Building artificial intelligence enabled resilient supply chain: a multi-method approach

被引:14
|
作者
Singh, Rohit Kumar [1 ]
Modgil, Sachin [1 ]
Shore, Adam [2 ]
机构
[1] Int Management Inst Kolkata, Kolkata, India
[2] Liverpool John Moores Univ, Liverpool Business Sch, Liverpool, England
关键词
Artificial intelligence; Transparency; Procurement strategy; Personalized solution; Last mile delivery; Reduced impact of disruption; Supply chain resilience; MANAGEMENT; TECHNOLOGY;
D O I
10.1108/JEIM-09-2022-0326
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
PurposeIn the uncertain business environment, the supply chains are under pressure to balance routine operations and prepare for adverse events. Consequently, this research investigates how artificial intelligence is used to enable resilience among supply chains.Design/methodology/approachThis study first analyzed the relationship among different characteristics of AI-enabled supply chain and how these elements take it towards resilience by collecting the responses from 27 supply chain professionals. Furthermore, to validate the results, an empirical analysis is conducted where the responses from 231 supply chain professionals are collected.FindingsFindings indicate that the disruption impact of an event depends on the degree of transparency kept and provided to all supply chain partners. This is further validated through empirical study, where the impact of transparency facilitates the mass customization of the procurement strategy to Last Mile Delivery to reduce the impact of disruption. Hence, AI facilitates resilience in the supply chain.Originality/valueThis study adds to the domain of supply chain and information systems management by identifying the driving and dependent elements that AI facilitates and further validating the findings and structure of the elements through empirical analysis. The research also provides meaningful implications for theory and practice.
引用
收藏
页码:414 / 436
页数:23
相关论文
共 50 条
  • [11] Building a resilient pork supply chain to Salmonella spp
    Mu, Wenjuan
    van Asselt, Esther
    van Wagenberg, Coen
    van der Fels-klerx, Ine
    RISK ANALYSIS, 2024, 44 (01) : 12 - 23
  • [12] Artificial Intelligence for Electricity Supply Chain automation
    Richter, Lucas
    Lehna, Malte
    Marchand, Sophie
    Scholz, Christoph
    Dreher, Alexander
    Klaiber, Stefan
    Lenk, Steve
    RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2022, 163
  • [13] Applying Artificial Intelligence in Supply Chain Management
    Alfawaz, Khaled Mofawiz
    Alshehri, Ali Abdullah
    COMMUNICATIONS IN MATHEMATICS AND APPLICATIONS, 2022, 13 (01): : 367 - 377
  • [14] Artificial intelligence applications in supply chain management
    Pournader, Mehrdokht
    Ghaderi, Hadi
    Hassanzadegan, Amir
    Fahimnia, Behnam
    INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2021, 241
  • [15] Prediction of Uncertain Parameters of a Sustainable Supply Chain Using an Artificial Intelligence Approach
    Massoumeh Nazari
    Mahmoud Dehghan Nayeri
    Kiamars Fathi Hafshjani
    Operations Research Forum, 6 (1)
  • [16] The Role of Artificial Intelligence in Supply Chain Agility: A Perspective of Humanitarian Supply Chain
    Pereira, Elisabeth T.
    Shafique, Muhammad Noman
    INZINERINE EKONOMIKA-ENGINEERING ECONOMICS, 2024, 35 (01): : 77 - 89
  • [17] RFID supply chain data deconstruction method based on artificial intelligence technology
    Zhang, Huiying
    Li, Ze
    OPEN COMPUTER SCIENCE, 2023, 13 (01)
  • [18] Business Modeling for Resilient Destination Development: A Multi-Method Approach for the Case of Destination Franconia, Germany
    Thees, Hannes
    Stoermann, Elina
    Pechlaner, Harald
    TOURISM PLANNING & DEVELOPMENT, 2023, 20 (02) : 212 - 235
  • [20] A multi-method examination of barriers to traceability in Industry 5.0-enabled digital food supply chains
    Sarkar, Bishal Dey
    Sharma, Isha
    Shardeo, Vipulesh
    INTERNATIONAL JOURNAL OF LOGISTICS MANAGEMENT, 2024,