Integrating hesitant fuzzy linguistic term sets with ordinal priority approach: Application to digital supply chain supplier selection

Background Multi-criteria decision making problems often involve multiple decision makers who express their preferences using imprecise linguistic terms rather than exact numerical values. The Ordinal Priority Approach (OPA) derives weights directly from ordinal preferences, but its standard form assumes crisp ranks and cannot capture hesitancy. Methodology This paper proposes a Hesitant Fuzzy Linguistic Term Set based Ordinal Priority Approach (HFLTS-OPA) for supplier selection in digital supply chains. Comparative linguistic expressions are converted into ordinal rank intervals and used to build a dual-bound OPA model whose paired optimistic and pessimistic programs yield interval priorities, ranked by possibility degree and non-dominance. The method is demonstrated on a manufacturing case with 3 decision makers, 8 criteria, and 10 suppliers. Findings Cost emerged as the dominant criterion, followed by quality and by information sharing and digital collaboration, confirming the growing importance of digital capabilities alongside conventional sourcing priorities. The method produced a clear supplier ordering that remained stable across all tested weight-sum ranges, and six HFLTS-based benchmark methods converged on the same leading alternatives. Originality The study is the first to integrate hesitant comparative linguistic expressions with OPA, preserving hesitation through to interval-valued priorities without pairwise comparisons or normalization.