However, it is well known how incentives lower forecast quality. We put other people into tiny boxes because that works to make our lives easier. If the forecast is greater than actual demand than the bias is positive (indicates over-forecast). Then, we need to reverse the transformation (or back-transform) to obtain forecasts on the original scale. For instance, even if a forecast is fifteen percent higher than the actual values half the time and fifteen percent lower than the actual values the other half of the time, it has no bias. Last Updated on February 6, 2022 by Shaun Snapp. It can be achieved by adjusting the forecast in question by the appropriate amount in the appropriate direction, i.e., increase it in the case of under-forecast bias, and decrease it in the case of over-forecast bias. This leads them to make predictions about their own availability, which is often much higher than it actually is. Let them be who they are, and learn about the wonderful variety of humanity. BIAS = Historical Forecast Units (Two months frozen) minus Actual Demand Units. 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. The classical way to ensure that forecasts stay positive is to take logarithms of the original series, model these, forecast, and transform back. "Armstrong and Collopy (1992) argued that the MAPE "puts a heavier penalty on forecasts that exceed the actual than those that are less than the actual". Forecasts can relate to sales, inventory, or anything pertaining to an organization's future demand. Your email address will not be published. How To Multiply in Excel (With Benefits, Examples and Tips), ROE vs. ROI: Whats the Difference? It keeps us from fully appreciating the beauty of humanity. In order for the organization, and the Sales Representative in the example to remove the bias from his/her forecast it is necessary to move to further breakdown the SKU basket into individual forecast items to look for bias. Here was his response (I have paraphrased it some): The Tracking Signal quantifies Bias in a forecast. This is a specific case of the more general Box-Cox transform. Although it is not for the entire historical time frame. The frequency of the time series could be reduced to help match a desired forecast horizon. There are two types of bias in sales forecasts specifically. He is a recognized subject matter expert in forecasting, S&OP and inventory optimization. By continuing to use this website, you consent to the use of cookies in accordance with our Cookie Policy. in Transportation Engineering from the University of Massachusetts. 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. False. 4 Dangerous Habits That Lead to Planning Software Abandonment, Achieving Nearly 95% Forecast Accuracy at Amarr Garage Doors. Exponential smoothing ( a = .50): MAD = 4.04. Once bias has been identified, correcting the forecast error is quite simple. Consistent with decision fatigue [as seen in Figure 1], forecast accuracy declines over the course of a day as the number . Forecast bias is distinct from forecast error in that a forecast can have any level of error but still be completely unbiased. A quotation from the official UK Department of Transportation document on this topic is telling: Our analysis indicates that political-institutional factors in the past have created a climate where only a few actors have had a direct interest in avoiding optimism bias.. What is the difference between forecast accuracy and forecast bias? Bias as the Uncomfortable Forecasting Area Bias is an uncomfortable area of discussion because it describes how people who produce forecasts can be irrational and have subconscious biases. For earnings per share (EPS) forecasts, the bias exists for 36 months, on average, but negative impressions last longer than positive ones. But just because it is positive, it doesnt mean we should ignore the bias part. Sales and marketing, where most of the forecasting bias resides, are powerful entities, and they will push back politically when challenged. demand planningForecast Biasforecastingmetricsover-forecastS&OPunder-forecast. As COO of Arkieva, Sujit manages the day-to-day operations at Arkieva such as software implementations and customer relationships. This relates to how people consciously bias their forecast in response to incentives. While you can't eliminate inaccuracy from your S&OP forecasts, a robust demand planning process can eliminate bias. It is a tendency for a forecast to be consistently higher or lower than the actual value. For instance, even if a forecast is fifteen percent higher than the actual values half the time and fifteen percent lower than the actual values the other half of the time, it has no bias. Separately the measurement of Forecast Bias and the efforts to eliminate bias in the forecast have largely been overlooked because most companies achieve very good results by only utilizing the forecast accuracy metric MAPE for driving and gauging improvements in quality of the forecast. Consistent negative values indicate a tendency to under-forecast whereas consistent positive values indicate a tendency to over-forecast. This is limiting in its own way. *This article has been significantly updated as of Feb 2021. 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. Bias is an uncomfortable area of discussion because it describes how people who produce forecasts can be irrational and have subconscious biases. A better course of action is to measure and then correct for the bias routinely. People are individuals and they should be seen as such. When evaluating forecasting performance it is important to look at two elements: forecasting accuracy and bias. Forecast bias is quite well documented inside and outside of supply chain forecasting. With statistical methods, bias means that the forecasting model must either be adjusted or switched out for a different model. All content published on this website is intended for informational purposes only. All Rights Reserved. Having chosen a transformation, we need to forecast the transformed data. 2020 Institute of Business Forecasting & Planning. It makes you act in specific ways, which is restrictive and unfair. If you continue to use this site we will assume that you are happy with it. Save my name, email, and website in this browser for the next time I comment. Optimism bias is the tendency for individuals to overestimate the likelihood of positive outcomes and underestimate the likelihood of negative outcomes. Positive bias may feel better than negative bias. Learning Mind 2012-2022 | All Rights Reserved |, What Is a Positive Bias and How It Distorts Your Perception of Other People, Positive biases provide us with the illusion that we are tolerant, loving people. We use cookies to ensure that we give you the best experience on our website. (Definition and Example). That is, we would have to declare the forecast quality that comes from different groups explicitly. If the organization, then moves down to the Stock Keeping Unit (SKU) or lowest Independent Demand Forecast Unit (DFU) level the benefits of eliminating bias from the forecast continue to increase. When the bias is a positive number, this means the prediction was over-forecasting, while a negative number suggests under forecasting. BIAS = Historical Forecast Units (Two-months frozen) minus Actual Demand Units. In fact, these positive biases are just the flip side of, Famous Psychics Known to Humanity throughout the Centuries, 10 Signs of Toxic Sibling Relationships Most People Think Are Normal, The Psychology of Anchoring and How It Affects Your Ideas & Decisions. All of this information is publicly available and can also be tracked inside companies by developing analytics from past forecasts. Forecast BIAS can be loosely described as a tendency to either, Forecast BIAS is described as a tendency to either. Supply Planner Vs Demand Planner, Whats The Difference? A Critical Look at Measuring and Calculating Forecast Bias, Case Study: Relaunching Demand Planning for an Aggressive Growth Strategy. Companies often do not track the forecast bias from their different areas (and, therefore, cannot compare the variance), and they also do next to nothing to reduce this bias. Managing Risk and Forecasting for Unplanned Events. Kakouros, Kuettner and Cargille provide a case study of the impact of forecast bias on a product line produced by HP. 1 What is the difference between forecast accuracy and forecast bias? It is an average of non-absolute values of forecast errors. Labelling people with a positive bias means that you are much less likely to understand when they act outside the box. This can improve profits and bring in new customers. Forecast bias is distinct from forecast error and is one of the most important keys to improving forecast accuracy. For example, a marketing team may be too confident in a proposed strategys success and over-estimate the sales the product makes. If you dont have enough supply, you end up hurting your sales both now and in the future. If the forecast is greater than actual demand than the bias is positive (indicatesover-forecast). As with any workload it's good to work the exceptions that matter most to the business. Analysts cover multiple firms and need to periodically revise forecasts. This bias is hard to control, unless the underlying business process itself is restructured. The MAD values for the remaining forecasts are. This bias is often exhibited as a means of self-protection or self-enhancement. Forecasting can also help determine the regions where theres high demand so those consumers can purchase the product or service from a retailer near them. It means that forecast #1 was the best during the historical period in terms of MAPE, forecast #2 was the best in terms of MAE. This data is an integral piece of calculating forecast biases. Reducing bias means reducing the forecast input from biased sources. How you choose to see people which bias you choose determines your perceptions. People rarely change their first impressions. At this point let us take a quick timeout to consider how to measure forecast bias in standard forecasting applications. But opting out of some of these cookies may have an effect on your browsing experience. The so-called pump and dump is an ancient money-making technique. A test case study of how bias was accounted for at the UK Department of Transportation. Forecasting bias can be like any other forecasting error, based upon a statistical model or judgment method that is not sufficiently predictive, or it can be quite different when it is premeditated in response to incentives. This may lead to higher employee satisfaction and productivity. It has nothing to do with the people, process or tools (well, most times), but rather, its the way the business grows and matures over time. Contributing Factors The following are some of the factors that make the optimism bias more likely to occur: This can cause organizations to miss a major opportunity to continue making improvements to their forecasting process after MAPE has plateaued. Accuracy is a qualitative term referring to whether there is agreement between a measurement made on an object and its true (target or reference) value. Jim Bentzley, an End-to-End Supply Chain Executive, is a strong believer that solid planning processes arecompetitive advantages and not merely enablers of business objectives. Of the four choices (simple moving average, weighted moving average, exponential smoothing, and single regression analysis), the weighted moving average is the most accurate, since specific weights can be placed in accordance with their importance. For judgment methods, bias can be conscious, in which case it is often driven by the institutional incentives provided to the forecaster. Do you have a view on what should be considered as best-in-class bias? It can be achieved by adjusting the forecast in question by the appropriate amount in the appropriate direction, i.e., increase it in the case of under-forecast bias, and decrease it in the case of over-forecast bias. However, once an individual knows that their forecast will be revised, they will adjust their forecast accordingly. This website uses cookies to improve your experience while you navigate through the website. Supply Chains are messy, but if a business proactively manages its cash, working capital and cycle time, then it gives the demand planners at least a fighting chance to succeed. It often results from the management's desire to meet previously developed business plans or from a poorly developed reward system. If there were more items in the Sales Representatives basket of responsibility that were under-forecasted, then we know there is a negative bias and if this bias continues month after month we can conclude that the Sales Representative is under-promising or sandbagging. This relates to how people consciously bias their forecast in response to incentives. Allrightsreserved. On this Wikipedia the language links are at the top of the page across from the article title. As Daniel Kahneman, a renowned. It has limited uses, though. Get the latest Business Forecasting and Sales & Operations Planning news and insight from industry leaders. This is a business goal that helps determine the path or direction of the companys operations. Sujit received a Bachelor of Technology degree in Civil Engineering from the Indian Institute of Technology, Kanpur and an M.S. He has authored, co-authored, or edited nine books, seven in the area of forecasting and planning. Forecast bias is well known in the research, however far less frequently admitted to within companies. Nearly all organizations measure their progress in these endeavors via the forecast accuracy metric, usually expressed in terms of the MAPE (Mean Absolute Percent Error). This is one of the many well-documented human cognitive biases. As COO of Arkieva, Sujit manages the day-to-day operations at Arkieva such as software implementations and customer relationships. Bias is based upon external factors such as incentives provided by institutions and being an essential part of human nature. This bias is a manifestation of business process specific to the product. Select Accept to consent or Reject to decline non-essential cookies for this use. 5. MAPE is the sum of the individual absolute errors divided by the demand (each period separately). Other reasons to motivate you to calculate a forecast bias include: Calculating forecasts may help you better serve customers. Forecast #3 was the best in terms of RMSE and bias (but the worst on MAE and MAPE). How to Market Your Business with Webinars. As George Box said, "All models are wrong, but some are useful" and any simplification of the supply chain would definitely help forecasters in their jobs. The formula for finding a percentage is: Forecast bias = forecast / actual result It is an interesting article, but any Demand Planner worth their salt is already measuring Bias (PE) in their portfolio. Forecasting bias is endemic throughout the industry. What you perceive is what you draw towards you. Are We All Moving From a Push to a Pull Forecasting World like Nestle? The forecast value divided by the actual result provides a percentage of the forecast bias. For instance, a forecast which is the time 15% higher than the actual, and of the time 15% lower than the actual has no bias. They should not be the last. Grouping similar types of products, and testing for aggregate bias, can be a beneficial exercise for attempting to select more appropriate forecasting models. Out of these cookies, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. (and Why Its Important), What Is Price Skimming? Which is the best measure of forecast accuracy? 3 For instance, a forecast which is the time 15% higher than the actual, and of the time 15% lower than the actual has no bias. A confident breed by nature, CFOs are highly susceptible to this bias. I can imagine for under-forecasted item could be calculated as (sales price *(actual-forecast)), whenever it comes to calculating over-forecasted I think it becomes complicated. This basket approach can be done by either SKU count or more appropriately by dollarizing the actual forecast error. They persist even though they conflict with all of the research in the area of bias. Although there has been substantial progress in the measurement of accuracy with various metrics being proposed, there has been rather limited progress in measuring bias. If future bidders wanted to safeguard against this bias . However, most companies refuse to address the existence of bias, much less actively remove bias. After all, they arent negative, so what harm could they be? And I have to agree. Properly timed biased forecasts are part of the business model for many investment banks that release positive forecasts on their own investments. This is not the case it can be positive too. For example, if the forecast shows growth in the companys customer base, the marketing team can set a goal to increase sales and customer engagement. The "availability bias example in workplace" is a common problem that can affect the accuracy of forecasts. Rick Glover on 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. Following is a discussion of some that are particularly relevant to corporate finance. Of the many demand planning vendors I have evaluated over the years, only one vendor stands out in its focus on actively tracking bias: Right90. A positive bias means that you put people in a different kind of box. Bias is a quantitative term describing the difference between the average of measurements made on the same object and its true value. 9 Signs of a Narcissistic Father: Were You Raised by a Narcissist? A positive bias works in the same way; what you assume of a person is what you think of them. If we know whether we over-or under-forecast, we can do something about it. On LinkedIn, I askedJohn Ballantynehow he calculates this metric. BIAS = Historical Forecast Units (Two-months frozen) minus Actual Demand Units. Forecast 2 is the demand median: 4. As pointed out in a paper on MPS by Schuster, Unahabhokha, and Allen: Although forecast bias is rarely incorporated into inventory calculations, an example from industry does make mention of the importance of dealing with this issue. Do you have a view on what should be considered as "best-in-class" bias? 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. What is the most accurate forecasting method? Examples: Items specific to a few customers Persistent demand trend when forecast adjustments are slow to True. Part of this is because companies are too lazy to measure their forecast bias. This website uses cookies to improve your experience. please enter your email and we will instantly send it to you. A necessary condition is that the time series only contains strictly positive values. The Institute of Business Forecasting & Planning (IBF)-est. The problem in doing this is is that normally just the final forecast ends up being tracked in 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. +1. Here are examples of how to calculate a forecast bias with each formula: The marketing team at Stevies Stamps forecasts stamp sales to be 205 for the month. In new product forecasting, companies tend to over-forecast. No product can be planned from a badly biased forecast. APICS Dictionary 12th Edition, American Production and Inventory Control Society. To improve future forecasts, its helpful to identify why they under-estimated sales. Its important to differentiate a simple consensus-based forecast from a consensus-based forecast with the bias removed. In this post, I will discuss Forecast BIAS. These cookies will be stored in your browser only with your consent. All Rights Reserved. A business forecast can help dictate the future state of the business, including its customer base, market and financials. Performance metrics should be established to facilitate meaningful Root Cause and Corrective Action, and for this reason, many companies are employing wMAPE and wMPE which weights the error metrics by a period of GP$ contribution. According to Chargebee, accurate sales forecasting helps businesses figure out upcoming issues in their manufacturing and supply chains and course-correct before a problem arises. There is probably an infinite number of forecast accuracy metrics, but most of them are variations of the following three: forecast bias, mean average deviation (MAD), and mean average percentage error (MAPE). These cases hopefully don't occur often if the company has correctly qualified the supplier for demand that is many times the expected forecast. Required fields are marked *. Second only some extremely small values have the potential to bias the MAPE heavily. It is still limiting, even if we dont see it that way. However, uncomfortable as it may be, it is one of the most critical areas to focus on to improve forecast accuracy. In some MTS environments it may make sense to also weight by standard product cost to address the stranded inventory issues that arise from a positive forecast bias. If the positive errors are more, or the negative, then the . To find out how to remove forecast bias, see the following article How To Best Remove Forecast Bias From A Forecasting Process.