Open AccessForecasting Model Number Production of Car Spare Parts at PT. Showa Katou Indonesia with Arima Method
Author(s) -
Cici Emilia Sukmawati,
Ayu Ratna Juwita
Publication year - 2022
Publication title -
jurnal sisfotek global
Language(s) - English
Resource type - Journals
eISSN - 2721-3161
pISSN - 2088-1762
DOI - 10.38101/sisfotek.v12i1.478
Subject(s) - production planning , production (economics) , spare part , schedule , autoregressive integrated moving average , production schedule , operations management , computer science , operations research , engineering , scheduling (production processes) , economics , time series , machine learning , macroeconomics , operating system
In the case of single part production planning at PT Showa Katou Indonesia The problem is the production plan is only a production schedule, the schedule is made only two times (morning and evening) in a day. The schedule is created after the Production Planning Inventory Control (PPIC) contacts the customer to ascertain what the customer needs. After knowing what the customer needs, a production schedule and planning are made. The impact of this erratic production schedule causes loss of production time because if there is no demand then nothing is done by workers and the machine stops production because they have to wait for an erratic production schedule. Another impact is the absence of stock in the warehouse and delays in delivery because they are only waiting for the production schedule from PPIC and waiting for finished goods to be produced. To reduce the bad impact, it is necessary to forecast production planning with data mining methods to help these problems. The method used is the ARIMA method with the model (p,d,q) (1,1,1). The results of testing using tools and manual testing showed significant values with MAD = 52.45, MSE = 3917.84, MAPE = 0.05.