Neural networks in supply chain management

Leung, Horris C.

doi:10.1109/iemc.1995.524607

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…Out of these, globalization is directly related to this research. Of this percentage (16%) refer to studies on how variables that favor integration are predicted or related (Leung, 1995;Chiu & Lin, 2004;Minis & Ampazis, 2006;Li, Lai & Nie, 2007;Lichun & Cai, 2008;Jeeva & Guo, 2010;Lee, Jung & Nie, 2010;Singh & Benyoucef, 2010;Bousqaoui, Said & Tikito, 2017;Yang, 2017;Kokkinos, Exadactylos, Vafidis & Hatziioannou, 2018;Liu & Ran, 2019). In the study by Chiu and Lin (2004), neural networks are used for the evaluation of collaborative planning in supply chains.…”

Section: Applications Of Neural Network In Supply Chainsmentioning

confidence: 99%

Application of neural networks in predicting the level of integration in supply chains

Muñoz

Cossío

Cedeño

et al. 2020

JIEM

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Purpose: This investigation is based on the theoretical analysis of the application of neural networks to the design and manage supply chains, along with an empirical approach, this investigation its developed with the prediction of the level of integration in the supply chain through neural networks.Design/methodology/approach: The methodology designed and used for the processing of data was the instruction of a neural network wich is used to predict the level of integration in a supply chain. This type of predictive application appears in the literature reviewed on supply chains. This analysis was carried out in a comparative way with the heterogeneous and homogeneous weights of the neuron training.Findings: The main results of this research focus on predicting the level of integration in the supply chain from the neoronal network. This provides a coached neuron that can be applied in other studies and, therefore, predict the outcome. On the other hand, it is shown that if the weights of the integration level variables are not homogeneous, the procedure presents different results depending on the context in which it is developed.Research limitations/implications: Among the limitations of the implementation of neural networks it should be noted, the necessary adaptation to the characteristics of the supply chains and the areas of performance of the business organizations under study, in the framework of activities productive or service itself, in addition to analyzing its corporate purpose in relation to the satisfaction of certain needs of the target markets.Originality/value: The literature shows multiple theoretical sources that refer to studies of neural networks in supply chains, observing the opportunity to apply this technique to predict the level of integration due to its benefits for decision making. The originality of this scientific work lies in the possibility of comparing the historical data of the level of integration and those predicted as a result of the coaching of the neuron with the weights of the heterogeneous and homogeneous variables.

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Section: Applications Of Neural Network In Supply Chainsmentioning

confidence: 99%

Application of neural networks in predicting the level of integration in supply chains

Muñoz

Cossío

Cedeño

et al. 2020

JIEM

View full text Add to dashboard Cite

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“…The applications of forecasting span a variety of fields, including highfrequency control (e.g. vehicle and robot control (Tang and Salakhutdinov 2019), data center optimization (Gao 2014)), business planning (supply chain management (Leung 1995), workforce and call center management (Chapados et al 2014; Copyright c 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.…”

Section: Introductionmentioning

confidence: 99%

Meta-Learning Framework with Applications to Zero-Shot Time-Series Forecasting

Oreshkin

Carpov

Chapados

et al. 2021

AAAI

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Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new TS coming from different datasets? This work provides positive evidence to this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms. Our theoretical analysis suggests that residual connections act as a meta-learning adaptation mechanism, generating a subset of task-specific parameters based on a given TS input, thus gradually expanding the expressive power of the architecture on-the-fly. The same mechanism is shown via linearization analysis to have the interpretation of a sequential update of the final linear layer. Our empirical results on a wide range of data emphasize the importance of the identified meta-learning mechanisms for successful zero-shot univariate forecasting, suggesting that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, resulting in performance that is at least as good as that of state-of-practice univariate forecasting models.

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“…Past and present research show that ANN as a problem solving methodology can be used for optimization (resources allocation and scheduling), predicting and forecasting (magnitude of future fire incidents), modeling and simulation (for dynamics of supply chain using techniques such as discrete event simulation and dynamic systems theory), coordination (for increasing coordination between different fire stations), and decision support (for management and analysis of data for the support of a decision; Leung, 1995). ANN are increasingly used for resource allocation and forecasting short or long-term demands for goods and services where they become a preferred alternative to regression techniques (Al-Saba and El-Amin, 1999;Cao et al, 2005;Denton, 1995;Kon and Turner, 2005;Wong et al, 2000).…”

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confidence: 96%

Modeling number of firefighters responding to an incident using artificial neural networks

Sadeghi‐Naini

Asgary

2013

International Journal of Emergency Services

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2014),"Safety climate, safety behaviors and line-of-duty injuries in the fire service"If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -A feed-forward back-propagation neural network (NN) is proposed to model number of firefighters responding to different fire incidents. Such a predictor model can estimate number of firefighter personnel required to tackle new incidents. This a priori information at the time of dispatch can help saving unnecessary efforts in low-risk incidents while focussing on high-risk ones to reduce overall damages and injuries caused by the fire incidents. Design/methodology/approach -A fully connected multilayer NN was adapted as the prediction model. The network was trained on a large number of fire incident records reported in Toronto area between 2000 and 2006 and then its performance was evaluated on another set of never seen records. Two types of prediction were done to model number of responding personnel: a rough category prediction and an exact number prediction. Findings -Results obtained reported a very promising ability of this approach to model number of firefighters responding to a fire incident. Originality/value -Such a model can significantly reduce uncertainties on the requirements needed for tackling a fire incident once it is reported.

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Neural networks in supply chain management

Cited by 10 publications

References 9 publications

Application of neural networks in predicting the level of integration in supply chains

Application of neural networks in predicting the level of integration in supply chains

Meta-Learning Framework with Applications to Zero-Shot Time-Series Forecasting

Modeling number of firefighters responding to an incident using artificial neural networks

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