Modelling and Simulation of Quality Risk Forecasting in a Supply Chain

Liu, C. H.; Xiong, W.

doi:10.2507/ijsimm14(2)co10

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…Further models may focus on different set of parameters and study how these key factors may impact on the sensitiveness of serial and divergent SCNs to shock demands by e.g. further analysing different forecasting methods [15,16].…”

Section: Resultsmentioning

confidence: 99%

Turbulence in Market Demand on Supply Chain Networks

Cannella¹,

Domínguez²,

Framiñán³

2016

Int. j. simul. model.

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Recent works have shown that the dynamic performance (i.e., the so-called bullwhip effect) of serial and divergent Supply Chain Networks (SCNs) under a sudden increase in the average customer demand may be substantially different. However, the effect of customer demand variability has not been investigated despite the fact that, in real-world business, a turbulence condition on the market usually generates an increase of customer demand variability. Herein we focus on this particular market condition by studying the dynamic behaviour of both serial and divergent SCNs under a sudden increase both in mean demand and variance. Results show how an isolated impulse in the variability of customer's orders results in the same bullwhip effect for both serial and divergent SCNs. From a managerial view point, this finding provides new interesting insights for SCN managers.

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Section: Resultsmentioning

confidence: 99%

Turbulence in Market Demand on Supply Chain Networks

Cannella¹,

Domínguez²,

Framiñán³

2016

Int. j. simul. model.

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“…From the manufacturer's perspective, Liu and Xiong proposed a method for predicting quality risks in the supply chain. ey deployed quality houses through quality functions to realize quality risk factors by using support vector machines to predict [16]. Ouyang studied the diffusion and identification of quality risk in the supply chain of dairy products based on CN [17].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Because of the complexity of quality risks in supply chain, the definitions are diverse from the different perspective of researchers. Based on previous studies, the quality risks refer to the probability of negative events caused by the conflict of quality requirements and quality reality [16]. And the quality risks are the sum of quality risks of all aspects in a supply chain [34].…”

Section: E Meaning and Characteristics Of Supply Chain Qualitymentioning

confidence: 99%

Quality Risk Propagation of Complex Product Collaborative Manufacturing Supply Chain Network Based on CN and SoV

Chen

Yuan

et al. 2020

Discrete Dynamics in Nature and Society

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The collaborative development of complex products has gradually developed into a “main manufacturer-suppliers” mode, under which the manufacturing enterprises form a complex product collaborative manufacturing supply chain network. Quality risks which bring enormous hidden danger to the product quality can be propagated and accumulate along the supply chain. It is of great significance to quantify the propagation mechanism of quality risk between supply chain network nodes and identify the key quality risk factor that causes fluctuation of product quality. This study for the first time applies the SoV into the research on quality risk propagation of complex product collaborative manufacturing supply chain network. Firstly, this paper uses the CN to construct a complex product collaborative manufacturing supply chain network according to its characteristics. Secondly, on the basis of SoV, the quality risk propagation model is established. Thirdly, we put forward a method to identify the key quality risk factors of supply chain network based on the risk propagation effect. Lastly, a numerical simulation is given to verify the effectiveness of the model and its identification method. The results reveal that the quality risk propagation includes the vertical propagation within enterprises and the horizontal propagation from the lower-level enterprises to the upper-level enterprises of the supply chain. The quality risks of an enterprise are determined by its own quality risk factors and the quality risk passed by the lower-level enterprises.

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“…The work that has been done on the subject of PD detection using classification algorithms is very diverse. Classification algorithms and other intelligent methods are offering experts support tools for predictions [25,26,27], although it is only possible to make accurate predictions to a certain degree [28]. There are a lot of studies done on the voice recordings, originally done at the University of Oxford by M.A.…”

Section: Introductionmentioning

confidence: 99%

Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks

Berus

Klančnik

Brezočnik

et al. 2018

Sensors

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In recent years, neural networks have become very popular in all kinds of prediction problems. In this paper, multiple feed-forward artificial neural networks (ANNs) with various configurations are used in the prediction of Parkinson’s disease (PD) of tested individuals, based on extracted features from 26 different voice samples per individual. Results are validated via the leave-one-subject-out (LOSO) scheme. Few feature selection procedures based on Pearson’s correlation coefficient, Kendall’s correlation coefficient, principal component analysis, and self-organizing maps, have been used for boosting the performance of algorithms and for data reduction. The best test accuracy result has been achieved with Kendall’s correlation coefficient-based feature selection, and the most relevant voice samples are recognized. Multiple ANNs have proven to be the best classification technique for diagnosis of PD without usage of the feature selection procedure (on raw data). Finally, a neural network is fine-tuned, and a test accuracy of 86.47% was achieved.

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Modelling and Simulation of Quality Risk Forecasting in a Supply Chain

Cited by 7 publications

References 17 publications

Turbulence in Market Demand on Supply Chain Networks

Turbulence in Market Demand on Supply Chain Networks

Quality Risk Propagation of Complex Product Collaborative Manufacturing Supply Chain Network Based on CN and SoV

Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks

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