MODELLING AND ANALYSIS OF SUPPLY CHAIN RISK IN PHARMACEUTICAL INDUSTRIES

Abstract

The pharmaceutical supply chain (PSC) is a crucial network dedicated to saving lives and enhancing the well-being of individuals. Its primary objective is to deliver medicines and equipment in the right quantity and quality in appropriate condition at the right time and place. The PSC is a complex system that plays a pivotal role in safeguarding human health and improving the quality of life. Suppliers, manufacturers, distributors, pharmacies, retailers, and patients form essential links within this network, working collaboratively to ensure a seamless flow of pharmaceutical products from production to patients who rely on them for treatment and well-being. As a sector that deals directly with human lives, the PSC demands precision, efficiency, and resilience to overcome challenges and disruptions, such as pandemics or raw material (RM) shortages, counterfeit drugs, and uneven drug distribution, to name a few, in order to fulfill its critical mission of saving lives and enhancing public health. Within the PSC, the pharmaceutical industry (PI) is responsible for manufacturing and providing uninterrupted access to quality medicines and equipment, which is of utmost importance. Given the direct impact on human lives, the inefficiency in PI poses greater and graver risks to the population and government as compared to other sectors. These risks, commonly classified as supply, operational, financial, and demand-related lead to unmet customer demands for essential drugs. These disruptions in the PSC, highlighted during recent events like the COVID-19 pandemic, have resulted in shortages of critical drugs, such as analgesics and antivirals, thereby endangering human lives. In order to improve performance, profitability, and resilience, risk management plays a pivotal role within the PI. It involves a scientific approach for identifying, assessing, and managing various unforeseen and uncontrollable occurrences. Risks, defined as unpredictable events, can arise from multiple factors, such as natural disasters, political instability, pandemics, etc., leading to disruptions and unpredictability, including shortages, fluctuations in demand, compromised quality, and poor coordination. For the PI, these disruptions have far-reaching consequences on human health. For instance, the unprecedented global crisis brought by COVID-19 severely disrupted global SCs, particularly in healthcare and pharmaceuticals, resulting in a public health emergency. The risk management for Indian pharmaceutical industry (IPI) is significant as it is the largest producer of generic medicines, the third-largest producer of bulk medicines, and one of the largest drug exporters in the world. IPI caters to the needs of essential drugs at low prices and optimum quality within India as well as across the world, highlighting its importance. However, despite this status, the accessibility and affordability of essential medicines remain significant issues, particularly in rural areas where connectivity poses major challenges. Consequently, enhancing the productivity, efficiency, and resilience of the IPI becomes essential, and this can be achieved through effective management of potential supply chain (SC) risks. In this context, the present research focuses on risk management of PSC risks within the Indian context, aiming to improve responsiveness, efficiency, resilience, and sustainability for PI. The initial step in this process involves identifying the potential risks within PSC. An extensive and systematic literature review has been conducted to identify these risks, which are then validated and supplemented through inputs from field experts. The identified risks have been categorized into seven main categories for ease of understanding and assessment. To prioritize these subrisks (within categories), the Delphi technique is employed to incorporate expert opinions. Additionally, a multi-criteria decision-making (MCDM) approach known as Intuitionistic Fuzzy (IF) Analytic Hierarchy Process (AHP) is implemented to determine the most critical sub-risks within and across the main categories. These results are compared with ranks obtained from traditional and Fuzzy AHP methods, and a sensitivity analysis using Spearman’s rank correlation coefficient has been conducted to assess the robustness of the findings.