Improve Forecast Accuracy by Managing Error

The Smart Forecaster

 Pursuing best practices in demand planning,

forecasting and inventory optimization

Improve Forecast Accuracy, Eliminate Excess Inventory, & Maximize Service Levels

In this video, Dr. Thomas Willemain, co-Founder and SVP Research, talks about improving Forecast Accuracy by Managing Error. This video is the first in our series on effective methods to Improve Forecast Accuracy.  We begin by looking at how forecast error causes pain and the consequential cost related to it. Then we will explain the three most common mistakes to avoid that can help us increase revenue and prevent excess inventory. Tom concludes by reviewing the methods to improve Forecast Accuracy, the importance of measuring forecast error, and the technological opportunities to improve it.

 

Forecast error can be consequential

Consider one item of many

  • Product X costs $100 to make and nets $50 profit per unit.
  • Sales of Product X will turn out to be 1,000/month over the next 12 months.
  • Consider one item of many

What is the cost of forecast error?

  • If the forecast is 10% high, end the year with $120,000 of excess inventory.
  • 100 extra/month x 12 months x $100/unit
  • If the forecast is 10% low, miss out on $60,000 of profit.
  • 100 too few/month x 12 months x $50/unit

 

Three mistakes to avoid

1. Ignoring error.

  • Unprofessional, dereliction of duty.
  • Wishing will not make it so.
  • Treat accuracy assessment as data science, not a blame game.

2. Tolerating more error than necessary.

  • Statistical forecasting methods can improve accuracy at scale.
  • Improving data inputs can help.
  • Collecting and analyzing forecast error metrics can identify weak spots.

3. Wasting time and money going too far trying to eliminate error.

  • Some product/market combinations are inherently more difficult to forecast. After a point, let them be (but be alert for new specialized forecasting methods).
  • Sometimes steps meant to reduce error can backfire (e.g., adjustment).
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        Four Useful Ways to Measure Forecast Error

        The Smart Forecaster

         Pursuing best practices in demand planning,

        forecasting and inventory optimization

        Improve Forecast Accuracy, Eliminate Excess Inventory, & Maximize Service Levels

        In this video, Dr. Thomas Willemain, co-Founder and SVP Research, talks about improving forecast accuracy by measuring forecast error. We begin by overviewing the various types of error metrics: scale-dependent error, percentage error, relative error, and scale-free error metrics. While some error is inevitable, there are ways to reduce it, and forecast metrics are necessary aids for monitoring and improving forecast accuracy. Then we will explain the special problem of intermittent demand and divide-by-zero problems. Tom concludes by explaining how to assess forecasts of multiple items and how it often makes sense to use weighted averages, weighting items differently by volume or revenue.

         

        Four general types of error metrics 

        1. Scale-dependent error
        2. Percentage error
        3. Relative error
        4 .Scale-free error

        Remark: Scale-dependent metrics are expressed in the units of the forecasted variable. The other three are expresses as percentages.

         

        1. Scale-dependent error metrics

        • Mean Absolute Error (MAE) aka Mean Absolute Deviation (MAD)
        • Median Absolute Error (MdAE)
        • Root Mean Square Error (RMSE)
        • These metrics express the error in the original units of the data.
          • Ex: units, cases, barrels, kilograms, dollars, liters, etc.
        • Since forecasts can be too high or too low, the signs of the errors will be either positive or negative, allowing for unwanted cancellations.
          • Ex: You don’t want errors of +50 and -50 to cancel and show “no error”.
        • To deal with the cancellation problem, these metrics take away negative signs by either squaring or using absolute value.

         

        2. Percentage error metric

        • Mean Absolute Percentage Error (MAPE)
        • This metric expresses the size of the error as a percentage of the actual value of the forecasted variable.
        • The advantage of this approach is that it immediately makes clear whether the error is a big deal or not.
        • Ex: Suppose the MAE is 100 units. Is a typical error of 100 units horrible? ok? great?
        • The answer depends on the size of the variable being forecasted. If the actual value is 100, then a MAE = 100 is as big as the thing being forecasted. But if the actual value is 10,000, then a MAE = 100 shows great accuracy, since the MAPE is only 1% of the actual.

         

        3. Relative error metric

        • Median Relative Absolute Error (MdRAE)
        • Relative to what? To a benchmark forecast.
        • What benchmark? Usually, the “naïve” forecast.
        • What is the naïve forecast? Next forecast value = last actual value.
        • Why use the naïve forecast? Because if you can’t beat that, you are in tough shape.

         

        4. Scale-Free error metric

        • Median Relative Scaled Error (MdRSE)
        • This metric expresses the absolute forecast error as a percentage of the natural level of randomness (volatility) in the data.
        • The volatility is measured by the average size of the change in the forecasted variable from one time period to the next.
          • (This is the same as the error made by the naïve forecast.)
        • How does this metric differ from the MdRAE above?
          • They do both use the naïve forecast, but this metric uses errors in forecasting the demand history, while the MdRAE uses errors in forecasting future values.
          • This matters because there are usually many more history values than there are forecasts.
          • In turn, that matters because this metric would “blow up” if all the data were zero, which is less likely when using the demand history.

         

        Intermittent Demand Planning and Parts Forecasting

         

        The special problem of intermittent demand

        • “Intermittent” demand has many zero demands mixed in with random non-zero demands.
        • MAPE gets ruined when errors are divided by zero.
        • MdRAE can also get ruined.
        • MdSAE is less likely to get ruined.

         

        Recap and remarks

        • Forecast metrics are necessary aids for monitoring and improving forecast accuracy.
        • There are two major classes of metrics: absolute and relative.
        • Absolute measures (MAE, MdAE, RMSE) are natural choices when assessing forecasts of one item.
        • Relative measures (MAPE, MdRAE, MdSAE) are useful when comparing accuracy across items or between alternative forecasts of the same item or assessing accuracy relative to the natural variability of an item.
        • Intermittent demand presents divide-by-zero problems which favor MdSAE over MAPE.
        • When assessing forecasts of multiple items, it often makes sense to use weighted averages, weighting items differently by volume or revenue.
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              What is the difference between Demand planning and Inventory optimization ?

              The Smart Forecaster

              Pursuing best practices in demand planning,

              forecasting and inventory optimization

              What is the difference between Demand planning and Inventory optimization ? 

              The Smart Demand Planning app (SDP) provides demand forecasts. The SDP forecasting engine is also the core of the Smart Inventory Optimization app (SIO), which stress-tests various inventory policies using a number of demand scenarios to find optimal inventory policy settings.

               

               

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                          Forecast Using Leading Indicators – Regression Analysis:

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                          forecasting and inventory optimization

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                          In this video tutorial Dr. Thomas Willemain, co–Founder and SVP Research at Smart Software, presents Regression Analysis, a specialized statistical modeling technique to identify and harness leading indicators to achieve more accurate forecasts.  Regression analysis is a statistical procedure to estimate the relationship between a response variable and one or more predictor variables. Housing starts, for example, might be a good leading indicator of vinyl siding demand.  Tom explains how and when to use regression analysis and works through a practical example.

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