How to Access Weighted Monetary Forecast Error Calculation

Executive Summary

  • There are common problems in measured forecast accuracy, however, making the forecast error monetized and weighed increases the ability to drive forecast improvement.
  • This forecast error measurement is available in the Brightwork Explorer.

Introduction

Forecast error measurement is hugely important. Without an accurate rating of your forecast accuracy, it is unknown what changes to the forecast process are desirable and will lead to forecast accuracy improvement.

However, while most of the discussion circulates the forecast error measurement type (MAPE, MAD, MSQE, etc..), these methods are normally ineffective in helping companies improve their forecast accuracy. This article is will explain why, and why there is a far more direct and effective alternative.

The Common Problems in Measuring Forecast Accuracy

A primary reason for this is that most forecasting software is designed around viewing the time series graphically and either making manual adjustments or fitting various forecasting models. The design orientation is not specifically around forecast error determination. This means measuring forecast accuracy given the standard tools is time consuming, difficult, itself error-prone. For these reasons and a host of others, forecast accuracy measurement in any depth tends not to occur.

While a forecast accuracy number is often known, the level of aggregation of the measurement is usually not…..which is of course not possible. Here are some common issues we see with forecast error measurement.

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  1. The forecast error is reported at too high of a level. While it is surprising to many, but in our experience companies only very rarely know their forecast error at the product location combination. And they never quantify monetarily the value of the forecast improvement.
  2. The forecast error is only measured and reported for the statistical forecast, with literally no other forecasts measured for error.
  3. There is quite frequently inadequate data maintained regarding manual overrides, meaning the forecast improvement (or degradation) due to overrides can be challenging to measure.

It is critical not to confuse performing a forecast at a level of aggregation by measuring the forecast at an aggregation. Measurement must be at the lowest level, or the level pertinent to supply planning. The supply planning process is the “customer” of the supply chain forecasting process.

Problem #1: Reporting Forecast Accuracy at a High Aggregation than the Product Location Combination

Companies will often say that they are reporting forecast error at an aggregation (product family, product group, etc..) because it reduces the forecast error. Or they might say that reporting forecast error at a grouping is how different departments “want to see the forecast error.”

While there will always be many different forecast error reports, the forecast error for supply chain planning must be measured at a specific product for a specific location. That is the measurement location is not optional. This leads companies to have a much lower forecast accuracy than they think they do. This led one executive on at a client of ours to state.

“I don’t get it. Our unit forecast accuracy is high, why is our service level so low?”

Higher aggregation means of course lower error. But the error cannot simply be measured wherever it is lowest!

To make forecast improvements, a company must know its forecast error, and then what techniques make what percentage improvement in the forecast error. If the company is “hazy” as to what a relevant forecast error is, or what their forecast error is, it becomes impossible to know what the impact is of changes to techniques.

Solution #1: Why…For Supply Chain Planning the Forecast Error Must be Measured at the Product Location Combination

The forecast error that is pertinent to supply planning is the error at the product location combination. No other aggregation higher than this has any meaning for supply planning as the supply plan must be generated at the product location.

Companies require an easy way to measure forecast error. Brightwork Forecast Explorer provides the easiest way to calculate forecast error, and it can be used to calculate any forecast interval (that is a grouping of periods) and many different forecasting inputs.

Problem #2: A Lack of Weighting of the Forecast Error

Most companies do not weigh their forecast error. This makes little sense, because without weighting, a product with an average demand of 10 units per month, counts the same as a product with an average of 20,000 units per month.

A primary reason that companies do not weight the forecast error, even though no average forecast error makes sense without it, with the standard error measurements, it is complicated to do. And it is also confusing to explain to people outside of those close to the forecasting function.

Solution #2: Natively Weighing the Forecast Error

The Weighted Monetary Forecast Error naturally weights the forecast error right within the Brightwork Explorer application. The Brightwork Explorer always calculates the error at the product location combination, but every line item of error is naturally weighed.

Problem #3: No Comparative Forecast Error

A forecast error without comparison loses much of its meaning. Let us say that you are informed that a product location using an advanced machine learning algorithm has a forecast error of 20% and an accuracy of 80%.

This sounds pretty good, doesn’t it?

However, what if the naive forecast for this same product location combination has a forecast error of 17% or 83% accuracy?

That changes the interpretation of the first description, doesn’t it?

Solution #3: Natively Comparing the Forecast Errors of Two Forecasts

The Brightwork Explorer is designed to have two forecasts uploaded along with the sales history so that at every product location combination the Weighted Monetary Forecast Error can be calculated.

The Brightwork Explorer can save any number of simulated comparisons of forecast errors in the application, which can be brought up at any time and reviewed and discussed.

This is just an example of the comparisons that we have saved in the Brightwork Explorer. Having these comparative errors saved in the BE allows the forecast error to be easily shared within the company. 

Problem #4: A Lack of Prioritization

What if one has the MAPE, MSE or MRSE calculations for a list of product locations. And the demand planner thinks that they should put effort into improving the forecast. In most cases, the demand planner will have no idea how to focus their attention. This is because forecasting systems provide no logic for prioritization.

The demand planner normally does not know the following important information.

  1. Which product locations have the highest potential for improved forecast accuracy with a new forecasting method.
  2. What is the financial value of the improved forecast error — as most forecasting applications do not have the part cost or price. Standard forecast errors work off of percentages, not units and not cost or price.

Solution #4: Monetization, Prioritization, and Native Sorting

With the Brightwork Explorer, monetization of the forecast error occurs right in the application. The prioritization is simple, as the forecast error can be easily sorted to tell the demand planner, which product locations have the biggest impact on inventory dollars based upon a change to a different forecast method than is currently used.

Problem #5: Only Measuring Forecast Error for the Statistical Forecast and For No Other Input

Forecasts come in from sales, marketing, and supply chain, among other sources.

Most companies put a great deal of emphasis on measuring the statistical forecast. However, they normally do not measure either manual overrides by demand planners or forecast inputs from sales and marketing. Often, all of these inputs are simply part of the final forecast.

If each of the inputs is not measured, how does anyone have any idea of what the value is to continue to include these different inputs in the future? Secondly, why measure the statistical forecast for accuracy, but not the other inputs. Why are sales and marketing exempt from being measured on the quality of their inputs?

To develop a high-quality consensus forecast, the error of each input must be evaluated.

  1. Most companies do not know the error of these inputs. Most sales and marketing departments can enter any forecast they wish, without their forecast error ever being measured. 
  2. Therefore they do not know how much to weight some inputs versus others.
  3. Companies that attempt to incorporate sales input into the forecasting process will nearly always confuse “getting input” with getting quality input and therefore will not set up the appropriate measurement mechanism and firewall between the sales forecast and the supply chain forecast.

Solution #5: Measuring the Forecast Error of Every Forecasting Input

The only successful consensus-based sales/supply chain forecasting projects are where sales provide but do not control which inputs get into the supply chain forecast. (this generalizes for all consensus inputs). This is because sales want the inventory always available, and can increase inventory by increasing their forecast. As they aren’t held responsible for inventory, they don’t care how much the cost of sale is, as it does not impact their quota.

Successful consensus-based sales/supply chain forecasting projects are rare.

There are several reasons why companies do not measure forecast accuracy outside of the statistical forecast. And one reason gets back to the previously mentioned issue that most forecasting systems are designed to produce forecasts, not to measure error. They have basic error measurement, but that is not the same as being purpose-built to specifically measure forecast error.

A primary reason for this is that companies find it too difficult to maintain the forecast error of each of these inputs. Therefore, low accuracy inputs end up being accepted into the consensus forecast. This is like baking a cake without worrying about where the ingredients came from or their quality.

Think of a simple comparison..were the ingredients of these cupcakes checked before the baking process began? If not, that is very similar to the forecasting process at most companies.

With Brightwork Forecast Explorer, you can know the forecast error — and the forecast error as it relates specifically to the product location combination for all inputs.

Taking Control of the Forecast Error Process

It is easy to get overwhelmed with forecast error measurement complexity. How we observe companies tend to measure forecast error indicates they are overwhelmed by these complexities. Don’t waste your time, or lose your mind trying to calculate forecast error.

None of the forecasting applications calculated forecast error the way the Brightwork Explorer does. It took years to figure out the lack of effectiveness, using the traditional error measurement methods and eventually coming up with our approach.

Forecast inputs should be determined by measuring each forecasting input error before being included as part of the final forecast.

Forecasting Testing — Everywhere

We mean that. We say it because use Brightwork Forecast Explorer ourselves to measure forecast error from every dimension, which is why we focused on this functionality in particular. Once forecast error measurement is semi-automated, there becomes a much higher ability and therefore interest in testing forecast error.

Brightwork Forecast Explorer is perfect for measuring any forecast error.

This includes:

  1. Statistical forecasting in different time buckets.
  2. Statistical forecasts from top-down techniques.
  3. Statistical forecasts using different history (for instance, sales orders versus consumption)
  4. Determining the statistical forecastability of different locations in the supply network (which may lead to placing forecasts in locations different from their current locations).
  5. Determining which data streams to use for generating a forecast (for example orders versus bookings or goods issues)
  6. Testing historical removal — that is removing history that is nonrepresentative to improve pattern recognition.
  7. Sales forecasts
  8. Marketing forecasts
  9. Etc..

We can count on one hand the number of companies that we have worked with that knows the forecast error of their sales or marketing forecasts!

And the end of the process, once the forecast error can be easily calculated (we always need the files in the format of the forecast being at the product location combination), this tells the entity that is creating the forecast, what is the best way to forecast.

Having Brightwork Create the Tested Forecast for You

The Brightwork Explorer is free for as many users at a company to use but requires a consulting contract. Customers pay an hourly rate for support along with a small base cost per month depending upon the size of the company.

However, the hourly support rate can also include Brightwork creating the forecasts by any method (such as those described above) and then loading the results into the Brightwork Explorer for you to review. We often recommend doing this because we are very efficient at creating any type of forecast that customers are interested in seeing tested. This is normally just for the testing phase, once the testing is complete, the standard approach is to configure the forecast within your forecasting system. Companies that only have an ERP system, or are producing forecasts with Excel or with Excel and a statistical plugin should ask us about their forecasting options.

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