Understanding Natural Disasters as Risks in Supply Chain Management through Web Data Analysis

Ong, Jimmy Boon Som; Wang, Zhaoxia; Goh, Rick Siow Mong; Xi, Yin; Xin, Xin; Fu, Xiuju

doi:10.17706/ijcce.2015.v4.391

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Most of those studies examined the SC integration in the context of SC resilience (Papadopoulos et al, 2017;Sheffi, 2015), sustainability (Papadopoulos et al, 2017;Wu et al, 2017), risk management (Ong et al, 2015), and agility (Giannakis and Louis, 2016). However, the research on this issue still strongly emphasizes theoretical development with limited studies of advanced data-mining modelling.…”

Section: Resultsmentioning

confidence: 99%

“…Most of those papers focus on the intelligent DSS that enables real-time control of the entire operational process in manufacturing (Dai et al, 2012;Krumeich et al, 2016;Zhang et al, 2017;Zhong et al, 2016), logistics/transportation (Delen et al, 2011;Dobre and Xhafa, 2014;, and SC agility (Ong et al, 2015;Papadopoulos et al, 2017). Not surprisingly, mathematical models are missing in many of those papers since such state-of-the-art systems normally require a complex mixture of various models and techniques from a number of data-mining disciplines.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Big data analytics in supply chain management: A state-of-the-art literature review

Nguyen

Zhou

Spiegler

et al. 2018

Computers & Operations Research

414

238

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Abstract:The rapidly growing interest from both academics and practitioners in the application of big data analytics (BDA) in supply chain management (SCM) has urged the need for review of up-to-date research development in order to develop a new agenda. This review responds to the call by proposing a novel classification framework that provides a full picture of current literature on where and how BDA has been applied within the SCM context. The classification framework is structurally based on the content analysis method of Mayring (2008) techniques are employed to develop these models? The discussion tackling these four questions reveals a number of research gaps, which leads to future research directions.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Big data analytics in supply chain management: A state-of-the-art literature review

Nguyen

Zhou

Spiegler

et al. 2018

Computers & Operations Research

414

238

View full text Add to dashboard Cite

show abstract

“…They offered to improve collaboration among researchers, analysts and healthcare professionals in healthcare sector. [8]Combination of blockchain and artificial intelligence is getting very popular because of its security, efficiency, productiveness in business force. In this paper, authors re-viewed the running state using AI in supply chains to see the activities of the technologies and future work exploration.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Efficiency in Healthcare Supply Chains: Leveraging Machine Learning for Optimized Operations

2021

IJFMR

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In this complex strings of healthcare supply chain, optimal efficiency is the most supreme to ensure cost and time effectiveness of medical resources. This paper scrabbles through Machine Learning (ML) classifiers such as, Na¨ıve Bayes, K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine (SVM) and Linear Regression to intensify the performance of healthcare supply chain management. This study focuses on five classes: ’Inspection Results’, ’Defect Rate’, ’Transportation Modes’, ’Routes’ and ’Cost’. According to my findings, Random Forest classifier indicated 87% accuracy in the ‘Inspection Results’ and ‘Transportation Modes’ classifications whereas KNN classifier signified an impressive accuracy of 86% in the ‘Routes’ classification. These findings underline the need for ML approaches within different classes. The diversified performances of classifiers within classes highlight the importance of selecting the most suitable algorithm based on supply chain aspect. This research not only shows the effectiveness of ML classifiers in healthcare supply chain optimization but also highlights the possibility of automation for different surface of supply chain management. It provides as a fundamental step for optimizing healthcare supply chain

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“…[17]- [19] Especially in times of disasters when social media turns out to be the only way possible to contact people. [20], [21] Image groups/video summarizations consist of summarizing a string of images or a video. Stories about the subject can be generated from a string of images as well.…”

Section: ][10] • Based On Languages: Multi Single Crossmentioning

confidence: 99%

Query-Based Summarization Methods for Conversational Agents: An Overview

Nimavat¹,

Joshiara²

2017

ijarcs

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Summarization is a topic that will be of a great important in the coming age since intelligent assistants especially the ones in the form of conversational agents will have to sift through the abundance of raw unstructured text data to provide relevant information. The data will be in the form of Social media posts, content websites and other user generated text content from which the user shall require tailored information from and about the data. The paper hence explores various methods for summarization and focuses particularly on extracting the gist from the perspective of a given keyword i.e. query based summarization from raw unstructured text data sources available at scale. Along with that, the need for a proper framework to mine relevant knowledge from the said data is acknowledged and the challenges that a conversational agent would hence face are identified. Various approaches that contribute to building a framework and solve the identified challenges are explored as well. It is hoped that the approaches discussed in the paper will be of use to researchers building algorithms in areas of knowledge mining and understanding, such as summarization, that deal with the challenges that are expected to arise.

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Understanding Natural Disasters as Risks in Supply Chain Management through Web Data Analysis

Cited by 6 publications

References 10 publications

Big data analytics in supply chain management: A state-of-the-art literature review

Big data analytics in supply chain management: A state-of-the-art literature review

Enhancing Efficiency in Healthcare Supply Chains: Leveraging Machine Learning for Optimized Operations

Query-Based Summarization Methods for Conversational Agents: An Overview

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