Business Data Analytics for Sustainable and Resilient Food Procurement: Integrating Expert Judgment and Objective Weighting under Uncertainty
Main article
Abstract
Food manufacturers increasingly face procurement decisions in which sustainability, continuity, food safety, social responsibility, and cost must be evaluated under uncertain and conflicting evidence. This study develops a business data analytics framework for sustainable and resilient food procurement by integrating expert judgment with objective weighting under uncertainty. The framework converts linguistic supplier assessments into uncertainty-aware evaluation scores, combines subjective criteria weights from cross-functional experts with objective weights derived from the information structure of supplier data, and ranks suppliers through a robust multi-normalization scoring process. A food-manufacturing case with six anonymized suppliers demonstrates how the framework identifies a preferred supplier while also generating diagnostics for supplier development, backup sourcing, and resilience investment. Results show that resilience and social responsibility criteria become decisive when supplier continuity, labor compliance, recovery capability, and traceability are explicitly modeled. Sensitivity analysis across subjective-objective weighting scenarios confirms the stability of the leading supplier and reveals where lower-ranked suppliers remain vulnerable. The study contributes to business data analytics by reframing supplier selection as an auditable analytics system rather than a one-time ranking exercise. It also provides managerial guidance for designing sustainable-resilient procurement scorecards, data-governance routines, and supplier improvement portfolios in disruption-sensitive food supply chains.
