Planning for disruptions in supply chain networks

Chaghooshi, Ahmad Jafarnejad; Momeni, Mansour; Abdollahi, Behzad; Safari, Hossein; Kamalabadi, Isa Nakhai

doi:10.5267/j.uscm.2017.8.003

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…On the other hand, other studies with a renewal approach such as artificial intelligence. For example: the blurry logic for the selection of variables (Bautista-Santos, Martínez-Flores, Fernández-Lambert, Bernabé-Loranca, Sánchez & Sablón-Cossío, 2015) and qualify the solutions to face the risk in the supply chain networks (Jafarnejad, Momeni, Abdollahi, Safari & Nakhai-Kamalabadi, 2018), the genetic algorithms (Liu & Ran 2019), the structural equation modeling for (Sreedevi & Saranga, 2017) and the neural networks for variable forecasting (Baharmand, 2019). This last perspective is the subject of this article.…”

Section: Introductionmentioning

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: Introductionmentioning

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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Risk Assessment in Supply Chain Networks of China–Pakistan Economic Corridor (CPEC)

Awan

Ali

2022

Chin. Polit. Sci. Rev.

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A graph-based model to measure structural redundancy for supply chain resilience

Tan

Zhang

Cai

2019

International Journal of Production Research

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Globalization and lean initiatives increase the vulnerabilities of the supply chains (SC), where disruptions in any plant in a supply chain network (SCN) can propagate throughout the whole SCN. Redundancy is part of the SC re-engineering to improve supply chain resilience (SCRES). This paper presents a conceptual model of an SCN using graph theory, considering the relationships between plants and materials. Based on the model, the structural redundancy of the SCN is measured, which is used to assess SCRES. This assessment approach focuses on the resilience of the SCN against disruptions. Case studies are discussed to illustrate the applicability of this model and show that increasing structural redundancy of the SCN improves SCRES against disruptions.

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Planning for disruptions in supply chain networks

Cited by 5 publications

References 28 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

Risk Assessment in Supply Chain Networks of China–Pakistan Economic Corridor (CPEC)

A graph-based model to measure structural redundancy for supply chain resilience

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