I am also active on LinkedIn. Many algorithms use MSE as it is faster to compute and easier to manipulate than RMSE. Here is a SKU count example and an example by forecast error dollars: As you can see, the basket approach plotted by forecast error in dollars paints a worse picture than the one by count of SKUs. Some supply chain departments report out aggregated forecast error, again to make the forecast error appear better than it is. Since MAPE is a measure of error, high numbers are bad and low numbers are good. As we saw above, in any model, the optimization of RMSE will seek to be correct on average. Forecast bias = forecast - actual result Here, bias is the difference between what you forecast and the actual result. Then to me, changing the forecast less than 2 days out is demand sensing. Few companies would like to do this. For instance, the following pages screenshot is from Consensus Point and shows the forecasters and groups with the highest net worth. This network is earned over time by providing accurate forecasting input. Follow settings are unclear for me: 1.) They have documented their project estimation bias for others to read and to learn from. The bias is defined as the average error: where n is the number of historical periods where you have both a forecast and a demand. Last Updated on February 6, 2022 by Shaun Snapp. Instead, I will talk about how to measure these biases so that onecan identify if they exist in their data. He has authored, co-authored, or edited nine books, seven in the area of forecasting and planning. To determine what forecast is responsible for this bias, the forecast must be decomposed, or the original forecasts that drove this final forecast measured. We are not remotely controlled by any vendor, consulting firm, etc.. 1998. The bias is gone when actual demand bounces back and forth with regularity both above and below the forecast. You might then prefer to minimize RMSE and to forecast the average (9.5) to avoid this situation. To calculate the Biasone simply adds up all of the forecasts and all of the observations seperately. 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. While several research studies point out the issue with forecast bias, companies do next to nothing to reduce this bias, even though there is a substantial emphasis on consensus-based forecasting concepts. Reducing bias means reducing the forecast input from biased sources. In comparison, a forecast minimizing RMSE will not result in bias (as it aims for the average). Your home for data science. I have watched the cult of the self grow. One only needs the positive or negative per period of the forecast versus the actuals, and then a metric of scale and frequency of the differential. 2. This article is an extract from my book Data Science for Supply Chain Forecasting. The role of demand forecasting in attaining business results. Bias is an uncomfortable area of discussion because it describes how people who produce forecasts can be irrational and have subconscious biases. The Mean Absolute Percentage Error (MAPE) is one of the most commonly used KPIs to measure forecast accuracy. To calculate either forecast accuracy or forecast bias you have to know two inputs which are the (Forecast and the sales). Dr. Chaman Jain is a former Professor of Economics at St. John's University based in New York, where he mainly taught graduate courses on business forecasting. Properly timed biased forecasts are part of the business model for many investment banks that release positive forecasts on their own investments. Indeed, unfortunately, the medians robustness to outliers can result in a very annoying effect for items with intermittent demand. Actually, many algorithms (especially for machine learning) are based on the Mean Squared Error (MSE), which is directly related to RMSE. Those forecasters working on Product Segments A and B will need to examine what went wrong and how they can improve their results. I think the question to ask also is what is the point of making the effort? We can then define RMSE% as such. You can select the article title to be taken to the article. Lets imagine that we sell a product to a single client. As a supply chain data scientist, you should experiment: if using MAE as a KPI results in a high bias, you might want to use RMSE. Select to see more of our articles on Forecasting. This is the reference list for the Forecast Basics articles, as well as interesting quotes from these references at Brightwork Research & Analysis. If the forecast undershoots the demand, then the error will be negative. You need to identify the outliers while you are building the model AND a final check of 2/3 std deviations at the end of the process. 1- BIAS forecast accuracy (consistent forecast error) 2-MAPE forecast accuracy (Mean Absolute Percentage Error) 3- MAE forecast accuracy (Mean Absolute Error) 4- RMSE forecast accuracy (Root Mean Squared Error) 5) Calculation of the Forecast Accuracy KPI Conclusion 1) Having a demand forecast The first step is to have a demand or sales forecast. The best way to avoid bias or inaccurate forecasts from causing supply chain problems is to use a replenishment technique that responds only to actual demand - for ex stock supply chain service as well as MTO. Forecast 2 is the demand median: 4. What mean this? It is a tendency for a forecast to be consistently higher or lower than the actual value. If you choose a bad forecasting application, obviously you will forecast at a low level. With statistical methods, bias means that the forecasting model must either be adjusted or switched out for a different model. Thanks in advance, Lee. Kakouros, Kuettner and Cargille provide a case study of the impact of forecast bias on a product line produced by HP. This category only includes cookies that ensures basic functionalities and security features of the website. 02-04-2021, 11:34 AM #2. Learn why so few entities in the IT space include references in their work. This will lead to the fastest results and still provide a roadmap to continue improvement efforts for well into the future. 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. You will learn how bias undermines forecast accuracy and the problems companies have from confronting forecast bias. However, this is the final forecast. This would drive appropriate changes to the production plan as soon as it could be changed (3 weeks out). We'll assume you're ok with this, but you can opt-out if you wish. The 1,9 example is contrived, but is an example that does happen in datasets we see all the time. Is it worse to aim for the median or the average of the demand? Companies cant do the most elementary forecasting properly. To simplify the following algebra, lets use a simplified version: the Mean Squared Error (MSE): If you set MSE as a target for your forecast model, it will minimize it. When a new method contradicts a large body of research, we have to sit back and take notice. Lets plot the demand we observed and these forecasts. Generally speaking, such a forecast history returning a value greater than 4.5 or less than negative 4.5 would be considered out of control. We then have an average weekly demand of 33 pieces and a demand median of 0. It can also be pathological, leaving some narcissistic people thin-skinned, easily hurt and likely to respond to real or imagined injury by attacking the person who hurt them. As with any workload it's good to work the exceptions that matter most to the business. By taking a top-down approach and driving relentlessly until the forecast has had the bias addressed at the lowest possible level the organization can make the most of its efforts and will continue to improve the quality of its forecasts and the supply chain overall. We have a whole category of photographs called "selfies.". I agree that such changes of forecast within lead time wont help you to balance supply and demand on supplier lead time (and will add some nervousness to the forecast), but in case of risk pooling you can balance positive and negative forecast errors. Still, MAE is only reduced by 3.6% (2.33 to 2.25), so the impact on MAE is nearly twice as low. 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. There is no complex formula required to measure forecast bias, and that is the least of the problem in addressing forecast bias. What happened if I adjust the BIAS of the forecast 2) Bias Adjustment Method Here is the same question as before. The bias is positive if the forecast is greater than actual demand (indicates over-forecasting). If narcissistic people had outstanding qualities to recommend them, such attributes would speak for themselves. Part of submitting biased forecasts is pretending that they are not biased. If we cant do that, we dont have a very good platform for proposing new and unproven methods. April 1, 1996. https://davestein.biz/2013/01/22/an-expert-talks-about-fixing-sales-forecasting-problems/, *https://www.amazon.com/Demand-Driven-Forecasting-Structured-Approach-Business/dp/0470415029. As forecast error cannot be calculated with much nuance or customizability within forecasting applications, this means that some automated method of measuring forecast error outside of forecasting applications is necessary. Is It Time to Renovate Your Supply Chain Planning Software? 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. I just want to second your point about finding an application which is good at doing this. 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. Its challenging to find a company that is satisfied with its forecast. 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. To learn about our improvement services for forecasting, select the chat bubble. If it is negative, company has a tendency to over-forecast. There are other forecast accuracy calculations that you can use, but make sure you find the most appropriate method for your needs, as it's important to understand how accurate your forecasting is for a number of reasons that we will now discuss. As soon as you have more than half of the periods without demand, the optimal forecast is 0! The applications simple bias indicator, shown below, shows a forty percent positive bias, which is a historical analysis of the forecast. Lets imagine a product with a low and rather flat weekly demand that has from time to time a big order (maybe due to promotions or to clients ordering in batches). The first one predicts 2 pieces/day, the second one 4, and the last one 6. Implementing Deep Learning With PyTorchImage Recognition, A New Paradigm: Identifying and Managing Climate Risk in Loan Portfolios, Set up an Airflow Environment on AWS in Minutes, Meandering Mondays #183 https://t.co/NMcPsQIMyP #BlogHopsandLinkParties https://t.co/PFzf9RoR7g.