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This paper focuses on assessing sustainability of supply chains. In this paper, at first, we propose network dynamic range adjusted measure (RAM) model. Then, inverse version of network dynamic RAM model is proposed. Our inverse network dynamic data envelopment analysis (DEA) model changes both inputs and outputs of decision making units (DMUs) so that current efficiency scores of DMUs remain unchanged. We change inputs and outputs without any change in efficiency score of DMU under evaluation while inputs and outputs may have large ranges. A case study shows efficacy of our proposed model.

PurposeThis paper discusses how learning-by-doing (LBD) criterion can be used to evaluate the sustainability of supply chains. This paper assesses the impacts of teamwork on the LBD criterion. Besides, the effect of the internship of new labors on the LBD criterion is discussed.Design/methodology/approachThe repeat of a task leads to a gradual improvement in the efficiency of production systems. LBD occurs by accumulating knowledge and skills in multiple periods. LBD can be used to study changes in the efficiency. Efficiency can be improved by accumulating knowledge and skills. In this paper, the LBD criterion is projected on learning curve (LC) models. Furthermore, the LC models are fitted to the supply chains. Each supply chain may have a unique LC model. A minimum difference is set between the current performance of decision making unit (DMU) and the estimated performance of DMU based on DMU's LC. Hence, a point in which the LBD occurs is determined.FindingsThis paper develops an inverse network dynamic data envelopment analysis (DEA) model to assess the sustainability of supply chains DMUs. Findings imply that the LBD criterion plays an important role in assessing the sustainability of supply chains. Furthermore, managers should increase the internships and teamwork to get more benefit from the LBD criterion.Originality/valueFor the first time, this paper uses the LBD criterion to assess the sustainability of supply chains given the LC equations.