Identification of supply chain disruptions with economic performance of firms using multi-category support vector machines

Ye, Shi Jie; Xiao, Zhi; Zhu, Guangfu

doi:10.1080/00207543.2014.974838

Cited by 23 publications

(9 citation statements)

References 40 publications

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“…Zage, Glass, and Colbaugh (2013) analyse large amounts of Web data using semi-supervised learning to determine the trustworthiness of vendors, thus allowing identification of deceivers. Risk identification is also explored by Ye, Xiao, and Zhu (2015), focusing, however, on financial risk. Publicly available economic performance data for Chinese firms are collected and are used to train multi-class Support Vector Machine classifiers.…”

Section: Machine Learning and Big Datamentioning

confidence: 99%

Supply chain risk management and artificial intelligence: state of the art and future research directions

Baryannis

Validi

Dani

et al. 2018

International Journal of Production Research

528

284

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Supply chain risk management (SCRM) encompasses a wide variety of strategies aiming to identify, assess, mitigate and monitor unexpected events or conditions which might have an impact, mostly adverse, on any part of a supply chain. SCRM strategies often depend on rapid and adaptive decision-making based on potentially large, multidimensional data sources. These characteristics make SCRM a suitable application area for artificial intelligence (AI) techniques. The aim of this paper is to provide a comprehensive review of supply chain literature that addresses problems relevant to SCRM using approaches that fall within the AI spectrum. To that end, an investigation is conducted on the various definitions and classifications of supply chain risk and related notions such as uncertainty. Then, a mapping study is performed to categorise existing literature according to the AI methodology used, ranging from mathematical programming to Machine Learning and Big Data Analytics, and the specific SCRM task they address (identification, assessment or response). Finally, a comprehensive analysis of each category is provided to identify missing aspects and unexplored areas and propose directions for future research at the confluence of SCRM and AI.

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Section: Machine Learning and Big Datamentioning

confidence: 99%

Supply chain risk management and artificial intelligence: state of the art and future research directions

Baryannis

Validi

Dani

et al. 2018

International Journal of Production Research

528

284

View full text Add to dashboard Cite

show abstract

“…Recently, there has been an AI resurgence due to the availability of increased computing power and large amounts of data, as well as the success of approaches within the broad area of machine learning. This has also led to SCRM researchers considering the potential of AI techniques in relation to tasks such as risk identification, prediction, assessment and response [6,7,8,9]. However, research is still at early stages, proposing either purely theoretical frameworks that have not been implemented and applied in real-world case studies [6,7], or ad-hoc solutions that are only applicable within the confines of a particular case study [8,9].…”

Section: Ai Techniques Have Received Relatively Little Attention In Rmentioning

confidence: 99%

“…Ye et al [9] investigate the applicability of machine learning techniques to identify supply chain disruptions that are rooted in the economic performance of firms within the chain. Publically available financial data, such as assetliability ratio, are obtained for Chinese firms and for periods before, during and after some form of supply chain disruption took place.…”

Section: Risk Identificationmentioning

confidence: 99%

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Predicting supply chain risks using machine learning: The trade-off between performance and interpretability

Baryannis

Dani

Antoniou

2019

Future Generation Computer Systems

168

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Managing supply chain risks has received increased attention in recent years, aiming to shield supply chains from disruptions by predicting their occurrence and mitigating their adverse effects. At the same time, the resurgence of Artificial Intelligence (AI) has led to the investigation of machine learning techniques and their applicability in supply chain risk management. However, most works focus on prediction performance and neglect the importance of interpretability so that results can be understood by supply chain practitioners, helping them make decisions that can mitigate or prevent risks from occurring. In this work, we first propose a supply chain risk prediction framework using data-driven AI techniques and relying on the synergy between AI and supply chain experts. We then explore the trade-off between prediction performance and interpretability by implementing and applying the framework on the case of predicting delivery delays in a real-world multi-tier manufacturing supply chain. Experiment results show that prioritising interpretability over performance may require a level of compromise, especially with regard to average precision scores.

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“…Supply chain risks have a significant impact on the operational, financial and market success of the firm (Craighead et al 2007;Singhal 2003, 2005;Micheli, Mogre, and Perego 2014;Ye, Xiao, and Zhu 2015;Ho et al forthcoming). In a recent report from Accenture and World Economic Forum (2012), it is noted that there is a need to develop new methods for identifying, highlighting and addressing supply chain risks.…”

Section: Introductionmentioning

confidence: 96%

Supply chain risk mitigation competency: an individual-level knowledge-based perspective

Ambulkar

Blackhurst

Cantor

2015

International Journal of Production Research

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Supply chain managers are responsible for making decisions regarding supply chain risk in order to mitigate the impact of supply chain disruptions. This study develops and tests a theoretical model that leverages the individual-level knowledge-based view perspective to understand the process through which risk mitigation orientation of the supply chain manager contributes to his/her absorptive capacity. A supply chain manager's absorptive capacity, in turn, enhances his/her ability to effectively mitigate supply chain risk. Study findings demonstrate that supply chain managers with high-risk mitigation orientation have greater level of absorptive capacity which enhances their risk mitigation competency. This study represents the first development and testing of a model that examines individual-level knowledge management factors that affect supply chain risk mitigation competency. This research emphasises the importance of the individual supply chain manager in managing risk and illustrates how theoretical perspectives from the knowledge management, supply chain risk and organisational behaviour literature can be fruitfully adopted to explain behaviour in the field of supply chain risk management.

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Identification of supply chain disruptions with economic performance of firms using multi-category support vector machines

Cited by 23 publications

References 40 publications

Supply chain risk management and artificial intelligence: state of the art and future research directions

Supply chain risk management and artificial intelligence: state of the art and future research directions

Predicting supply chain risks using machine learning: The trade-off between performance and interpretability

Supply chain risk mitigation competency: an individual-level knowledge-based perspective

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