Home Journals IJTER Archives Vol. 2, No. 4 SKILL GAP ANALYSIS IN SEAFOOD PROCESSING AND EXPORT UNITS US...

International Journal of Technology and Emerging Research

e-ISSN: 3068-109X p-ISSN: 3068-1995 DOI: 10.64823/ijter Volume: 2 — Issue 4 (2026)
Article Info
Open Access Research Article
7 pages PDF

SKILL GAP ANALYSIS IN SEAFOOD PROCESSING AND EXPORT UNITS USING AI

by ORCID iD

International Journal of Technology and Emerging Research 2026 , 2 (4) , 118–124

10.64823/ijter.2604014
Received: 01 Apr 2026 Published: 22 Apr 2026
View PDF Download

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: supply chain management, machine learning, IoT, Sustainability, Predictive Analytics, Keywords: Artificial Intelligence, Skill Gap Analysis, Seafood Processing, Export Units, Workforce development, Quality Control, Compliance

© 2026 The Author(s). Published by IORO Publications. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original author and source are credited, a link to the license is provided, and any changes are indicated.

IORO Support

Usually replies in minutes

Common Questions

Leave us a message: