Ripple effect mitigation capabilities of a hub and spoke distribution network: an empirical analysis of pharmaceutical supply chains in India

Sindhwani, Rohit; Jayaram, Jayanth; Saddikuti, Venkataramanaiah

doi:10.1080/00207543.2022.2098073

Cited by 16 publications

(6 citation statements)

References 78 publications

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“…Table 1 reveals several studies using mathematical, survey and empirical analysis regarding supply chain disruption. However, limited research has been performed using SBOM for recovery planning and managing SC risks (Ivanov, 2019;Rahman et al, 2021;Sindhwani et al, 2022). Sindhwani et al (2022) listed the methods used for analyzing the ripple effects, evaluating them on the criteria of network, process, control, and a hybrid combination of networkprocess, network-control, and process-control levels.…”

Section: Supply Chain Disruption Propagationmentioning

confidence: 99%

“…However, limited research has been performed using SBOM for recovery planning and managing SC risks (Ivanov, 2019;Rahman et al, 2021;Sindhwani et al, 2022). Sindhwani et al (2022) listed the methods used for analyzing the ripple effects, evaluating them on the criteria of network, process, control, and a hybrid combination of networkprocess, network-control, and process-control levels. The findings revealed that Bayesian networks, complexity theory, Markov chains, Petri Nets, Reliability Theory, Entropy analysis, Graph theory, control theory, statistical analysis, and Multi-criteria decision-making methods were widely used techniques.…”

Section: Supply Chain Disruption Propagationmentioning

confidence: 99%

“…However, Stewart and Ivanov (2019) identified products and demand for each customer assessment as significant constraints for facility locations. In recent years factory location and direct distance between customers and DCs are becoming challenging constraints for managers due to disruptions (Sindhwani et al, 2022).…”

Section: The Case Studymentioning

confidence: 99%

“…Ivanov and Dolgui (2021) theorized and conceptualized GFA by applying ALX software. This paper adopted the recommendations of Sindhwani et al (2022) and Ivanov (2021a) to formulate the GFA framework and analysis approach represented in Fig. 4.…”

Section: Green Field Analysis (Gfa)mentioning

confidence: 99%

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RETRACTED ARTICLE: Responsive strategies for new normal cold supply chain using greenfield, network optimization, and simulation analysis

et al. 2023

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The global–local supply chains are affected by the forward and downward propagation of COVID-19. The pandemic disruption is a low-frequency and high-impact (black swan) event. Adapting to the “New Normal” situation requires adequate risk mitigation strategies. This study proposes a methodology to implement a risk mitigation strategy during supply chain disruptions. Random demand accumulation strategies are considered to identify the disruption-driven challenges under different pre and post-disruption scenarios. The best mitigation strategy and the optimal location of distribution centers to maximize the overall profit were determined using simulation-based optimization, greenfield analysis, and network optimization techniques. The proposed model is then evaluated and validated using appropriate sensitivity analysis. The main contribution of the study is to (i) perform cluster-based supply chain disruption analysis, (ii) propose a resilient and flexible model to illustrate the proactive and reactive measures for the ripple effect, (iii) prepare the supply chain for future pandemic-like crises, and (v) reveal the relationship between the pandemic impact and supply chain resilience. A case study of an ice cream manufacturer is used to demonstrate the proposed model.

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Section: Supply Chain Disruption Propagationmentioning

confidence: 99%

Section: Supply Chain Disruption Propagationmentioning

confidence: 99%

Section: The Case Studymentioning

confidence: 99%

Section: Green Field Analysis (Gfa)mentioning

confidence: 99%

See 2 more Smart Citations

RETRACTED ARTICLE: Responsive strategies for new normal cold supply chain using greenfield, network optimization, and simulation analysis

et al. 2023

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“…They ranked different sets of problem solutions using TOPSIS. Sindhwani, et al [21] centered on moderating the impacts of COVID-19 widespread in India's pharmaceutical dissemination organization. They have gotten the finest sedate distribution location utilizing stochastic optimization and Lagrange discharge.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of the hierarchical supply chain in the pharmaceutical industry

Rahmaty,

Nozari

2023

2576-8484

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This paper discusses the modeling and solution of a hierarchical supply chain problem in the pharmaceutical industry. The considered supply chain includes the levels of suppliers, production centers, and distribution, which are taken into account in the decisions of the location of capacity facilities, optimal allocation of flow, and vehicle routing at the same time. Due to the indeterminacy of the problem environment, the two-stage probabilistic programming method has been used to control the model, and the new WOGA algorithm has been used to solve the problem. The presented algorithm is a combination of the Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA) algorithms, which are used to minimize the costs of the entire designed network. The results obtained from the model analysis show that WOGA has a high efficiency in solving the developed mathematical model compared to GA and WOA. There was no significant difference between the averages of the objective function and the computational time between different solution methods. Since the perishability of the drug in transportation was considered in this article, it was observed that the cost of the entire network reaches its highest level if the period of perishability is 1. Because the production and distribution centers cannot have inventory in their warehouses and must meet the demand of pharmacies in every period.

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Application of BP Neural Network and Artificial Intelligence Algorithm in Pharmaceutical Production

Lin

2024

Lecture Notes on Data Engineering and Communications Technologies

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Ripple effect mitigation capabilities of a hub and spoke distribution network: an empirical analysis of pharmaceutical supply chains in India

Cited by 16 publications

References 78 publications

RETRACTED ARTICLE: Responsive strategies for new normal cold supply chain using greenfield, network optimization, and simulation analysis

RETRACTED ARTICLE: Responsive strategies for new normal cold supply chain using greenfield, network optimization, and simulation analysis

Optimization of the hierarchical supply chain in the pharmaceutical industry

Application of BP Neural Network and Artificial Intelligence Algorithm in Pharmaceutical Production

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