Operations Analysis of Walmart

Introduction

Background and Significance

The retail industry is highly competitive, and efficient inventory and supply chain management are crucial to the success of any retailer. Walmart is the largest retailer in the world, and with thousands of stores globally, managing inventory levels and supply chains can be a daunting task. Therefore, developing a sales forecasting model that incorporates different factors affecting sales can help Walmart optimize their inventory levels and supply chain management, ultimately leading to increased sales and customer satisfaction.

Moreover, understanding the correlation between store type and size with average weekly sales data can help Walmart make strategic decisions about store locations and layouts. For example, Walmart can use this information to determine which store types and sizes are most successful in which locations and use that data to make informed decisions about expanding or consolidating stores.

Furthermore, the project's significance goes beyond Walmart's operations and can be applicable to the broader retail industry. Developing a model that can accurately predict weekly sales and optimize inventory and supply chain management can help other retailers reduce waste, lower costs, and increase profitability.

Overall, the significance of this project lies in the potential to improve Walmart's operations, increase profitability, and provide a framework that other retailers can follow to improve their operations as well.

Project Objective

The goal of this project is to develop a sales forecasting model for individual Walmart stores that incorporates factors that affect weekly sales. The objective is to provide Walmart store managers with insights and recommendations that can be used to make data-driven decisions in areas such as sales forecasting, marketing strategies, inventory management, pricing, employee management, and customer retention.

Literature Review

Overview of Sales Forecasting Models

Sales forecasting models are essential for businesses to predict future sales and adjust production, inventory, and supply chain management accordingly. Qualitative, quantitative, and hybrid models are commonly used. Qualitative models use expert opinions and surveys, while quantitative models use statistical analysis to forecast weekly sales based on historical sales data and relevant variables. Time-series forecasting and multiple linear regression models are common types of quantitative models. Accurate sales forecasting is critical to a company's profitability and operational efficiency.

Correlation Analysis in Retail Operations

Correlation analysis is a critical tool in retail operations to understand the relationships between different factors impacting sales. This could include store location, customer behavior, pricing, marketing, and inventory levels. Several research studies have utilized correlation analysis to identify the impact of promotions, weather patterns, or customer demographics on sales. Different statistical methods such as multiple regression analysis can be employed to conduct correlation analysis. However, there are limitations and challenges of using correlation analysis in retail, such as the difficulty of establishing causation, the potential for spurious correlations, and the need to account for confounding variables. Future research directions could include identifying new variables that impact sales, using more sophisticated statistical methods, or developing predictive models based on correlation analysis to forecast sales more accurately.

Methodology

Data Cleaning

The dataset used in this analysis was obtained from two sources - the Walmart Sales Forecast dataset on Kaggle and a Walmart.csv file from a GitHub repository. The data spans an 8-store period from November 2010 to October 2012, and includes weekly sales data for each store.

Data Preprocessing

The data was preprocessed by removing missing values and duplicates, checking for data consistency, and converting categorical variables into numerical ones. We also analyzed the data to identify outliers and removed them to prevent their impact on the model.

Correlation Analysis

We conducted correlation analysis to identify the relationships between different factors that impact sales, including store type, size, and location, as well as marketing, pricing, and inventory management. We used Pearson's correlation coefficient to measure the strength and direction of the linear relationship between variables.

Model Development

We utilized different techniques to develop our sales forecasting model. For time series analysis, we employed exponential smoothing and moving average techniques to forecast the future sales of individual Walmart stores. Additionally, we used trend projection, which considers the historical sales data, to forecast the future trends. Moreover, we utilized multiple linear regression, which considers various factors such as store type and size, holidays, and promotions, to develop a robust model. To evaluate the accuracy of our models, we utilized Mean Squared Error (MSE) and R-squared values.

Recommendations

Based on the results of the sales forecasting model, we provided recommendations for store managers on areas such as sales forecasting, marketing strategies, inventory management, pricing, employee management, and customer retention. These recommendations were derived from the correlation analysis and feature selection steps in our methodology.

Our Prediction Model

After the implementation of different forecasting techniques of Moving Average, Exponential Smoothing, Trend Line Projection, and Multiple Linear Regression, comparing the Mean Squared Error gave us the conclusion that Multiple Linear Regression is the most accurate method to build our prediction model.

Assumptions

It is assumed that the data is accurate and consists of all influencing factors and thus the prediction model is created based on the analysis of this data.

Limitations

• Firstly, Walmart sells multiple products, but the data was not available for the sales of each individual product, thus we could not go deep into the inventory management aspect of individual products.

• Secondly, sales patterns may change over time, and using outdated or irrelevant data could lead to incorrect conclusions.

• Thirdly, the analysis only captures correlations and not causation. There may be other underlying factors that are causing changes in sales patterns such as demographics, environmental factors, etc.

• Finally, the analysis may not capture all relevant factors. There may be other variables that could influence sales, such as marketing campaigns or changes in product availability, that are not included in the analysis.

Analysis and Conclusion

From the analysis, we can conclude:

• Type A stores have maximum sales, suggesting the potential for opening more stores in big cities.

• Store 8 has the highest contribution, followed by Stores 1, 2, and 6. The contribution percentage may differ from the actual revenue generated by each store.

• The top 10 departments that Walmart should focus on are Candy & Tobacco; Personal Care, Health and Beauty Aids; Girl’s Wear; Ladies’ Wear; Plus Size; Ladies Outerwear; Pharmacy; OTC Pharmacy, Health, and Wellness; Menswear; and Horticulture and Live Plants.

• Sales are highest during November and December, being the holiday peak season.

• The most significant variable in the analysis of the sales forecast is the size of the store, and the weekly sales are least dependent on the Fuel Price.

• Table 1 – 4 suggests the top 5 departments whose products should always be in stock for Store 1 – 4.

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Recommendations

Resource Allocation

A store manager can use the predicted sales figures to allocate resources such as labor, inventory, and marketing budgets to meet the expected demand. This can help them optimize the use of resources to maximize profitability and avoid unnecessary costs.

Forecasting Revenue

Predicting the week's aggregate sales can help a store manager forecast revenue for the upcoming week. This information can be used to develop better financial plans, budgets, and business strategies that are more aligned with the store's goals.

Setting Sales Targets

The predicted sales figures can be used to set sales targets for the store's staff. This can help motivate employees and provide them with clear goals to strive for, which can lead to increased productivity and better sales performance.

Inventory Management

A store manager can use predicted sales figures to manage inventory levels better. By forecasting the sale for different departments, they can ensure that they have enough inventory on hand to meet customer demand, but not so much that it leads to overstocking or waste.

Marketing and Promotions

Predicted sales figures can help a store manager design targeted promotions and marketing campaigns to boost sales. For example, they may use this information to create time-limited offers on products that are predicted to have high demand during the upcoming week.