A New Product’s Demand Forecasting Using Artificial Neural Network

Natapat Areerakulkan, Chanicha Moryadee, Lamphai Trakoonsanti, Martusorn Khaengkhan, Natpatsaya Setthachotsombut

2024

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%.

Paper Citation

in Harvard Style

Areerakulkan N., Moryadee C., Trakoonsanti L., Khaengkhan M. and Setthachotsombut N. (2024). A New Product’s Demand Forecasting Using Artificial Neural Network. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 417-424. DOI: 10.5220/0012734800003690

in Bibtex Style

@conference{iceis24,
author={Natapat Areerakulkan and Chanicha Moryadee and Lamphai Trakoonsanti and Martusorn Khaengkhan and Natpatsaya Setthachotsombut},
title={A New Product’s Demand Forecasting Using Artificial Neural Network},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={417-424},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012734800003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - A New Product’s Demand Forecasting Using Artificial Neural Network
SN - 978-989-758-692-7
AU - Areerakulkan N.
AU - Moryadee C.
AU - Trakoonsanti L.
AU - Khaengkhan M.
AU - Setthachotsombut N.
PY - 2024
SP - 417
EP - 424
DO - 10.5220/0012734800003690
PB - SciTePress