Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal International Journal of Supply Chain Management International Journal of Technology and Modeling
Sharma, Arjun
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Transportation

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Managing Complex Supply Chains: Lessons from Military Logistics Sharma, Arjun
International Journal of Supply Chain Management Vol 12, No 6 (2023): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v12i6.6215

Abstract

The prerogative of modern life is the door-step delivery of the desired products within days or a few hours of placing the order with the vendor/s. This necessitates the creation, maintenance and management of an efficient network of people and processes for production and timely delivery of the product in demand – a robust supply chain. The article is a comprehensive approach towards modern supply chain management. The purpose of this article is to illustrate the complexities faced by commercial organization in managing their supply chains, especially the ones that are spread across the globe. This article explains how the commercial world can learn from the military the ways to handle their supply chains effectively even in disruptive situations such as diseases and natural disasters in order to ensure non disruption of operations. Supply chains being lifeline of any business it pays to learn from those who are the best in managing supply chains under dire circumstances. Though blindly copying and trying to replicate supply chain management (SCM) practices adopted by the military may prove futile, a modified version to suit company-specific needs will make important differences in the efficiency of commercial operation of enterprises. We scrutinized an assortment of secondary sources including peer reviewed journal, magazines, online publications, books and newspapers to arrive at this conclusion.
The Future Delivered: Rethinking Last Mile in the Age of Instant Gratification Sharma, Arjun
International Journal of Supply Chain Management Vol 13, No 1 (2024): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v13i1.6218

Abstract

This study explains the problems faced in last mile delivery (LMD) and suggests innovative solutions for them. The demand for home delivery services for delivering small package to the customers’ doorsteps has expanded rapidly due to the exponential growth of online shopping. Last mile delivery has now become a key success factor for any business, thanks to the rapid development of information and communication technology (ICT), e-commerce and the COVID-19 pandemic. Final-mile delivery on the very same day as placing the order for a product, has turned out to be a critical success factor for businesses, since modern-day customers have grown new habits and hence new expectations - the expectation of expedited demand fulfillment through door-step delivery of the products they shop online. Though the costliest leg of the supply chain, LMD will always be what creates the competitive edge for the e-commerce. It is necessary for sustenance and success.
Advancing Medical Diagnostics with Deep Learning: A Novel Approach to Disease Detection and Prediction Patel, Priya; Sharma, Arjun; Mehta, Rahul; Iyer, Ananya
International Journal of Technology and Modeling Vol. 2 No. 2 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i2.109

Abstract

Deep learning has revolutionized various fields, including medical diagnostics, by enabling more accurate and efficient disease detection and prediction. This paper explores the latest advancements in deep learning applications for medical diagnostics, emphasizing how convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models enhance diagnostic accuracy. The study discusses the integration of deep learning with medical imaging, electronic health records (EHRs), and genomic data to improve early disease detection and personalized treatment strategies. Additionally, ethical considerations, challenges, and future directions in deep learning-based diagnostics are analyzed. The findings highlight the potential of deep learning to transform healthcare by reducing diagnostic errors, optimizing treatment plans, and improving patient outcomes.
Co-Authors Iyer, Ananya Mehta, Rahul Patel, Priya