Open AccessAnalisis Peramalan Penjualan dan Penggunaan Metode Linear Programming dan Decision Tree Guna Mengoptimalkan Keuntungan pada PT Primajaya Pantes Garment
Author(s) -
Inti Sariani Jianta Djie
Publication year - 2013
Publication title -
journal the winners
Language(s) - English
Resource type - Journals
eISSN - 2541-2388
pISSN - 1412-1212
DOI - 10.21512/tw.v14i2.651
Subject(s) - exponential smoothing , computer science , linear programming , production (economics) , operations research , linear regression , decision tree , operations management , product (mathematics) , production planning , goal programming , simplex algorithm , demand forecasting , mathematics , economics , artificial intelligence , algorithm , microeconomics , geometry , machine learning , computer vision
Primajaya Pantes Garment is a company that runs its business in garment sector. However, due to various numbers of requests each month, the company is difficult to determine the amount of production per month that is appropriate to maximize profits. The purpose of this study is to determine the appropriate forecasting method that can be used as a reference to determine the amount of production in the next period and to find a combination of products to maximize profits. Research used forecasting methods, including naive method, moving averages, weighted moving averages, exponential smoothing, exponential smoothing with trend, and linear regression. In addition, this study also used Linear Programming method with Simplex method to determine the best combination of products for the company and to choose a decision using a decision tree to determine which alternative should be done by the company. Results of this study found that the linear regression method is the most appropriate method in determining the forecast demand in the next period. While in the Linear Programming method, constraints used were the constraints of raw materials, labor hours, and limited demand for the product. The result of the decision tree is to increase production capacity.