Future Smart Logistics Technology Based on Patent Analysis Using Temporal Network

Koopo Kwon and Jaeryong So ()
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Koopo Kwon: Department of Shipping and Air Cargo & Drone Logistics, Youngsan University, 142, Bansong-sunhwan-ro, Haeundae-gu, Busan 48015, Republic of Korea
Jaeryong So: Department of Industrial Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea

Sustainability, 2023, vol. 15, issue 10, 1-17

Abstract: This study aims to predict new technologies by analyzing patent data and identifying key technology trends using a Temporal Network. We have chosen big data-based smart logistics technology as the scope of our analysis. To accomplish this, we first extract relevant patents by identifying technical keywords from prior literature and industry reports related to smart logistics. We then employ a technology prospect analysis to assess the innovation stage. Our findings indicate that smart logistics technology is in a growth stage characterized by continuous expansion. Moreover, we observe a future-oriented upward trend, which quantitatively confirms its classification as a hot technology domain. To predict future advancements, we establish an IPC Temporal Network to identify core and converging technologies. This approach enables us to forecast six innovative logistics technologies that will shape the industry’s future. Notably, our results align with the logistics technology roadmaps published by various countries worldwide, corroborating our findings’ reliability. The methodology presents in this research provides valuable data for developing R&D strategies and technology roadmaps to advance the smart logistics sector.

Keywords: smart logistics; patent analysis; technology growth curve; time series; network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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