An Interpretive Structural Modelling (ISM) Approach to Evaluate Barriers to Resiliency in Indian Pharmaceutical Supply Chains

The COVID-19 global pandemic, over the last year and a half, has managed to create massive disruptions in global supply chains and exposed their vulnerabilities, thereby reemphasizing the importance of resiliency. The current study aims to identify and prioritize, through the quantitative decision-making technique of Interpretive Structural Modelling (ISM), a set of barriers to resiliency for the pharmaceutical supply chain in India. The rationale behind choosing the Indian pharmaceutical supply chain was that the pharmaceutical sector in India supplies over half of the global demand for vaccines and generic drugs, and the trajectory of growth is indicated around US$100 billion by the year 2025, along with exporting pharmaceutical products to nearly 200 destination countries. The findings of the current study are expected to aid the decision-makers in evaluating the relative criticality and the interrelationship between the potential (and critical) barriers to supply chain resiliency, and in turn to develop strategic plans. This, in turn, can help to combat unforeseen supply chain disruptors such as COVID-19. This methodology and the findings of the study can be generalized for other supply chains. © 2023 K.J. Somaiya Institute of Management Studies and Research.

Mutual Resiliency and Lean Analysis for Supply Chain Management in a COVID-19 Ventilator Production Case Study

Lean management and resilience are studied as significant paradigms in the supply chain in unaggregated domain. As studied in the literature, the resiliency can contribute to the flexibility of supply chains for fulfilling the fluctuations by enabling redundancy. However, the level of redundancy can be an interesting topic from lean management perspective to be managed. This paper has focused on the context of aggregation of Lean and resiliency management these and has proposed a trade-off matrix method. The integration and aggregation of lean and resilient supply chain (SC) practices are investigated with a special focus for creation of synergy. The paper also has investigated the impact of this integration on SC performance through a case study in context of the COVID-19 ventilator production. This case has been designed to highlight the tradeoff analysis among lean and resilience concepts. By applying an Analytic Hierarchy Process model, the paper has enabled a decision-making framework for determination of redundancy level. The data collection for including and documentation of information about various sections of the supply chain is conducted to determine the optimal solution by service level evaluation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Regional and plant-size impacts of COVID-19 on beef processing.

During the COVID-19 pandemic, U.S. consumers witnessed changes in the volume and type of meat products available at retail and food service markets. Simultaneously, widening farm-to-wholesale price spreads fueled calls for industry change and several related policy proposals. The objective of this study is to document fed cattle slaughter and evaluate the structure and performance of the beef processing industry during the early stages of the COVID-19 pandemic. For major beef-producing regions, the 2019-2020 change in federally inspected U.S. cattle slaughter volumes varied in isolated instances with regional reliance on larger processing facilities. Implications of this are discussed both for current policy and industry discussions, as well as to encourage additional future research.

A supply chain disruption risk mitigation model to manage COVID-19 pandemic risk.

This study develops a mathematical model to mitigate disruptions in a three-stage (i.e., supplier, manufacturer, retailer) supply chain network subject to a natural disaster like COVID-19 pandemic. This optimization model aims to manage supply chain disruptions for a pandemic situation where disruptions can occur to both the supplier and the retailer. This study proposes an inventory policy using the renewal reward theory for maximizing profit for the manufacturer under study. Tested using two heuristics algorithms, namely the genetic algorithm (GA) and pattern search (PS), the proposed inventory-based disruption risk mitigation model provides the manufacturer with an optimum decision to maximize profits in a production cycle. A sensitivity analysis was offered to ensure the applicability of the model in practical settings. Results reveal that the PS algorithm performed better for such model than a heuristic method like GA. The ordering quantity and reordering point were also lower in PS than GA. Overall, it was evident that PS is more suited for this problem. Supply chain managers need to employ appropriate inventory policies to deal with several uncertain conditions, for example, uncertainties arising due to the COVID-19 pandemic. This model can help managers establish and redesign an inventory policy to maximize the profit by considering probable disruptions in the supply chain network.