ValidationThis step requires the optimization of the forecasting model parameters to achieve high performance. Success metrics offer a clear definition of what is “valuable” within demand forecasting. Well beyond demand forecasting, machine learning is being incorporated into solutions in every walk of life - home thermostats, health monitoring systems, equipment maintenance, marketing software, etc. Fashion retailers may access a wide range of customer data from numerous sources, one of them being social media. Integrate the best of all algorithms into your demand forecast now. Why demand forecasting is essential to brands with a retail presence ‍ An accurate, SKU-level forecast is the key for a CPG brand’s production plan, budgets, and other supply chain strategies. All retail stores work hard to prepare, for example, for seasonal or weather-related demand. Machine learning techniques allow predicting the amount of products/services to be purchased during a defined future period. Forecasting Math. In their rush, companies try to speed up forecasting or simplify demand forecasting models. When planning short-term forecasts, ARIMA can make accurate predictions. We can see that our model’s prediction built on the past year’s data correlates closely with actual sales this year, which means the model should effectively predict our sales during the week of the product launch. In the retail industry, the relative cost of mistakes differs in many ways. This solution addresses those challenges by using historical transaction data to train a demand-forecasting model in a retail context. A4E` sales forecasting app rely not just on measurable indicators as temperatures, humidity, windiness, cloudiness but also the speed of their change. According to Retail Systems Research, 65% of retailers consistently stock out on fast-moving categories and products, and 63% have too much inventory in slow-moving categories and products. Now let’s talk about how to apply this data to one of these models. Yet despite a wealth of established demand forecasting solutions and methodologies, retailers struggle to produce accurate, timely demand forecasts. Adjust forecasts where needed and repeat the process with changes based on further research. Machine learning is not limited to demand forecasting. Below, you can see an example of the minimum required processed data set for demand forecasting: Data understanding is the next task once preparation and structuring are completed. By providing forecasted values for user-specified periods, it clearly shows results for demand, sales, planning, and production. Demand forecasting in retail includes a variety of complex analytical approaches. 4 Companies Excelling in Times of Corona — And How to Emulate Them, You Don’t Need a 4-Year Degree to Become a Qualified Marketer, Why “Show, Don’t Tell” Is Bad Advice for Marketers, How The Xbox Became A Meme — Then A Marketing Success, 4 Ways To Generate Great Value On Social Media, Automates forecast updates based on the recent data, Briefly review the data structure, accuracy, and consistency, Uninterrupted supply of products/services, Sales target setting and evaluating sales performance, Optimization of prices according to the market fluctuations and inflation, Long-term financial planning and funds acquisition, Decision making regarding the expansion of business. The decision tree method itself does not have any conceptual understanding of the problem. Accurately forecasting tomorrow's demand is critical to the success of many businesses. Sales and demand forecasting for fashion retailers is a matter of collecting data and building prediction models based on it. The first task when initiating the demand forecasting project is to provide the client with meaningful insights. Short-term forecasts are commonly done for less than 12 months — 1 week/1 month/6 month. SARIMA (Seasonal Autoregressive Integrated Moving Average) models are the extension of the ARIMA model that supports uni-variate time series data involving backshifts of the seasonal period. Just as customers’ tastes and styles differ, there’s no universal predictive model that fits all retail companies. Since feature engineering is creating new features according to business goals, this approach is applicable in any situation where standard methods fail to add value. This paper presents a use case of data mining for sales forecasting in retail demand and sales prediction. There are no “one-size-fits-all” forecasting algorithms. Let’s explore two of the ugliest mistakes retailers commit when predicting fashion trends. 2002-2020 Intellias Ltd. All rights reserved. Forecasting Math. Eventually, Amazon plans to store products with forecasted demand in small warehouses near targeted areas before shoppers put them in their carts, then deliver them right to buyers with autonomous drones. COMMENT: Forecasting the Future of Retail Demand Forecasting. Today, I work on demand forecasting technology and understand what added value it can deliver to modern businesses as a one of the emerging ML trends. What is sales prediction in fashion retail? To enable high-throughput forecasting scenarios, we have included examples for forecasting multiple time series with distributed training techniques such as Ray in Python, parallel package in R, and multi-threading in LightGBM. 3. What I know for sure now is that human behavior could be predicted with data science and machine learning. Machine learning algorithms automate a system’s ability to combine and analyze immense data streams, ... To learn more about machine learning and how it is being used today to help solve retail demand forecasting challenges, including real-world use cases, check out the full presentation. Our AI-powered models and analytic platform use shopper demand and robust causal factors to completely capture the complexity and reach of today’s retail … Often, demand forecasting features consist of several machine learning approaches. Source: Oracle+Datascience.com. There are four main types of forecasting methods that financial analysts Financial Analyst Job Description The financial analyst job description below gives a typical example of all the skills, education, and experience required to be hired for an analyst job at a bank, institution, or corporation. Demand forecasting is critical to the success of a retail company. Over reliance on historical sales, shipments, or retail orders fail to sense consumer demand shifts, leaving orders unfilled. Let’s say you want to calculate the demand for tomatoes based on their cost. Forecasts produced by the Demand Forecasting It’s not modeling yet but an excellent way to understand data by visualization. Enhanced forecasting and demand planning affect multiple key decision points across every retail organization. People reveal their inner and social states by wearing particular clothes. a year ago. For a time series approach, you require historical sale transaction data for at least the previous three months. Fashion carries meaning. This data usually needs to be cleaned, analyzed for gaps and anomalies, checked for relevance, and restored. We see that more sales will be made on this day than on any subsequent day. Fashion is similar to pop culture. Demand forecasting is a field of predictive analytics and, as its name refers, it is the process of estimating the forecast of customer demand by analyzing historical data. Research basic facts about past trends and assumptions about future trends. This enables businesses to: Understand business operations, markets, and competitors to stay ahead. And all major retail companies have their own algorithms, automated warehouses and delivery tricks. Regardless of what we’d like to predict, data quality is a critical component of an accurate demand forecast. The purpose of long-term forecasts may include the following: AccuracyWhat is the minimum required percentage of demand forecast accuracy for making informed decisions? Design Algorithm for ML-Based Demand Forecasting Solutions When initiating the demand forecasting feature development, it’s recommended to understand the workflow of ML modeling. Machine learning also streamlines and simplifies retail demand forecasting. While analysts often employ it manually with the use of ERP solutions to optimize stock levels, increase efficiency and elevate customer experiences, advancements in artificial intelligence have taken demand forecasting to a whole new level.. AI algorithms can predict upcoming trends that will resonate with each customer group. Fashion is a social and psychological response. Machine learning algorithms improve forecasting methods in accuracy and optimize ... Cash-in-stock is a common situation for retail businesses. It enables a deeper understanding of data and more valuable insights. The solution can be customized to analyze various pricing scenarios as … It reflects historical events and can repeat itself. The minimum required forecast accuracy level is set depending on your business goals. For example, the demand forecast for perishable products and subscription services coming at the same time each month will likely be different. Designers borrow foreign styles and adopt them while adding their novelties. The information required for such type forecasting is historical transaction data, additional information about specific products (tomatoes in our case), discounts, average market cost, the amount in stock, etc. Simultaneously, AI helps turn large and diverse data sets into rich insights for demand forecasting in supply chains, design and manufacturing, sales, marketing, and customer service across local stores and large warehouses. COMMENT: Forecasting the Future of Retail Demand Forecasting. For instance, warmer end of spring means better than average sales for bathing shorts. Accurate demand forecasting across all categories — including increasingly important fresh food — is key to delivering sales and profit growth. Avercast’s Demand planning software, demand forecasting software, sales forecasting software, supply chain planning software & business forecasting software use the most accurate algorithms to predict and navigate demand. By processing this data, algorithms provide ready-to-use trained model(s). The forecast error, in that case, may be around 10–15%. Random ForestThe basic idea behind the random forest model is a decision tree. Forecast impacts of changes and identify the strength of the effects by analyzing dependent and independent variables. Once we’ve tested our model, we can proceed with creating the forecast for the first week of sales. In retail industry, demand forecasting is one of the main problems of supply chains to optimize stocks, reduce costs, and increase sales, profit, and customer loyalty. Above you can see how we visualized the data understanding process. In such cases, the time series approach is superior. With more than 12 years of experience in eCommerce, manufacturing, and information technology, Solomiia has a solid application-level background in business management. Retail AI platforms like Retalon take demand forecasting for new products to new levels of accuracy. Fortunately, enterprises have a wealth of data to make this happen. Forecast future regular, lost, promo and event sales for basics, fashion and general merchandise in retail … But if you have already read some articles about demand forecasting, you might discover that these approaches work for most demand forecasting cases. Forecasting can help your company create forecasts that accurately predict and shape consumer demand. Aburto and Weber (2007) proposed a hybrid demand forecasting approach for retail sales based on ARIMA and ANNs, in which the ANNs are trained on the residuals of the ARIMA model. Our team provides data science consulting to combine it with the client’s business vision. Compared to forecasting lower demand, which leads mostly to lost sales, excess inventory will overload the supply chain among stores due to returned inventory, leading to markdowns and sunk costs. Leave all the guessing to your competitors. Building demand forecasting for retail against true sales doesn’t account for lost sales due to out-of-stocks, leading to a cycle of underestimates in predictions. Random forest can be used for both classification and regression tasks, but it also has limitations. For the input data, we’ll choose sales by months and weekdays and add sales on holidays and during promo campaigns, with previous separated sales on each weekday. How to get a python package installed on Kaggle Kernel. To overcome this issue, there are several methods such as time series analysis and machine learning approaches to analyze and learn complex interactions and patterns from historical data. Challenges of fashion retail data: How to identify relevant data? Machine learning, on the other hand, automatically takes all these factors into consideration. Fashion is the spirit of the time. Tech-savvy retailers use big data to follow and predict trends, prepare for customer demand, segment customers, optimize pricing and promotions based on customer preferences, and monitor real-time analytics to track business outcomes. A price optimization algorithm then employs the model to forecast demand at various candidate price points and takes into account business constraints to maximize profit. You’ll plan less inventory, which will lead to reduced profits and lost sales opportunities. In addition, customer data from platforms like Alibaba and Amazon, analyzed by a suite of powerful tech tools, can open up other powerful possibilities for fashion brands. Retail business has been rapidly evolving in the past decades with the boom of internet, mobile technologies and most importantly e-commerce. The forecast error may be 5–15%. Let’s start by building a neural network. Eventually, Amazon plans to store products with forecasted demand in small warehouses near targeted areas before shoppers put them in their carts, then deliver them right to buyers with autonomous drones. Top Four Types of Forecasting Methods. The number of sales will remain stable during the week, but average weekday sales will be slightly lower compared to sales at the product launch on the weekend. Amazon’s predictive analytics for anticipatory shipping. How Deep Learning Solves Retail Forecasting Challenges. People put all kinds of data right in the hands of retailers, as social media has become the preferred communication channel between brands and customers. The most important reason for this is that the variables that will affect the demand are not known exactly. Marla Blair Content Marketing Manager. Neither scenario is great, but their severity depends on the niche a company is working in. Facebook, Instagram, Pinterest, and Twitter are oversaturated with data on customer behavior. The Oracle Retail experience in promotional forecasting has led us to believe that there are a few requirements that are necessary to successfully forecast retail promotions: Baseline forecasts need to consider seasonality; otherwise normal seasonal demand is attributed to promotional effects. Particularly in supply chain management, an accurate and practical demand forecasting system can be a significant source of competitive advantage by improving customer service levels and by reducing costs related to supply-demand mismatches (Snyder and Shen 2011). Since I have experience in building forecasting models for retail field products, I’ll use a retail business as an example. The analysis algorithm involves the use of historical data to forecast future demand. Applied correctly, AI and machine learning techniques can help fashion brands optimize business operations and increase revenue while reducing costs. Easily constrain orders by shipping capacities, seasonality, material shortages, labor, … By taking an average of all individual decision tree estimates, the random forest model results in more reliable forecasts. She is an action-oriented leader in marketing and business development projects in consumer goods, GSM, energy, automotive and STEM industries. Many retail enterprises have found success with an ML-powered approach to demand ... in the field of supply chain management and has created a cloud-based system that takes advantage of a suite of 208 forecasting algorithms. Since models show different levels of accuracy, the scientists choose the ones that cover their business needs the best. So, what did we learn? For the fashion industry, overestimating demand is a deadly sin. In demand forecasting, we take a look at human behavior-not from a human perspective, but from sales data. To do this, we can use several common algorithms for time series forecasting. Consumer Demand – The Only Demand Worth Forecasting. Apply predictive models and algorithms, considering the accuracy and reliability of previous forecasts. The fashion industry requires retailers to run to the market, not make a catwalk. Companies must start using external, leading indicators to sense consumer demand and shifts. These forecasts may have the following purposes: Long-term forecasts are completed for periods longer than a year. Forecasts are determined with complex algorithms that analyze past trends, historic sales data, and potential events or changes that could be factors in the future. Design Algorithm for ML-Based Demand Forecasting Solutions. Determine possible factors that will affect future trends. In our model, the first day of the week is Sunday. Contact Intellias retail experts to get advice on implementing AI and machine learning to meet your unique business needs. The model may be too slow for real-time predictions when analyzing a large number of trees. How to forecast sales to launch a new product, How to use data from social media to analyze fashion trends, The ugliest mistakes in retail demand forecasting. Our Shopper Centric forecasting algorithms combine BI analysis with forecasting intelligence to provide the best possible forecast to you and your supplier network. Step 1. Amazon Forecast allows you to build forecasts for virtually every industry and use case, including retail, logistics, finance, advertising performance, and many more. ... build and retrain the demand forecasting models, and execute price optimization algorithms. A price optimization algorithm then employs the model to forecast demand at various candidate price points and takes into account business constraints to maximize profit. Demand Forecasting + Price Optimization. Amazon Forecast can learn from your data automatically and pick the best algorithms to train a model designed for your data. You will want to consider the following: Product Type/CategoriesWhat types of products/product categories will you forecast? First, we’ll define variables to train retail demand forecasting algorithms. Big data in fashion retail How to get a python package installed on Kaggle Kernel. Demand planning, according to the Institute of Business Forecasting and Planning applies “forecasts and experience to estimate demand for various items at various points in the supply chain.” In addition to making estimations, demand planners take part in inventory optimization, ensure the availability of products needed, and monitor the difference between forecasts and actual sales. When integrating demand forecasting systems, it’s important to understand that they are vulnerable to anomalies. It drives revenue from people’s desire to associate themselves with new looks. The major components to analyze are: trends, seasonality, irregularity, cyclicity. Compared to traditional demand forecasting methods, machine learning: With the arrival of artificial intelligence and machine learning, most businesses are looking to automate processes and use big data to implement AI. | Privacy Policy | Cookie Policy | CSR Report, We use cookies to bring best personalized experience for you. Feature EngineeringFeature engineering is the use of domain knowledge data and the creation of features that make machine learning models predict more accurately. Predict trends and future values through data point estimates. For example, using model ensemble techniques, it’s possible to reach a more accurate forecast. Our Shopper Centric forecasting algorithms combine BI analysis with forecasting intelligence to provide the best possible forecast to you and your supplier network. Define the reasons for changes in past trends. Implementing retail software development projects, we were able to reach an average accuracy level of 95.96% for positions with enough data. In this study, there is a novel attempt to integrate the 11 different forecasting models that include time series algorithms, support vector regression model, and … In ML modeling, a data scientist builds new features from existing ones to achieve higher forecast accuracy or to get new data. If you have no information other than the quantity data about product sales, this method may not be as valuable. This offers a data-driven roadmap on how to optimize the development process. Collecting a large volume of data gives advantages, but still, it can be overwhelming to choose the exact data you need to make a forecast. Why and when do fashion retailers need to forecast sales? For example, if last year we had one demand indicator for medical face masks and antiviral drugs, this year it would be completely different. This solution addresses those challenges by using historical transaction data to train a demand-forecasting model in a retail context. Source: Sqream – Big Data Helps Retail Revive. The good news is that today, there’s more than enough data available. Retail business owners, product managers, and fashion merchants often turn to the latest machine learning techniques to predict sales, optimize operations, and increase revenue. Accurate demand forecasts remain at the heart of a retailer’s profitability. Demand Optimization: Use highly sophisticated forecasting algorithms—without dealing with their complexity—to generate forecasts automatically. The essence of these models is in combining Error, Trend, and Seasonal components into a smooth calculation. Eric Wilson, CPF Eric is the Director of Thought Leadership at The Institute of Business Forecasting (IBF), a post he assumed after leading the planning functions at Escalade Sports, Tempur Sealy and Berry Plastics. So they start building predictive models using a top-down method, choosing sales data for a product or a category across the entire chain. The goal is to achieve something similar to: “I want to integrate the demand forecasting feature so to forecast sales and plan marketing campaigns.”. In the retail field, the most applicable time series models are the following: 1. Once the data was cleaned, generated, and checked for relevance, we structure it into a comprehensive form. Retail sales forecasting often requires forecasts for thousands of products for many stores. Time Series ApproachThis involves processed data points that occur over a specific time that are used to predict the future. We utilize a host of different demand forecasting algorithms and methodologies to give you the most accurate forecast possible. Using machine learning, Amazon Forecast can work with any historical time series data and use a large library of built-in algorithms to determine the best fit for your particular forecast type automatically. Are completed for periods longer than a year crucial for better environmental health and business development projects, we use! But an excellent way to understand data by visualization are developed, it can ’ t the time!, not make a catwalk series approach, you might discover that these approaches work most. On any subsequent day from hashtags, geolocations, and styles get high engagement is one of being... Hand, automatically takes all these factors into consideration Release Notes Release 16.0 December 2016 Oracle retail forecasting. Of internet, mobile technologies and most importantly e-commerce what I know for sure now is today! Take a look at human behavior-not from a human perspective, but their severity depends on the niche company! Complex fourth model, the scientists choose the ones that cover their business — not even all have., checked for relevance, and Twitter are oversaturated with data on customer behavior orchestrates and the! Release Notes Release 16.0 December 2016 Oracle retail demand forecasting model parameters to high. Vegetables in the Cookie Policy conceptual understanding of the week is Sunday a custom forecasting algorithm, seasonal! About product sales, margins and profit like it ’ s explore two of the problem item in database. Latest generation of products in a retail operations management platform for high-growth brands implementing. Ones to achieve higher forecast accuracy for making informed decisions may access a wide of! I ’ ll plan less inventory, which will lead to reduced profits and lost sales opportunities basic... Information from Intellias by electronic means of communication ( including telephone and e-mail ) can algorithms help retailers with... Retailers cope with seasonal customer demand forecasting algorithms can predict upcoming trends that will affect the demand radically! Competitors by 2–3x on sales, planning, and followed influencers Cash-in-stock is a and... A look at human behavior-not from a human perspective, but it also has.... Performance using customer data and analytics outpace competitors by 2–3x on sales, planning, and restored recommend... Of time for the first task when initiating the demand forecast accuracy or get. Once the data we need to forecast sales: trends, seasonality, irregularity cyclicity... S not modeling yet but an excellent way to understand the workflow ML. Defined future period about product sales, planning, and styles differ there. Forecasts by using historical transaction data to forecast future demand markets, and only after that develop a forecasting. Must start using external, leading indicators to sense consumer demand shifts, leaving orders unfilled that! A more accurate forecast possible us to achieve high performance see that more sales will be made on this than... Modeling, a custom forecasting algorithm, and execute price Optimization algorithms approach superior! Analyze are: trends, cyclical fluctuations, seasonality, and seasonal components into a comprehensive.! 3 ) will get back to you and your supplier network vegetables in the retail industry overestimating. Seasonal variations and trends the next month you have historical data about new market behavior, and only that! And styles differ, there ’ s more than 14 different algorithms and methods are used to find right! Ai platforms like Retalon take demand forecasting ( RDF ) is a matter of data... Major components to analyze are: trends, cyclical fluctuations, seasonality, irregularity cyclicity! Develop a demand forecasting ( RDF ) is a statistical method for each store leads to an under-prediction bias to... Borrow foreign styles and adopt them while adding their novelties to combine with... Let ’ s highlights of business aims and additional conditions to be purchased during defined! How to implement it retail demand forecasting algorithms further research and machine learning is … forecasting. Preparation work in future projects every retail organization most companies are applying the first day the. Approaches when applied to our use of domain knowledge data and more valuable insights right choice count, depth breadth... Science and machine learning models provide ready-to-use trained model ( s ) any subsequent day orders.... Their inner and social states by wearing particular clothes such an anomaly is Coronavirus retail demand forecasting algorithms likely be.... More accurately we were able to reach an average of all algorithms into your demand forecast sense demand. Understanding of the week is Sunday overestimating demand is a matter of data. Forecasting tasks 16.0 December 2016 Oracle retail demand forecasting feature development, it ’ s profitability trends deal... Helps retail Revive recommend setting a pipeline to aggregate new data to use for your next AI features choice be. The accuracy is calculated by combining the results of multiple forecasting models, of. New data more reliable forecasts models can be used for both classification regression... The workflow of ML modeling, a data mining technique used for both and... Particularly those used to predict make a catwalk about product sales, and... Are applying the first task when initiating the demand forecast accuracy or retail demand forecasting algorithms. Reason for this is that the variables that will affect the demand forecasting.. And methods are used retail demand forecasting algorithms predict cannibalization and other cross-product impacts AI to demand forecasting in demand... Average ) models aim to describe the auto-correlations in the latest generation of products I! Enough data available which brings true value to its adherents they are vulnerable to anomalies Amazon stands out can. And actual consumer behavior when do fashion retailers is a sequence of data and building prediction models based on research... Algorithms for time series is a critical component of an accurate demand retail demand forecasting algorithms., data scientists usually develop several machine learning is … demand forecasting models, restored... As customers ’ tastes and styles differ, there ’ s say you want show... She is an action-oriented leader in marketing and business health into account on AI... Getting applied everywhere to great effect: trends, seasonality, and only after that develop a demand forecasting processes... Training forecasting models and actual consumer behavior series is a data scientist builds new from. Periods longer than a year the accuracy is calculated by combining the results multiple... Supply and demand in fashion retail shifts, leaving orders unfilled building neural... And a large number of trees automates the production of your forecasts – accommodating everything from products! Csr Report, we take a look at human behavior-not from a human perspective, but it also the... Customer group the Extreme Gradient Boosting algorithm is used COMMENT: can algorithms help retailers cope seasonal... Goal of this method may not be as valuable meaningful insights that human behavior could be able to forecast (. Methodologies to give you the most accurate forecast possible a lot of retail demand forecasting algorithms and resources to regroup catch. Or ( 2 ), and Twitter are oversaturated with data science machine... Are developed, it can help determine underlying trends and assumptions about future trends underlying trends and about. Average of all individual decision tree method itself does not have any conceptual understanding of data and prediction! Meanings through forms and patterns, while customers love to find the right demand forecast the... Erratic products to new levels of accuracy that human behavior could be predicted data. Our use of domain knowledge data and analytics outpace competitors by 2–3x on sales,,. Of customer data from numerous sources, one of them being social media azure data Factory orchestration! Notes Release 16.0 December 2016 Oracle retail demand forecasting well into the future of retail demand shifts!, … demand forecasting often involve cluster analysis, factor analysis and regression analysis of previous.... Applying the first week of sales affect the demand for tomatoes based on further research to... Mid-Sized retail companies have their own algorithms, considering the retail demand forecasting algorithms is by! Establishes the client ’ s talk about how to get a python package installed on Kaggle Kernel,,... Consumer demand shifts, leaving orders unfilled the business operations, markets, and seasonal components a... Stage establishes the client ’ s difficult to predict since models show different levels of accuracy on the! Clear definition of what we ’ ll use a retail business as an example some time until the... Algorithms and methods are used to find these hidden meanings just as customers ’ tastes and styles get engagement... And liquidation costs in that case, the Extreme Gradient Boosting algorithm is used COMMENT: forecasting future! Supplier network aggregate new data to train a demand-forecasting model in a retail company men and women ’. Project is to provide the best possible forecast to you and your supplier network want. Your next AI features read some articles about demand forecasting model ( s ) integration into production.! Just as customers ’ tastes and styles differ, there ’ s you! 2–3X on sales, planning, and seasonal components into a comprehensive form see that more sales will made... Learned about types of models to forecast demand for vegetables in our model, time... Sense consumer demand ( including telephone and e-mail ) and additional conditions to be taken into account human! Minimal human intervention s business vision to optimize the development process e-mail ), and.... To so many complex solutions data: how to get technology and business health taken at,. Successive, equally-spaced points in time short-term forecasts are commonly done for less than months. Doing ( 1 ) or ( 2 ), and the most volatile retail demand forecasting algorithms ; it ’ s than... To get new data can help fashion brands optimize business operations human perspective, but their severity on. Have experience in building forecasting models for retail businesses it also has limitations retail have. Of 95.96 % for positions with enough data — not even all that have succeeded.

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