Implementation of Simple and Weighted Moving Average for  Forecasting Tela-Tela Production in MSME X

Sariati Sariati; Borneo Satria Pratama; Ferdy  Albar; Dea Yolanda Putri Sitio; Dimas Dwika Riandry; Marcho Antonio; Sri Januarti; Bayu Rimba  Aliansyah; Th. Candra Wasis Agung Sutignya; Maidia Solfianti; Erwan Erwan; Loko Jeremia Sembiring

doi:10.31004/riggs.v4i4.5205

Authors

Sariati Sariati Politeknik Negeri Pontianak
Borneo Satria Pratama Politeknik Negeri Pontianak
Ferdy Albar Politeknik Negeri Pontianak
Dea Yolanda Putri Sitio Politeknik Negeri Pontianak
Dimas Dwika Riandry Politeknik Negeri Pontianak
Marcho Antonio Politeknik Negeri Pontianak
Sri Januarti Politeknik Negeri Pontianak
Bayu Rimba Aliansyah Politeknik Negeri Pontianak
Th. Candra Wasis Agung Sutignya Politeknik Negeri Pontianak
Maidia Solfianti Politeknik Negeri Pontianak
Erwan Erwan Politeknik Negeri Pontianak
Loko Jeremia Sembiring Politeknik Negeri Pontianak

DOI:

https://doi.org/10.31004/riggs.v4i4.5205

Keywords:

Tela-Tela, Forecasting, Simple Moving Average, Weighted Moving Average, MAPE

Abstract

Accurate production forecasting is essential for micro, small, and medium enterprises (MSMEs) to support effective production planning, inventory control, and decision-making. This study evaluates the performance of the Simple Moving Average (SMA) and Weighted Moving Average (WMA) methods in forecasting tela-tela production demand at MSME X using different historical period lengths. Production data from November 2023 to October 2024 were analyzed, and forecasting accuracy was assessed using the Mean Absolute Percentage Error (MAPE). The results indicate that forecasting accuracy varies depending on both the length of the moving average period and the weighting scheme applied. The WMA model with a 4-period window (n = 4) achieved the highest accuracy, producing the lowest MAPE value of 8.36%, which is classified as highly accurate. The SMA model with n = 4 also demonstrated good performance, with a MAPE value of 14.40%. Meanwhile, models employing longer historical periods (n = 8) yielded MAPE values of 16.20% for WMA and 19.82% for SMA, both falling within the good forecasting performance category but exhibiting lower responsiveness to recent demand changes. These findings highlight that shorter historical periods, when combined with appropriate weighting, can more effectively capture recent demand patterns in dynamic production environments. Accordingly, the WMA method with a 4-period window is recommended for MSME X as a reliable and accurate approach to support production planning, optimize resource allocation, and reduce the risk of overproduction or stock shortages.

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