Improving Supply Chain Visibility With Artificial Neural Networks

Silva, Nathalie; Ferreira, L. S.; Silva, Cristóvão; Magalhães, Vanessa S.M.; Neto, Pedro

doi:10.1016/j.promfg.2017.07.329

Cited by 37 publications

(14 citation statements)

References 16 publications

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“…This illustrates how adaptable the company is when it comes to implementing and incorporating emerging strategies and technology to establish a competitive market advantage [62]. According to the supply chain, forming a collective culture is a crucial phase in supply chain management.…”

Section: Visibility For Integratingmentioning

confidence: 99%

Digital Twins Driven Supply Chain Visibility within Logistics: A New Paradigm for Future Logistics

Moshood

Nawanir

Sorooshian

et al. 2021

ASI

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The supply chains shaping their distribution networks become more diverse as companies respond to global markets’ stringent criteria. This is also counterproductive to the visibility of the supply chain within the company and can adversely affect the organization’s core business. This paper attempts to evaluate how organizations can benefit from introducing Digital Twins to enhance their logistics supply network visibility. Additionally, deployment issues and technologies supporting Digital Twins were reviewed. This study used ATLAS.ti 9 software tools to save, classify, and evaluate the data for this analysis to systematically review the literature. We reviewed, compiled, and sorted papers from 227 publications for this article and then recognized 104 as critical to the work scope; this analysis’ quest date was set from 2002 to 2021. This article represents the first attempt at dealing with the issue of supply chain visibility through the Digital Twins in the logistics field. The research outcomes found that Digital Twins would help companies develop predictive metrics, diagnostics, projections, and physical asset descriptions for their logistics. This study also suggested some steps to overcome the challenges in implementing a Digital Twins in the logistics industry. For researchers, this review offers the possibility to unify and expand existing solutions and to identify links and interfaces that are still needed. As for managerial implications, this study can be used to identify future strategies and technologies to fulfil certain logistics tasks and develop new technological solutions for current and future demands.

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Section: Visibility For Integratingmentioning

confidence: 99%

Digital Twins Driven Supply Chain Visibility within Logistics: A New Paradigm for Future Logistics

Moshood

Nawanir

Sorooshian

et al. 2021

ASI

View full text Add to dashboard Cite

show abstract

“…The combination of cloud computing and neural network can automatically combine prediction indicators and prediction models, predict every possibility, automatically simulate and analyze the prediction result and the actual conditions so as to choose the best prediction. Then the model and result are returned to the decision-makers [10] [11].…”

Section: Related Workmentioning

confidence: 99%

Study of Economic Management Forecast and Optimized Resource Allocation Based on Cloud Computing and Neural Network

He¹

2020

Preprint

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As various factors which affect the development of global market economy become increasingly uncertain, economy and commodity will become more and more fluctuating in economic operation. With its strong non-linear mapping capacity, artificial neural network has already been applied in many fields, time series analysis and trend prediction, for instance.Cloud computing can interact fast with service provider at the minimum management cost. This paper proposes an economic forecast and optimized resource allocation model based on cloud computing and BP neural network. Its main goal is to break down a complex prediction task into several sub-tasks, effectively reduce the workload of a single computer and enhance the operating efficiency. Meanwhile, this method can also choose the optimal neural network prediction model at an adaptive learning rate so as to make the prediction and analysis result more accurate.

show abstract

Section: Related Workmentioning

confidence: 99%

Study of Economic Management Forecast and Optimized Resource Allocation Based on Cloud Computing and Neural Network

He¹

2020

Preprint

View full text Add to dashboard Cite

Abstract As various factors which affect the development of global market economy become increasingly uncertain, economy and commodity will become more and more fluctuating in economic operation. With its strong non-linear mapping capacity, artificial neural network has already been applied in many fields, time series analysis and trend prediction. Cloud computing can interact fast with service provider at the minimum management cost. This paper proposes an economic forecast and optimized resource allocation model based on cloud computing and BP neural network. Its main goal is to break down a complex prediction task into several sub-tasks, effectively reduce the workload of a single computer and enhance the operating efficiency. Simulation results show that the proposed method does not rely on gradient information, and has strong optimization calculation ability. At the same time, it can analyze and predict economic management, so as to provide strong decision support for decision makers.

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Improving Supply Chain Visibility With Artificial Neural Networks

Cited by 37 publications

References 16 publications

Digital Twins Driven Supply Chain Visibility within Logistics: A New Paradigm for Future Logistics

Digital Twins Driven Supply Chain Visibility within Logistics: A New Paradigm for Future Logistics

Study of Economic Management Forecast and Optimized Resource Allocation Based on Cloud Computing and Neural Network

Study of Economic Management Forecast and Optimized Resource Allocation Based on Cloud Computing and Neural Network

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