Managing the risk of supply chain bankruptcy in supply chain network redesign

Jahani, Hamed; Alavifard, Farzad; Ivanov, Dmitry; Ghasemishabankareh, Behrooz

doi:10.1016/j.ifacol.2019.11.571

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Por último, y no menos importante se presentan aportes de los AG en el área de almacenamiento y gestión de inventarios [45][46][47][48][49][50][51] y en el área de selección, evaluación de proveedores respecto a una demanda variable [52][53][54][55][56][57].…”

Section: Algoritmos Genéticosunclassified

Oportunidades para la transformación digital de la cadena de suministro del sector bananero basado en software con inteligencia artificial

Palacio

2021

Rev. politec.

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La inteligencia artificial ofrece grandes oportunidades para la cadena de suministro, siendo esto una ventaja competitiva para el mercado cambiante de hoy en día. Este artículo tiene como objetivo identificar los impactos y oportunidades que puede ofrecer el software con inteligencia artificial para facilitar la operación y mejorar el desempeño de la cadena de suministro en el sector bananero de Colombia. La metodología de trabajo consta de seis pasos en donde se obtuvo un total de 72 investigaciones. Las fuentes de información fueron cuatro bases de datos. Como conclusión principal, la cadena de suministro del sector bananero tiene todo lo necesario para que se implementen soluciones basadas en software inteligente con el fin de lograr una adaptación, flexibilidad y sensibilidad al contexto y dominio de ejecución. Artificial intelligence offers great opportunities for the supply chain, making it a competitive advantage for today's changing market. This paper aims to identify the impacts and opportunities that artificial intelligence software can offer to supply chain in the Colombian banana sector to facilitate the operation and improve the performance. The searching method consists of six steps getting 72 investigations finally. The sources of information were four databases. The main conclusion is the supply chain of the banana sector has everything for implementation of solutions based on intelligent software in order to achieve adaptation, flexibility and context awarenes and execution domain.

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Section: Algoritmos Genéticosunclassified

Oportunidades para la transformación digital de la cadena de suministro del sector bananero basado en software con inteligencia artificial

Palacio

2021

Rev. politec.

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“…Economic sustainability is important to pursue, as financial difficulties and failures of critical suppliers trigger announcements of supply chain disruptions that severely penalize business operations [9][10][11]. Therefore, companies in a supply chain must maintain a healthy financial state to achieve the successful integration of their activities and proper coordination of all their key processes.…”

Section: Introductionmentioning

confidence: 99%

“…He considered that the X 5 ratio (net sales/total assets) was influenced by the company's sector or industry, so he decided to make a new model valid for all industries or sectors, which he called the Z"-score [37]. Nevertheless, in recent academic articles, this Z model is likely to continue to be used instead of the latest update [9,44].…”

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confidence: 99%

Analysis of the Economic Sustainability of the Supply Chain Sector by Applying the Altman Z-Score Predictor

2022

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This paper fills the gap in the financial perspective of supply chain performance measurement, related to the lack of a bankruptcy probability indicator, and proposes a predictor which is the eighth-model of the Altman Z-Score Logistic Regression. Furthermore, a bankruptcy probability ranking is established for the companies’ supply chains, according to the industry to which they belong. Moreover, the values are set to establish three categories of companies according to predictor. The probability of bankruptcy is analysed and studied for the supply chain of different industries. The building industry is revealed to have the highest probability of bankruptcy.

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“…In modern industry, artificial intelligence (AI) can be applied in almost every link in the manufacturing process. This gives birth to intelligent manufacturing (IM) system and related techniques [1][2][3][4]. The IM system is defined by the autonomy of manufacturing units and the self-organization of the system.…”

Section: Introductionmentioning

confidence: 99%

Influencing Factors and Evaluation Model of Quality Risks in Intelligent Manufacturing Mobile Supply Chain

He¹

2020

JESA

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The intelligent manufacturing (IM) supply chain (SC) involves multiple distributed agents. The mobile supply chain (MSC) technology supports the real-time management of key information resources in the supply chain of IM products. This paper explores the influencing factors and evaluation model of quality risks in IM MSC, trying to make realistic evaluation of the actual quality risks of the enterprise. Firstly, the authors constructed a quality risk identification framework for IM MSC, and a hierarchical evaluation index system (EIS) based on the factors affecting quality risks. Besides, the features and attributes of four dimensions of quality risks were specified, and the corresponding intuitive triangular fuzzy numbers were given. Next, an evaluation model was established for the quality risks of IM MSC based on backpropagation neural network (BPNN). After the evaluation of quality risks, a contract model was designed for the quality risk control in IM MSC. The proposed EIS and models were proved effective through experiments.

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Managing the risk of supply chain bankruptcy in supply chain network redesign

Cited by 6 publications

References 15 publications

Oportunidades para la transformación digital de la cadena de suministro del sector bananero basado en software con inteligencia artificial

Oportunidades para la transformación digital de la cadena de suministro del sector bananero basado en software con inteligencia artificial

Analysis of the Economic Sustainability of the Supply Chain Sector by Applying the Altman Z-Score Predictor

Influencing Factors and Evaluation Model of Quality Risks in Intelligent Manufacturing Mobile Supply Chain

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