Blockchain is a technology with unique combination of features such as decentralized structure, distributed notes and storage mechanism, consensus algorithm, smart contracting, and asymmetric encryption to ensure network security, transparency and visibility. Blockchain has immense potential to transform supply chain (SC) functions, from SC provenance, business process reengineering to security enhancement. More and more studies exploring the use of blockchain in SCs have appeared in recent years. In this paper, we consider a total of 178 articles and examine all the relevant research done in the field associated with the use of blockchain integration in SC operations. We highlight the corresponding opportunities, possible societal impacts, current state-of-the-art technologies along with major trends and challenges. We examine several industrial sectors such as shipping, manufacturing, automotive, aviation, finance, technology, energy, healthcare, agriculture and food, e-commerce, and education among others that can be successfully revamped with blockchain based technologies through enhanced visibility and business process management. A future research agenda is established which lays the solid foundation for further studies on this important emerging research area.

Blockchain technology in supply chain operations: Applications, challenges and research opportunities

A review of logistics Internet-of-Things: Current trends and scope for future research

With new global regulations on supply chains (SCs), sustainable regulation mechanisms have become subject to controversy. The intention is to create and expand green and sustainable supply chains (SSC) to meet environmental and economic standards and to boost one’s position in competitive markets. This study examines the resilient sustainable reverse logistics network (RLN) process for end-of-life vehicles (ELVs) in Iran. We pursue both actual and uncertain situations that possess big data characteristics (3 V’s) in information between facilities of the proposed reverse logistics (RL), and we consider recycling technology due to its societal impacts. Due to unpredictable environmental and social factors, the various proposed network facilities may not utilize their full capacity, so we also consider situations in which the network facility capacity is disrupted. Our primary objective is to minimize the total cost of the resilient sustainable RLN. For most parameters, finding the best solution through traditional methods is time-consuming and costly. Hence, to enhance decision-making power, the value of model parameters in each scenario is considered. A Cross-Entropy (CE) algorithm with basic scenario concepts is used in robust model optimization. The results demonstrate that changing the scenario situation significantly impacts optimal environmental and social costs. In particular, when the situation is “pessimistic,” environmental impact costs are at their highest levels. Hence, scenario-based modeling of the network is a good approach to implement under uncertainty conditions. On the other hand, results show that cost savings for organizations are achieved through optimal planning of the centers' capacity to save cost, increase services, and ensure effective government response to cost-effective and instrumental market competition.

Robust network design for sustainable-resilient reverse logistics network using big data: A case study of end-of-life vehicles

A reverse supply chain for medical waste: A case study in Babol healthcare sector

A novel multi-objective scenario-based optimization model for sustainable reverse logistics supply chain network redesign considering facility reconstruction

A multiobjective optimization model for sustainable reverse logistics in Indian E-commerce market

Indian pharmaceutical industry is witnessing enormous challenges due to varying patent laws, increasing demand, and continuous pressure from the government to provide medicines at a lower price. To overcome these challenges, there is a need for a more robust supply chain (SC) which will help in information sharing and reduce overall cost. The chapter determines the key drivers of Indian pharmaceutical SC, and draws the attention of industry, stakeholders, and top management to emphasise on these drivers to enhance the performance and profitability of SC. An interpretive structural modelling-based approach has been employed to model the pharmaceutical SC key drivers. The 16 key parameters have been identified across all major dimensions such as SC, HR, & organizational, market, technology, and reverse logistics. Further fuzzy MICMAC analysis is done to categorize based on their driving and dependence power. The factors like collaborative relationship among SC partners, quality regulations, third party logistics, and end-to-end responsive SC are found to be more important enablers. Exploring Key Success Factors of Indian Pharmaceutical Supply Chain Using Interpretive Structural Modelling

Anurag Mishra

Papers

A multiobjective optimization model for sustainable reverse logistics in Indian E-commerce market

Exploring Key Success Factors of Indian Pharmaceutical Supply Chain Using Interpretive Structural Modelling