Supply chain diagnostics with dynamic Bayesian networks

Kao, Han-Ying; Huang, C.-L.; Li, Hanlin

doi:10.1016/j.cie.2005.06.002

Cited by 39 publications

(18 citation statements)

References 4 publications

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“…A Bayesian Network (BN) which consists of a set of nodes and links is a causal representation model and is useful to model uncertainty [15]. A BN assumes a form of Directed Acyclic Graph (DAG) in which every node within BN (we called as BN variables) denotes random variables and every link within BN characterizes probabilistic dependences of BN variables [16].…”

Section: Bayesian Network and Naïve Bayesmentioning

confidence: 99%

On-the-Fly Performance-Aware Human Resource Allocation in the Business Process Management Systems Environment Using Naïve Bayes

Wibisono

Nisafani

Bae

et al. 2015

Lecture Notes in Business Information Processing

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Abstract. Traditionally, resource allocation problem has been considered as one of the important issues in business process management to maintain the acceptable level of each activity completion time which can reduce the total completion time. Especially, the complexity of managing resources increases when the resource type is human because performance of each human resource might fluctuate over time due to various unpredicted factors. Hence, upfront planning of the resource allocation might be unsuitable in this matter. Therefore, this study proposes an on-the-fly resource allocation using Naïve Bayes to manage human resources more efficiently. The term on-the-fly here indicates that the resource allocation planning will be frequently updated and executed during the execution time by considering recent human resource performances. In this paper, we will show the proposed approach exceeds other resource allocation approaches in terms of total completion time.

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Section: Bayesian Network and Naïve Bayesmentioning

confidence: 99%

On-the-Fly Performance-Aware Human Resource Allocation in the Business Process Management Systems Environment Using Naïve Bayes

Wibisono

Nisafani

Bae

et al. 2015

Lecture Notes in Business Information Processing

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“…Earlier on, Bayesian networks (BNs) have been used by Kao et al (2005), Lockamy and McCormack (2010), and Deleris and Erhun (2011) to model the probability distributions and dependencies of risky variables. BNs are graphical probabilistic models for structuring probabilistic information (Darwiche 2010).…”

Section: Earlier Approaches To Disruptions In Supply Networkmentioning

confidence: 99%

Disruptions in Supply Networks: A Probabilistic Risk Assessment Approach

Käki

Salo

Talluri

2015

J of Business Logistics

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Supply disruptions are attracting growing attention. Even in geographically, politically and economically stable locations, companies are exposed to disruptions, because they depend on their suppliers and suppliers' suppliers. The analysis of these disruptions helps mitigate risks: for example, instead of relying on local measures such as safety stock or insurance, a company can introduce new supply contracts or backup risky suppliers. In this article, we analyze risks caused by supplier disruptions by introducing concepts from probabilistic risk assessment (PRA), which is a widely employed methodology for the risk analysis of complex engineering systems. We apply PRA to examine simple networks such as triads analytically, and use simulation to analyze disruption risks in random networks of realistic size. We also illustrate how PRA can support strategic decisions such as whether or not to use single or multiple suppliers; which suppliers are more risky than others; and what impacts the complexity of the supply base has on the reliability of the supplier network.

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“…BBN has also been used as the knowledge base of reasoning systems for supply chain diagnostics and prediction, vendor appraisal, customer assessment, evaluation of strategic or technical alliance (Kao et al, 2005). The participating enterprises in the supply chain can solve the reasoning problems based on the networks.…”

Section: Bayesian Belief Networkmentioning

confidence: 99%

Decision support under uncertainties based on robust Bayesian networks in reverse logistics management

Shevtshenko

Wang

2009

IJCAT

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One of the major challenges for product lifecycle management systems is the lack of integrated decision support tools to help decision-making with available information in collaborative enterprise networks. Uncertainties are inherent in such networks due to lack of perfect knowledge or conflicting information. In this paper, a robust decision support approach based on imprecise probabilities is proposed. Robust Bayesian belief networks with interval probabilities are used to estimate imprecise posterior probabilities in probabilistic inference. This generic approach is demonstrated with decision-makings in design for closed-loop supply chain. The ultimate goal of robust intelligent decision support systems is to enhance the effective use of information available in collaborative engineering environments.

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Supply chain diagnostics with dynamic Bayesian networks

Cited by 39 publications

References 4 publications

On-the-Fly Performance-Aware Human Resource Allocation in the Business Process Management Systems Environment Using Naïve Bayes

On-the-Fly Performance-Aware Human Resource Allocation in the Business Process Management Systems Environment Using Naïve Bayes

Disruptions in Supply Networks: A Probabilistic Risk Assessment Approach

Decision support under uncertainties based on robust Bayesian networks in reverse logistics management

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