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All Journal PROZIMA (Productivity, Optimization and Manufacturing System Engineering)
Mohammad Buchori
Program Studi Teknik Industri, Fakultas Teknik, Universitas Muhammadiyah Sidoarjo
Industrial & Manufacturing Engineering

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Production Forecasting Using Autoregressive Integrated Moving Average (ARIMA) Method at PT. XYZ Mohammad Buchori; Tedjo Sukmono
PROZIMA (Productivity, Optimization and Manufacturing System Engineering) Vol 2 No 1 (2018): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/prozima.v2i1.1290

Abstract

In production planning and control the first step is to forecast to determine how much production, the company forecasting is still not optimal, because forecasting has an important role in a company. PT. XYZ is a food company that produces chicken meatballs and chicken dumplings. So from that this study uses the forecasting method Autoregressive Integreted Moving Average (ARIMA). ARIMA is often also called the Box-Jenkins time series method. ARIMA is very good for short-term forecasting, while for long-term forecasting the forecasting accuracy is not good. The purpose of this research is to get a good ARIMA model, used to forecast production in the company. So that the production becomes optimal and not excessive which can cause waste of raw materials, which will make production costs a lot. Data processing is done with the help of an Eviews computer program to determine a good ARIMA model, from processing data obtained by ARIMA (1.0,0). With the results obtained forecasting in the period 37 to period 48.
Co-Authors Tedjo Sukmono