

    Well the goal is to achieve forecast accuracy, since if we can achieve being as accurate as possible, we will achieve at the same time schedule adherence, reduction ACR, and impovement of FCR.
    Finally, I want to compare forecast vs. actual call volume, Service Level and Abandoned Call Rate achievement, and scheduled FTEs.

    Then, I will show the impact to the whole business. Please correct me if I’m wrong.

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    #190077

    Hody
    Member

    I suggest that your Goal, CTQ, Big-Y, whatever you want to call it is Reducing Customer Wait Time. That wait time, in turn, is a function of by call volumes, FTEs to answer calls, and call lengths when answered. There is also going to be variation inside each of those components (inaccurate forecasting, FTE-to-FTE differences, sick-rate of FTEs, and call-to-call resolution time.

    So don’t get paralyzed worrying about your accuracy rate. Start some regression analysis or General Linear Models (GLMs) to determine which of those Xs is the biggest impact on the Y and why. Maybe it turns out your effort would be better spent on FTE training and tools to reduce resolution times and your 80% volume accuracy isn’t painful anymore since your customer wait times will go down… Just say’n…

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    #190170

    Kluttz
    Member

    You’re going to run into a couple problems if you’re trying to use regression analysis;
    1. regressions are only as good as the factors you have as inputs to them. If you’re not capturing all your input variables (you mentioned a few above), you’re not going to get an r-sq large enough to build a model of any value. And even if you did, it wouldnt be beneficial for daily forecasting. You could try monte carlo simulation, but you’d have to create several different models for day of the week, week of the month, month, etc.
    In summary, you can definitely use statistical tools to improve your forecasting, but you’d be way better off looking at improving your call centre processes instead – make them robust enough to deal with reasonable variation in call volume changes. Because no matter how tight your forecasting is, you’re still going to have common cause volume variation that would be essentially impossible to account for.
    I’d focus on improving your average handle time and first call resolution via DMAIC.

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