Demand forecasting with univariate times series model: a case study in electric and electronic manufacturing company
The Electric and Electronic manufacturing companies are among industries that need forecasting the most. The need to forecast in the management and operations is increasing in order to achieve its objectives. The company currently facing the problem of over produce inventory for some of the product...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/4437/ http://eprints.uthm.edu.my/4437/1/ICO037.pdf |
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| Summary: | The Electric and Electronic manufacturing
companies are among industries that need forecasting the most.
The need to forecast in the management and operations is
increasing in order to achieve its objectives. The company
currently facing the problem of over produce inventory for some
of the product and the problem might be due to inconsistent
demand. Thus, data pattern of inventory demand need to identify
before choosing the forecasting methods. Comparison among
methods was conducted to determine the best method for the
company in order to prepare them for future inventory. This
study was conducted using the case study method. The data of
demand for one of the product series for five consecutive years
were collected and forecasted using Risk Simulator software for
the purpose of this study. From the result, the selected
forecasting methods to implement were: Single Moving Average,
Single Exponential Smoothing, Double Exponential Smoothing,
Double Moving Average, Regression Analysis and ARIMA
(Autoregressive Integrated Moving Average). It were selected
based on the trend and cyclical data patterns. The finding
revealed that, Single Moving Average method is the best
forecasting method when comparison forecast accuracy is made |
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