Open AccessIntegrasi Linear Regression dan Aggregate Planning untuk Perencanaan dan Pengendalian Produksi Leaf Spring Hino OW 190/200 di Industri Komponen Otomotif
Author(s) -
Deni Ahmad Taufik,
Indra Setiawan,
Muhammad Wahid,
Abdul Rochim .,
Muhammad Tosin
Publication year - 2021
Publication title -
operations excellence
Language(s) - English
Resource type - Journals
eISSN - 2654-5799
pISSN - 2085-4293
DOI - 10.22441/oe.2021.v13.i2.023
Subject(s) - raw material , spring (device) , automotive industry , production (economics) , aggregate (composite) , production planning , engineering , agricultural engineering , agricultural science , mathematics , operations management , environmental science , economics , mechanical engineering , chemistry , materials science , organic chemistry , composite material , macroeconomics , aerospace engineering
Production planning and control in a manufacturing company involves all production activities from raw material requirements to finished products. The Jakarta Automotive Components Industry is engaged in manufacturing which produces leaf spring products that are sent to several regular customers, namely the Automotive Assembly Industry. Leaf spring Hino OW 190/200 is the type of spring ordered and shipped to PT. HMMI. Based on data for the January-December 2019 period, the demand for Hino OW 190/200 leaf spring has fluctuated quite significantly. The purpose of this study was to plan and control the production process of Leaf Spring Hino OW 190/200. Forecasting for the next 12 periods is based on demand plots from the previous 12 periods, calculating the Aggregate production plan, determining the Master Production Schedule (MPS), calculating raw material requirements using the Hybrid and Lot for Lot methods. The results showed that to support the smooth production, it can be seen that the production planning for forecasting calculations using the Linear Regression method generates a model Y=319,575+3,723X. Calculation of the need for main raw materials and components in 2020 uses the Hybrid and Lot for Lot method, which is 256,182.88 kg, much smaller than the company's calculations based on 2019 data, namely 259,827.40 kg.