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About Demand Planning LLC

Demand Planning LLC, based in Boston MA, is a consulting boutique comprised of seasoned experts with real-world supply chain experience and subject-matter expertise in demand forecasting, S&OP, Customer planning, and supply chain strategy.

We provide process and solutions consulting, as well as customized training across a variety of industries.

Through our knowledge portal DemandPlanning.Net, we offer a full menu of training programs through in-person and online courses, as well as a variety of informational articles, downloadable calculation templates, and a unique Demand Planning discussion forum.

  • 23Mar

    Companies are rushing to upgrade from the APO 4.1 and 5.0 versions to the new SAP SCM 7.0  As the purse strings are loosening up this year, companies are spending more money in ECC and application upgrades.

    What are the big differences between the 4.0/5.0 vs. 7.0?  There are some marginal improvements that the tech shop may admire but anything for the planning community?!

    We also hear that the planners have not been using the Statistical modeling features in APO.  Will upgrading to 7.0 persuade the planners to use the Stat Models more?  Not just more, just even barely?

    IT implementation teams say that Stat models are not a priority given the budget constraints they have.  So more millions before and no stat models.  Now five years and many millions later, we have a shiny new upgrade and again the stat models are not a priority.

    I have been preaching Usability for the past few years.

    Put together fine tools  – But help the users in making the transition to the tool – give them better understanding – Make the new tool more usable!

    Give them the reports they need.  Provide them an exception based workflow!

    APO has good statistical models.  They will help you move the peanut forward but only if these models are better understood and leveraged.

    We just re-launched the marketing campaign for our Usability Consulting – Model tuning and model matching to product profiles are important elements of the Usability training. 

    Once implemented the Usability project will harmonize the use of models across planners from various geographies for the same business/product family.  There will be streamlined work flow.

    We help you answer the following questions:

    1. Am I using a Pareto Approach in my APO planning process?
    2. How can I leverage APO DP to improve our forecast accuracy?
    3. Why does APO mostly give me flat forecasts? How do I fix this?
    4. What are Alpha, Beta, Gamma, Sigma and Theta? How do I leverage these parameters?
    5. What is the correct level to model so as to improve the overall accuracy at the SKU level?
    6. What are weighting profiles? How does it affect my final forecast?
    7. How can I control time trend using trend dampening profiles?
    8. Are there products and customers that are better left to APO’s automated modeling strategy?
    9. Which models to choose for what family of SKUs?
    10. What are custom modeling profiles?
    11. How is APO helping us simplify and improve the promotional planning process?
    12. How do I create Multiple Linear Regression Models in APO?
    13. Are we using the system defined error metrics in APO? Why are they different from the classic MAPE calculations?
    14. How do you conduct phase-in/phase-out of products?
    15. When should I not use the Croston’s Model?
    16. Why am I getting 9,000+ alerts every morning?

    Perhaps the stress test of your new implementation or upgrade will be to ask the team if they can answer the above questions.

    Our Model tuning and training project can be launched any time -but it is better to launch it sometime during the middle of the configuration calendar before the go-live.  This way the training to the planners can be combined with the Navigation training.

    The benefits will be substantial:  improved forecasting process, workflow and significant supply chain benefits and cost savings.

    What does it cost you?  Not much – perhaps one-fiftieth of what you paid for the APO DP implementation or some where in that ball park.  We finish everything in ten to twelve calendar weeks.

    The detailed brochure on this Model Tuning and Training service is available for download at

    Happy Forecasting!

    Mark Chockalingam

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  • 10Jul

    A user in an APO forum lamented:

    “I am trying to use the Moving Average option in APO DP but I get a static forecast that remains constant for all the future months based on the history.  I would like the statistical forecast to be a moving target not a constant.  Not sure how to achieve this?!

    “All I get is the same statistical forecast volume for all 24 months. This is not want we are looking for.  The system is giving me a forecast like this:  month1 – 915, month2 – 915,… month 24 – 915

    “But I want the system to give me forecast that is different each month: 
    Future: month1 – 915, month2 – 902,… month 24 – 908.”

    Before we go too far with the micro details of these numbers, let us first realize some qualities of a good statistical forecast. 

    One of the important qualities of a forecast is robustness. Robustness means the forecast does not change like a yo-yo just based on new historical data point.

    So the objective is not to mimic the history but to produce a forecast that minimizes the error. Theoretically, Moving averages should produce a constant forecast into the future at least after the first two periods.  The idea of the moving average is that it will change by at least one third of the impact of the new observation that is different from the

    Other than this, let us understand the difference in error in what is being proposed here.  A forecast that is 915 each month is off by 1% from another forecast that varies between 902 and 915 over the entire forecast horizon. 

    A contrived model that looks fancier with oscillations in results between 902 and 915 perhaps can be 1% better on average than another model that proposes to use a constant 915 every month. I don’t think the trade-off to improve forecast quality by 1% is worth the model search to come up with a more complex model that mimics
    the history better.

    The fact that you are choosing moving average means that the data series is relatively more stable. We as planners should let moving average do its job and move on to more complex items that need your attention – items that
    have a persistent trend or seasonality or both.

    Trying to fit a holt-winters model when the series begs you for a constant model is NOT a good use of time. Note that SAP APO classifies moving average under constant models.  In fact, specifying that you want Holt-Winters models in such a scenario will give you a First Order smoothing model which again gives you constant forecasts into the horizon. 

    If you want to learn more about error metrics, you may be interested in

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  • Hi Mark!!! Fantastic post! I found your reflections on forec...