Managing Product Returns at the Source: Associations between SCOR Processes and Return Performance - Multimethod Evidence from the Gaziantep Carpet Manufacturing Industry

Dilara Berrak TARHAN (Ankara Medipol University, Faculty of Economics, Administrative and Social Sciences, Department of International Trade and Finance, Türkiye)
Hasan Aksoy (Gaziantep University, Faculty of Economics and Administrative Sciences, Department of International Trade and Logistics, Gaziantep, Türkiye)

Journal of Transportation and Logistic
Vol. 1 No. 2 (2026): August 2026

Product returns are not merely a post-sale reverse-logistics activity; they can also represent the visible outcome of deficiencies in planning, sourcing, production, delivery, and supply chain governance. This study examines associations between the Plan, Source, Make, Deliver, and Enable processes in the SCOR Model 12.0 and a perception-based Return construct and identifies processes that merit managerial attention for return prevention. A multimethod design is employed. A total of 119 valid questionnaires collected from carpet manufacturing firms operating in Gaziantep, Türkiye, were analyzed via exploratory factor analysis, correlation analysis, and multiple linear regression. Evaluations of 28 SCOR metrics obtained from 10 supply chain managers were then weighted via the entropy method, and semistructured interviews with four stakeholders connected to one large carpet manufacturer were used for contextual interpretation. Because the survey is cross-sectional and all the constructs were measured from the same respondents at one measurement occasion, the regression coefficients are interpreted as associations rather than causal effects, and common-method variance cannot be ruled out. The main regression model accounts for 75.7% of the variance in the Return construct (R² = .757; adjusted R² = .747; p < .001). Plan is the strongest predictor (β = -.559; p < .001), followed by Enable (β = -.253; p = .004), Make (β = -.200; p = .017), and Deliver (β = -.170; p = .021); Source is not statistically significant (β = -.091; p = .115). Exploratory item-level regressions identify supply chain data and information management (β = -.668; p < .001) and engineer-to-order production (β = -.546; p < .001) as the strongest nominal associations, but these item-level p values are unadjusted for multiple testing and are treated as hypothesis-generating. Within the 10-manager panel, entropy-derived weights rank Plan first, and an indicator-count-normalized sensitivity check also leaves Plan first, although the ordering of intermediate processes changes. Overall, the findings are consistent with a preventive, upstream approach to returns centered on planning, information governance, customized production, and fulfillment control.