International Journal of Supply and Operations Management
Volume:9 Issue: 4, Autumn 2022

Covid-19 pandemic affected millions of people across the globe. Healthcare professionals need various kind of medical product like drugs, vaccines, other biologicals, and diagnostic equipment to combat pandemics. Fake vendors introduced falsified medical products in national and international markets during pandemic. These counterfeit products are life threatening due to inferiority in quality and available in noncompliance of label claim. Europol confiscated 34,000 counterfeit surgical masks in just one coordinated assignment of fake goods. The data for the unauthorized medical product sell is higher than expectation during this Covid-19 pandemic. World Health Organization reported that up to $200 billion worth of counterfeit pharmaceutical products are sold globally every year.It is a challenge to track and trace counterfeit medical products because these products must pass through many complicated distribution channels which allows opportunity for counterfeit drugs to enter in supply chain. In current supply chain methods, central authorities control transacted data among parties. Multiple intermediates needed to enable activities and creating trust. In this scenario, there is chance of manipulation in data fabrication. Blockchain protects supply chain and maintain a shared source of data information. Trust enabled by cryptographic algorithms and immutability of data preserved in Blockchain.In this paper, a Theoretical Blockchain Architecture Model (t-BAM) proposed using Hyperledger Fabric as a Blockchain platform and Byzantine Fault Tolerance (BFT) Algorithm for mutual consensus in supply chain of medical products during COVID-19 pandemic. This model validated for immutability, Mutual consensus, Transparency and Accountability, Privacy and Security, Temperature and Humidity control parameters.

The supply chain ecosystem is currently benefiting from a great dynamic resulting from the digitalization of organizations and trades. For all the stakeholders in the area, this is a real breakthrough, and machine learning is at the core of this revolution. It has profoundly revolutionized companies in many aspects including the evolution of communication methods, the automation of many processes, the growing importance of information systems, etc. With shrinking margins and more demanding customers, supply chain management in increasingly becoming a source of competitive advantage. Its management and optimization requires a factual to Supply Chain decision making at strategique, tactical and operational levels. In this context and data rich environment, machine learning approaches and techniques find numerous useful applications for supply chain decision making. Today, companies have no choice but to apply Machine Learning solutions in almost every part of their processes. This fact seems even clearer in markets where competition is fierce. While Machine Learning does not redefine the enterprise, it is certainly a powerful asset for both marketing and process optimization purposes. It is so ingrained in the strategies of companies that now most of them rely heavily on it for all processes from creation, to product quality control, to public relations. In recent years, a series of practical applications of machine learning (ML) for supply chain decisions have been introduced.

The need for effective use of assets has become more important in the design of supply chain networks in today’s competitive environment. Sale and leaseback (SLB) agreements are one of the appropriate tools to achieve this important goal. The proper use of these agreements increases the liquidity of assets, and provides financial resources required for other activities. However, the consideration of SLB possibility in a Closed-Loop Supply Chain (CLSC) that aims to minimize 〖CO〗_2 emissions as well as maximizing profit has never studied before. Therefore, this paper proposes a bi-objective two-stage stochastic program for designing a CLSC network considering SLB agreements. The objective functions are: to maximize profit after tax and to minimize 〖CO〗_2 emissions of the supply chain. To assess the performance of the proposed model, 30 different-sized test problems are generated and solved by both LP-metric and max-min methods. Finally, sensitivity analysis is performed to assess the impact of SLB related parameters (the safety stock coefficient, the fair value of the leased asset, the interest rate implicit in the lease, and the lessee's incremental borrowing rate) on the objectives. The results show significant superiority of the proposed model over which do not consider SLB possibility. Outcomes also indicates that Lp-metric method provides better solutions for the problem. Finally, some managerial insights are suggested.

In the article the absence of an institution for monitoring and substantiating a macroeconomic strategy for countering the COVID-19 pandemic and the inability of governments to adequately respond to the spread and deepening of the coronavirus crisis, which is characteristic in the modern world for almost all countries, has been identified. The managerial concept of government actions is substantiated using the behavioral theory in the context of national business cultures, which determines the choice of a behavioral imperative and regulatory decisions taking into account changes in the stability of economic management objects. The characteristics of the national business cultures of individual countries of the world are highlighted, which can act as stimulating factors in the fight against the COVID-19 pandemic and overcoming its consequences.

In this research, spare parts logistics in the aviation industry has been investigated. Since each aircraft consists of several components, the availability, ordering, and delivery of them are important during aircraft repair and maintenance operations and delays in supplying component spare parts will have a direct impact on the delivery of aircraft. This paper aims to optimize a supply of component spare parts using a programming model considering minimize the holding and purchasing costs. Moreover, two critical constraints including the purchase budget and repair capacity which are rarely mentioned in the previous papers have been noted in this research. FARSCO aviation maintenance & overhaul center as the biggest dedicated MRO center in the Middle East has been considered in this paper as the case study. Based on its capabilities, FARSCO provides services to all airlines and 90% of domestic airlines send their aircraft to this center for performing heavy checks and maintenance. The proposed model was verified and solved by using CPLEX solver and is constructed for the case by considering the numerical data. The results are obtained in two average demand and pessimistic modes (worst case scenario). Finally, a sensitivity analysis of the model is carried out to investigate the applicability of the problem. Results demonstrate the optimal number of maintenance jobs that can be completed to deliver at each period, as well as the order quantity of spare parts and the shortages of spare parts which are important for managers to deliver aircraft to the customers on time.

The purpose of this study is to examine the impact of the COVID-19 epidemic on the energy supply chain and some key economic sectors of the Vietnamese economy in order to assist Vietnamese policymakers in solving the extremely difficult issues of whether or not the government should limit the energy supply when the economic demand falls. The researcher applies the Structure Path Analysis method to evaluate the impact of the energy supply chain on the main business sectors of the economy from 2015 to 2020; and the Constrained Fixed method Price Multiplier to determine the effect of COVID-19 on the energy supply chain before and after the epidemic. The results show that when the government is forced to limit the supply chain of coal, oil and electricity by 10-15%, it does not make much impact on the economy. However, if the government limits the supply chain by 20-25%, then it will have negative effect on the major industries of the economy, especially tourism, trade, construction, transportation and public services. The study also recommends how policymakers should limit the energy supply chain to a safe range for the economy of less than 15%

Development of supply chains is one of the practical concepts in the field of production and sales in competitive conditions. Accordingly, it is necessary to properly study the competitive conditions in which supply chain networks can be designed. In this regard, the present research contributes to the field by incorporating the market share and customer satisfaction to the competitive conditions of supply chains. For this purpose, a nonlinear mathematical model is presented in order to find locations and perform distributions in a closed-loop supply chain under competitive conditions. This model has two objectives including profit maximization and market share maximization. To solve the model, LP-metric and goal programming are implemented, and then the results of these two methods are discussed. Comparisons are also made in terms of the value of the objective functions as well as the solution time. Finally, the simple weighted sum method is used to select the superior method. The results show that the LP-metric method is worth performing to solve the mathematical model of the research.