Rethinking 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.

Accuracy is represented as a percentage between zero and 100, while error percentages start at zero but have no upper limit. Reports of MAPE (mean absolute percent error) or other error metrics can be titled “forecast accuracy” reports, which blurs the distinction. So, you may want to know how to convert from the error viewpoint to the accuracy viewpoint that your company espouses. This blog describes how with some examples.

Accuracy metrics are computed such that when the actual equals the forecast then the accuracy is 100% and when the forecast is either double or half of the actual, then accuracy is 0%. Reports that compare the forecast to the actual often include the following:

The Actual
The Forecast
Unit Error = Forecast – Actual
Absolute Error = Absolute Value of Unit Error
Absolute % Error = Abs Error / Actual, as a %
Accuracy % = 100% – Absolute % Error

Look at a couple examples that illustrate the difference in the approaches. Say the Actual = 8 and the forecast is 10.

Unit Error is 10 – 8 = 2

Absolute % Error = 2 / 8, as a % = 0.25 * 100 = 25%

Accuracy = 100% – 25% = 75%.

Now let’s say the actual is 8 and the forecast is 24.

Unit Error is 24– 8 = 16

Absolute % Error = 16 / 8 as a % = 2 * 100 = 200%

Accuracy = 100% – 200% = negative is set to 0%.

In the first example, accuracy measurements provide the same information as error measurements since the forecast and actual are already relatively close. But when the error is more than double the actual, accuracy measurements bottom out at zero. It does correctly indicate the forecast was not at all accurate. But the second example is more accurate than a third, where the actual is 8 and the forecast is 200. That’s a distinction a 0 to 100% range of accuracy doesn’t register. In this final example:

Unit Error is 200 – 8 = 192

Absolute % Error = 192 / 8, as a % = 24 * 100 = 2,400%

Accuracy = 100% – 2,400% = negative is set to 0%.

Error metrics continue to provide information on how far the forecast is from the actual and arguably better represent forecast accuracy.

We encourage adopting the error viewpoint. You simply hope for a small error percentage to indicate the forecast was not far from the actual, instead of hoping for a large accuracy percentage to indicate the forecast was close to the actual. This shift in mindset offers the same insights while eliminating distortions.

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).

Managing Spare Parts Inventory: Best Practices

In this blog, we’ll explore several effective strategies for managing spare parts inventory, emphasizing the importance of optimizing stock levels, maintaining service levels, and using smart tools to aid in decision-making. Managing spare parts inventory is a critical component for businesses that depend on equipment uptime and service reliability. Unlike regular inventory items, spare parts often have unpredictable demand patterns, making them more challenging to manage effectively. An efficient spare parts inventory management system helps prevent stockouts that can lead to operational downtime and costly delays while also avoiding overstocking that unnecessarily ties up capital and increases holding costs.

12 Causes of Overstocking and Practical Solutions

Managing inventory effectively is critical for maintaining a healthy balance sheet and ensuring that resources are optimally allocated. Here is an in-depth exploration of the main causes of overstocking, their implications, and possible solutions.

FAQ: Mastering Smart IP&O for Better Inventory Management.

Effective supply chain and inventory management are essential for achieving operational efficiency and customer satisfaction. This blog provides clear and concise answers to some basic and other common questions from our Smart IP&O customers, offering practical insights to overcome typical challenges and enhance your inventory management practices. Focusing on these key areas, we help you transform complex inventory issues into strategic, manageable actions that reduce costs and improve overall performance with Smart IP&O.

Inventory Optimization for Manufacturers, Distributors, and MRO

Managing Spare Parts Inventory: Best Practices
In this blog, we’ll explore several effective strategies for managing spare parts inventory, emphasizing the importance of optimizing stock levels, maintaining service levels, and using smart tools to aid in decision-making. Managing spare parts inventory is a critical component for businesses that depend on equipment uptime and service reliability. Unlike regular inventory items, spare parts often have unpredictable demand patterns, making them more challenging to manage effectively. An efficient spare parts inventory management system helps prevent stockouts that can lead to operational downtime and costly delays while also avoiding overstocking that unnecessarily ties up capital and increases holding costs. […]
Innovating the OEM Aftermarket with AI-Driven Inventory Optimization
The aftermarket sector provides OEMs with a decisive advantage by offering a steady revenue stream and fostering customer loyalty through the reliable and timely delivery of service parts. However, managing inventory and forecasting demand in the aftermarket is fraught with challenges, including unpredictable demand patterns, vast product ranges, and the necessity for quick turnarounds. Traditional methods often fall short due to the complexity and variability of demand in the aftermarket. The latest technologies can analyze large datasets to predict future demand more accurately and optimize inventory levels, leading to better service and lower costs. […]
Future-Proofing Utilities: Advanced Analytics for Supply Chain Optimization
Utilities in the electrical, natural gas, urban water, and telecommunications fields are all asset-intensive and reliant on physical infrastructure that must be properly maintained, updated, and upgraded over time. Maximizing asset uptime and the reliability of physical infrastructure demands effective inventory management, spare parts forecasting, and supplier management. A utility that executes these processes effectively will outperform its peers, provide better returns for its investors and higher service levels for its customers, while reducing its environmental impact. […]
Centering 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. […]

Blog Categories

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.

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.

Inventory Optimization for Manufacturers, Distributors, and MRO

Managing Spare Parts Inventory: Best Practices
In this blog, we’ll explore several effective strategies for managing spare parts inventory, emphasizing the importance of optimizing stock levels, maintaining service levels, and using smart tools to aid in decision-making. Managing spare parts inventory is a critical component for businesses that depend on equipment uptime and service reliability. Unlike regular inventory items, spare parts often have unpredictable demand patterns, making them more challenging to manage effectively. An efficient spare parts inventory management system helps prevent stockouts that can lead to operational downtime and costly delays while also avoiding overstocking that unnecessarily ties up capital and increases holding costs. […]
Innovating the OEM Aftermarket with AI-Driven Inventory Optimization
The aftermarket sector provides OEMs with a decisive advantage by offering a steady revenue stream and fostering customer loyalty through the reliable and timely delivery of service parts. However, managing inventory and forecasting demand in the aftermarket is fraught with challenges, including unpredictable demand patterns, vast product ranges, and the necessity for quick turnarounds. Traditional methods often fall short due to the complexity and variability of demand in the aftermarket. The latest technologies can analyze large datasets to predict future demand more accurately and optimize inventory levels, leading to better service and lower costs. […]
Future-Proofing Utilities: Advanced Analytics for Supply Chain Optimization
Utilities in the electrical, natural gas, urban water, and telecommunications fields are all asset-intensive and reliant on physical infrastructure that must be properly maintained, updated, and upgraded over time. Maximizing asset uptime and the reliability of physical infrastructure demands effective inventory management, spare parts forecasting, and supplier management. A utility that executes these processes effectively will outperform its peers, provide better returns for its investors and higher service levels for its customers, while reducing its environmental impact. […]
Centering 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. […]

The Smart Forecaster

Pursuing best practices in demand planning,

forecasting and inventory optimization

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

Forecast error can be consequential

Three mistakes to avoid

RECENT POSTS

Managing Spare Parts Inventory: Best Practices

12 Causes of Overstocking and Practical Solutions

FAQ: Mastering Smart IP&O for Better Inventory Management.

Recent Posts

Inventory Optimization for Manufacturers, Distributors, and MRO

Blog Categories

The Smart Forecaster

Pursuing best practices in demand planning,

forecasting and inventory optimization

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

Four general types of error metrics

1. Scale-dependent error

2. Percentage error

3. Relative error

4 .Scale-free error

1. Scale-dependent error metrics

2. Percentage error metric

3. Relative error metric

4. Scale-Free error metric

The special problem of intermittent demand

Recap and remarks

RECENT POSTS

Managing Spare Parts Inventory: Best Practices

12 Causes of Overstocking and Practical Solutions

FAQ: Mastering Smart IP&O for Better Inventory Management.

Recent Posts

Inventory Optimization for Manufacturers, Distributors, and MRO

Blog Categories