Sales Forecasting Analysis Using Time Series Method: A Study on Chocolate Banana Snack Business
DOI:
https://doi.org/10.29407/kilisuci.v3i.7133Keywords:
Sales Forecasting, Time Series, Single Exponential Smoothing, Mean Absolute Precentage Error, Small BusinessAbstract
Research aim :
The purpose of this research is to analyze the sales trend of the Chocolate Banana snack business in the next period and analyze the accuracy level of chocolate banana snack sales forecasting.
Design/Methode/Approach :
This research is descriptive research with a quantitative approach. There are two stages of data analysis, namely calculating sales forecasting for the next period using time series methods (Moving Average, Weighted Moving Average, and Exponential Smoothing methods. There are 3 types of Exponential Smoothing methods, namely Single Exponential Smoothing (SES) with constant α 0.1 and constant α 0.4, and Linear Exponential Smoothing (LES) with constant α 0.1 / β 0.2). While the second stage is to evaluate the results of the accuracy level of the sales forecasting method with the MAPE technique.
Research Finding :
The result of this study is that the most effective method is the single exponential smoothing method (α 0.4) with the sales forecasting results for the next period of 594, with an accuracy level with MAPE of 5.67%.
Theoretical contribution/Originality :
This research contributes to the application of time series forecasting methods in small-scale food businesses, especially in improving the accuracy of sales predictions.
Practitionel/Policy implication :
The results of this study provide insights for small business owners in the food sector, emphasizing the importance of applying sales forecasting methods to improve decision-making.
Research limitation :
This research is limited to 10 weeks of sales data, making it less accurate for the long term. Further studies are recommended using longer data and more complex methods.
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