A review on role of artificial intelligence in food processing and manufacturing industry

Ramírez-Asís, Edwin; Vilchez-Carcamo, Juan; Thakar, Chetan M.; Phasinam, Khongdet; Kassanuk, Thanwamas; Naved, Mohd

doi:10.1016/j.matpr.2021.11.616

Cited by 35 publications

(19 citation statements)

References 17 publications

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“…The adoption of AI in agri-food supply chain has directed its attention to the three dimensions of ESG for various reasons. Firstly, the agriculture and food industry has been associated with substantial environmental impacts, including soil degradation, water pollution (Javaid et al , 2023) and food waste (Ramirez-Asis et al , 2022). Consequently, prioritizing environmental sustainability has become critical in mitigating these adverse effects.…”

Section: Discussion With Practical Implicationsmentioning

confidence: 99%

Artificial intelligence in supply chain decision-making: an environmental, social, and governance triggering and technological inhibiting protocol

Hao

Demir

2023

JM2

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Purpose Decision-making, reinforced by artificial intelligence (AI), is predicted to become potent tool within the domain of supply chain management. Considering the importance of this subject, the purpose of this study is to explore the triggers and technological inhibitors affecting the adoption of AI. This study also aims to identify three-dimensional triggers, notably those linked to environmental, social, and governance (ESG), as well as technological inhibitors. Design/methodology/approach Drawing upon a six-step systematic review following the preferred reporting items for systematic reviews and meta analysis (PRISMA) guidelines, a broad range of journal publications was recognized, with a thematic analysis under the lens of the ESG framework, offering a unique perspective on factors triggering and inhibiting AI adoption in the supply chain. Findings In the environmental dimension, triggers include product waste reduction and greenhouse gas emissions reduction, highlighting the potential of AI in promoting sustainability and environmental responsibility. In the social dimension, triggers encompass product security and quality, as well as social well-being, indicating how AI can contribute to ensuring safe and high-quality products and enhancing societal welfare. In the governance dimension, triggers involve agile and lean practices, cost reduction, sustainable supplier selection, circular economy initiatives, supply chain risk management, knowledge sharing and the synergy between supply and demand. The inhibitors in the technological category present challenges, encompassing the lack of regulations and rules, data security and privacy concerns, responsible and ethical AI considerations, performance and ethical assessment difficulties, poor data quality, group bias and the need to achieve synergy between AI and human decision-makers. Research limitations/implications Despite the use of PRISMA guidelines to ensure a comprehensive search and screening process, it is possible that some relevant studies in other databases and industry reports may have been missed. In light of this, the selected studies may not have fully captured the diversity of triggers and technological inhibitors. The extraction of themes from the selected papers is subjective in nature and relies on the interpretation of researchers, which may introduce bias. Originality/value The research contributes to the field by conducting a comprehensive analysis of the diverse factors that trigger or inhibit AI adoption, providing valuable insights into their impact. By incorporating the ESG protocol, the study offers a holistic evaluation of the dimensions associated with AI adoption in the supply chain, presenting valuable implications for both industry professionals and researchers. The originality lies in its in-depth examination of the multifaceted aspects of AI adoption, making it a valuable resource for advancing knowledge in this area.

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Section: Discussion With Practical Implicationsmentioning

confidence: 99%

Artificial intelligence in supply chain decision-making: an environmental, social, and governance triggering and technological inhibiting protocol

Hao

Demir

2023

JM2

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“…En un plano metodológico el estudio aporta con la identificación del efecto que tiene la AEE sobre el DE, el cual se basa en la teoría de aprendizaje social desarrollado por Bandura (2019) quien alude que la motivación tiene un papel importante, expresada a través de la AEE, en asumir actitudes empresariales que impulsen su desarrollo, estos emprendedores se fijan metas difíciles, pero alcanzables, exhiben perseverancia, ponen en una gran cantidad de esfuerzos hacia las tareas empresariales, y se recuperan rápidamente de sus fracasos (Torres-Coronas & Vidal-Blasco, 2019); además, debido a estos esfuerzos persistentes, un resultado positivo suele ser evidente (Salem, 2014;Ramirez-Asis et al, 2022). Finalmente, en un plano práctico y social, este estudio tiene gran relevancia en vista que permite conocer medios por el cual se pueda mejorar el desarrollo de las MYPES peruanas, siendo esto de suma importancia en vista del valor que tiene sobre la economía del país.…”

Section: Modelo Estructuralunclassified

Autoeficacia emprendedora y desempeño de micro y pequeñas empresas peruanas

R.¹,

Ramírez-Asís²,

Figueroa³

et al. 2023

RVG

Self Cite

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La autoeficacia emprendedora ha ganado importancia en los últimos años en el campo del emprendimiento, con base en ello el objetivo del presente estudio fue determinar el efecto de la autoeficacia emprendedora sobre el desempeño empresarial de las micro y pequeñas empresas de cuatro provincias peruanas, para ello se evaluó a un total de 382 emprendedores de diversas instituciones quienes fueron evaluados a través de una encuesta con propiedades psicométricas adecuadas. Dentro de los principales resultados se determinaron valores significativos a un nivel de confianza del 95% (p=0.00; p<0.01) en la relación entre las dimensiones de la autoeficacia emprendedora con el desempeño empresarial, donde resalta que la dimensión de propiciar relaciones con inversionistas aporta en gran medida al incremento de los resultados de las empresas, mientras que la dimensión fomentar un entorno innovador su contribución es menor; en conclusión, se evidencia que la autoeficacia emprendedora tiene un efecto positivo sobre el desempeño empresarial esto indica que en tanto un sujeto tenga mejores aptitudes para diseñar nuevos productos se obtendrán mejoras en el desempeño dentro de las micro y pequeñas empresas peruanas.

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“…Currently, an AI revolution is happening in almost all industries (including the agriculture and food industry) on a global scale. Two examples of the application of AI in the agri-food sector are discussed in this section, while many other applications can be found in other recent publications ( Addanki et al, 2022 ; Ben Ayed and Hanana, 2021 ; Ramirez-Asis et al, 2022 ; Talaviya et al, 2020 ).…”

Section: Interconnection Between Vegetal and Digital Trendsmentioning

confidence: 99%

Emerging trends in the agri-food sector: Digitalisation and shift to plant-based diets

Hassoun

Boukid²,

Pasqualone³

et al. 2022

Current Research in Food Science

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A review on role of artificial intelligence in food processing and manufacturing industry

Cited by 35 publications

References 17 publications

Artificial intelligence in supply chain decision-making: an environmental, social, and governance triggering and technological inhibiting protocol

Artificial intelligence in supply chain decision-making: an environmental, social, and governance triggering and technological inhibiting protocol

Autoeficacia emprendedora y desempeño de micro y pequeñas empresas peruanas

Emerging trends in the agri-food sector: Digitalisation and shift to plant-based diets

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