Development of a knowledge-based intelligent decision support system for operational risk management of global supply chains

Park, Yang Byung; Yoon, Sung Joon; Yoo, Jun Su

doi:10.1504/ejie.2018.089878

Cited by 18 publications

(6 citation statements)

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“…Considering the cascading effect of risks on global SNs, Ojha et al (2018) used the Bayesian network theory to analyse SNs faced with simultaneous disruptions, providing a holistic measurement approach for predicting the complex behaviour of risk propagation for improved SN risk management. Park, Yoon, and Yoo (2018) proposed a knowledge-based intelligent decision support system for SC risk management, providing SC managers with a practical tool to accurately predict and effectively control operational risk. Selecting better enterprise locations and minimising transportation costs are required to construct a resilient SN (Altiparmak et al 2006).…”

Section: Studiesmentioning

confidence: 99%

“…The construction of SNs that are resilient against disruptions has been extensively studied from several perspectives such as the risk management perspective (e.g. Chopra and Sodhi 2004;Nickel, Saldanha-da-Gama, and Ziegler 2012;Ledwoch, Yasarcan, and Brintrup 2018;Ojha et al 2018;Park, Yoon, and Yoo 2018;Nooraie et al 2019), the optimisation problem perspective (e.g. Altiparmak et al 2006;Wang and Ip 2009;Cardoso et al 2015;Dixit, Seshadrinath, and Tiwari 2016;Kamalahmadi and Mellat-Parast 2016b), the sourcing strategy perspective (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Research on supply network resilience considering random and targeted disruptions simultaneously

Shi

Long

et al. 2019

International Journal of Production Research

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Supply networks (SN) must maintain operations and connectedness under disruptions to remain competitive; this is referred to as SN resilience. Building a resilient SN is an underlying challenge in supply chain management. In this paper, SN resilience is examined from the complex network topology perspective to understand how supply chain managers construct resilient networks. The proposed growth model considers enterprises leaving the network, which previous studies have ignored. Considering the heterogeneous roles of enterprises, new metrics based on a new proposed sub-network concept are presented to evaluate resilience. Using a computer simulation, the resilience of the SN generated by the model proposed in this paper is compared with that of other models, and the results indicate that (i) the proposed model can be tuned to generate a desired resilient network; (ii) the proposed metrics capture the resilience requirements of the SN very well; (iii) the more uniform the distribution of the enterprises, the more resilient the corresponding SN; and (iv) the higher the values of α and β, the lower the SN resilience, and β affects the resilience more than α does.

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Section: Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on supply network resilience considering random and targeted disruptions simultaneously

Shi

Long

et al. 2019

International Journal of Production Research

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“…In the literature, several applications of PCA have been proposed in the context of SCM, showing relevant benefits on the supplier selection problem in multi-item/multi-supplier environments [38] or on the extraction of the most relevant sustainability indicators to conduct ecoe ciency performance analyses in industrial companies [39]. PCA can also contribute to the identification of operational risk sources (see, e.g., [40]) and, for our case in particular, to better comprehend the risk profiles of the di↵erent inventory items according to the logistic features associated with them. With this knowledge base, we expect that company experts can develop more e↵ective action plans to improve and support the inventory control decision-making process.…”

Section: Modeling Frameworkmentioning

confidence: 99%

<i>K</i>-means clustering combined with principal component analysis for material profiling in automotive supply chains

Gonçalves

Cortez

Carvalho

2021

EJIE

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At a time where available data is rapidly increasing in both volume and variety, descriptive Data Mining (DM) can be an important tool to support meaningful decision-making processes in dynamic Supply Chain (SC) contexts. Up until now, however, scarce attention has been given to the application of DM techniques in the field of inventory management. Here, we take advantage of descriptive DM to detect and grasp important patterns among several features that coexist in a real-world automotive electronics SC. Concretely, Principal Component Analysis (PCA) is employed to analyze and understand the interrelations between ten quantitative and dependent variables in a multi-item/multi-supplier environment. Afterwards, the principal component scores are characterized via a K-means clustering, allowing us to classify the samples into four clusters and to derive di↵erent profiles for the multiple inventory items. This work provides evidence that descriptive DM contributes to find interesting feature-patterns, resulting in the identification of important risk profiles that may e↵ectively leverage inventory management for superior performance.

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“…Although in recent years there has been a plethora of studies on technology readiness and technology acceptance from the consumer's perspective, industrial context, or in the organizational adoption as a whole [5,10,14,[27][28][29][30][31][32][33][34][35][36][37], there is scarcity of research from an employee's perspective as the unit of analysis for readiness and technology acceptance [11,36,[38][39][40][41][42] in e-business environments. Moreover, no studies of such nature are found for technology intensive environments.…”

Section: Employees Customersmentioning

confidence: 99%

Sustainable Change Management through Employee Readiness: Decision Support System Adoption in Technology-Intensive British E-Businesses

2019

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Technology brings green sustainable management practices to the workplace. It is important to ascertain the factors that enable or inhibit employees' perceptions towards technology adoption. Corporate sustainability and sustainable management practices partially depend on employees for the successful implementation of technological changes in the workplace. This study aims at applying the technology acceptance model (TAM) from an employees' user-perspective. It addresses those factors that form employee readiness for e-business and enable their intention to use e-business technologies such as decision support systems (DSS). It focuses on technology intensive firms while combining Davis' technology acceptance model and Lai and Ong's employee readiness for e-business (EREB) model. A survey questionnaire was used to collect the data for this cross-sectional study from 331 employees of 28 well-established small and medium-sized e-businesses located in the United Kingdom. The outcomes show that the four dimensions of EREB explain the 58.2% of variance in perceived ease of use and the 50.2% of variance in perceived usefulness. Together, perceived usefulness and perceived ease of use explain the 51.8% of variance in intention to use while fully mediating the relationship between higher order EREB construct and intention to use DSS.

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Development of a knowledge-based intelligent decision support system for operational risk management of global supply chains

Cited by 18 publications

References 0 publications

Research on supply network resilience considering random and targeted disruptions simultaneously

Research on supply network resilience considering random and targeted disruptions simultaneously

<i>K</i>-means clustering combined with principal component analysis for material profiling in automotive supply chains

Sustainable Change Management through Employee Readiness: Decision Support System Adoption in Technology-Intensive British E-Businesses

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