Data Analysis And Forecasting Report Of Tesco Plc Case Study

Introduction - Big Data Analytics And Sales Forecasting At Tesco Plc

In the dynamic landscape of the retail industry, data analytics has become an essential tool for informed decision-making. This report delves into the field of Big Data Analytics and focuses on Tesco Plc, a leading company in the Retail industry. As technology advances, businesses need to understand and harness the power of data to stay competitive. The following sections explain big data concepts, explore the nuances of data warehousing, and explore different storage methods.

Additionally, a comprehensive analysis of Tesco sales data for the past three years is performed using forecasting techniques to predict future trends. A comparison of different big data frameworks and a discussion of ethics, privacy, and security issues related to data analysis illuminates the diverse facets of this evolving field. As Tesco moves into a data-driven world, this report aims to provide valuable insights and recommendations to use information effectively responsibly and ethically.

Big Data Definition and Data Storage

In an ever-expanding data space, understanding big data is essential. Big data includes huge data sets characterized by volume, velocity, variety, and complexity. For Tesco Plc, these datasets come from various sources such as transactions, customer interactions, and supply chain processes.

Effective management of such data requires appropriate storage and storage systems. Data warehousing is the centralization of disparate data into one cohesive repository for streamlined access and analysis (Moura, 2021). Tesco introduces advanced data warehousing mechanisms to optimize structured and unstructured data storage. Different types of data storage, such as relational databases, NoSQL databases, and cloud-based storage, play a key role in accommodating the various data formats found in retail. The use of Excel and PowerBi in this analysis is consistent with current industry practice and provides powerful tools for data interpretation and visualization.

Tesco Plc sales forecast

The focus then shifts to his Tesco sales forecast, which is an important aspect of strategic planning. Viewing sales series visualizes trends, patterns, and fluctuations and provides the basis for predictive modelling. Four-period moving averages and centred moving averages (CMA) help smooth out irregularities and improve distinguishing between underlying trends.

Calculating trends provides a better understanding of Tesco's sales trends over the study period. Identifying seasonality in sales data is essential to making accurate forecasts (Catalão, 2022). Seasonal fluctuations, or recurring patterns, influence consumer behavior and impact sales. Precisely specifying and deseasonalizing the data enables accurate forecasting and gives Tesco insight into peak and off-peak periods. The highlight of these analyses is the fourth-year sales forecast. The forecast integrates historical data, trend analysis and seasonal considerations, giving Tesco a forward-looking perspective for strategic decision-making.

Metrics such as mean absolute deviation (MAD) and mean squared error (MSE) are important when evaluating forecast accuracy (ANDRICORY, 2023). These metrics quantify the difference between predicted and actual values and provide a comprehensive understanding of forecast accuracy. The next section summarizes the findings in a concise report, highlighting Tesco's sales forecasts and their associated errors. This analysis serves as a basis for the subsequent investigation of various big data frameworks and their impact on Tesco Plc.

Sales Forecasting for Tesco PLC

The sales Forecast table provides a thorough forecast of Tesco sales in 2024, carefully designed to provide a nuanced understanding of expected trends and patterns. A comprehensive analysis of these forecasts provides key insights into Tesco's potential performance in the coming quarters.

Seasonality and dissonance: Seasonality acts as a lens into periodic patterns in sales data, and dissonance is a quantification of the average deviation from these observed patterns. It will be a scale. The presence of negative seasonality, particularly in Q4 2021 and Q4 2022, would result in significant negative seasonality (Li, 2023). This indicates a period of subdued sales relative to the moving average and indicates potential challenges or special circumstances that may impact consumer behavior in these quarters.

FTrend (Final Trend) and Forecasting: FTrend plays an important role in understanding the underlying trend, taking into account seasonality. Combined with the seasonality of the forecast column, it provides a comprehensive sales forecast for each quarter of 2024. This integrated approach aims to more accurately represent expected sales performance based on short-term and long-term fluctuations and current trends.

Error, MAD Error, and MSE: The error column is an important indicator in evaluating the deviation between actual sales and forecast values. The errors in Q4 2021 and Q4 2022 are particularly large, highlighting potential challenges in accurately forecasting revenue for these particular quarters (Lokanan, 2021). Mean absolute deviation error (MAD) and mean squared error (MSE) also help evaluate the accuracy of a predictive model. Lower MAD error and MSE values mean a more accurate and reliable predictive model.

Sales/CMA Ratio: The Sales/CMA ratio has proven to be an important relative measure, regarding the agreement of sales with the Centered Moving Average (CMA). Gain insight. Deviations from a ratio close to 1 indicate a possible trend or anomaly in sales behavior. This metric helps you understand how sales will develop in 2024 compared to the smoothed average.

Notable Observations: A closer look at the forecast period reveals corresponding discontinuities, FTrend, and seasonal variations that affect the forecast values. In particular, a reduction in the effects of seasonality in 2023 and 2024 suggests a potential stabilization of sales patterns, allowing for a more predictable and manageable forecasting

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Error evaluation: Error evaluation is the most important to understand the accuracy of a predictive model (Al-Suraihi, et al. 2020). The margin of error is particularly large in the fourth quarter of 2021 and the fourth quarter of 2022, which may require further scrutiny of the factors that help improve predictive ability in future forecasts.

Model Validation: Checking MAD errors and MSE values adds a layer of model validation. Relatively small errors in the later stages indicate that the model has evolved and adapted, learning from past patterns and improving accuracy.

In summary, this detailed sales forecast analysis provides Tesco Plc with actionable insights for strategic planning in the dynamic retail industry. Forecast values and related metrics serve as essential tools for making informed decisions and proactively responding to market trends.

The dashboard provides a comprehensive overview of Tesco's sales forecast analysis. It contains the essential elements needed to understand time series forecasting calculations and visualize results. Specifically, the tabular data shows Tesco's actual quarterly sales from the first quarter of 2021 to the fourth quarter of 2024. Next, you will see the 4-period moving average, centered moving average (CMA), seasonal index, and seasonal index (Monte, 2021). The analysis calculates quarterly D-data, trend forecasts, sales forecasts, and forecast errors. These metrics allow analysts to decompose time series into underlying components such as trend, seasonality, and noise to develop accurate predictive models. CMA helps quantify seasonality by calculating the ratio of actual sales to CMA. A ratio greater than 1 indicates a period of high seasonal sales. Desasonized data shows the underlying trends by removing seasonal effects. Predictive trends use linear regression of non-seasonalized data to predict future values. Sales forecast numbers combine trend and seasonal effects. The error indicates the deviation between the actual value and the prediction and is used to calculate accuracy metrics such as mean absolute deviation (MAD) and mean squared error (MSE).

Visualization condenses this analysis into a clear line diagram and compares actual values with predictions. This shows a clear pattern of seasonality for Tesco, with sales peaking around the holidays in the fourth quarter of each year and declining in the first quarter after the holidays. The red predicted line overlaps well with the black actual line, qualitatively indicating the accuracy of the model.

While there is some over- and under-forecasting, his overall adjustment over the four years analyzed is solid. Quantitatively lower MAD is 218, 500, and MSE is 47. The number 7 million indicates the reliable predictability of Tesco's volatile sales pattern using time series analysis. This model allows for rational sales and inventory planning but expects some degree of uncertainty from quarter to quarter (ZXhang, et al. 2023). The line chart extends through 2024, so long-term forecasts may be less accurate, but it provides a useful approximation of revenue performance.

Overall, the comprehensive time series analysis and insightful visualizations are a great starting point for Tesco executives to set sales goals and make data-driven decisions on marketing, operations, finance, etc. Provide points. Despite the limited accuracy of statistical predictions, this dashboard provides significant business value.

Comparison of Big Data Frameworks

When comparing big data frameworks, Tesco Plc can consider different tools such as descriptive, diagnostic, predictive, or prescriptive frameworks. Each framework provides different tools for analyzing historical and current datasets. Descriptive frameworks provide a comprehensive overview, diagnostic frameworks diagnose problems, and predictive frameworks predict trends and prescriptive frameworks suggest optimal actions. The evaluation will focus on selecting the best solution for Tesco's business case based on attention to detail, quality of work and academic criteria (Naveen, et al. 2022). This strategic analysis provides Tesco with a versatile tool to gain valuable insights from a wide range of retail industry data sets. This report adopts a descriptive analysis of the forecasted values of 2024.

The descriptive statistics table provides a comprehensive overview of Tesco Plc's forecast values for 2024, identifying the central tendency, variability, and distribution characteristics. I'm emphasizing it. Analysis of these key numbers provides valuable insights into the “Big Data Framework”.

Central tendency: The median quarterly revenue forecast for 2024 is approximately £17,396,419.7, which is roughly in line with the median and a relatively balanced allocation It shows that (Evans, 2022). However, the forecasting tool yields a slightly lower average, suggesting that there may be a discrepancy between predictions and actual sales.

Variability: Standard deviation and variance reveal the spread of predicted results. Significant standard deviations for prediction and error metrics (117,624.48 and 119,857 pounds). (92) demonstrate significant variation and highlight the need for a robust analytical framework that takes variation into account.

Distribution properties: Kurtosis and skewness provide insight into the shape and symmetry of a distribution. Kurtosis values close to zero indicate a relatively normal distribution, while skewness values indicate asymmetry. Positive skewness of an error metric means a bias toward positive deviations from the mean, and you should be aware of the possibility of overestimation in your predictions.

Range and extreme values: This range highlights a range of values, with a prediction range and error margin of over £200,000 (Mariani, and Wamba, 2020). This highlights the large dispersion of predicted results. In particular, the minimum and maximum values highlight the edges of the dataset and provide insight into possible outliers and exceptional performance scenarios.

Interpretation: Detailed descriptive analysis helps Tesco understand the nuances of the forecast results. The small difference between the mean and median suggests a relatively balanced distribution. However, the significant variability and skewness of the error metrics highlight the dynamic nature of the prediction challenge.

In the context of comparing big data frameworks, Tesco should consider the robustness of these frameworks in handling different data sets, adapting to fluctuations, and considering potential biases in predictions (Hassanin, and El-Sayed, 2023). The focus should be on choosing a framework that matches the observed characteristics and ensures accurate analysis and decision-making based on the predicted results. Descriptive statistics serve as a critical foundation for Tesco to address the complexities of big data frameworks and optimize its evolving retail industry analytics approach.

Recommendation for Tesco PLC.

Here are some recommendations from Tesco Plc. on sales forecasts and opportunities for improvement, based on the sales forecasts for 2024, Tesco will generate between £3,00 and £480,000 per quarter compared to actual A slight downward forecast should be taken into account. Although the overall forecast model error is relatively low, it is important to prepare operations for higher-than-expected demand during the peak holiday season in the fourth quarter to prevent inventory buildup due to seasonal spikes.

Increased storage capacity and staffing flexibility to better respond when sales volumes exceed expectations (Dicuonzo, et al. 2019). Maintaining lean operations during the first quarter downturn is recommended, but creating contingency plans to rapidly expand distribution centers and stores will help attract marketing campaigns and external trends. It may be able to maximize its profits during the Christmas season when shoppers arrive in droves.

In the marketing space, Tesco has a significant opportunity to use advanced analytics to improve its advertising strategy for the peak holiday season in 2024. Detailed tracking of which product categories and consumer segments led to above-average sales growth compared to the previous year's forecasts allows for more targeted targeting. By tailoring product bundle offers, personalized coupons, and loyalty program bonuses based on location and demographics, shoppers can more effectively earn additional benefits.

If actual demand increases faster than predicted, a high degree of supply chain integration with selected suppliers by leveraging volume contracts or revenue-sharing agreements may also benefit both parties. Joint planning of production scale and storage buffers could help balance supply in the face of demand uncertainty. Big data analytics, which leverages internal data in combination with macroeconomic signals, can also further improve the accuracy of statistical forecasts (HAO, 2023). However, tactical flexibility to respond to deviations is still important.

In summary, increasing operational and marketing flexibility to deal with fluctuations in demand, targeting sales growth opportunities, and building win-win supply partnerships have helped Tesco consistently exceed sales forecasts. It can help the firm to better meet the customers' needs. Leveraging unexpected demand dynamics can generate long-term revenue synergies through expanding the customer base. A resilient, data-driven and collaborative mindset will maximize Tesco's future success.

Ethical, Privacy, and Security Issues

As mass data collection and predictive analytics become the norm, Tesco must proactively address new ethical concerns around transparency, privacy and security. As customer interactions and transactions generate large amounts of data, Tesco must ensure informed consent, anonymity of data, and protection against unauthorized external use. Aggregating information creates great value, but customers naturally expect strong data management.

As cyber risks increase exponentially, continued investment in resilient systems and comprehensive staff training, in addition to redundancy mechanisms, are essential (Flynn, 2020). As it strives for commercial success through cutting-edge analytics, Tesco must simultaneously maintain its brand reputation and customer trust by embedding ethical responsibility at every step. Industry collaboration with technology partners and transparent communication with regulators enables a balance between innovation and responsibility. Ultimately, Tesco's vision should go beyond short-term profits and aim for long-term relationships powered by data ethics and integrity.

Conclusion

In conclusion, the advanced predictive models and insightful visualizations developed in this project offer great potential for data-driven planning and strategic growth. By effectively using predictive capabilities and considering the inherent statistical limitations, Tesco can extend its retail market leadership through agile and analytical decision-making capabilities. As the external environment evolves rapidly, continuous improvement of modeling technology and appropriate response to dynamic markets accelerate future readiness. Responsible data management and proactive collaboration with stakeholders across the retail ecosystem will further drive sustainable success. With its ethical, resilient and innovative mindset, Tesco is well-placed to lead the analytics revolution in retail.

References

Journals

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