Bayesian network modelling for supply chain risk propagation

Ojha, Ritesh; Ghadge, Abhijeet; Tiwari, Manoj Kumar; Bititci, Umit

doi:10.1080/00207543.2018.1467059

Cited by 238 publications

(117 citation statements)

References 52 publications

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“…Research in SC reaction to disturbances is related to the semantic network analysis level with a focus on structural properties, complexity roles, and node/arc criticality . The studies (Basole and Bellamy 2014;Dolgui 2014a, 2014b;Kim, Chen, and Linderman 2015;Brintrup, Wang, and Tiwari 2015;Sawik 2017;Macdonald et al 2018;Yoon et al 2018;Scheibe and Blackhurst 2018;Pavlov et al 2018;Ojha et al 2018;Giannoccaro, Nair, and Choi 2017;Ivanov 2018Ivanov , 2019Dolgui, Ivanov, and Sokolov 2018;Li et al 2019;Pavlov et al 2019b) recognised the structural SC properties as crucial determinant to maintain stability and robustness and to achieve resilience. Another important observation in literature is a linkage of SC complexity and resilience (Blackhurst et al 2005;Nair and Vidal 2011;Bode and Wagner 2015;Dubey et al 2019a;Tan, Cai, and Zhang 2020).…”

Section: Viability Vs Stability Robustness and Resilience Of Scsmentioning

confidence: 99%

Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak

Ivanov

Dolgui

2020

International Journal of Production Research

1,229

919

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An intertwined supply network (ISN) is an entirety of interconnected supply chains (SC) which, in their integrity secure the provision of society and markets with goods and services. The ISNs are open systems with structural dynamics since the firms may exhibit multiple behaviours by changing the buyer-supplier roles in interconnected or even competing SCs. From the positions of resilience, the ISNs as a whole provide services to society (e.g. food service, mobility service or communication service) which are required to ensure a long-term survival. The analysis of survivability at the level of ISN requires a consideration at a large scale as resilience of individual SCs. The recent example of coronavirus COVID-19 outbreak clearly shows the necessity of this new perspective. Our study introduces a new angle in SC resilience research when a resistance to extraordinary disruptions needs to be considered at the scale of viability. We elaborate on the integrity of the ISN and viability. The contribution of our position study lies in a conceptualisation of a novel decision-making environment of ISN viability. We illustrate the viability formation through a dynamic game-theoretic modelling of a biological system that resembles the ISN. We discuss some future research areas.

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Section: Viability Vs Stability Robustness and Resilience Of Scsmentioning

confidence: 99%

Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak

Ivanov

Dolgui

2020

International Journal of Production Research

1,229

919

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“…As far as we are aware, while our function is highly complex, our study is the first major study to integrate decision making into the definition of the conditional probability tables of the Bayesian Networks for the purpose of supply chain risk propagation. Put differently, most if not all studies in supply chain risk propagation that leverage a Bayesian Network modelling approach assume that the structure of the network is static (Garvey et al, 2015;Qazi et al, 2018;Ojha et al, 2018). Our study is the first of it's kind that attempts to understand the nature and dynamics of risk propagation through the lens of decision-dependent and risk-interdependent Bayesian Networks, where the probabilities are not head constant or determined by data-driven methods, but rather are a function of specific decisions.…”

Section: Resultsmentioning

confidence: 99%

“…Since we will leverage Bayesian Networks in our simulation and our model, a brief review of them is necessary. A more thorough review is conducted in Heckerman (1998) as well as Ojha et al (2018). Bayesianism rests on the general principle that probability is inherently a subjective belief regarding an event that one holds, measured on a 0 to 1 scale (0 being not likely, 1 being certain).…”

Section: A Review Of Bayesian Networkmentioning

confidence: 99%

“…However, in order to measure risk propagation, we will need to choose a framework of metrics to leverage. Some initial frameworks have been proposed in the literature (Blackhurst et al, 2008;Zhao et al, 2019;Qazi et al, 2018;Ojha et al, 2018).…”

Section: Supply Chain Risk Propagation Measurementmentioning

confidence: 99%

“…Furthermore, the reverse flow of risk (from downstream to upstream) has been explored from a variation in payment perspective (Serrano et al, 2018). More recently, Ojha et al (2018) extended the work of Garvey et al (2015), Shin et al (2012), and Qazi et al (2018) by proposing another framework of measures more specific to traditional supply chain performance metrics such as fragility, inventory costs, and service levels.…”

mentioning

confidence: 99%

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The rippled newsvendor: A new inventory framework for modeling supply chain risk severity in the presence of risk propagation

Garvey

Carnovale

2020

International Journal of Production Economics

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How should managers take into account the propagation of supply chain disruptions and risks (i.e. the ripple effect) when they design their inventory policies? For over 60 years, various extensions and applications to the popular newsvendor model have been suggested, where cost/profit are often the focal objective. Accordingly, we propose a new version of the traditional single-period newsvendor model -the "Rippled Newsvendor" -with supply chain severity (i.e. risk propagation) as the primary objective while taking into account network structure. Our model considers exogenous and endogenous risk(s) of disruption while exploring the tension between under-supply and "wear-and-tear" (i.e system breakdown).To model the intricacies of this trade-off whilst minimizing the potential spread of risk, we leverage a Bayesian Network whereby the conditional probability distributions are functions of the inventory ordering decisions. We use a simulation study to understand the nature of our objective function as well as to gain insight into the potential optimal ordering policies of this new model. Furthermore, the simulation seeks to understand how the various factors in our system impact total risk severity, and if they do so in different ways. Our simulations indicate that local exogenous risk is of greater importance than non-local exogenous risk.Furthermore, we show that the type of risk, as well as the structural characteristics of the supply chain and inventory system, impact risk severity differently. As far as we are aware, this is the first framework to incorporate supply network structure, risk propagation, and inventory decision making.

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Interpretable biomanufacturing process risk and sensitivity analyses for quality‐by‐design and stability control

Xie

Wang

et al. 2021

Naval Research Logistics

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While biomanufacturing plays a significant role in supporting the economy and ensuring public health, it faces critical challenges, including complexity, high variability, lengthy lead time, and very limited process data, especially for personalized new cell and gene biotherapeutics. Driven by these challenges, we propose an interpretable semantic bioprocess probabilistic knowledge graph and develop a game theory based risk and sensitivity analyses for production process to facilitate quality-by-design and stability control. Specifically, by exploring the causal relationships and interactions of critical process parameters and product quality attributes, we create a Bayesian network based probabilistic knowledge graph characterizing the complex causal interdependencies of all factors. Then, we introduce a Shapley value based sensitivity analysis, which can correctly quantify the variation contribution from each input factor on the outputs (i.e., productivity, product quality). Since the bioprocess model coefficients are learned from limited process observations, we derive the Bayesian posterior distribution to quantify model uncertainty and further develop the Shapley value based sensitivity analysis to evaluate the impact of estimation uncertainty from each set of model coefficients. Therefore, the proposed bioprocess risk and sensitivity analyses can identify the bottlenecks, guide the reliable process specifications and the most informative data collection, and improve production stability.

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Bayesian network modelling for supply chain risk propagation

Cited by 238 publications

References 52 publications

Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak

Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak

The rippled newsvendor: A new inventory framework for modeling supply chain risk severity in the presence of risk propagation

Interpretable biomanufacturing process risk and sensitivity analyses for quality‐by‐design and stability control

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