Artificial intelligence in supply chain management: A systematic literature review

Toorajipour, Reza; Sohrabpour, Vahid; Nazarpour, Ali; Oghazi, Pejvak; Fischl, Maria

doi:10.1016/j.jbusres.2020.09.009

Cited by 420 publications

(237 citation statements)

References 132 publications

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“…AI has the potential to facilitate smart and agile decision making in the supply chain to anticipate problems. Hence, a proactive system of AI helps in enhancing the quality of service and in delighting customers through on-time and undamaged deliveries (Toorajipour et al, 2021;Wamba et al, 2020b). AI facilitates automated compliance that results in lower costs and the efficient functioning of a value chain network (Treleaven & Batrinca, 2017).…”

Section: Artificial Intelligencementioning

confidence: 99%

Artificial intelligence for supply chain resilience: learning from Covid-19

Modgil

Singh

Hannibal

2021

IJLM

216

104

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PurposeMany supply chains have faced disruption during Covid-19. Artificial intelligence (AI) is one mechanism that can be used to improve supply chain resilience by developing business continuity capabilities. This study examines how firms employ AI and consider the opportunities for AI to enhance supply chain resilience by developing visibility, risk, sourcing and distribution capabilities.Design/methodology/approachThe authors have gathered rich data by conducting semistructured interviews with 35 experts from the e-commerce supply chain. The authors have adopted a systematic approach of coding using open, axial and selective methods to map and identify the themes that represent the critical elements of AI-enabled supply chain resilience.FindingsThe results of the study highlight the emergence of five critical areas where AI can contribute to enhanced supply chain resilience; (1) transparency, (2) ensuring last-mile delivery, (3) offering personalized solutions to both upstream and downstream supply chain stakeholders, (4) minimizing the impact of disruption and (5) facilitating an agile procurement strategy.Research limitations/implicationsThe study offers interesting implications for bridging the theory–practice gap by drawing on contemporary empirical data to demonstrate how enhancing dynamic capabilities via AI technologies further strengthens supply chain resilience. The study also offers suggestions for utilizing the findings and proposes a framework to strengthen supply chain resilience through AI.Originality/valueThe study presents the dynamic capabilities for supply chain resilience through the employment of AI. AI can contribute to readying supply chains to reduce their risk of disruption through enhanced resilience.

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Section: Artificial Intelligencementioning

confidence: 99%

Artificial intelligence for supply chain resilience: learning from Covid-19

Modgil

Singh

Hannibal

2021

IJLM

216

104

View full text Add to dashboard Cite

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“…On the other hand, as the globalization of economic life raises competition to a fundamentally different level, many studies have emerged in the area of business and resource management system efficiency [71][72][73], analytical capabilities, and trend forecasting in the economy [15], as well as radical improvements in labor efficiency, reducing the financial and labor costs of doing business [18,74], increasing sustainable economic growth through green economy development [75], and so on. Recently, scientists have been particularly interested in studying AI in terms of the ability to make the right business decisions and reduce business costs [76]. As the commercial potential of AI technologies moved beyond clearly laboratory research, they become of economic value.…”

Section: Artificial Intelligence In the Energy Sectormentioning

confidence: 99%

Trends, Impacts, and Prospects for Implementing Artificial Intelligence Technologies in the Energy Industry: The Implication of Open Innovation

Dudnik

Vasiljeva²,

Kuznetsov

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

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This research aims to substantiate the impact of using open innovation (OI) in the energy sector in readiness to implement artificial intelligence (AI) technologies and their effectiveness. The empirical method was proposed to determine the readiness level of OI for the implementation of AI technologies by comparing Russian and French energy companies. Readiness level indicators of companies for AI implementation using the Fibonacci sequence, Student’s t-test, and the method of fuzzy sets were empirically determined. The integrated readiness indicator for AI implementation by companies was calculated using the method of fuzzy sets and expressed through variance, allowing for these significant factors. Russian companies are at a low level of developmental readiness to implement AI, which is in contrast to companies operating in a developed market where the determining factor is the AI technology cost. The example of the innovative business model “Energy-as-a-Service” shows the synergistic effects of OI use and AI technology introduction. This paper is novel because it seeks to contribute to the current debate in the literature, justifying the position that energy companies that have in the past actively applied the concept of open innovation in business, are the most competitive and most efficient in implementing AI technologies.

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“…No caso da blockchain, esta tecnologia tem aparecido lado a lado com as cadeias de suprimento [Hu et al 2021]. Os dados, uma vez lançados na rede Edge, para uma rede mais interna da organização, podem fazer uso de IA para detectar padrões (correlações e variações), [Toorajipour et al 2020] e [Min 2010]. Outras formas de detecção de eventos são via regras de negócios ou gatilhos em aplicações e bancos de dados (via triggers) que também são uma forma de reforçar políticas comerciais, [Leveling et al 2014].…”

Section: Trabalhos Relacionadosunclassified

Combinando Internet das Coisas, Inteligência Artificial e Blockchain para Monitorar a Cadeia de Agroquímicos

Monteiro

Mignoni

Righi

et al. 2021

Anais Do XIII Simpósio Brasileiro De Computação Ubíqua E Pervasiva (SBCUP 2021)

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A movimentação de agroquímicos na cadeia produtiva é imensa, oportunizando roubos, mal uso e evasão de impostos. O controle atual é precário e necessita de melhorias, possibilitando um vasto campo de pesquisa. Conforme a literatura na área, as propostas atuais de rastreabilidade usam apenas poucos sensores, além do fato de não existir combinação e integração entre eles. Nesse contexto, este trabalho apresenta um modelo que permite a rastreabilidade de defensivos agrícolas, combinando sensores de Internet das Coisas (IoT), aprendizado de máquina, redes de neblina (fog), identificação por rádio frequência (RFID) e blockchain. O diferencial do modelo está na contribuição de uma proposta modular e que permite segurança e confiabilidade no controle dos produtos. O modelo desenvolvido apresentou resultados concisos e promissores. Quando realizada a movimentação de objetos dentro de containers e fora destes, sensores capturam e enviaram os dados das ações para armazenamento e análise das camadas seguintes do modelo.

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Artificial intelligence in supply chain management: A systematic literature review

Cited by 420 publications

References 132 publications

Artificial intelligence for supply chain resilience: learning from Covid-19

Artificial intelligence for supply chain resilience: learning from Covid-19

Trends, Impacts, and Prospects for Implementing Artificial Intelligence Technologies in the Energy Industry: The Implication of Open Innovation

Combinando Internet das Coisas, Inteligência Artificial e Blockchain para Monitorar a Cadeia de Agroquímicos

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