Drug counterfeits have been an international issue for almost two decades, and the latest statistics show that fake medications will continue to penetrate legitimate pharmaceutical supply chains (PSCs). Therefore, identifying the issues faced by PSCs is essential to combat the counterfeit drug problem, which will require the implementation of technologies in various phases of the PSC to gain better visibility. In this regard, a literature review was conducted to fulfill the following objectives: (i) review the application of traceability technologies in various PSC phases to detect counterfeits; (ii) analyze the various barriers affecting the establishment of a safe PSC and the critical success factors used to overcome those barriers; and (iii) develop a conceptual framework and guidelines to demonstrate the influence of traceability technologies and success factors on overcoming the various barriers in different phases of the PSC. The major finding of this review was that traceability technologies and the critical success factors have a significant influence on overcoming the barriers to establishing a safe PSC.

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Research Highlights• We develop a decision model to integrate three decisions pertaining to location, allocation, and routing of different varieties of recycled plastics.• We validate the model using data retrieved from a case study in India using a conventional decomposed modelling approach.• Our integrated model reduces over ten percent of total recycling costs for both single and multiple products in the Indian context.• Managers need to cluster the customers based on the facility location and offer attractive incentives to reduce cost and increase benefits. Integrated optimization model and methodology for plastics recycling: Indian empirical evidence AbstractWe develop a decision model to integrate three decisions pertaining to location, allocation, and routing of different varieties of recycled plastics. Our decision model allocates recycling collection points to the available centralized return center based on its capacity and ensures similar allocation to all facilities via optimal routing for trucks. The study addresses several pertinent questions such as how to deal with higher collection cost, how to develop a model that jointly considers operational cost reduction along with the achievement of higher environmental benefits, how to reduce sub optimization which is quite common when using standalone decision models, and what are the feasible ways to increase the utilization of collection facilities. We validate the model using data retrieved from a case study in India using a conventional decomposed modelling approach. Our findings demonstrate that the proposed integrated model reduces over ten percent of total recycling costs for both single and multiple products. The results also suggest managers to reduce variances in product quality levels in order to achieve substantial total costs reduction. In addition, to increase the plastic recycling returns and reduce the operational cost, managers need to cluster the customers based on the facility location and offer attractive incentives.

The COVID-19 pandemic impact on the global supply systems is continuously on the rise creating an increasing ripple effect across the automotive supply chain network. In this disruptive setting, Additive manufacturing (AM) is perceived by many supply chain professionals as well as by many global automakers amongst best options to handle the disruptions and boost the automotive supply chain resilience. This research is dedicated to analysis up to which extent Additive manufacturing (AM) is a miraculous remedy to automotive supply chain disruptions. To this end, we use the Indian automotive industry as case-model to identify major barriers towards AM wide deployment and derive recommendations on how AM can be successfully used to cope with automotive supply chain disruptions.