Title:
Artificial Intelligence-enabled Business Analytics in the Lobster Industry: A Conceptual Framework for Demand, Quality, Traceability, and Supply Chain Decisions

Authors:
Sheng Zheng, USA

Abstract:

Lobster businesses operate in a high-value but operationally demanding environment shaped by perishability, seasonal landings, variable quality, fragmented trading relationships, and exposure to market and logistics disruptions. This article develops a business-oriented framework for applying artificial intelligence (AI) and business analytics across the lobster value chain. A targeted synthesis of research on AI capability, business analytics, supply chain management, digital food systems, traceability, and lobster computer vision connects technical applications with recurring managerial decisions. The framework organizes value creation into four linked elements: data foundations; descriptive, predictive, prescriptive, and computer-vision capabilities; managerial actions in procurement, inventory, pricing, logistics, grading, traceability, and marketing; and business outcomes involving margin, waste reduction, service reliability, trust, resilience, and sustainability. Five propositions identify the conditions under which predictive analytics, prescriptive analytics, computer vision, and digital traceability may contribute to business value. The article also outlines a staged implementation path in which firms begin with decision visibility and standardized product information, advance to forecasting and exception management, and adopt optimization only after operational constraints and accountability mechanisms are clearly defined. The resulting framework provides a research agenda and a practical structure for evaluating AI investments in lobster businesses and other perishable, traceability-sensitive markets.

DOI: https://doi.org/10.51505/IJEBMR.2026.10903