Penerapan Time Series Forecasting untuk Optimalisasi Penjualan pada Resto X di Bekasi

Abstract

The advancement of information technology has enabled more efficient processing of restaurant sales data through the application of forecasting methods. This study aims to implement time series forecasting techniques to predict daily sales in the food and beverage industry. Utilizing historical transaction data processed through Business Intelligence (BI) tools, specifically Power BI, the study analyzes trends and patterns in sales over time. The forecasting methods employed include Single Exponential Smoothing and Single Moving Average. The application of these methods is particularly crucial given that the restaurant under study still faces limitations in leveraging information technology, especially in terms of data management and sales analytics. In line with the rapid development of digital technologies, the integration of data-driven systems has become an essential requirement for businesses. Therefore, the findings of this study are expected to contribute to the digitalization of operational processes, enhance managerial efficiency, and strengthen the restaurant’s competitiveness in an increasingly dynamic market environment.