Business Intelligence for Improving Supply Chain Risk Management

Liu, L.; Daniels, Hennie; Oosterhout, Marcel van; Dalen, Jan van

doi:10.1080/2287108x.2013.11006084

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…It is clear that organizations cannot manage their risk without managing their data and information/knowledge (Neef, 2005); thus, many organizations have started to use automated risk management frameworks to compete in today's knowledge driven business environment (Haksöz, 2013;Wu, Chen, & Olson, 2014). Despite the high interest in industry, academia has been lagging in terms of the use of BI for management of SC risks (Liu, Daniels, & Hofman, 2014;Wu, Chen, & Olson, 2014;Aruldoss, Travis, & Venkatesan, 2015). BI contains the databases, tools, methods, processes and technologies for the transformation of raw data into meaningful and useful Er , "A data mining-based framework for supply chain risk management", Computers & Industrial Engineering, Accepted.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The RDW also contains risk metadata (Vaisman & Zimányi, 2014). The metadata layer is very important for the DM-based SCRM model because it includes information about risk indicators such as the source of data, related risk factors, location of the risk (e.g., business process such as ordering, supplier selection, shipping), owner of the risk, and data collection frequency and methods (Kayis & Karningsih, 2012;Liu, Daniels, & Hofman, 2014). It can be seen as a roadmap to access risk data.…”

Section: Building a Risk Data Warehousementioning

confidence: 99%

“…Anomaly detection may be used to detect unusual events and patterns such as abnormal transactions. Interesting and unusual patterns in risk factors and their sub-factors should be evaluated to examine their effects on the occurrence of risk events (Lee et al, 2013;Liu, Daniels, & Hofman, 2014). The reason for such patterns should be determined to develop effective risk mitigation strategies.…”

Section: Supply Chain Risk Management Problemmentioning

confidence: 99%

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A Data Mining-Based Framework for Supply Chain Risk Management

Er¹,

Firat²,

Ghadge

2019

SSRN Journal

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Increased risk exposure levels, technological developments and the growing information overload in supply chain networks drive organizations to embrace data-driven approaches in Supply Chain Risk Management (SCRM). Data Mining (DM) employs multiple analytical techniques for intelligent and timely decision making; however, its potential is not entirely explored for SCRM.The paper aims to develop a DM-based framework for the identification, assessment and mitigation of different type of risks in supply chains. A holistic approach integrates DM and risk management activities in a unique framework for effective risk management. The framework is validated with a case study based on a series of semi-structured interviews, discussions and a focus group study.The study showcases how DM supports in discovering hidden and useful information from unstructured risk data for making intelligent risk management decisions.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Building a Risk Data Warehousementioning

confidence: 99%

Section: Supply Chain Risk Management Problemmentioning

confidence: 99%

See 1 more Smart Citation

A Data Mining-Based Framework for Supply Chain Risk Management

Er¹,

Firat²,

Ghadge

2019

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…They are intricately intervened with technologies used for data integration and analysis. Liu et al (2014) point out that data integration is a significant challenge in the supply chain environment. They used an integrated data pipeline for recording transactions among the supply chain partners and the ETL process is used to load data in a common data warehouse.…”

mentioning

confidence: 99%

Data Integration Technologies

Chandra

Grabis

2016

Supply Chain Configuration

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The models presented in the previous chapters use knowledge of supply chain structure to represent the supply chain. Additionally, the parameters of the models were assumed as given and limited attention was devoted to estimation of these parameters. Data driven and statistical methods on the other hand can be used to uncover unknown structural relationships within the supply chain and provide methods for gathering and estimation of input data necessary for supply chain decision-making. Additionally, the data availability recently has increased dramatically making data driven approaches and attractive alternative for strategic supply chain analysis. That has opened a way to a range of new data gathering and supply chain analysis methods based on data integration from various sources. These methods follow a data driven approach implying that the primary means of analysis and decision-making are data processing operations. They are intricately intervened with technologies used for data integration and analysis. Liu et al. (2014) point out that data integration is a significant challenge in the supply chain environment. They used an integrated data pipeline for recording transactions among the supply chain partners and the ETL process is used to load data in a common data warehouse. Business intelligence methods are used for data analysis supported by a common managerial model. Data integration allows for evidence based supply chain risk assessment. Hahn and Packowski (2015) suggest application of in-memory analytics for processing large data volumes in supply chains and also develop a framework for analytics applications in supply chain management. The framework shows application of data-driven approaches in data exploration and monitoring approaches. In line with latest developments in computing, Neaga et al. (2015) propose using a cloud based platform for big data analytics supporting logistics service. The key principles of the platform design are data integration to support various types of information and end-users,

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Sustainability, Risk, and Business Intelligence in Supply Chains

Kara

Fırat

2016

Practice, Progress, and Proficiency in Sustainability

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Sustainable Supply Chain Management has become one of the most important managerial trends nowadays. Companies can optimize their supply chains by integrating risk management and sustainability programs. This chapter explains sustainability and risk management concepts in supply chains and discusses the relationship between these two issues in a multidirectional way. Business Intelligence tools are addressed in terms of their role and importance in supporting the management of supply chains, their risks and sustainability. The aim of the chapter is to provide a background to better understand the link between supply chain risk management, sustainability and business intelligence.

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Business Intelligence for Improving Supply Chain Risk Management

Cited by 4 publications

References 17 publications

A Data Mining-Based Framework for Supply Chain Risk Management

A Data Mining-Based Framework for Supply Chain Risk Management

Data Integration Technologies

Sustainability, Risk, and Business Intelligence in Supply Chains

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