Examining the Use of Business Intelligence on Revenue Forecasting in Quick Service Restaurants Using Multivariate Linear Regression Model: A Case of Simbisa Brands
- Author
- Chawora, Ronald
- Title
- Examining the Use of Business Intelligence on Revenue Forecasting in Quick Service Restaurants Using Multivariate Linear Regression Model: A Case of Simbisa Brands
- Abstract
-
The aim of this research was to unpack the use of Business Intelligence (BI) tools to improve
revenue forecasting in quick service restaurants (QSRs) by using a multivariate linear regression
model. The study involved collecting and assessing historical data on sales, turnover, purchases,
implementing BI tools such as data visualization and predictive analytics. The intended outcome
was to discover how BI methods could be utilized to estimate future revenue, streamline operations
and enable more data-driven decisions. The study's main objectives were to examine the impact of
various factors such as demand, price, and inflation on revenue and to evaluate the role played by
Business Intelligence systems in revenue forecasting. The study's findings showed that purchases,
cost, profit, and inventory were good predictors of turnover while factors such as customers and
inflation had minimal impact. Findings from this research provides useful insights into the factors
that can affect a QSR's performance and revenue forecasting, allowing businesses to make betterinformed strategic decisions. By using a model for evaluating historical data and predicting future
performance, businesses can identify trends and patterns which can be used to inform their revenue
forecasting. It can also make necessary changes to their strategies in real-time and optimize
performance to maximize revenue. - Date
- 2023
- Publisher
- BUSE
- Keywords
- Business Intelligence
- Revenue Forecasting
- Supervisor
- Miss. Pagan’a
- Item sets
- Department of Statistics and Mathematics
