A time series analysis for sales of chicken based food product

Hamzah, Diyana Izyan Amir; Rusiman, Mohd Saifullah; Him, Norziha Che; Shafi, Muhammad Ammar; Alma, Özlem Gürünlü; Suhartono, Suhartono

doi:10.1063/5.0053699

Cited by 3 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It implied that after adjusting the data, the seasonal pattern was removed. According to Hamzah et al (2021), the ARIMA method is more accurate and can be used to predict chicken-based food products on weekly sales data. Other than that, Li et al (2021) develop research using a two-step product lifecycle forecast approach for consumer technology products with inadequate sales data.…”

Section: Comparison Of Actual and Intervention Forecasting Methodsmentioning

confidence: 99%

The Use of Intervention Approach in Individual and Aggregate Forecasting Methods for Burger Patties: A Case in Indonesia

Neisyafitri

Ongkunaruk

2022

J AGRIB RURAL DEV RES

View full text Add to dashboard Cite

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.

show abstract

Section: Comparison Of Actual and Intervention Forecasting Methodsmentioning

confidence: 99%