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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.

Download the Presentation

 

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).
Leave a Comment

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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.

      Download the Presentation

       

      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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                  Forecasting Techniques for a more profitable business

                  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, defines and compares the most useful Forecasting Techniques: Exponential Smoothing, Single Exponential Smoothing, Holt’s Method and Winter’s Method.  These videos explain the basic thinking under each technique as well as the math behind them, how they are used in practice and the tradeoff of each method.

                  Forecasting Techniques for a more profitable business
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                    In this blog, the spotlight is cast on the software that creates reports for management, the silent hero that translates the beauty of furious calculations into actionable reports. Watch as the calculations, intricately guided by planners utilizing our software, seamlessly converge into Smart Operational Analytics (SOA) reports, dividing five key areas: inventory analysis, inventory performance, inventory trending, supplier performance, and demand anomalies. […]
                  • You need to team up with the algorithms for Inventory ManagementYou Need to Team up with the Algorithms
                    This article is about the real power that comes from the collaboration between you and our software that happens at your fingertips. We often write about the software itself and what goes on “under the hood”. This time, the subject is how you should best team up with the software. […]
                  • Rethinking forecast accuracy, A shift from accuracy to error metricsRethinking forecast accuracy: A shift from accuracy to error metrics
                    Measuring the accuracy of forecasts is an undeniably important part of the demand planning process. This forecasting scorecard could be built based on one of two contrasting viewpoints for computing metrics. The error viewpoint asks, “how far was the forecast from the actual?” The accuracy viewpoint asks, “how close was the forecast to the actual?” Both are valid, but error metrics provide more information. […]
                  • Using Key Performance Predictions to Plan Stocking Policies
                    I can't imagine being an inventory planner in spare parts, distribution, or manufacturing and having to create safety stock levels, reorder points, and order suggestions without using key performance predictions of service levels, fill rates, and inventory costs. […]
                  • Every forecasting model is good for what it is designed forEvery Forecasting Model is Good for What it is Designed for
                    With so much hype around new Machine Learning (ML) and probabilistic forecasting methods, the traditional “extrapolative” or “time series” statistical forecasting methods seem to be getting the cold shoulder. However, it is worth remembering that these traditional techniques (such as single and double exponential smoothing, linear and simple moving averaging, and Winters models for seasonal items) often work quite well for higher volume data. Every method is good for what it was designed to do. Just apply each appropriately, as in don’t bring a knife to a gunfight and don’t use a jackhammer when a simple hand hammer will do. […]

                    Inventory Optimization for Manufacturers, Distributors, and MRO

                    • Top Differences between Inventory Planning for Finished Goods and for MRO and Spare PartsTop Differences Between Inventory Planning for Finished Goods and for MRO and Spare Parts
                      In today’s competitive business landscape, companies are constantly seeking ways to improve their operational efficiency and drive increased revenue. Optimizing service parts management is an often-overlooked aspect that can have a significant financial impact. Companies can improve overall efficiency and generate significant financial returns by effectively managing spare parts inventory. This article will explore the economic implications of optimized service parts management and how investing in Inventory Optimization and Demand Planning Software can provide a competitive advantage. […]
                    • Centering Act Spare Parts Timing Pricing and ReliabilityCentering Act: Spare Parts Timing, Pricing, and Reliability
                      In this article, we'll walk you through the process of crafting a spare parts inventory plan that prioritizes availability metrics such as service levels and fill rates while ensuring cost efficiency. We'll focus on an approach to inventory planning called Service Level-Driven Inventory Optimization. Next, we'll discuss how to determine what parts you should include in your inventory and those that might not be necessary. Lastly, we'll explore ways to enhance your service-level-driven inventory plan consistently. […]
                    • 5 Steps to Improve the Financial Impact of Spare Parts Planning5 Steps to Improve the Financial Impact of Spare Parts Planning
                      In today’s competitive business landscape, companies are constantly seeking ways to improve their operational efficiency and drive increased revenue. Optimizing service parts management is an often-overlooked aspect that can have a significant financial impact. Companies can improve overall efficiency and generate significant financial returns by effectively managing spare parts inventory. This article will explore the economic implications of optimized service parts management and how investing in Inventory Optimization and Demand Planning Software can provide a competitive advantage. […]
                    • Bottom Line strategies for Spare Parts Planning SoftwareBottom Line Strategies for Spare Parts Planning
                      Managing spare parts presents numerous challenges, such as unexpected breakdowns, changing schedules, and inconsistent demand patterns. Traditional forecasting methods and manual approaches are ineffective in dealing with these complexities. To overcome these challenges, this blog outlines key strategies that prioritize service levels, utilize probabilistic methods to calculate reorder points, regularly adjust stocking policies, and implement a dedicated planning process to avoid excessive inventory. Explore these strategies to optimize spare parts inventory and improve operational efficiency. […]