Predictive Inventory Analytics and Demand Forecasting in Malaysian Electronics Manufacturing

Authors

  • Nurshamimah Samsuddin Faculty of Technology Management and Technopreneurship, UTeM
  • Maliza Mohd Nor Faculty of Business, MMU Malacca
  • Masri Sulaiman Honda Malaysia Sdn. Bhd., Malaysia

DOI:

https://doi.org/10.54554/jtmt.2024.12.02.002

Abstract

This study investigates the role of predictive inventory analytics in enhancing demand forecasting accuracy within Malaysia’s electronics manufacturing sector. As global supply chains become increasingly volatile, electronics manufacturers face challenges in managing inventory for components with long lead times and fluctuating demand. Leveraging machine learning (ML) models such as XGBoost, LSTM, and Random Forest, this research evaluates how predictive analytics can reduce inventory waste, improve responsiveness, and support strategic planning. Findings from case studies and data analysis reveal that ML-driven forecasting significantly improves inventory performance, especially during periods of global disruption and product lifecycle transitions.

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Published

2024-12-31

How to Cite

Samsuddin, N. ., Mohd Nor, M. ., & Sulaiman, M. . (2024). Predictive Inventory Analytics and Demand Forecasting in Malaysian Electronics Manufacturing. Journal of Technology Management and Technopreneurship (JTMT), 12(2). https://doi.org/10.54554/jtmt.2024.12.02.002

Issue

Vol. 12 No. 2 (2024): (IN PRESS)

Section

Journal of Technology Management and Technopreneurship

License

Copyright (c) 2024 Journal of Technology Management and Technopreneurship (JTMT)

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