Critical Analysis of Technologies Enhancing Supply Chain Collaboration in the Food Industry: A Nigerian Survey

Logistics, Journal Year: 2025, Volume and Issue: 9(1), P. 8 - 8

Published: Jan. 9, 2025

Background: Supply chain collaboration technologies (SCCTs) are digital tools designed to enhance communication, coordination, and integration among supply stakeholders. These essential for enhancing transparency, efficiency, traceability within complex networks, particularly in the food industry. Methods: This study focuses on statistical analysis of survey data evaluate adoption impact SCCTs, including blockchain, Internet Things (IoT), enterprise resource planning (ERP), artificial intelligence (AI), Nigeria’s Results: The results reveal critical insights into barriers, perceived benefits, gaps implementation. Descriptive inferential techniques highlight significant variations technology across different sectors, uncovering key factors influencing SCCTs. findings demonstrate that while hold substantial potential optimize performance, their acceptance is constrained by infrastructural deficiencies, regulatory challenges, under-developed trust-building mechanisms, limited technical expertise. Conclusions: paper underscores importance targeted interventions, policy support, allocation foster effective utilization provides data-driven recommendations improving uptake, contributing sustainability competitiveness chain.

Language: Английский

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Leveraging Artificial Intelligence (AI) for Resilience in Industry 5.0

Advances in business strategy and competitive advantage book series, Journal Year: 2025, Volume and Issue: unknown, P. 35 - 68

Published: Jan. 31, 2025

The incorporation of Artificial Intelligence (AI) within Industry 5.0 significantly enhances resilience among small businesses. This chapter explores how AI transforms resilience, sustainability, and customer engagement strategies. With Small businesses can analyze large datasets to identify risks, optimize operations, deliver personalized experiences that align with consumer expectations. AI's ability process data efficiently allows anticipate market changes navigate uncertainties. Additionally, adopting fosters a culture encouraging employees embrace change. also supports sustainable practices by optimizing resource use reducing waste. Customer improves through AI-driven personalization, allowing tailor products services individual preferences. concludes recommendations for businesses: invest in employee training collaboration, ensure leadership commitment, prioritize foster adaptability thrive today's 5.0.

Language: Английский

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The Role of Generative Artificial Intelligence in Digital Agri-Food

Journal of Agriculture and Food Research, Journal Year: 2025, Volume and Issue: unknown, P. 101787 - 101787

Published: March 1, 2025

Language: Английский

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Harnessing artificial intelligence for advancements in Rice / wheat functional food Research and Development

Food Research International, Journal Year: 2025, Volume and Issue: unknown, P. 116306 - 116306

Published: March 1, 2025

Language: Английский

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Closing the loop: technological innovations in food waste valorisation for global sustainability

Discover Sustainability, Journal Year: 2025, Volume and Issue: 6(1)

Published: April 8, 2025

Language: Английский

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Circular bioeconomy in carbon footprint components of nonthermal processing technologies towards sustainable food system: A review

Trends in Food Science & Technology, Journal Year: 2024, Volume and Issue: 149, P. 104520 - 104520

Published: April 30, 2024

Language: Английский

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Artificial intelligence on the agro-industry in the United States of America

AIMS Agriculture and Food, Journal Year: 2024, Volume and Issue: 9(4), P. 959 - 979

Published: Jan. 1, 2024

<p>Integrating artificial intelligence (AI) into agriculture is a pivotal solution to address the pressing challenges posed by rapid population growth and escalating food demand. Traditional farming methods, unable cope with this surge, often resort harmful pesticides, deteriorating soil health. However, advent of AI promises transformative shift toward sustainable agricultural practices. In context United States, AI's historical trajectory within sector showcases remarkable evolution from rudimentary applications sophisticated systems focused on optimizing production quality. The future American lies in AI-driven innovations, spanning various facets such as image sensing for yield mapping, labor management, optimization, decision support farmers. Despite its numerous advantages, deployment does not come without challenges. This paper delved both benefits drawbacks adoption domain, examining impact agro-industry environment. It scrutinized emergence robot farmers role reshaping practices while acknowledging inherent problems associated implementation, including accessibility, data privacy, potential job displacement. Moreover, study explored how tools can catalyze development agribusiness, offering insights overcoming existing through innovative solutions. By comprehensively understanding opportunities obstacles entailed integration, stakeholders navigate landscape adeptly, fostering more resilient system generations.</p>

Language: Английский

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Revolutionizing food quality assessment: Unleashing the potential of artificial intelligence for enhancing honey physicochemical, biochemical, and melissopalynological insights

Journal of the Saudi Society of Agricultural Sciences, Journal Year: 2024, Volume and Issue: 23(4), P. 312 - 325

Published: Jan. 23, 2024

In the pursuit of advancing food quality assessment, this study employs sophisticated data-driven techniques to delve into complex realm honey analysis. With aim unraveling multifaceted nature quality, Self-Organizing Maps (SOMs) and Principal Component Analysis (PCA) were employed scrutinize interplay physicochemical, biochemical, melissopalynological attributes in samples collected from diverse drylands Algeria. The dataset comprised 62 eight crucial parameters. These parameters span climate zones (arid vs. desertic), honeybee breeds (Tellian, Saharan, hybrid), extraction methods (manual pressing electric centrifugation), beekeeping systems (modern traditional). Using SOMs, categorized distinct clusters that reflect variations across these four honey-related variables. Additionally, SOM heatmaps offer granular insights individual parameters' distribution map. Our analysis revealed nuanced distinctions North African regions, with specific playing a pivotal role defining quality. On average, exhibited following characteristics: water content 15.14%, an electrical conductivity 0.5 µS/cm, pH 4.20, total sugar 83%, reducing 63.83%, proline concentration 382.7 mg/kg honey, hydroxymethylfurfural level 77.4 mg/kg, average pollen grain density 4.56 × 105 grains per 10 g honey. Notably, identified clear demarcations linked characteristics associated bee techniques. results underscored significance selected as key indicators This analytical approach not only offered comprehensive assessment but also holds potential for broader applications within industry. findings invite further exploration ecological genetic dimensions practices Africa deepen our understanding honey's attributes. showcased efficacy SOMs PCA fabric assessment. techniques, complemented by structured used, provided valuable contributed enhancing scientific By elucidating relationships between research paves way future studies field promise monitoring.

Language: Английский

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Fruits and vegetables preservation based on AI technology: Research progress and application prospects

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 226, P. 109382 - 109382

Published: Aug. 27, 2024

Language: Английский

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Advancing Food Manufacturing: Leveraging Robotic Solutions for Enhanced Quality Assurance and Traceability Across Global Supply Networks

Trends in Food Science & Technology, Journal Year: 2024, Volume and Issue: 153, P. 104705 - 104705

Published: Sept. 10, 2024

Language: Английский

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