KnE Social Sciences

ISSN: 2518-668X

The latest conference proceedings on humanities, arts and social sciences.

Determining the Appropriate Demand Forecasting Using Time Series Method: Study Case at Garment Industry in Indonesia

Published date:Oct 22 2018

Journal Title: KnE Social Sciences

Issue title: International Conference on Economics, Business and Economic Education 2018 (ICE-BEES 2018)

Pages:553–564

DOI: 10.18502/kss.v3i10.3156

Authors:
Abstract:

PT XYZ is a subsidiary company which responsible to distribute the clothing line product. This company could be categorized as merchandising business because it only sells products or finished goods from parent company. The method that used by XYZ to predict future demand is based on the judgment of previous sales and does not apply forecasting methods to predict demand. As distributor, it is important to reach sales target from parent company. To maintain the sustainability of the company, this can be prevented by improving the method of forecasting their demand. This research will be analyzed using time series method including moving average, simple exponential smoothing, holt’s model and winter’s model. Mean Absolute Deviation (MAD) is used to calculate the error and to compare the model in terms of their forecast performance since the characteristic of their forecast errors are not symmetric distribution and Tracking Signal (TS) to track and control whether the method is still appropriate or not. The calculation concluded that simple moving average is the best method to applied for predicting the future demand. To utilize the methods provided in this study, director of XYZ required to record the historical sales data systematically, measure the forecast error properly, use the proposed method in the right time period, and commit to learn the proposed demand forecasting method.

 

 

Keywords: supply chain management, operation management, demand forecasting

References:

[1] Chopra, S., and Meindl, P. (2016). Supply Chain Management; Strategy, Planning, and Operation. Edinburgh Gate: Pearson.


[2] Heizer, J., and Render, B. (2014). Operations Managament; Sustainability and Supply Chain Management. Edinburgh Gate: Pearson.


[3] Özlem İpek KALAOGLU1, E. S. (2015). RETAIL DEMAND FORECASTING IN CLOTHING INDUSTRY. TEKSTİL ve KONFEKSİYON 25(2), 171.


[4] S. Lakshmi Anusha, S. A. (2014). Demand Forecasting for the Indian Pharmaceutical Retail: A Case Study. Journal of Supply Chain Management Systems, 5.

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