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Companies attempt to improve the performance of their supply chain (SC) by distinguishing and presenting feasible sustainable development practices (SDP). Considering SDP without focusing on sustainability risks may disturb the company’s future. Very few studies in the extant literature have dealt with the impact of (SDP) on the supply chain risk management (SCRM). In fact, the aim of this paper is to classify and prioritize SDPs according to their priority for better risk management and effective SC performance. The proposed approach comprises two phases. First, 14 SDPs are identified and selected from the literature. Second, MICMAC (Matrice d’impacts croisés multiplication appliquée à un classement) method as a structural analysis method applies to identify and assess sustainable supply chain risk management (SSCRM) practices which reduce risk in the SC. The input data for each phase are based on Delphi technique, which is a process group used to collect the opinions of experts in the field. The aim of the proposed approach is to prioritize SSCRM practices and classify them into influential, non-influential, independent and dependent practices and their mutual relationships. The six key findings SSCRM practices from direct and indirect classification include the following elements: (1) Delayed differentiation, (2) Information sharing with upstream and/or downstream partners, (3) Simplification of product dismantling/anticipation of product end of life, (4) Supplier/subcontractor’s performance assessment, (5) establishing shared supply management and (6) establishment of contracts with transporters.

Seaports are important infrastructures to support international trade. Therefore, it is vital that port efficiency and productivity are continuously evaluated and improved. In this context, the objective of this article is to evaluate both the technical efficiency and the change in productivity of the six most important Tunisian commercial seaports, Bizerte, Rades, Sousse, Sfax, Gabes, and Zarzis, over a period of twelve years from 2005 to 2016. To achieve this objective, the data envelopment analysis (DEA) method is applied. The first output-oriented DEA application is about efficiency evaluation, which, for each seaport, allows the estimation of overall technical efficiency, pure technical efficiency, and scale efficiency. The second application concerns the evolution of the productivity of Tunisian seaports during the study period using the Malmquist DEA-based productivity index. The productivity analysis is performed according to the year (period) and according to each studied seaport. The first output-oriented DEA method provides that the overall technical efficiency in the above-mentioned ports is 69.4% while the pure technical efficiency is 83.3%. Furthermore, the average scale efficiency is about 82.6%, which implies that the decreasing type of returns to scale dominates in this study. Regarding the second DEA application for productivity evolution, the obtained results from the data analysis revealed that it fell by 6.7%, mainly due to the degradation of the technological change (8.3%). The results obtained provide useful basic criteria for establishing efficiency improvement strategies for each studied seaport.