Authors:
Natapat Areerakulkan
;
Chanicha Moryadee
;
Lamphai Trakoonsanti
;
Martusorn Khaengkhan
and
Natpatsaya Setthachotsombut
Affiliation:
College of Logistics and Supply Chain, Suan Sunandha Rajabhat University, Dusit, Bangkok, Thailand
Keyword(s):
Demand Forecasting, Mobile Phone, Artificial Neural Network.
Abstract:
This paper presents the means to improve new product (mobile phone) demand forecasting that led to total cost reduction and more efficient inventory management. The selected forecast methods, namely Holt-Winters (HW), Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and Artificial Neural Network (ANN), are implemented, where the most accurate method, ANN is selected to forecast demand of the new product (sixth generation mobile phone) for the following year. In addition, the comparison between the original and ANN method shows that ANN is 51.28% more accurate. After that, we develop the proposed solution plan that links improved demand forecasting to calculate the suitable inventory quantities and production rates for both finished goods and work in process. The proposed solution scenario when compared with problem scenario can reduce loss sales and inventory carrying costs by $1,400,626.80 or equivalent to 27.71%.