Open AccessThe Use of Intervention Approach in Individual and Aggregate Forecasting Methods for Burger Patties: A Case in Indonesia
Author(s) -
Rendayu Jonda Neisyafitri,
Pornthipa Ongkunaruk
Publication year - 2022
Publication title -
agraris
Language(s) - English
Resource type - Journals
eISSN - 2527-9238
pISSN - 2407-814X
DOI - 10.18196/agraris.v8i1.12842
Subject(s) - demand forecasting , indonesian , promotion (chess) , aggregate (composite) , economic shortage , aggregate data , econometrics , sales forecasting , computer science , government (linguistics) , economics , statistics , operations management , mathematics , linguistics , philosophy , materials science , politics , political science , law , composite material
The Indonesian beef consumption increases sharply during Ramadan and made a difference between supply and demand. The research aimed to study the demand pattern of burger patties and determine a suitable forecasting method compared between quantitative and intervention forecasting methods. The actual demand was intervened by experts based on reasons such as supply shortage, holidays, promotion, and government projects. The daily sales of burger patties were collected for a year. Then, the data were divided into training and testing data. Later, time-series forecasting was performed by software. Then, the best forecasting method for daily data was selected between Individual forecasting and Top-Down forecasting. Similarly, for weekly data, the best forecasting method was compared between aggregate forecasting and Bottom-Up forecasting. Then, repeat the process for the intervened sales data. The result revealed that the mean absolute percentage error was improved after intervention by about 3.64%-58.83%. The combination of quantitative and qualitative approaches improved forecast accuracy. In addition, the aggregate level or weekly sales forecast had higher forecast accuracy than the disaggregated level. The Bottom-Up forecast performs better than the aggregate forecast. Hence, we recommended the company plans based on weekly data and implement Every Low Price to reduce the demand fluctuation.