Demand forecasting<\/a> is an intricate science with many moving parts, and it can be challenging to get right, especially if you\u2019re just getting started. With ecommerce continuing to grow, creating a similar shopping experience across channels creates a level of demand from customers that can be very challenging to plan for.\u00a0However, forecast errors in your supply chain can be devastating to your business\u2019s productivity and bottom line, so you must avoid mistakes as much as possible.<\/p>\n With the proper planning processes in place, it is possible to achieve demand forecast accuracy and enjoy the benefits of demand planning without the risk. This article will discuss how demand forecasting errors affect your business and where they originate.<\/p>\n Forecast error occurs when there is a difference between the forecast demand and the actual demand.\u00a0Determining the difference\u00a0can involve various calculations, but in general, the more significant the difference between these two factors, the greater the impact on your bottom line and the greater the risks will be. Dangers of forecast error include:<\/p>\n Forecast accuracy determines all kinds of factors about how you run your business. It helps you decide what to buy and when to buy it. It also tells you what to stock and where to do so. It could even determine how you hire personnel and where you allocate your resources. In the end, forecast accuracy determines whether you can meet your customers\u2019 needs.<\/p>\n To avoid forecasting errors effectively, it helps to understand the different types you may encounter. Being prepared for potential issues can help you\u00a0mitigate inaccuracy and generate reliable forecasts<\/a>\u00a0that keep your business moving forward.<\/p>\n Underestimation occurs when forecasted demand is lower than actual demand, while overestimation is the opposite. If you expect demand to be lower than what it actually is, you could have many dissatisfied customers and lost sales. Overestimating demand can lead to excess inventory and potential waste. Keeping up with\u00a0technological advancements can improve accuracy<\/a>\u00a0and reduce errors.<\/p>\n Random errors are the unpredictable variations between your forecasted and actual demands. These variations can come from various uncontrollable events, making them difficult to eliminate completely. You can minimize random errors by reviewing real-time data and continuously updating your forecasting models.<\/p>\n Consistent inaccuracies in your forecasting processes are known as systematic errors. These errors are due to factors such as outdated forecasting models, flawed or inadequate methodologies or underestimation of an external factor’s impact. Regularly reviewing your forecasting techniques and processes can reveal systematic errors. Artificial intelligence (AI) tools can also\u00a0help\u00a0generate accurate demand forecasts<\/a>\u00a0with minimal systematic errors.<\/p>\n Failing to consider the effects of delays and seasonal demands can lead to forecasting errors. Delays can happen for various reasons, from weather events and holidays to driver shortages and other supply-chain-specific challenges. Factoring in delays and surges in demand related to the season can help you effectively manage inventory needs.<\/p>\n Forecasting should be a collaborative process within your organization. You need data from all sources to make informed decisions about meeting customer demands. Every department has valuable information for your forecasting process. Your sales team, for example, will know what demand shifts are happening, while your marketing team may conduct promotions that increase demand for certain items.<\/p>\n If your data is incorrect, your forecasts will be inaccurate as well. Organizing, acquiring\u00a0and checking data is a significant source of delay in implementing forecasting software because so many businesses neglect data until forecasting brings data issues to light. Therefore, keeping up with and mastering your supply chain data is critical to accurately forecasting your inventory demand and ensuring proper inventory levels.<\/p>\n Data anomalies are also something to keep in mind. Even if your forecasting data is up to date and perfect, inaccurate or inconsistent data can easily drive forecasts off track if you don\u2019t manage your data properly.\u00a0These potential risks make it\u00a0critical to measure forecast error and make error calculations.<\/p>\n Businesses use\u00a0various forecasting methods\u00a0to\u00a0calculate their demand. For example, traditional forecasting techniques are referred to as extrapolative methods. These methods attempt to find patterns in an item\u2019s demand history, then project that same pattern into the future.<\/p>\n Some of these methods include exponential smoothing and moving averages. However, these methods are designed to work on consistent and regular data, not intermittent data. If you try to use them with intermittent data, your results will be inaccurate. Beyond that, with the last two years of shifting purchase and demand behavior, historical data needs to be carefully reviewed to ensure current conditions are met.<\/p>\n Big-ticket and slow-moving items usually involve intermittent data that must be calculated another way. In these cases, regression analysis, also known as causal modeling, may be used. These models use data other than an item\u2019s demand history to forecast demand. However, they require more skill to use.<\/p>\n Picking the correct forecasting method for your products is crucial to getting the right results. So know your product, know your business, and you will know which model to choose.<\/p>\n While organizations may think they are working together, different people will have different priorities depending on their departments.\u00a0Alternatively, the mistake may be evaluating the forecasts incorrectly. If time isn\u2019t taken to assess each forecast properly, collaboratively, the business\u00a0risks\u00a0error in its estimates.<\/p>\n There are many phases in a forecasting process, and things can go wrong.\u00a0Forecasting has a\u00a0team component to\u00a0it, and this is where the process may get messy. Sales and Operations Planning is often where the collaborative aspect comes into play.<\/p>\n During these meetings, different departments come together to determine what the official forecast will be for the company. This\u00a0collaboration\u00a0enables more effective communication to occur, so that all departments are aligned on goals and strategies.\u00a0Clear communication between departments\u00a0helps improve forecast accuracy, adding to better inventory management and ultimately increased customer satisfaction.<\/p>\n You can use several different types of calculations<\/a> to determine your forecast error. These calculations range in complexity and the type of information they reveal, but they can all help you make necessary adjustments to improve forecast accuracy and reliability. Forecast\u00a0error values can reveal forecast quality<\/a>\u00a0and opportunities for improvement.<\/p>\n Two of the most common forecast error calculations include:<\/p>\n You don\u2019t have to\u00a0handle your\u00a0demand forecasting<\/u><\/a>\u00a0all on your own. Blue Ridge Global\u00a0offers a\u00a0<\/u>supply chain management\u00a0<\/u>tool<\/u><\/a>\u00a0that can help you with forecasting at all steps of the supply chain to ensure that you can confidently predict your business\u2019s needs and deliver your customers\u2019 satisfaction. Our smart software incorporates your data and can help you identify risks, optimize planning\u00a0and even improve pricing.\u00a0Try out<\/u><\/a>\u00a0Blue Ridge Global software for yourself and see what we can do.[\/vc_column_text][image_with_animation image_url=”37152″ image_size=”full” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”” border_radius=”20px” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default” img_link=”https:\/\/blueridgeglobal.com\/request-demo\/”][\/vc_column][\/vc_row]\n","protected":false},"excerpt":{"rendered":" [vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” equal_height=”yes” content_placement=”middle” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” bg_color=”#202945″ bg_image=”36226″ bg_position=”left top” background_image_loading=”default” bg_repeat=”no-repeat” scene_position=”center” top_padding=”1%” constrain_group_1=”yes” bottom_padding=”1%” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” enable_shape_divider=”true” shape_divider_color=”#ffffff” shape_divider_position=”bottom” shape_divider_height=”0″…<\/p>\n","protected":false},"author":36,"featured_media":0,"parent":39596,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-37148","page","type-page","status-publish"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/blueridgeglobal.com\/wp-json\/wp\/v2\/pages\/37148","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blueridgeglobal.com\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/blueridgeglobal.com\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/blueridgeglobal.com\/wp-json\/wp\/v2\/users\/36"}],"replies":[{"embeddable":true,"href":"https:\/\/blueridgeglobal.com\/wp-json\/wp\/v2\/comments?post=37148"}],"version-history":[{"count":6,"href":"https:\/\/blueridgeglobal.com\/wp-json\/wp\/v2\/pages\/37148\/revisions"}],"predecessor-version":[{"id":46468,"href":"https:\/\/blueridgeglobal.com\/wp-json\/wp\/v2\/pages\/37148\/revisions\/46468"}],"up":[{"embeddable":true,"href":"https:\/\/blueridgeglobal.com\/wp-json\/wp\/v2\/pages\/39596"}],"wp:attachment":[{"href":"https:\/\/blueridgeglobal.com\/wp-json\/wp\/v2\/media?parent=37148"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}What Is Forecast Error?<\/h2>\n
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Types of Forecasting Errors<\/h2>\n
Under and Overestimation<\/h3>\n
Random<\/h3>\n
Systematic<\/h3>\n
Underestimating Lead Times<\/h3>\n[\/vc_column_text][image_with_animation image_url=”37151″ image_size=”full” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”” border_radius=”20px” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default”][vc_column_text]Accurately predicting lead times is critical to receiving the products and materials you need right when you need them. If you underestimate lead times, you risk disappointing customers and failing to meet their demands. Monitoring and predicting lead times with precision will help you minimize related forecasting errors.<\/p>\n
Ignoring Potential Delays and Seasonal Variance<\/h3>\n
Overlooking Other Departments<\/h3>\n
Three Sources of Forecast Error<\/h2>\n
No. 1: Data Problems<\/h3>\n
No. 2: Wrong Forecasting Method<\/h3>\n
No. 3: Flaws in the Forecasting Process<\/h3>\n
How to Calculate Forecast Error<\/h2>\n
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Reduce Errors in Your Demand Forecasting<\/h2>\n