Your source for Oracle Retail customer content, event proceedings, and solution updates. Forecast Scorecard Dashboard: Evaluate forecast accuracy and identify opportunities. Aggregate the preprocessed continuous day level promotional variables to the week level. The Oracle Retail Demand Forecasting 13.3 Functional Implementer Essentials (1Z0-463) exam is designed for individuals who possess a strong foundation and expertise in … Each time a source-level forecast is generated, a PAE is calculated for that level. Retail Demand Forecasting (RDF) Increase forecast accuracy across product life cycles with Retail Demand Forecasting. A final-level forecast is generated for each product/location combination using each potential source generation level. This document defines and identifies the Oracle Retail Demand Forecasting patches and minimum releases that are required for the Oracle products to address the security vulnerabilities announced in the Advisory for July 2020. See how Oracle Retail's demand forecasting solution helps retailers deliver financial performance in … Prior versions of Oracle Retail Demand Forecasting (RDF) use only the Forecast procedure both to generate the forecast and to estimate the promotion effects. Compare all candidate forecasts using BIC Criterion. To do this, the promotional lifts are filtered from the historical sales and applied on top of the item's rate of sale. NOTE: Forecast150 is released in v15, and the Forecast special expression is decommissioned as of v15.0.1. The difficulty of automatically matching a new product to a previous product or profile. The Profile-based forecasting method can be successfully used to forecast new items. The selected model is recorded in the database. Causal Forecasting Method can calculate not only each individual promotion effect, but also the overlapping promotions effects. The SimpleES model is applied to the time series unless a large number of transitions from non-zero sales to zero data points are present. The final selection between the resulting models is made according to a performance criterion that involves a trade-off between the model's fit over the historic data and its complexity. Oracle provides Critical Patch Updates (CPU) to its customers to fix security vulnerabilities. ORACLE RETAIL DEMAND FORECASTING. The best aggregation status keeps track of which sub-problems have been performed and which sub-problems remain. A promotion variable can represent an individual promotion or a combination of overlapping promotions. The system determines the multiplicative and additive weights that best fit the data on hand. A style/store forecast is generated, and the forecast data is spread back down to the item/store level. The complexity penalty is necessary to avoid over fitting. Retail Cloud Get History and Forecast 2020-2027, new areas for expansion, increase your customer base, Breakdown Data by Manufacturers. Retail Demand Forecasting Cloud Service Configuration Consultant {username} : {useremail} Please provide us with feedback on your Oracle Learning Subscription experience! Time series methods extrapolate features from the past (in this case, past sales data) to the future. Retail Cloud Set achievable targets for commercial growth, sales, and latest product developments In season, the pre-season forecast serves as a forecast plan to the Bayesian forecasting method. Oracle Retail recently released our next generation Oracle Retail Demand Forecasting (RDF) Cloud Service. If the DD value is used to forecast, the history (if it exists) of the product is ignored. One or more of these subtasks is performed during each period that the computer is idle. The binary writes the winning promotional variables effects back to the database. 3038984 Mar 11, 2016 4:19 PM In the forecasting process when RDF sees a regular price change, we know that it applies an elasticity value, decay factor and effective periods. The time series methods that the system offers include: Exponential Smoothing (ES) Forecasting Methods - Automatic Exponential Smoothing (AutoES), Simple/Intermittent Exponential Smoothing, Multiplicative Winters Exponential Smoothing, Seasonal Exponential Smoothing (SeasonalES). RDF is able to use several time series methods to produce forecasts. Holt exponential smoothing treats data as linearly trended but non-seasonal. Update the week-to-day profile of w36 so that the weight of Thursday is doubled (the multiplicative factor is 2): Finally, spread the forecast of w36 using the normalized profile. The current RDF Seasonal Regression forecasting model is designed to address these needs. By using standard statistical distributional assumptions, RDF develops measures of uncertainty associated with forecast point estimates from these models. A description of the competing models used within AutoES is described in "Exponential Smoothing (ES) Forecasting Methods". The primary process by which RDF automatically fits an exponential smoothing model to a time series is called Automatic Exponential Smoothing (AutoES). In some instances, no promotional variables are found to be statistically significant. These forecast updates can be critical to a company's success and can be used to increase or cancel vendor orders. For a particular series, even if the amount of available history allows one to fit a complex model (that is, one with seasonal components), the resulting model is not necessarily superior to a simpler model. The Seasonal Regression Model is included in the AutoES family of forecasting models and is thus a candidate model that is selected if it best fits the data. Manage, control, and perform seamless execution of day-to-day merchandising activities, including purchasing, distribution, order fulfillment, and financial close. Drive optimal strategies in planning, increase inventory productivity in supply chains, decrease operational costs, and deliver customer satisfaction from engagement to sale to fulfilment, Maximize forecast accuracy for the entire product lifecycle with tailored approaches for short- and long-lifecycle products, Adapt to recent trends, seasonality, out-of-stocks, and promotions, and reflect retailers’ unique demand drivers, Anticipate customer demand by maximizing the value of your data through the application of retail sciences that draw from machine learning, artificial intelligence, and decision-science disciplines, Simplify forecast management by maximizing the productivity of your team with exception-driven processes paired with our experience-inspired user interface, Inspire new ways to engage customers and augment the forecasting process while maximizing the agility of your business with extensible science, workflows, and operations. The binary records the forecast and the baseline in the database. This method captures the trend of a series through the slope of the regression line while the series shifted by a cycle provides its seasonal profile. If no, move on to Step 9. READ OUR RETAIL FORECASTING BLOG REQUEST A DEMO Engage with … If less than two years of data is available, a Seasonal Regression model is used. Within RDF, a few modifications to the standard selection criteria have been made. Leverage forecasted demand across all commerce channels to guide a time-phased inventory ordering, allocation, replenishment, and delivery plan to all levels of the distribution network. At this point, there is no reason to mistrust the sales plan. Overall, a forecast point estimate is evaluated as: a function of level, trend, seasonality, and trend dampening factor. For example, the shape for certain fashion items might show sales ramping up quickly for the first four weeks and then trailing off to nothing over the next eight weeks. Content will be entered on the day of the Critical Patch Update release. Put simply, the better the history of the variable being forecast, the stronger these statistical patterns are. If the regression finds no significant promotional variables, the casual method is considered to have failed to fit. Through training, you will learn about traditional forecasting through a variety of forecast methods and how to leverage this solution to help your business align operations across global networks. It should be noted that just because promotional forecasting is selected, it does not necessarily imply that a promotional forecast results. Built-in artificial intelligence and intuitive dashboards help retailers prevent overstocking and boost customer satisfaction. If no, move on to Step 9. Figure 3-6 Multiplicative Winters Exponential Smoothing. If no, move on to Step 3. These baselines are then spread back to the item/store level and then loaded in the RDF Causal Engine. The ratio of the magnitude estimate over the frequency estimate is the forecast level reported for the original series. These changes tend to favor the seasonal models to a slightly higher degree that improves the forecasts on retail data, especially for longer forecast horizons. Helps FEMSA/OXXO upgrade RDF from version 10.0 to version 13.1, Oracle Retail Demand Forecasting Data sheet Oracle While providing invaluable information regarding the best aggregate level for source forecasts, the Automatic Forecast Level Selection process may be very CPU intensive. You have the option of accepting the system-generated source-level selection or manually selecting a different source-level to be used. Oracle Inventory Optimization Enables Retailers to Navigate Uncharted Demand ... Service can sit between a retailer's forecasting and supply chain systems to help highlight ... Oracle Retail. In order to make this feasible in a retail environment, Oracle Retail has developed a number of different meta-methods that can automatically select the best method among a number of competing models. Retail Demand Forecasting Cloud Service Forecast Analyst {username} : {useremail} Please provide us with feedback on your Oracle Learning Subscription experience! If yes, generate the forecast and statistics using the Croston's method and move on to the next time series. This was the motivation for developing an approach that would combine the two forecasts in a reasonable manner. If yes, generate a forecast and statistics using the SimpleES method and move on to Step 4. When AutoES forecasting is chosen in RDF, a collection of candidate models is initially considered. The forecast, generated over the train period, can be compared to the actual sales figures in the test period to calculate the Percent Absolute Error (PAE) between the two. The problem arises when attempting to forecast products with little or no history. The Engine uses the baseline along with the historic promotional data and future planned promotions data to create the system forecast, which is the baseline with the lifts, which were calculated from the promotional data, applied on top. Results included: - Improved flash sale revenue, unit sales and margin by over 300% - Improved demand forecasting accuracy for new products The Simple forecast is re-seasonalized using the profiles. Does the time series contain the minimum data points to qualify to forecast using the Holt method? Built-in artificial intelligence and intuitive dashboards help retailers prevent overstocking and boost customer satisfaction. (Doc ID 1265403.1) Last updated on DECEMBER 03, 2019. For further details on prediction interval calculations, refer to Char&Yatfield, International Journal of Forecasting, March 1992. Check the spelling of your keyword search. The Level at the end of the series (time t) is: The Trend at the end of the series (time t) is: The Seasonal Index for the time series (applied to the forecast horizon) is: Oracle Winters, calculates initial seasonal indices from a baseline Holt forecast. Drive optimal recommendations for promotions, markdowns, and targeted offers that maximize profits and sell through. and the Trend at the end of the series (time t) is: and the Seasonal Index for the time series (applied to the forecast horizon) is: Oracle Winters is a Winters-based decomposition approach to update the level, trend, and seasonal indexes. The Holt model provides forecast point estimates by combining an estimated trend (for the forecast horizon - h) and the smoothed level at the end of the series. The data is de-seasonalized using the profile and then fed to Simple method. Implementing Oracle Retail Demand Forecasting. In order to do that, we need to have a profile (which can be copied from an item that shares the same seasonality) and a number that specifies the de-seasonalized demand (DD value). Since this model does not use a smoothing parameter to place added weight on more recent historic values, a Simple Moving Average model is not actually in the exponential smoothing family. The forecast is calculated using the DD value multiplied by the profile. This would be equivalent to using the effects at the source-level for time series that have no causal variable instances in the history. Recently, Oracle Retail evaluated the next-generation, cloud-native, retail demand forecasting solution against Best Buy’s current on-premises version where end-users were manually adjusting 50 percent of forecasts and found a 70% improvement of promotional forecasts. Generally the time series provided is past sales history for a given item/store that is used to predict what future demand might be. The scheduling of the Automatic Forecast Level Selection process (AutoSource) must be integrated with the schedules of other machine processes. Any retail scenario or marketing activity can be modeled in the solution, allowing you to make better planning and merchandising decisions based on better predictions. Figure 3-3 is an example of a forecast in which data seems to be un-trended and un-seasonal; note the flat appearance of the forecast. This offering, powered by machine learning, can sit between a retailer’s forecasting and supply chain systems to help highlight the best actions they can take to optimize inventory. However, they were not designed to work with sales histories of shorter than two years. Promotional variables, internal promotional variables, promotional variable types, and the series itself are passed to the stepwise regression routine, with the historic data serving as the dependent variables. This means that they are based solely on the history of one variable, such as sales. Does the time series contain enough relevant data to generate a forecast? For any assistance regarding the above and other forecasting changes that you may be experiencing please set up a call for assistance or email Guiming Miao , Oracle Retail Director of Science, for more tips. Oracle Retail Demand Forecasting (RDF) empowers retailers to centralize demand forecasts for their omnichannel enterprise — from operations and vendor collaboration to planning and optimization to marketing and insights — accurately and efficiently. The spreading utilizes causal daily profiles, thus obtaining a causal forecast at the day granularity. If you want to force certain promotional variables into the model, this can be managed through forecasting maintenance parameters. Causal Forecasting uses stepwise regression to determine which causal variables are significant. All rights reserved. Bayesian forecasting is primarily designed for product/location positions for which a plan exists. Oracle Retail Demand Forecasting is highly flexible, and can be configured to take into account your unique demand drivers, like pricing or promotions. Copyright © 2018, Oracle and/or its affiliates. In some instances, especially in retail, pure time series techniques are inadequate for forecasting demand. Oracle Retail Demand Forecasting Cloud Service Empower Demand-Driven Retailing Maximize forecast accuracy for the entire product lifecycle with next-generation retail science paired with exception-driven processes and delivered on our platform for modern retailing. As forecasting consultants and software providers, Oracle Retail assists clients in obtaining good forecasts for future demands for their products based upon historical sales data and available causal information. User input in overriding the automatic training horizon further enhances the simple robustness of this model for base-level data. Sunday is reserved for generating forecasts. Identifying the best aggregation levels for sets of products and locations can be divided into a number of sub-problems: Determining the best source-level forecast. Oracle Retail’s Demand Forecasting Cloud Service (RDF CS) empowers retailers to centralize demand forecasts — from operations and vendor collaboration to … Seasonal indices, level, and trend are then updated in separate stages, using Winter's model as a basis for the updates. In actual practice these algorithms have been and can be used to forecast a myriad of different data streams at any product/location level (shipment data at item/warehouse, financial data at dept./chain, and so on). IT creates optimized inventory targets by item by location to meet demand and satisfy business and financial objectives. The following is an example of a typical schedule for the Automatic Forecast Level Selection process: Monday through Thursday, the selection process starts at midnight and runs for eight hours. The approach to use the continuous promotion indicators to generate an accurate causal forecast at the day level is as follows: Calculate the weekly multiplicative effect for the promotion using the standard causal forecasting system with continuous indicators. The Logic team brings the hands-on supply chain experience your organization needs to successfully implement, deploy, and manage Oracle Retail’s leading Supply Chain Management & Optimization solutions. They are exponential smoothing models because the weighting uses decays at an exponential rate. Typically, moving average forecasts are generated at the final forecast level (for example, item/store) and their results used to spread more sophisticated higher-level forecasts (for example, those generated with exponential smoothing). All of these methods attempt to best capture the statistical probability distribution previously discussed, and they do this by fitting quantitative models to statistical patterns from historical data. Does that mean that at 12 weeks the time series results are irrelevant and that at 14 weeks the sales plan has no value? To solve this problem, the task of selecting best aggregation levels for product/location combinations is decomposed and processed piecemeal during times when the computer would normally be idle. There are a few solutions that make use of the effects from other similar time series. This release features robust machine learning, artificial intelligence and decision science, enabling retailers to gain pervasive value across forecasting and planning processes. This produces cleaner signals and alleviates issues involved in forecasting new items and new stores and issues involving data sparsity. Daily profiles are calculated using the Curve module. Balance inventory throughout the supply chain to efficiently achieve desired service levels to customers by providing optimized replenishment recommendations. Filter all leading zeros in the input data that is within the training window. The BIC criterion attempts to balance model complexity with goodness-of-fit over the historical data period (between history start date and forecast start date). Retail Demand Forecasting For On Premise Forecast Analyst {username} : {useremail} Please provide us with feedback on your Oracle Learning Subscription experience! Applies to: Oracle Retail Data Warehouse - Version 13.1 and later Information in this document applies to any platform. If there is too little data to create a seasonal forecast (in general, less than 52 weeks), the system selects from the Simple ES, Trend ES, and Intermittent ES methods. A combination of several seasonal methods. This method lets the Multiplicative Seasonal and Additive Seasonal models compete and picks the one with the better fit. A simple moving average forecast involves taking the average of the past n time periods and using that average as the forecast for all future time periods (where n is the length of fitting period). First, the baseline is generated. A wide variety of statistical forecasting techniques are available, ranging from very simple to very sophisticated. In this case, the forecast equals the baseline. Oracle's Supply Chain Optimization retail-specific offering is tailored to deliver benefits for or across all retail formats: Organization-wide demand forecast reflecting all key demand influencers Time-phased inventory replenishment throughout the supply chain Oracle Retail Inventory Optimization Cloud Service comes with pre-built machine learning models that more accurately predict overall inventory levels; recommend inventory re-distribution; balance supply and demand to free up money tied up in excess inventory; and more. Your search did not match any results. Engage with Oracle Retail Planning and Optimization Learning Subscription and maximize your planning and optimization solution investment with an all-new, modern learning experience. The binary reads the type of each promotional variable into the system. Demand Forecasting: Base Releases: 16.0: Release Notes: Installation Guide(Rev 2) User Guide RPAS Classic Client(Rev. The final selection between the competing models is made according to a performance criterion that involves a trade-off between the model's fit over the historic data and its complexity. Try one of the popular searches shown below. To forecast short-lifecycle promotional items, Causal deprices, depromotes, and smoothes the forecasting data source to generate the short lifecycle forecast causal baseline. Oracle Learning Subscriptions | Learn Oracle ... Oracle Learning Subscriptions Feedback Retail Demand Forecasting Cloud Service Introduction {username} : {useremail} Please provide us with feedback on your Oracle Learning Subscription experience! Oracle Retail Demand Forecasting (RDF) provides retail marketers with the ability to find meaningful patterns within consumer data, plan an accurate demand forecast, and … The Seasonal Regression Model uses simple linear regression with last year's data as the predictor variable and this year's sales as the target variable. The Bayesian forecast is the causal baseline for short lifecycle items.The next step is to calculate the promotional lifts. The most common statistical methodologies used are univariate. Description. Initially the implementation of RDF was going to cover FMCG, hardlines, textiles and electronics and complete within one year. Suppose for a certain product, the profile is as follows: Suppose that in the past, the promotion was held on Wednesday, Thursday, and Friday of week w6: Then the continuous weekly indicator for this promotion in w6 should be set to 0.4, which is the sum of the weights of Wednesday, Thursday, and Friday. Refer to Figure 3-1, "AutoES Flowchart". The difficulty comes in deciding which products/locations will benefit from this technique and from what level in the hierarchy these source-level forecasts should be spread. A forecasting algorithm was developed that merges a customer's sales plans with any available historical sales in a Bayesian fashion (that is, it uses new information to update or revise an existing set of probabilities. A Simple Exponential Smoothing model is then applied to each of these newly created series to forecast a magnitude level as well as a frequency level. This curve represents the pre-season baseline forecast. Note that this profile is already computed for spreading the weekly forecasts to the day level. Seasonal Regression is an Oracle Retail specific extension of this procedure for use in seasonal models with between one and two years of history. Our intuition tells us that instead of a hard-edge boundary existing, there is actually a steady continuum where the benefits from the sales plan decrease as we gather more historic sales data. The second noise-driven concession is to check the slope to determine if it is either too slight or too great. In order to improve inventory accuracy and optimise sales forecasts, they decided to bring forecasting systems and processes in-house using Oracle RDF. The inability to include planners' intuition into a forecasting model. Forecasts for short horizons can be estimated with Simple Exponential Smoothing when less than a year of historic demand data is available and acts-like associations are not assigned in RDF. A sales last year forecast is based entirely on sales from the same time period of last year. This method does not generate confidence and cumulative intervals. Causal, or promotional, forecasting requires four input streams: Promote decomposes the problem of promotional forecasting into two sub-tasks: Estimating the effect that promotions have on demand, Applying the effects on the baseline forecasts. If a simpler model (for example, a model with only a level component or level and trend components) fits as well as a seasonal model, the AutoES forecasting process finds the simpler model to be preferable. Retailers can now improve inventory management through a single view of demand throughout their entire product lifecycle with the next generation Oracle Retail Demand Forecasting (RDF) Cloud Service. Overview Dashboard: Contextualize forecasting impacts to key performance indicators. Oracle Retail recently released our next generation Oracle Retail Demand Forecasting (RDF) Cloud Service. Oracle Retail Demand Forecasting is highly flexible, and can be configured to take into account your unique demand drivers, like pricing or promotions. If the effects are calculated at higher level than item/store, the effects are replicated down to item/store since the effects are multiplicative. The binary creates an internal promotional variable to allow the modeling of trend. Then the forecast data source is depriced, depromoted and smoothed. The profiles are multiplied by the causal effects and then the profiles have to renormalize. If yes, generate a forecast and statistics using the Seasonal Regression method and move on to Step 6. RDF uses a damped Holt model that decays the trend component so that it disappears over the first few weeks. This forecast represents the final forecast. If no, move on to Step 9. Because sales histories of longer than two years are often difficult to obtain, many retail environments need a seasonal forecast that can accommodate sales data histories of between one and two years. Coupled with our 24/7 retail learning subscriptions, your team will build individual competencies that maximize the usage of your investments. Given that both sales plans and time series forecasts are available, an obvious question exists: When should the transition from sales plan to time series forecasting occur? That is, when aggregate forecasts can be calculated for long and less noisy aggregate time series, Simple Moving Average models provide an adequate (and computationally quick) forecast to determine the ratios needed for RDF spreading. Oracle Retail Science Platform Cloud Service, Oracle Retail Offer Optimization Cloud Service, Oracle Retail Assortment and Item Planning Cloud Service, Oracle Retail Advanced Inventory Planning, Planning and Optimization Retail Learning Subscription. This forecasting guidebook covers two case studies executed with MIT and Oracle Retail on how adaptive intelligent applications leverage machine learning and AI to deliver significant results for retailers. Companies with a truly demand-driven supply chain can grow sales by 4%, cut operations cost by 10%, and reduce inventory by 30%. Oracle Retail Demand Forecasting (RDF) is a statistical and promotional forecasting solution. 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