Lead-time and risk reduction assessment of a sterile drug product manufacturing line using simulation

Spindler, Jannik; Kec, Thomas; Ley, Thomas

doi:10.1016/j.compchemeng.2021.107401

Cited by 18 publications

(4 citation statements)

References 19 publications

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“…Therefore, there is a need to identify the most critical ones by quantifying their contribution to the overall industry risk value. As per the authors' best knowledge, the studies in the literature have identified and prioritized the pharmaceutical industry risks (Coudeville et al, 2020;Silva et al, 2020;Spindler et al, 2021); however, there seems to be a lack of studies focusing on risk quantification in the IPI for performance improvement. This is the main research gap which is addressed in this study.…”

Section: Risk Assessment Of Pharma Industrymentioning

confidence: 99%

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Supply chain risk factor assessment of Indian pharmaceutical industry for performance improvement

Sharma

Kumar

Arora

2022

IJPPM

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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.

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Section: Risk Assessment Of Pharma Industrymentioning

confidence: 99%

“…, 2020; Silva et al. , 2020; Spindler et al. , 2021); however, there seems to be a lack of studies focusing on risk quantification in the IPI for performance improvement.…”

Section: Literature Reviewmentioning

confidence: 99%

Supply chain risk factor assessment of Indian pharmaceutical industry for performance improvement

Sharma

Kumar

Arora

2022

IJPPM

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“…In pharmaceutical SC, Spindler et al [96] investigated the benefits of a simulation-based model and evaluated the potential for the adoption in a scalable Digital Twins system. Park et al [97] suggested a distributed DT simulation-based cyber physical production systems to reduce the differences among assets and develop a production plan based on the results of DT simulation.…”

Section: Application Of Dtmentioning

confidence: 99%

Digital supply chain: literature review of seven related technologies

Zhang,

Yu,

Wan

et al. 2024

Manufacturing Rev.

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This paper systematically reviews literature related with digital supply chains (DSC) and investigates the application status and development trend of different digital technologies in supply chain management. The review is conducted from the perspective of seven key digital supply chain technologies, i.e. Internet of Things (IoT) & Radio Frequency Identification (RFID), 5th Generation Mobile Communication Technology (5G), 3D Printing, Big data (BD), Blockchain, Digital Twins (DT), and Intelligent autonomous vehicles (IAVs). It highlights the main limitations and opportunities of the various DSC technologies, provides an overview of prior studies, and identifies knowledge gaps by outlining the advantages, weaknesses and restrictions of individual technology. The paper also aims at providing a development framework as a roadmap for the match of different digital technologies with different strategic goals.

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“…Supplier Management Discrete event simulation was used to investigate the impact of the COVID-19 pandemic on food retail supply chains resilience. [74] Developed a simulation model for a sterile pharma products factory line to investigate the sensitivity of steps involved, cycle design and batch change circumstances, multiple shift models, scheduling methodologies, and transportation failure risk assessments.…”

Section: Robotics In Manufacturingmentioning

confidence: 99%

Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics

et al. 2021

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Background: As the Internet of Things (IoT) has become more prevalent in recent years, digital twins have attracted a lot of attention. A digital twin is a virtual representation that replicates a physical object or process over a period of time. These tools directly assist in reducing the manufacturing and supply chain lead time to produce a lean, flexible, and smart production and supply chain setting. Recently, reinforced machine learning has been introduced in production and logistics systems to build prescriptive decision support platforms to create a combination of lean, smart, and agile production setup. Therefore, there is a need to cumulatively arrange and systematize the past research done in this area to get a better understanding of the current trend and future research directions from the perspective of Industry 4.0. Methods: Strict keyword selection, search strategy, and exclusion criteria were applied in the Scopus database (2010 to 2021) to systematize the literature. Results: The findings are snowballed as a systematic review and later the final data set has been conducted to understand the intensity and relevance of research work done in different subsections related to the context of the research agenda proposed. Conclusion: A framework for data-driven digital twin generation and reinforced learning has been proposed at the end of the paper along with a research paradigm.

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Lead-time and risk reduction assessment of a sterile drug product manufacturing line using simulation

Cited by 18 publications

References 19 publications

Supply chain risk factor assessment of Indian pharmaceutical industry for performance improvement

Supply chain risk factor assessment of Indian pharmaceutical industry for performance improvement

Digital supply chain: literature review of seven related technologies

Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics

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