Financial Performance Measurement and Analysis in Quick Service Restaurant

Abstract

This paper presents a comprehensive review of Power BI to analyze the financial and operational performance of a large fast-food chain in India, specifically in the Quick Service Restaurant (QSR) sector. It starts by explaining the company’sstructure andthenfocuses onanalyzingits financial data, such as financial statements, budgeting, and future projections, tailored tothe needs of the QSR industry. Key tothe analysis is organizing and cleaning data, creating unique product IDs, and ensuring accuracy for budget comparisons. Theprojectcoversin-depthdatamanipulationandmodelingto ensure reliable insights into key financial metrics, such as revenue and profit. Advanced visualizations, including charts and Pareto analysis, help present a clear picture of the company’s performance. Thisresearch paper offers a detailed approach for using Power BI in the QSR sector, providing insights for improving business operations and profitability.

Introduction

The financial performance analysis of Quick Service Restaurants (QSRs) involves managing extensive data from sales, inventory, labor, and costs. Due to tight profit margins and a fast-paced environment, QSRs need quick, data-driven decisions to optimize costs and improve profitability. However, integrating data from diverse systems like POS, HR, and inventory software is challenging.

This project proposes a system that leverages Python for data preprocessing and advanced analytics, and Power BI for real-time interactive visualization of financial metrics such as revenue growth, cost efficiency, and profit margins. The integration of these tools enables automated data collection, cleaning, and analysis, providing actionable insights through dynamic dashboards that help managers make strategic decisions.

The system is designed to handle data from multiple sources, unify it into a centralized database, and apply predictive analytics and scenario modeling for forecasting and optimization. Role-based secure access ensures appropriate use by stakeholders. The scalable solution aims to enhance operational efficiency, reduce costs, and support sustainable growth in the competitive QSR industry.

The literature review highlights prior research in predictive analytics, customer segmentation, and performance measurement in foodservice, reinforcing the importance of combining advanced analytics with customized dashboards. This approach empowers QSR managers with timely, accurate, and comprehensive financial insights, overcoming limitations of traditional fragmented reporting systems.

Conclusion

This research paper introduces financial performance measurement and analysis system developed for Quick Service Restaurants (QSRs) provides a comprehensive framework for improving operational efficiency and profitability.ByintegratingadvancedtoolslikePowerBIfor visualization and Python for data processing, the system enablesreal-time analysis of critical metrics such as revenue growth, profit margins, and cost structures. This approachnot only automates complex calculations but also enhances decision-making by presenting actionable insights through interactive dashboards. The project demonstrates the potential of data-driven strategies in addressing the challenges faced by QSRs, such as managing high data volumes, optimizing costs, and adapting to market demands. This system lays a foundation for scalable and sustainable growth, equipping QSRs with the tools to stay competitive.

References

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Copyright

Copyright © 2025 Sarthak Deshmukh, Neha Kothavade, Vedangi Pagar, Harshali Thakor, Prof. R. S. Jagale. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.