Coupled with our 24/7 retail learning subscriptions, your team will build individual competencies that maximize the usage of your investments. When inventory levels are optimized, lost sales due to product stock-outs are greatly reduced, as are the costs incurred by overstocking. Built-in artificial intelligence and intuitive dashboards help retailers prevent overstocking and boost customer satisfaction. The following are major problems in automatically developing these forecasts: The lack of substantial sales history for a product (which especially makes obtaining seasonal forecasts very difficult). This means that they are based solely on the history of one variable, such as sales. In order to improve inventory accuracy and optimise sales forecasts, they decided to bring forecasting systems and processes in-house using Oracle RDF. In that case, the effect for that variable would not be computed at all, thus affecting the accuracy of the forecast. Advanced Inventory Planning - Oracle Retail Oracle Retail Demand Forecasting Related Parameters 7 .3 documentation My Oracle Support Documents Oracle Retail Demand Forecast EP interface design and documentation 2. With Oracle Retail Demand Forecasting Cloud Service, you stay on the cutting edge of forecasting science and get the most for your team. This method performs best when dealing with highly seasonal sales data with a relatively short sales history. The Profile-based forecasting method generates a forecast based on a seasonal profile. With Oracle Retail Demand Forecasting RETAIL MARKET REALITIES THE UPSIDE OF UPGRADING MODERN RETAIL IMPERATIVES FUTURE PROOF INVESTMENT With over 5,280 customers worldwide, Oracle is the platform for modern retail around the globe. The confidence interval is set to 1/3 of the DD value. 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. You have the option of accepting the system-generated source-level selection or manually selecting a different source-level to be used. However, it is an adequate model to use when low-level (final forecast) ratios are needed for RDF's spreading of high-level (aggregate) forecasts. If the DD value is used to forecast, the history (if it exists) of the product is ignored. 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. The technical methods used are driven by the goal to provide the most accurate forecasts possible in an automatic and efficient manner. 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. The causal forecasting process has been simplified by first estimating the effects of promotions. Oracle Retail recently released our next generation Oracle Retail Demand Forecasting (RDF) Cloud Service. Our client is one of the largest hypermarket chains in the world and had been using an outsourced service to calculate sales forecast. 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. Goal Bayesian forecasting, as developed by Oracle Retail, uses a sales plan as the starting point that is adjusted based on observed data. 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. If yes, generate the forecast and statistics using the Croston's method and move on to the next time series. If source-level forecasting is used and causal method is used both at the source-level and at the final-level, the effects from the final-level is used. Automatic Exponential Smoothing (AutoES) is an example of one such method that clients can select. In some instances, no promotional variables are found to be statistically significant. Retail Demand Forecasting - Elasticity measures. The binary writes the winning promotional variables effects back to the database. If no, move on to Step 9. The difficulty of automatically matching a new product to a previous product or profile. Developing compelling and unique assortments through optimized retail planning continues to be the key for retailers to compete in this increasingly complex industry. Oracle's Retail Demand Forecasting Cloud Service aims to help retailers boost inventory management by providing a single view of demand through the product lifestyle. 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. If no, move on to Step 9. 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. In the Oracle Retail approach to causal forecasting, the causal effects are obtained by fitting a stepwise linear regression model that determines which variables are most relevant and what effect those relevant variables have on the series. Does the time series qualify to forecast using the Multiplicative Winters method? Retail Demand Forecasting Cloud Service Forecast Analyst {username} : {useremail} Please provide us with feedback on your Oracle Learning Subscription experience! 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. 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. Low selling or relatively new products can use aggregated data from similar products/locations at a higher level in the hierarchy, generate forecasts using this data, and then spread these higher level forecasts back down to provide more accurate forecasts. Included is a discussion of the importance of confidence intervals and confidence limits, the time series methods used to generate forecasts, and how the best forecasting method is selected from a list of candidate models. When AutoES forecasting is chosen in RDF, a collection of candidate models is initially considered. If multiple plans are to be set up for different time periods, the domain should be set up with different forecasting levels for each time period of interest. If yes, generate a forecast and statistics using the Seasonal Regression method and move on to Step 6. 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). Causal Forecasting Method can calculate not only each individual promotion effect, but also the overlapping promotions effects. It is especially effective for new products with little or no historic sales data. The alpha is capped by 0.5 by default or the Max Alpha (Profile) value entered by the user. A Simple Moving Average model assumes that historical data is too short or noisy to consider seasonal effects or local trend and is based on the level of the series. 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). The regression method provides a much better forecast of the series than was possible with the other exponential smoothing algorithms. Demand Forecasting: Base Releases: 16.0: Release Notes: Installation Guide(Rev 2) User Guide RPAS Classic Client(Rev. When this forecasting method is selected, the forecasts are seen as trending either up or down, as shown in Figure 3-5. This curve represents the pre-season baseline forecast. 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. 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. RDF uses a variety of predictive techniques to generate forecasts of demand. For each product/location combination at the final forecast level, the problem consists of: Generating forecasts at each unique aggregation level, Using the train-test approach to evaluate the percent absolute error statistics for each. Helps FEMSA/OXXO upgrade RDF from version 10.0 to version 13.1, Oracle Retail Demand Forecasting Data sheet Oracle IT creates optimized inventory targets by item by location to meet demand and satisfy business and financial objectives. By using standard statistical distributional assumptions, RDF develops measures of uncertainty associated with forecast point estimates from these models. Predictions from these various models gives the estimated mean outcome. Does the time series contain the minimum data points to qualify to forecast using Winters methods? Sometimes it is difficult to capture seasonality, trend, or causal effects on the final-level (item/store) due to scarcity of the data. The system cannot distinguish between the promotional effect and the normal seasonality of the product. Croston's method is used when the input series contains a large number of zero data points (that is, intermittent demand data). Forecast Scorecard Dashboard: Evaluate forecast accuracy and identify opportunities. The binary reads the type of each promotional variable into the system. Bayesian Forecasting assumes that the shape that sales takes is known, but the scale is uncertain. If the effects are calculated at higher level than item/store, the effects are replicated down to item/store since the effects are multiplicative. These forecast updates can be critical to a company's success and can be used to increase or cancel vendor orders. The problem arises when attempting to forecast products with little or no history. In season, the pre-season forecast serves as a forecast plan to the Bayesian forecasting method. 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. Oracle Retail Demand Forecasting is highly flexible, and can be configured to take into account your unique demand drivers, like pricing or promotions. Put simply, the better the history of the variable being forecast, the stronger these statistical patterns are. In essence, this is the causal forecasting process where the generation of the baseline and the estimation of the promotional lifts are modified for items with short lifecycle. One solution would be to do source-level causal forecasting and then spread down to the final-level. Figure 3-6 Multiplicative Winters Exponential Smoothing. The resulting feed is aggregated and then spread down using rate of sales as a profile to create a lifecycle curve. The absence of a check mark in this measure causes the system to default to the Default Source level or the Source Level Override value if this has been set by you. Figure 3-2 Forecast Level Selection Process. 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. Using this method, the resulting forecast for the original series is calculated. The client selected Oracle Retail Demand Forecasting (RDF) and set up a project to implement the system and create a new centralised forecasting team. 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. Note that since each member of the model candidate list is actually a family of models, an optimization routine to select optimal smoothing parameters is required to minimize s for each model form (that is, to select the best model). Retail; Storage Management. In this case, the forecast equals the baseline. The scheduling of the Automatic Forecast Level Selection process (AutoSource) must be integrated with the schedules of other machine processes. These product/locations can be, but are not limited to: items new in the assortment, fashion items, and so on. 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). If this is the case, the method rejects itself out of hand and allows one of the other competing methods to provide the forecast. 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. Try one of the popular searches shown below. 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. 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. ORACLE RETAIL DEMAND FORECASTING. 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. The Simple forecast is re-seasonalized using the profiles. We have experimentally proven that source-level forecasting technique often improves the accuracy on the final-level. Shape is the selling profile or lifecycle that can be derived from a sales plan. Applies to: Oracle Retail Data Warehouse - Version 13.1 and later Information in this document applies to any platform. 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. Providing multiple forecasting methods is only valuable if the appropriate model can be selected in an accurate and efficient manner. Overall, a forecast point estimate is evaluated as: a function of level, trend, seasonality, and trend dampening factor. The purpose of statistical forecasting is to make the process of predicting future events both objective and quantitative. The forecast is calculated using the DD value multiplied by the profile. Cancel Submit Feedback. Initially the implementation of RDF was going to cover FMCG, hardlines, textiles and electronics and complete within one year. To accomplish the first task, a stepwise regression sub-routine is used. Oracle Retail Demand Forecasting enables you to manage a single forecast to drive profitable planning and operations reflecting customer preferences. Archive and Data Protection; Support Services. The profile may be loaded, manually entered, or generated by Curve. Link to Product Website: https://www.oracle.com. A promotion variable can represent an individual promotion or a combination of overlapping promotions. Based on the assumptions of the model that this method is trying to describe, versus the noisy data it is likely to receive, several exceptions to this regression technique are caught and corrected. We suggest you try the following to help find what you’re looking for: 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. The second noise-driven concession is to check the slope to determine if it is either too slight or too great. A combination of several seasonal methods. Increased forecast accuracy depends on the strength of these patterns in relation to background irregularities. Aggregate the preprocessed continuous day level promotional variables to the week level. 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. This release features robust machine learning, artificial intelligence and decision science, enabling retailers to gain pervasive value across forecasting and planning processes. For example, the overall sales level of the product, how quickly the product takes off, how the product's sales is affected by planned promotions. The combined effect is most likely attributed to one or the other event. Causal effects are applied to the daily profiles. Return the corresponding forecast and statistics for the system-selected forecast method and move on to the next time series. For each product/location combination, the best source forecast level identified by RDF appears in the Optimal source-level measure on this view. The following topics present fundamentals of the RDF statistical forecasting processes. Oracle Retail’s Demand Forecasting Cloud Service (RDF CS) empowers retailers to centralize demand forecasts — from operations and vendor collaboration to … In addition, the Additive and Multiplicative Winters models search for short-term trends and have difficulties with trends occurring inside the seasonal indices themselves. 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