SKILL GAP ANALYSIS IN SEAFOOD PROCESSING AND EXPORT UNITS USING AI

This paper examines the application of Artificial Intelligence (AI) for conducting skill gap analysis in seafood processing and export units, with particular relevance to emerging seafood hubs where traditional workforce assessment methods remain prevalent. The seafood industry faces persistent challenges in maintaining product quality, regulatory compliance, operational efficiency, and sustainability, many of which stem from deficiencies in workforce skills. Conventional approaches are often slow, subjective, and incapable of delivering real-time insights. In contrast, AI enables data-driven, scalable, and predictive assessment of workforce competencies. The study proposes a multi-phase AI-driven framework that integrates machine learning, natural language processing, IoT-based monitoring, and predictive analytics to identify, measure, and address skill gaps across processing, logistics, compliance, and sustainability functions. Data sources include employee profiles, performance records, training histories, and industry benchmarks. AI tools such as computer vision for quality inspection, digital twins for process optimization, blockchain for traceability, and AR/VR platforms for training are analyzed for their role in enhancing workforce capability. Findings from a case study of a mid-sized seafood processing unit reveal significant deficiencies in advanced quality testing and export documentation knowledge. AI-based adaptive training improved compliance rates by 25%, reduced processing errors by 15%, and shortened export cycle time by 10%, demonstrating measurable operational gains. However, implementation challenges persist, including limited data availability, workforce resistance, digital literacy gaps, and high initial investment costs. The paper recommends the development of unified data platforms, change-management initiatives, scalable cloud-based AI solutions, and policy support through subsidies and skill-development programs. Overall, the study concludes that AI-enabled skill gap analysis can significantly enhance productivity, sustainability, and global competitiveness in seafood processing and export industries, providing a strategic pathway for modernization in an increasingly technology-driven global market.

@article{ijter2026212604017809,
  author = {Dr, Sipra Karmakar},
  title = {SKILL GAP ANALYSIS IN SEAFOOD PROCESSING AND EXPORT UNITS USING AI},
  journal = {International Journal of Technology &  Emerging Research },
  year = {2026},
  volume = {2},
  number = {4},
  pages = {118-124},
  doi =  {10.64823/ijter.2604014},
  issn = {3068-109X},
  url = {https://www.ijter.org/article/212604017809/skill-gap-analysis-in-seafood-processing-and-export-units-using-ai},
  abstract = {This paper examines the application of Artificial Intelligence (AI) for conducting skill gap analysis in seafood processing and export units, with particular relevance to emerging seafood hubs where traditional workforce assessment methods remain prevalent. The seafood industry faces persistent challenges in maintaining product quality, regulatory compliance, operational efficiency, and sustainability, many of which stem from deficiencies in workforce skills. Conventional approaches are often slow, subjective, and incapable of delivering real-time insights. In contrast, AI enables data-driven, scalable, and predictive assessment of workforce competencies. The study proposes a multi-phase AI-driven framework that integrates machine learning, natural language processing, IoT-based monitoring, and predictive analytics to identify, measure, and address skill gaps across processing, logistics, compliance, and sustainability functions. Data sources include employee profiles, performance records, training histories, and industry benchmarks. AI tools such as computer vision for quality inspection, digital twins for process optimization, blockchain for traceability, and AR/VR platforms for training are analyzed for their role in enhancing workforce capability. Findings from a case study of a mid-sized seafood processing unit reveal significant deficiencies in advanced quality testing and export documentation knowledge. AI-based adaptive training improved compliance rates by 25%, reduced processing errors by 15%, and shortened export cycle time by 10%, demonstrating measurable operational gains. However, implementation challenges persist, including limited data availability, workforce resistance, digital literacy gaps, and high initial investment costs. The paper recommends the development of unified data platforms, change-management initiatives, scalable cloud-based AI solutions, and policy support through subsidies and skill-development programs. Overall, the study concludes that AI-enabled skill gap analysis can significantly enhance productivity, sustainability, and global competitiveness in seafood processing and export industries, providing a strategic pathway for modernization in an increasingly technology-driven global market.
  
  },
  keywords = {Keywords: Artificial Intelligence; Skill Gap Analysis; Seafood Processing; Export Units; Workforce development; Machine Learning; IoT; Predictive Analytics; Quality Control; Supply Chain Management; Compliance; Sustainability},
  month = {Apr},
}

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