Many household products are sold by various subsidiaries of the retail store network which are geographically located at various locations. Supply chain inefficiencies will occur at different locations when the market potential will not evaluated by the retailers. Many times it is not easy for the retailers to understand the market condition at various geographical locations. The organization of retail store network has to understand the market conditions to intensify its goods to be bought and sold so that many number of customers get attracted in that direction. Business forecast helps retailers to visualize the big picture by forecasting the sales we get a general idea of coming years if any changes are needed then those changes are done in the retail store's objective so that success is achieved more profitably .It also helps the customers to be happy by providing the products desired by them in desired time, when the customers are happy then they prefer the store that provides all the resources they need to their satisfaction by this the sales in the particular store in which the customers purchase more items increases causing more profit. The forecasting of sales helps to know the retailers the demand of the product. In this paper we make an attempt by understanding the retail store business's driving factors by analyzing the sales data of Walmart store that is geographically located at various locations and the forecast of sales for coming 39 weeks is done. By sales forecasting the retail networks are supported so that the resources can be managed efficiently.
See Full PDF See Full PDFTo solve all kinds of problems in sales forecasting in our country, we must fully understand the impact of traditional sales forecasting at present, and understand the innovative development that big data will bring to sales forecasting through case analysis, which is also the purpose and significance of this paper. Firstly, this paper introduces the background and importance of the topic; secondly, it studies the new characteristics of sales forecasting in the big data environment, and discusses the current situation based on the analysis of big data; thirdly, it focuses on the impact of significant data sales forecasting, and emphasizes the opportunities brought by big data technology while analyzing the existing problems in China at the present stage; finally, combined with the current situation, it puts forward the suggestions for the development of big data technology Strategy.
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International Journal for Research in Applied Science & Engineering Technology (IJRASET)
In this contemporary era, supermarket and general stores have been scrutinizing the sales record for knowing the demands of customers and to find the straggles in general trend. So, as the data is available a predictive model has been built using various algorithms such as Polynomial regression, XGBoost, Linear regression, and Ridge regression techniques for forecasting the sales of a business in advance. The prediction is based on sales of supermarket for various outlets to calibrate the business model to expected outcomes. With the results and analysis provided by the model retailers can know the sales volume in advance.
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International Journal of Scientific Research in Science, Engineering and Technology
Big Marts, which are distribution centers for supermarket chains, now keep tabs on sales volume and revenue numbers for each product to anticipate domestic consumption and adjust inventory control. Examining the data warehouse's server database often reveals inconsistencies and overarching patterns. Companies like Big Mart can use the data with a variety of machine learning techniques to predict future product sales. Many different machine learning algorithms, including Linear Regression, Ridge Regression, Lasso Regression, Decision Tree Regression, Random Forest Regression, Support Vector Regressor, Adaboost Regressor, and XGBoost Regression, have been employed in this project to forecast Big Mart product sales. We find that XGBoost Regression performs the best in predicting sales volume among the listed algorithms. To this end, we have developed a model with XGBoost Regression and optimized it for maximum precision. This model is available through a flask application; users simply log in, specify the product's parameters, and receive sales forecasts.
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International Journal for Research in Applied Science & Engineering Technology (IJRASET)
Currently, Big Marts, the equivalent of supermarket run-canters, keep track of each item's sales data in order to forecast implicit consumer demand and update force operation. In order to estimate the volume of bargains for each item for the association's stock control, transportation, and logistical services, each request aims to offer verified and limited time deals to attract numerous guests over time. By intentionally entangling the data store of the data storage, anomalies and broad trends are continuously uncovered. Retailers like Large Mart can use the performing data to predict future transaction volume utilising a variety of machine learning techniques, such as big bazaar. The present machine learning algorithm is very sophisticated and offers methods for predicting or reading deals with any kind of association, which is very beneficial to Always better prophecy is useful in creating and refining commercial marketing plans, which is particularly useful. The development of a prediction model utilising linear retrogression and Ridge retrogression methods for analysing the transactions of a company like Big-Mart, and it was found to perform better than models themselves. additional Measurable factors methods with regression, machineaccumulative (ARIMA), and Integrated Using Moving Average, (ARMA) machine-cumulative Moving normal, create many transactions that read morality.
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Supermarkets and their franchises are increasing a lot in recent times. At this moment to increase their sales, they have to predict the sales of the items. Thereby they can protect themselves from losses and they can generate profits. So, this analysis will require a lot of time and effort. So, we proposed a machine learning model that will use the XGBoost Regressor to predict the sales of the items. Thereby marts can plan their recruitment strategy, perceive challenges early, motivate the sales team, predict revenue, aid future marketing plans, and helps in many more ways.
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International Journal of Advanced Trends in Computer Science and Engineering
Sales forecasting is an important when it comes to companies who are engaged in retailing, logistics, manufacturing, marketing and wholesaling. It allows companies to allocate resources efficiently, to estimate revenue of the sales and to plan strategies which are better for company's future. In this paper, predicting product sales from a particular store is done in a way that produces better performance compared to any machine learning algorithms. The dataset used for this project is Big Mart Sales data of the 2013.Nowadays shopping malls and Supermarkets keep track of the sales data of the each and every individual item for predicting the future demand of the customer. It contains large amount of customer data and the item attributes. Further, the frequent patterns are detected by mining the data from the data warehouse. Then the data can be used for predicting the sales of the future with the help of several machine learning techniques (algorithms) for the companies like Big Mart. In this project, we propose a model using the Xgboost algorithm for predicting sales of companies like Big Mart and founded that it produces better performance compared to other existing models. An analysis of this model with other models in terms of their performance metrics is made in this project. Big Mart is an online marketplace where people can buy or sell or advertise your merchandise at low cost. The goal of the paper is to make Big Mart the shopping paradise for the buyers and a marketing solutions for the sellers as well. The ultimate aim is the complete satisfaction of the customers. The project "SUPERMARKET SALES PREDICTION" builds a predictive model and finds out the sales of each of the product at a particular store. The Big Mart use this model to under the properties of the products which plays a major role in increasing the sales. This can also be done on the basis hypothesis that should be done before looking at the data.
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