Analyzing supply chain technology trends through network analysis and clustering techniques: a patent-based study

Sajjad Shokouhyar (), Mehrdad Maghsoudi (), Shahrzad Khanizadeh () and Saeid Jorfi ()
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Sajjad Shokouhyar: Australian Institute of Business
Mehrdad Maghsoudi: Shahid Beheshti University
Shahrzad Khanizadeh: Shahid Beheshti University
Saeid Jorfi: University of Hagen

Annals of Operations Research, 2024, vol. 341, issue 1, No 13, 313-348

Abstract: Abstract The supply chain forms the backbone of the modern consumer economy, weaving an intricate network of stakeholders across geographical and socioeconomic divides. While new technologies have enhanced supply chain management, the market dynamism and network complexities continue to challenge decision-makers. This study employs social network analysis and text mining to unravel technological patterns within the patent landscape of supply chain management. The analysis draws on a dataset of over 32,000 supply chain patents from Lens.org spanning 2000–2022. Network analysis reveals cooperation patterns and key players, while text mining and clustering identify five technology clusters: secure access control, manufacturing, logistics, data management, and RFID. Technology life cycle analysis indicates that secure access control, data management, and RFID have reached maturity, while logistics is still growing and manufacturing faces saturation. The findings highlight that despite maturity, these technologies warrant continued investment to resolve persistent challenges. The technology trends and maturity insights uncovered can help enterprises make informed strategic decisions by aligning R&D initiatives with technology lifecycles. This pioneering study bridges innovation research and technology management, offering a nuanced understanding of supply chain technologies. The framework presented can be extended to analyze other domains, opening avenues for further research. Overall, this study decodes the patent landscape to decode the future.

Keywords: Supply chain; Technology forecasting; Patent mining; Text clustering; Social network analysis (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-024-06119-w

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