International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 07 | July 2024
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p-ISSN: 2395-0072
Sales forecasting using machine learning V SAI UJWAL Student-Jain University-Banglore ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Accurate sales It is essential to forecast for
learning algorithms may improve their predictions by learning from past data, adjusting to shifting trends, and producing more accurate forecasts.
retail management, staffing levels, and marketing strategies. Walmart, the world's largest retailer, faces the challenge of predicting sales across its vast network of stores and a broad variety of products. The application of machine learning (ML) to sales forecasting has become increasingly popular. ML enables companies to examine past data, spot trends, and project future sales trends with confidence. This paper explores the application of ML for sales forecasting in Walmart. It discusses various ML algorithms that have been used for sales forecasting at Walmart, as well as the challenges and opportunities associated with implementing ML solutions in this domain. The paper also presents an analysis of using ML for anticipating sales at Walmart, demonstrating the effectiveness of ML can increase the accuracy of sales forecasting. The results of this study demonstrate the significance of ML for sales forecasting in Walmart and provide valuable insights for businesses seeking to adopt ML for their sales forecasting needs. By leveraging ML,Walmart can accomplish its business objectives, increase consumer satisfaction, and improve operational efficiency.
1.1 Benefits of Machine Learning for Walmart Implementing ML for sales forecasting can bring significant benefits to Walmart, including:
Key Words: Machine Learning , Data , Predictions , Forecasting
1.
Improved inventory management: Accurate sales forecasts enable Walmart to optimize inventory levels, lowering the possibility of overstocks and stock outs. Better customer happiness, lower expenses, and higher profitability result from this.
2.
Enhanced staffing planning: By predicting sales trends, Walmart can make informed decisions about personnel levels, making sure that there are enough workers on hand to satisfy client demand. This results in lower labour expenses and improved customer service.
3.
Targeted marketing campaigns: ML-based sales forecasts can be used to identify high demand products and target marketing campaigns accordingly. This leads to more effective promotions, increased customer engagement, and higher sales.
1.INTRODUCTION In the competitive retail landscape, accurate For firms to maximise inventories, sales forecasting is essential. management, employee numbers and marketing strategies. Walmart, the world's largest retailer, faces the challenge of predicting sales across its vast network of stores and A vast assortment of goods.(ML) offers a powerful tool is of sAles forecasting allows companies to examine past data, spot trends, and project future sales trends with confidence.
1.2 Challenges Involved in Machine Learning Implementation Despite the potential benefits, implementing ML for sales forecasting poses certain challenges: A.
Why Machine Learning for Sales Forecasting? Traditional forecasting methods, such as exponential smoothing and moving averages, rely on historical data and simple statistical techniques. While these methods can be effective for short-term forecasting, they often fail to capture the complex dynamics of retail sales, which are impacted by a number of variables, including consumer behaviour, economic trends, promotions, and holidays. Large and complicated datasets can be handled more effectively by machine learning algorithms, which can also spot subtle patterns and relationships that conventional methods can miss. Even in the face of uncertainty, machine
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Data integrity: A major factor influencing machine learning models' accuracy is the calibre of the training data. Walmart needs to guarantee the accuracy, consistency, and completeness of its sales data across various product categories and retailers.
B. Feature engineering: Selecting the right features from the available data is crucial for building effective ML models. Walmart needs to identify the features that are most relevant to sales forecasting and extract them from its complex data landscape. C. Model selection and tuning: Different ML Different algorithms have varying advantages and dis disadvantages .Walmart must decide which appropriate algorithm for its particular sales forecasting needs and carefully tune its parameters to achieve optimal performance.
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