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Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap

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Bibliographic Details
Title: Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap
Authors: Koopo Kwon, Sungchan Jun, Yong-Jae Lee, Sanghei Choi, Chulung Lee
Source: Sustainability, Vol 14, Iss 5430, p 5430 (2022)
Publisher Information: MDPI AG
Publication Year: 2022
Collection: Directory of Open Access Journals: DOAJ Articles
Subject Terms: retail logistics, technology roadmap, patent analysis, time series, clustering, latent dirichlet allocation, Environmental effects of industries and plants, TD194-195, Renewable energy sources, TJ807-830, Environmental sciences, GE1-350
Description: The rapid advancement of digital technologies has fundamentally changed the competitive dynamics of the logistics industry. For players in the logistics industry, digitization has become an unavoidable situation to achieve survival and sustainable competitiveness. A technology strategy is essential for digitization, and identifying opportunities and threats of technology development through technology trend exploration is important for technology strategy. In addition, to enable the implementation of the technology strategy, it is necessary to detect the change in technology and search for the technology that is expected to have a practical development effect. The purpose of this study is to identify opportunities and areas for technology development through patent data in establishing technology strategies. Previous research mainly relied on the expert interview method, and there was also a patent analysis study based on topic modeling, but only to grasp technology trends. This paper aims to propose a new framework for the extension to the stage for establishing a technology roadmap. By using the Word2Vec algorithm, we will investigate the patent search formula that reflects the trend, the prediction of changes in logistics technology through LDA (Latent Dirichlet Allocation) clustering of patent data, and the derivation of vacant technology by experimental methods. The proposed framework is expected to be utilized for predicting technological change and deriving promising technologies for establishing technology roadmaps in logistics companies.
Document Type: article in journal/newspaper
Language: English
Relation: https://www.mdpi.com/2071-1050/14/9/5430; https://doaj.org/toc/2071-1050; https://doaj.org/article/fdd6bfbe84fa4867952f375b9c554e28
DOI: 10.3390/su14095430
Availability: https://doi.org/10.3390/su14095430
https://doaj.org/article/fdd6bfbe84fa4867952f375b9c554e28
Accession Number: edsbas.A2690A44
Database: BASE
Description
Abstract:The rapid advancement of digital technologies has fundamentally changed the competitive dynamics of the logistics industry. For players in the logistics industry, digitization has become an unavoidable situation to achieve survival and sustainable competitiveness. A technology strategy is essential for digitization, and identifying opportunities and threats of technology development through technology trend exploration is important for technology strategy. In addition, to enable the implementation of the technology strategy, it is necessary to detect the change in technology and search for the technology that is expected to have a practical development effect. The purpose of this study is to identify opportunities and areas for technology development through patent data in establishing technology strategies. Previous research mainly relied on the expert interview method, and there was also a patent analysis study based on topic modeling, but only to grasp technology trends. This paper aims to propose a new framework for the extension to the stage for establishing a technology roadmap. By using the Word2Vec algorithm, we will investigate the patent search formula that reflects the trend, the prediction of changes in logistics technology through LDA (Latent Dirichlet Allocation) clustering of patent data, and the derivation of vacant technology by experimental methods. The proposed framework is expected to be utilized for predicting technological change and deriving promising technologies for establishing technology roadmaps in logistics companies.
DOI:10.3390/su14095430