Add to ORKGAbstract. Supply Chain Disruptions (SCDs) such as labor disputes, defective materials, and transportation matters can make a huge impact on both the responsiveness and effectiveness of a supply chain. Therefore, it is necessary to develop a reliable tool to foresee SCDs in their early stages. In this study, we aim to develop a reliable method to detect disruption in the supply chain process (SCP) using Bayesian Network (BN) and K2 Algorithm. The prominent feature of this study is its ability to support many process structures such as sequence, XOR, and iterative
Although predictive machine learning for supply chain data analytics has recently been reported as a...
Despite their fame and capability in detecting out-of-control conditions, control charts are not eff...
Knowing which products and hazards to monitor along the food supply chain is crucial for ensuring fo...
International audienceRobust dynamic Bayesian network (DBN) is a valid tool for disruption propagati...
The supply chain is an integrated process of suppliers, plants, warehouses, and manufacturers all wo...
International audienceDynamic Bayesian network (DBN) theory provides a valid tool to estimate the ri...
In the broad sense, the Bayesian networks (BN) are probabilistic graphical models that possess uniqu...
The article of record may be found at https://doi.org/10.1007/s12247-019-09396-2Purpose: Clinical tr...
<p><strong>Purpose:</strong> Increase of costs and complexities in organizations beside the increase...
The COVID-19 pandemic outbreak has greatly impacted the daily lifestyle of consumers, so that they s...
International audienceIt is vital for supply chains (SCs) to survive the dramatic and long-term impa...
This study proposes a method to estimate the posterior distribution of multivariate categorical data...
While there is growing interest in supply chain resilience, conceptualization of the main constructs...
Supply chains have become complex and vulnerable and therefore, researchers are developing effective...
International audienceThe ripple effect could occur when a disruption in supplier base cannot be loc...
Although predictive machine learning for supply chain data analytics has recently been reported as a...
Despite their fame and capability in detecting out-of-control conditions, control charts are not eff...
Knowing which products and hazards to monitor along the food supply chain is crucial for ensuring fo...
International audienceRobust dynamic Bayesian network (DBN) is a valid tool for disruption propagati...
The supply chain is an integrated process of suppliers, plants, warehouses, and manufacturers all wo...
International audienceDynamic Bayesian network (DBN) theory provides a valid tool to estimate the ri...
In the broad sense, the Bayesian networks (BN) are probabilistic graphical models that possess uniqu...
The article of record may be found at https://doi.org/10.1007/s12247-019-09396-2Purpose: Clinical tr...
<p><strong>Purpose:</strong> Increase of costs and complexities in organizations beside the increase...
The COVID-19 pandemic outbreak has greatly impacted the daily lifestyle of consumers, so that they s...
International audienceIt is vital for supply chains (SCs) to survive the dramatic and long-term impa...
This study proposes a method to estimate the posterior distribution of multivariate categorical data...
While there is growing interest in supply chain resilience, conceptualization of the main constructs...
Supply chains have become complex and vulnerable and therefore, researchers are developing effective...
International audienceThe ripple effect could occur when a disruption in supplier base cannot be loc...
Although predictive machine learning for supply chain data analytics has recently been reported as a...
Despite their fame and capability in detecting out-of-control conditions, control charts are not eff...
Knowing which products and hazards to monitor along the food supply chain is crucial for ensuring fo...