BIAS = Historical Forecast Units (Two-months frozen) minus Actual Demand Units. To determine what forecast is responsible for this bias, the forecast must be decomposed, or the original forecasts that drove this final forecast measured. A primary reasonfor this is that sales wantto ensure product availability, and sales are not measured by inventory turns on inventory investment. In contexts where forecasts are being produced on a repetitive basis, the performance of the forecasting system may be monitored using a tracking signal, which provides an automatically maintained summary of the forecasts produced up to any given time. He has authored, co-authored, or edited nine books, seven in the area of forecasting and planning. And say.. If it is negative, a company tends to over-forecast; if positive, it tends to under-forecast. Forecast Accuracy Formula: 4 Calculations In Excel - AbcSupplyChain This website uses cookies to improve your experience. Now, if we forecast the demand median (0), we obtain a total absolute error of 100 (MAE of 33) and a total squared error of 10.000 (RMSE of 58). You might then prefer to minimize RMSE and to forecast the average (9.5) to avoid this situation. Forecast Bias can be described as a tendency to either over-forecast (forecast is more than the actual), or under-forecast (forecast is less than the actual), leading to a forecasting error. We have never received a request to reduce forecast bias. Note that you can choose to report forecast error with one or more KPIs (typically MAE & bias) and use another one (RMSE?) Its tough to find a company that is satisfied with its forecast. Uplift is an increase over the initial estimate. Advocates seek tenant protections from bias, lead paint, rent hikes, more General ideas like using more sophisticated forecasting methods or changing the forecast error measurement interval are typically dead ends. We'll assume you're ok with this, but you can opt-out if you wish. If it is positive, bias is downward, meaning company has a tendency to under-forecast. It is a tendency for a forecast to be consistently higher or lower than the actual value. You can use the bias as a complement, to detect recurrent errors you wont notice with MAE alone. Far more important is for the planner to focus on forecast bias. Likewise, if the added values are less than -2, we find the forecast to be biased towards under-forecast. It is amusing to read other articles on this subject and see so many of them focus on how to measure forecast bias. One could think that using RMSE instead of MAE or MAE instead of MAPE doesnt change anything. Tracking Signal is the gateway test for evaluating forecast accuracy. Typically a person who is 100% biased will make a statement like the following. In forecasting, bias occurs when there is a consistent difference between actual sales and the forecast, which may be of over- or under-forecasting. What's Your Forecast Accuracy Target? If chosen correctly and measured properly, it will allow you to reduce your stock-outs, increase your service rate and reduce the cost of your Supply Chain. A normal property of a good forecast is that it is not biased.[1]. And if you prove that their forecast was biased with all the numbers, they will often still say it wasnt by coming up with an excuse for why something changed and that this was why their forecast was off. I agree with your recommendations. For judgment methods, bias can be conscious, in which case it is often driven by the institutional incentives provided to the forecaster. As the name implies, it is the mean of the absolute error. Lets take some time to discuss the impact of choosing either RMSE or MAE on bias, sensitivity to outliers, and intermittent demand. Not only for general ease-of-use but because adjusting for bias is about more than identification and adjustment. What is MAPE and bias in forecasting? There are many ways to get this global indicator. The first one predicts 2 pieces/day, the second one 4, and the last one 6. Familiarity with forecasting basics is an important part of being effective with the software tools designed to exploit this efficiency. This is simply the length of time into the future for which forecasts are to be prepared. Removing forecast bias is tricky. See the example: Conversely if the organization has failed to hit their forecast for three or more months in row they have a positive bias which means they tend to forecast too high. This is covered in more detail in the article Managing the Politics of Forecast Bias. In the vast majority of cases, institutions cannot address forecast bias without bringing in outside help so that the foreign entity can bear the responsibility of implementing a new forecasting process that addresses bias. But opting out of some of these cookies may have an effect on your browsing experience. If these equations are unclear to you, this is not an issue dont get discouraged. Companies are seeking to implement (or re-implement) planning technology solutions, tune and optimize existing methodologies towards tighter variances, and integrate more accurate information into their planning processes. This is irrespective of which formula one decides to use. Forecast bias is a tendency for a forecast to be consistently higher or lower than the actual value. What's Your Forecast Accuracy Target? - ToolsGroup Compared to MAE, RMSE does not treat each error the same. That is if a person likes a certain type of movie, they can be said to be biased. When this is described as a preference. Many criteria, including can identify bias. A forecaster loves to see patterns in history, but hates to see patterns in error; if there are patterns in error, there's a good chance you can do something about it because it's unnatural. Sales forecasting is a very broad topic, and I wont go into it any further in this article. Lets now reveal how these forecasts were made: Before discussing the different forecast KPIs further, lets take some time to understand why a forecast of the median will get a good MAE and a forecast of the mean a good RMSE. But it is not scaled to the original error (as the error is squared), resulting in a KPI that we cannot relate to the original demand scale. It is a subject made even more interesting and perplexing in that so little is done to minimize incentives for bias. I'm in the process of implementing WMAPE and am adding bias to an organization lacking a solid planning foundation. A forecast that is, on average, 15% lower than the actual value has a 15% error and a 15% bias. Some research studies point out the issue of forecast bias in supply chain planning. The process consists of identifying baseline demand, making an intuitive prediction based on the available evidence (such as a promotion or new customer), and then adjusting the baseline . But a significant reason for their trepidation is they have never actually measured their forecast bias from all the forecast inputs. I therefore recommend you to use the MAE to calculate your KPI, it is simple to implement and interpret. Very good article Jim. The easiest way to remove bias is to remove the institutional incentives for bias. The new software is usually seen as a magic bullet but can only be part of the solution. A forecast history entirely void of bias will return a value of zero, with 12 observations, the worst possible result would return either +12 (under-forecast) or -12 (over-forecast). One of the easiest ways to improve the forecast is right under almost every companys nose, but they often have little interest in exploring this option. Learn in 5 steps how to master forecast accuracy formulas and implement the right KPI in your business. These types of dashboards should be considered a best practice in forecasting software design. What Is Forecast Bias? | Demand-Planning.com To be transparent with you, while this method is ideal, I have rarely seen it used. We measure bias on all of our forecasting projects. If the bias of the forecasting method is zero, it means that there is an absence of bias. The advantage of this formula is that it strongly penalizes large forecast errors. For example, a median-unbiased forecast would be one where half of the forecasts are too low and half too high: see Bias of an estimator. This discomfort is evident in many forecasting books that limit the discussion of bias to its purely technical measurement. APICS Dictionary 12th Edition, American Production and Inventory Control Society. They want forecast accuracy improvement but are generally blind to the topic of bias. It facilitates performance tracking and allows for effective communication with your colleagues in sales forecasting. If the forecast is greater than actual demand than the bias is positive (indicatesover-forecast). If we look at the KPI of these two forecasts, this is what we obtain: Interestingly, by just changing the error of this last period by a single unit, we decrease the total RMSE by 6.9% (2.86 to 2.66). This button displays the currently selected search type. Just skip them and jump to the conclusion of the RMSE and MAEparagraphs. Actually, many algorithms (especially for machine learning) are based on the Mean Squared Error (MSE), which is directly related to RMSE. Such a forecast history returning a value greater than 4.5 or less than negative 4.5 would be considered out of control. Sujit received a Bachelor of Technology degree in Civil Engineering from the Indian Institute of Technology, Kanpur and an M.S. Just as for MAE, RMSE is not scaled to the demand. The problem with simple measures of forecast accuracy is that it is sometimes difficult to work out what they mean and even trickier to work out what you need to do. If you dont want this to be too difficult to maintain, I really recommend creating a single table or database that centralizes all this data. Because of these tendencies, forecasts can be regularly under or over the actual outcomes. How much institutional demands for bias influence forecast bias is an interesting field of study. Over a 12-period window, if the added values are more than 2, we consider the forecast to be biased towards over-forecast. This blog post is the third part of a Chainalytics' Integrated Demand and Supply Planning practice five-part series on improving statistical forecasting. MAE, also known as MAD (Mean Absolute Deviation) or WAPE (Weighted Absolute Percentage Error), is the calculation of the average of weighted absolute errors. This is covered in more detail in the article Managing the Politics of Forecast Bias. Well, the answer is not black and white. This provides a quantitative and less political way of lowering input from lower-quality sources. Ill walk you through step-by-step on how to do this, from selecting the parameters to the details of the calculation. To me, it is very important to know what your bias is and which way it leans, though very few companies calculate itjust 4.3% according to the latest IBF survey. Biased forecasts can cause higher inventory and logistics costs As COO of Arkieva, Sujit manages the day-to-day operations at Arkieva such as software implementations and customer relationships. For some, having a forecast bias is an essential part of their business model. He is a recognized subject matter expert in forecasting, S&OP and inventory optimization. Most organizations have a mix of both: items that were over-forecasted and now have stranded or slow moving inventory that ties up working capital plus other items that were under-forecasted and they could not fulfill all their customer demand. When a bias is demonstrated in this way, it's more difficult to dispute. The only difference is the forecast on the latest demand observation: forecast #1 undershot it by 7 units and forecast #2 by only 6 units. Measuring Forecast Accuracy: Approaches to Forecasting : A Tutorial A forecast bias is an instance of flawed logic that makes predictions inaccurate. Bias tracking should be simple to do and quickly observed within the application without performing an export. Companies are not environments where truths are brought forward and the person with the truth on their side wins. This is one of the many well-documented human cognitive biases. The formula is very simple. This extends beyond forecasting as people generally think they are far more objective than they are. The tracking signal in each period is calculated as follows: AtArkieva, we use the Normalized Forecast Metric to measure the bias. Forecast Error Metrics | Institute of Business Forecasting In the following example, a sales forecast was calculated at the item level for the month of May. The Political Implications of Pointing Out Forecast Bias, Keeping the Presence of Objectivity Alive. A) It simply measures the tendency to over-or under-forecast. Note that if the forecast overshoots the demand with this definition, the error will be positive. Table of Contents: Select a Link to be Taken to That Section, Last Updated on February 6, 2022 by Shaun Snapp. On LinkedIn, I askedJohn Ballantynehow he calculates this metric. Video Introduction: How to Understand Forecast Bias, Bias as theUncomfortableForecasting Area. If you have an ERP or other software, you probably already have forecasts. So, I cannot give you best-in-class bias. To determine the bias of a forecast, one must have the ability to measure comparative forecast accuracy efficiently. ), but the average is now 18.1. High forecast accuracy leads to lower required inventory levels, fewer lost sales, and optimized working capital. No product can be planned from a severely biased forecast. One of the simplest (although not the easiest) ways of improving the forecastremoving the biasis right under almost every companys nose. If you really cant wait, you can have a look at my article: Forecasting in Excel in 3 Clicks: Complete Tutorial with Examples, where I provide easy methods to forecast in Excel in less than 5 minutes. Forecast bias is distinct from forecast error and is one of the most important keys to improving forecast accuracy. We can simply say that MAPE promotes a very low forecast as it allocates a high weight to forecast errors when the demand is low. All are likely to nudge the forecast in a direction that is favorable to their goals. Qualitative forecasting is a type of forecasting that involves more subjective, intuitive, or experiential approaches. Everything from the use of promotions to the incentives they have set up internally to poorly selected or configured forecasting applications stand in the way of accurate forecasts. A Critical Look at Measuring and Calculating Forecast Bias, The Art of Demand Planning: Understanding the Market & Creating Consensus, S&OP Leadership & Building Effective Teams, 5 Major Benefits of S&OP For Your Company. Rick Gloveron LinkedIn described his calculation of BIAS this way: Calculate the BIAS at the lowest level (for example, by product, by location) as follows: The other common metric used to measure forecast accuracy is the tracking signal. Reprint: R0707K The primary goal of forecasting is to identify the full range of possibilities. Ok, I admit I might be a little bit biased. Forecast bias is well known in the research; however far less frequently admitted to within companies. To solve this, it is common to divide MAE by the average demand to get a %: MAPE/MAE Confusion It seems that many practitioners use the MAE formula and call it MAPE. What is "A Good Forecast" by Nelson Hartunian | Feb 12, 2013 Tremendous cost-saving efficiencies can result from optimizing inventory stocking levels using the best predictions of future demand. Lets start by defining the error as the forecast minus the demand. These two things dont have much to do with each other. Measuring forecast accuracy (FA) determines the degree to which an organization can accurately predict sales. Grouping similar types of products, and testing for aggregate bias, can be a beneficial exercise for attempting to select more appropriate forecasting models. Many people benefit from providing forecast bias. However, most companies use forecasting applications that do not have a numerical statistic for bias. For example, if your MAE is 20%, then you have a 20% error rate and 80% forecast accuracy. We also use third-party cookies that help us analyze and understand how you use this website. Do you have a view on what should be considered as best-in-class bias? If we know whether we over-or under-forecast, we can do something about it. Beyond the impact of inventory as you have stated, bias leads to under or over investment and suboptimal use of capital.