And these are also to departments where the employees are specifically selected for the willingness and effectiveness in departing from reality. One can minimize a mathematical function by setting its derivative to zero. The tracking signal in each period is calculated as follows: AtArkieva, we use the Normalized Forecast Metric to measure the bias. These forecasts are updated each month and take into account the order history: in January, the forecast for May indicated sales of 500 quantities. However, once an individual knows their forecast will be revised, they will adjust accordingly. To account for both positive and negative errors, we compute the average of percentage errors with signs ignored, that is, average of absolute percentage error. Some commonly used metrics include: Mean Absolute Deviation (MAD) = ABS (Actual - Forecast) A forecast that is, on average, 15% lower than the actual value has a 15% error and a 15% bias. Forecasting bias can be like any other forecasting error, based upon a statistical model or judgment method that is not sufficiently predictive. It can be quite different when premeditated in response to incentives. It has developed cost uplifts that their project planners must use depending upon the type of project estimated. Definition of Forecast Accuracy - Gartner Sales Glossary Six Rules for Effective Forecasting by Paul Saffo From the Magazine (July-August 2007) Summary. In other words, we are looking for a value that splits our dataset into two equal parts. Some companies are unwilling to address their sales forecast bias for political reasons. Forecast accuracy is the degree to which sales leaders successfully predict sales (in both the long and short term). But nothing is less true. But how? The forecast accuracy formula is simply the average of the error percentages: This method is really not recommended, because there is no weighting, neither on quantities nor on values. This is covered in more detail in the article Managing the Politics of Forecast Bias. No product can be planned from a severely biased forecast. It also promotes less participation from weak forecasters, as they can see that their input has less impact on the forecast. Bias is an uncomfortable area of discussion because it describes how people who produce forecasts can be irrational and have subconscious biases. Let's now reveal how these forecasts were made: Forecast 1 is just a very low amount. In my view, it can be rationally explained by the fact that judgment methods are known to have more bias than statistical methods. Sales and marketing, where most of the forecasting bias resides, are powerful entities that will push back politically when challenged. Just as for MAE, RMSE is not scaled to the demand. This is most commonly in daily, weekly, or monthly buckets. Accurate sales forecasts are essential for making key decisions about short-term spending and deals for key accounts. If it is bad, what should you do? It could revolve around elements like knowledge of a business's customer journey, market research, or company leadership's personal experience in a field. When you think about it, if you have to be 'off' slightly, this is . 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). Grouping similar types of products, and testing for aggregate bias, can be a beneficial exercise for attempting to select more appropriate forecasting models. Drilling deeper the organization can also look at the same forecast consumption analysis to determine if there is bias at the product segment, region or other level of aggregation. Forecast #3 was the best in terms of RMSE and bias (but the worst on MAE and MAPE). 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. 1982, is a membership organization recognized worldwide for fostering the growth of Demand Planning, Forecasting, and Sales & Operations Planning (S&OP), and the careers of those in the field. Well, the answer is not black and white. The problem is that procurement lead times are very often item or supplier-specific. Yet, few companies actually are interested in confronting the incentives they create for forecast bias. Due to this, optimizing MAPE will result in a strange forecast that will most likely undershoot the demand. Very good article Jim. How To Best Remove Forecast Bias From A Forecasting Process I am sometimes asked by a director, who is worn out by funding continuous improvement initiatives for forecasting. He published Data Science for Supply Chain Forecasting in 2018 (2nd edition in 2021) and Inventory Optimization: Models and Simulations in 2020. 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. Forecast accuracy is, in large part, determined by the demand pattern of the item being forecasted. Therefore, adjustments to a forecast must be performed without the forecasters knowledge. 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. In organizations forecasting thousands of SKUs or DFUs, this exception trigger is helpful in signaling the few items that require more attention versus pursuing everything. Lets imagine we want to compare two slightly different forecasts. So you would end up with item-specific horizons, and item-specific forecast accuracy KPIs. Still, MAE is only reduced by 3.6% (2.33 to 2.25), so the impact on MAE is nearly twice as low. Forecast bias is well known in the research; however far less frequently admitted to within companies. So ideally, you should choose a 90-day horizon in your forecast accuracy computation: the reason is that after February, you could not react anymore. These performance dashboards exist in a few vendors, but forecasting accuracy could be significantly improved if they were universal. These skewed demand distributions are widespread in supply chains as the peaks can be due to periodic promotions or clients ordering in bulk. On an aggregate level, per group or category, the +/- are netted out revealing the overall bias. Similar results can be extended to the consumer goods industry where forecast bias isprevalent. Forecast bias is distinct from forecast error and is one of the most important keys to improving forecast accuracy. The quickest way of improving forecast accuracy is to track bias. Forecast 2 is the demand median: 4. But what pulled my attention, that not all . High forecast accuracy leads to lower required inventory levels, fewer lost sales, and optimized working capital. And if possible, by week. Few companies would like to do this. As the name implies, it is the mean of the absolute error. The inverse, of course, results in a negative bias (indicates under-forecast). The problem in doing this is that normally just the final forecast ends up being tracked in the forecasting application (the other forecasts are often in other systems), and each forecast has to be measured for forecast bias, not just the final forecast, which is an amalgamation of multiple forecasts. Bias, on the other hand, is a much easier thing to grasp. As we cover in the article How to Keep Forecast Bias Secret, many entities (companies, government bodies, universities) want to continue their forecast bias. I recommend this method only in the context of an ABC classification. Video Introduction: How to Understand Forecast Bias, Bias as theUncomfortableForecasting Area. Six Rules for Effective Forecasting - Harvard Business Review There are manyreasons why such bias exists including systemic ones as discussed in a prior forecasting bias discussion. 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. Far more important is for the planner to focus on forecast bias. Tracking Signal is the gateway test for evaluating forecast accuracy. You can try this for yourself and reduce the error of one of the most accurate periods to observe the impact on MAE and RMSE. This is going to be kept very simple. Confronting forecast bias means risking yourself politically because many people in the organization want to continue to work their financial bias into the forecast. We have to understand that a significant difference lies in the mathematical roots of MAE & RMSE. The first step is to have a demand or sales forecast. What Is Forecast Bias. Forecast bias is a tendency for a forecast to be consistently higher or lower than the actual value. In contrast, MAE's optimization will try to be as often overshooting the demand as undershooting the demand, which means targeting the demand median. The main difference between biased forecasts and unbiased forecasts is that the 'dart pattern' of an unbiased forecast shows dart throws spread equally around the bullseye, as seen in the diagram below. Forecast #3 was the best in terms of RMSE and bias (but the worst on MAE and MAPE). The first step in managing this is retaining the metadata of forecast changes. Just avoid it. This is the exact definition of the median. So start recording historical data by article. The Mean Absolute Percentage Error (MAPE) is one of the most commonly used KPIs to measure forecast accuracy. Not only for general ease-of-use but because adjusting for bias is about more than identification and adjustment. However, the reasons provided dont change a bad or biased forecast. Products of same segment/product family shares lot of component and hence despite of bias at individual sku level , components and other resources gets used interchangeably and hence bias at individual SKU level doesn't matter and in such cases it is worthwhile to, Retired Senior Supply Chain Officer at Retired. I often arrive at companies and deliver the bad news about how their forecast systems are mismanaged. It is difficult for even salespeople that they may have some bias in presenting their products versus a competitors products. Its important to differentiate a simple consensus-based forecast from a consensus-based forecast with the bias removed. 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. If the bias of the forecasting method is zero, it means that there is an absence of bias. Politeness often seems to end up being not pointing out financial bias and allowing the financially biased individual to continue to misinform others that they are as objective or nearly as objective as anyone else. Most of the organizations calculated the FA% (Forecast accuracy) and FB% (Forecast bias) monthly basis which is logical as S&OP is a monthly cycle. This is irrespective of which formula one decides to use. However, it is preferable if the bias is calculated and easily obtainable from within the forecasting application. Companies often measure it with Mean Percentage Error (MPE). However, it is well-known how incentives lower forecast quality. Bias is an uncomfortable area of discussion because it describes how people who produce forecasts can be irrational and have subconscious biases. Uplift is an increase over the initial estimate. For judgment methods, bias can be conscious, in which case it is often driven by the institutional incentives provided to the forecaster. Unfortunately, our unique client seems to make an order one week out of three without any kind of pattern. Table of Contents: Select a Link to be Taken to That Section, Last Updated on February 6, 2022 by Shaun Snapp. You can read my other articles here. Lets do an example with a dummy demand time series. 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. But for mature products, I am not sure. There is, unfortunately, no definitive answer. At the top the simplistic question to ask is, Has the organization consistently achieved its aggregate forecast for the last several time periods?This is similar to checking to see if the forecast was completely consumed by actual demand so that if the company was forecasted to sell $10 Million in goods or services last month, did it happen? Mean Absolute percentage error (MAPE). If you want to see our references for this article and other Brightwork related articles, see this link. When you think about it, if you have to be 'off' slightly, this is a more ideal bias scheme, because if you sum the differences of the individual attempts, you get . Forecasting and demand planning teams measure forecast accuracy as a matter of fact. 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. The MSE is the average squared error per article. If these equations are unclear to you, this is not an issue dont get discouraged. The formula is very simple. Learn in 5 steps how to master forecast accuracy formulas and implement the right KPI in your business. Cognitive biases are part of our biological makeup and are influenced by evolution and natural selection. Murphy used 9 different attributes for forecast quality - Bias, Association, Accuracy, Skill, Reliability, Resolution, Sharpness, Discrimination and Uncertainty. Eliminating bias can be a good and simple step in the long journey to anexcellent supply chain. Accuracy is critical because its downstream effects are far-reaching and can have unintended . In summary, it is appropriate for organizations to look at forecast bias as a major impediment standing in the way of improving their supply chains because any bias in the forecast means that they are either holding too much inventory (over-forecast bias) or missing sales due to service issues (under-forecast bias). Within any company or any entity, large numbers of people contribute information to various planning processes that have an easily measurable bias, and they do not appreciate having it pointed out. Just skip them and jump to the conclusion of the RMSE and MAEparagraphs. stella February 27, 2023 resource 0 Comments. And if there is no cost to them, they will continue to provide a forecast with bias. 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. 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. That is to say that optimizing MSE aims to produce a prediction that is correct on average and, therefore, unbiased. General ideas like using more sophisticated forecasting methods or changing the forecast error measurement interval are typically dead ends. Note that if the forecast overshoots the demand with this definition, the error will be positive. A primary reasonfor this is that sales wantto ensure product availability, and sales are not measured by inventory turns on inventory investment. To be able to perform the calculations, you need to have access to two sets of data: the forecast history and the demand history. The bias is gone when actual demand bounces back and forth with regularity both above and below the forecast. PDF Forecast Accuracy and Inventory Strategies THINK Blog - IBM Anyone can come up with an excuse as to why something they predicted did not occur. Forecast bias can always be determined regardless of the forecasting application used by creating a report. For those interested in removing forecast bias, software designed to mitigate forecast bias can help highlight bias and provide mechanisms to adjust it within the application. 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. 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. Bias | IBF If the demand was greater than the forecast, was this the case for three or more months in a row in which case the forecasting process has a negative bias because it has a tendency to forecast too low. They have documented their project estimation bias for others to read and learn from. BIAS = Historical Forecast Units (Two-months frozen) minus Actual Demand Units. Lets plot the demand we observed and these forecasts. I am also active on LinkedIn. Companies often measure it with Mean Percentage Error (MPE). Investment banks promote positive biases for their analysts, just as supply chain sales departments promote negative biases by continuing to use a salespersons forecast as their quota. A forecasting process with a bias will eventually get off-rails unless steps are taken to correct the course from time to time. One aims at the median, the second aims at the average. As COO of Arkieva, Sujit manages the day-to-day operations at Arkieva such as software implementations and customer relationships. To stop algorithmic bias, we first have to define it | Brookings So what? Forecast Bias He founded his consultancy company SupChains in 2016 and co-founded SKU Science a fast, simple, and affordable demand forecasting platform in 2018. improve or degrade the forecast error. Some items are easy to forecast, and some are difficult. Part of submitting biased forecasts is pretending that they are not biased. [bar group=content]. These cookies will be stored in your browser only with your consent. We'll assume you're ok with this, but you can opt-out if you wish. In this case, a person with a financial bias will try to conflate a preference, with a financial bias. MAPE is the sum of the individual absolute errors divided by the demand (each period separately). How much institutional demands for bias influence forecast bias is an interesting field of study. A typical measure of bias of forecasting procedure is the arithmetic mean or expected value of the forecast errors, but other measures of bias are possible. This comes at a cost: a sensitivity to outliers. A) It simply measures the tendency to over-or under-forecast. In new product forecasting, companies tend to over-forecast. I cannot discuss forecasting bias without mentioning MAPE, but since I have written about those topics in the past, in this post, I will concentrate on Forecast Bias and the Forecast Bias Formula. 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. These institutional incentives have changed little in many decades, even though there is never-ending talk of replacing them. We also use third-party cookies that help us analyze and understand how you use this website. This website uses cookies to improve your experience while you navigate through the website. People are considering their careers and try to bring up issues only when they think they can win those debates. As can be seen, this metric will stay between -1 and 1, with 0 indicating the absence of bias. Unbiased forecasts. Only experimentation will reveal which method works best for a dataset. We can both remove forecast bias from forecasts, and continue to have movie preferences, and root for our favorite sports team. Conclusion to optimize a forecasts MSE, the model will have to aim for the total forecast to be equal to the total demand. There are two approaches at the SKU or DFU level that yielded the best results with the least efforts within my experience. Nevertheless, lets now imagine that we have one new demand observation of 100. MAPE stands for Mean Absolute Percent Error - Bias refers to persistent forecast error - Bias is a component of total calculated forecast error - Bias refers to consistent under-forecasting or over-forecasting - MAPE can be misinterpreted and miscalculated, so use caution in the interpretation. Time Series Forecasting Performance Measures With Python When discussing forecast error with someone, I would always advise you to explicitly show how you compute the forecast error to be sure to compare apples and apples. 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. Still, they often have little interest in exploring this option. Forecast model bias Absolute size of the forecast errors Can be used to: Compare alternative forecasting models Identify forecast models that need adjustment (management by exception) Measures of Forecast Accuracy E rror = A ctual demand - F orecast OR e t = A t - F t h2. There is a bit of math ahead. Forecast bias is generally not tracked in most forecasting applications in terms of outputting a specific metric. Then, the KPI is derived from the overall % error we just calculated.