Data Mining For Inventory Forecasting Using Double Exponential Smoothing Method

被引：0

作者：

Hasmin, Erfan ^{[1
]}

Aini, Nurul ^{[2
]}

机构：

[1] Dipanegara Comp Sci Coll, Informat Engn, Makassar, Indonesia

[2] Dipanegara Comp Sci Coll, Software Engn Dept, Makassar, Indonesia

来源：

PROCEEDINGS OF ICORIS 2020: 2020 THE 2ND INTERNATIONAL CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEM (ICORIS) | 2020年

关键词：

data mining; double exponential smoothing; forecasting; KDD; MAPE;

D O I：

10.1109/ICORIS50180.2020.9320765

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

PT. Sentral 88 Maros is a company engaging in food and house appliances procurement. In its daily activities, PT. Sentral 88 Maros has problems in determining its stock, where it is usually available in excess, nor it can cause loss with the additional cost for storage to keep the stock. One way to meet the availability of the goods is by identifying the number of demand for the following period, so the company can provide goods without overloading or running out of stock in the warehouse. Based on this background, the research built a system using Double Exponential Smoothing method. Forecasting with time series is a quantitative method in processing the data of some previous periods that were collected using the right technique. The results of the forecasting were used as a reference in forecasting value in the next period. The existing data set were used from April 2019 to September 2019 and the conclusion for forecasting the frozen food (So Good Bakso Goreng 120gr) was 14 with an error level of MAPE calculation by 2.1%.

引用

页码：43 / 47

页数：5

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