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All Journal International Journal of Supply Chain Management Indonesian Journal of Electrical Engineering and Computer Science
Hasan, Mohd Hilmi
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Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Transportation

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A Case Study to Explore IoT Readiness in Outbound Logistics Hasan, Mohd Hilmi; Khairuddin, Aimi Amirah; Akhir, Emelia Akashah Patah
International Journal of Supply Chain Management Vol 8, No 2 (2019): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v8i2.3013

Abstract

Most of the logistics companies are facing problems with tracking and tracing in their logistics networks that led to poor last-mile service quality. This problem can be solved by improving technology such as the Internet of Things (IoT). Midway through the second decade of the millennium, the rapid development of IoT has influenced the company’s outbound logistics operations such as last-mile delivery. Implementation of IoT will help courier companies to optimize their delivery process. Despite its popular perceived benefit in assisting last-mile delivery, IoT remains a new technology and its adoption rate is still low in Malaysia. Thus, the main purpose of this research is to explore the status of IoT readiness among logistics companies in Malaysia. Apart from that, this research also intends to propose the best practice for courier companies to implement IoT. The finding of this research will indicate the factors affecting the readiness of the organizations to adopt IoT and the best practice for the implementation of IoT for the last-mile of a parcel delivery service in Malaysia will be proposed. This research is carried out by making use of qualitative methods with a number of courier companies in Malaysia as case studies. The case study provided is related to a courier company in Malaysia. In the preliminary study phase, results show that IoT helps to improve productivity and enhance the efficiency of the company.
Remaining useful life estimation of turbofan engine: a sliding time window approach using deep learning Alqushaibi, Alawi; Hasan, Mohd Hilmi; Abdulkadir, Said Jadid; Taib, Shakirah Mohd; Al-Selwi, Safwan Mahmood; Sumiea, Ebrahim Hamid; Ragab, Mohammed Gamal
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp283-299

Abstract

System degradation is a common and unavoidable process that frequently oc curs in aerospace sector. Thus, prognostics is employed to avoid unforeseen breakdowns in intricate industrial systems. In prognostics, the system health status, and its remaining useful life (RUL) are evaluated using numerous sen sors. Numerous researchers have utilized deep-learning techniques to estimate RUL based on sensor data. Most of the studies proposed solving this problem with a single deep neural network (DNN) model. This paper developed a novel turbofan engine RUL predictor based on several DNN models. The method includes a time window technique for sample preparation, enhancing DNN’s ability to extract features and learn the pattern of turbofan engine degradation. Furthermore, the effectiveness of the proposed approach was confirmed using well-known model evaluation metrics. The experimental results demonstrated that among four different DNNs, the long short-term memory (LSTM)-based predictor achieved the better scores on an independent testing dataset with a root mean-square error of 15.30, mean absolute error score of 2.03, and R-squared score of 0.4354, which outperformed the previously reported results of turbofan RULestimation methods.
Co-Authors Abdulkadir, Said Jadid Akhir, Emelia Akashah Patah Al-Selwi, Safwan Mahmood Alqushaibi, Alawi Khairuddin, Aimi Amirah Ragab, Mohammed Gamal Sumiea, Ebrahim Hamid Taib, Shakirah Mohd