PurposeThe purpose of the present work is to improve the industry performance by identifying and quantifying the risks faced by the Indian pharmaceutical industry (IPI). The risk values for the prominent risks and overall industry are determined based on the four risk parameters, which would help determine the most contributive risks for mitigation.Design/methodology/approachAn extensive literature survey was done to identify the risks, which were also validated by industry experts. The finalized risks were then evaluated using the fuzzy synthetic evaluation (FSE) method, which is the most suitable approach for the risk assessment with parameters having a set of different risk levels.FindingsThe three most contributive sub-risks are counterfeit drugs, demand fluctuations and loss of customers due to partners' poor service performance, while the main risks obtained are demand, financial and logistics. Also, the overall risk value indicates that the industry faces medium to high risk.Practical implicationsThe study identifies the critical risks which need to be mitigated for an efficient industry. The industry is most vulnerable to the demand risk category. Therefore, the managers should minimize this risk by mitigating its sub-risks, like demand fluctuations, bullwhip effect, etc. Another critical sub-risk, the counterfeit risk, should be managed by adopting advanced technologies like blockchain, artificial intelligence, etc.Originality/valueThere is insufficient literature focusing on risk quantification. Therefore, this work addresses this gap and obtains the industry's most critical risks. It also discusses suitable mitigation strategies for better industry performance.

PurposeThis study aims to utilize DMAIC methodology along with value stream mapping and other Lean Six Sigma tools in a major automobile light manufacturing industry to reduce defect rates and increase production capacity in their manufacturing line. The study also proposes a modified framework based on lean principles and FlexSim to identify and reduce waste in the selected industry.Design/methodology/approachA Lean Six Sigma modified framework has been deployed with DMAIC to reduce the defect rate and increase the production rate. Various tools like value stream mapping, brainstorming, Pareto charts, 5S, kanban, etc. have been used at different phases of DMAIC targeting wastes and inventory in the production line. Also, a simulation model has been utilized for the automobile light manufacturing industry to improve the machine utilization time with varying batch sizes.FindingsThe results of the study indicated a 53% reduction in defect rates. Thus, there would be an expected improvement in sigma value from 3.78 to 3.89 and a reduction in defects per million opportunities (DPMO) from 11,244 to 8,493. Additionally, simulation model using FlexSim was developed, and the optimum ordering batch size of raw material was obtained. It was also analyzed that idle time for various stations could be reduced by up to 30%.Practical implicationsThe utilized framework helps identify defects for managers to increase production efficiency. The workers, operators and supervisors on the production line also need to be trained regularly for identifying the areas of improvement.Originality/valueThe modified Lean Six Sigma framework used in this study includes FlexSim simulation to make the framework robust, which has not been used with LSS tools in the literature studied. Also, the LSS finds very less application in the manufacturing domain, considering which this study tends to add value in existing literature taking a case of an automobile light manufacturing industry.

The pharmaceutical industry is the backbone of the healthcare system for any country. However, this industry faces various risks, which hamper its efficient working in providing life-saving medicines/services to the people. In this context, the purpose of the study is to improve the resilience and performance of the pharmaceutical industry (PI) with the identification, and assessment of supply chain (SC) risks. A case illustration has also been presented in the Indian context. The study utilizes an extensive literature survey and the Delphi method for identifying, finalizing, and classifying the risks into seven categories. The Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) has been used to analyze and prioritize the risks to determine their criticality. The results show that the three most important risks are financial, supplier, and demand/customer/market. Within these risks, the three most critical sub-risks are found to be loss of customers, raw material (RM) issues, and bad reputation of the company, respectively. The study provides managers with an extensive list of PI risks for their consideration. The results also present the critical risks which need to be mitigated for enhanced performance and resilience of the industry. The study also emphasizes the importance of interconnection between various SC partners for better risk management.

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