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Data Intelligence and Risk Analytics


Special issue call for papers from Industrial Management & Data Systems

Guest editors:

Desheng Dash Wu,School of Economics and Management, University of Chinese Academy of Sciences. Risk Lab, University of Toronto. dash@risklab.ca

Shulin Lan, School of Innovation and Entrepreneurship, University of Chinese Academy of Sciences. lanshulin@ucas.ac.cn

Chen Yang, HKU-ZIRI Lab for Physical Internet, Dept. of Industrial and Manufacturing Systems Engineering, The University of Hong Kong. wzhyoung@163.com

David L. Olson, College of Business Administration, University of Nebraska Lincoln. dolson3@unl.edu

Special Issue Focus

Data intelligence and risk analytics develop at fast pace but still lack of relevant research work. This special issue contributes to innovative research methodologies based on data intelligence and risk analytics. It will cover several topics such as technologies, decision models, policies, successful cases, and best practices for risk analytics. Current challenges such as data collection, data processing and data-based models for data intelligence and risk analytics will be covered in this issue. For practical implementations, the case studies and best practices will be reported so that the lessons and insights from the successful cases could be followed by practitioners.

Data intelligence is the analysis of various forms of data in such a way that it can be used by companies to expand their services or investments (McAfee et al. 2012). It has caused great repercussions in both academics and the industry, such as American e-commerce giants tech firms. (Athey and Imbens 2016). Risk analytics has become an important topic in today’s more complex, interrelated global environment, replete with threats from natural, engineering, economic, and technical sources (Olson and Wu 2015).  Barrett and Baum (2017) discussed the application of data intelligence in risk analysis.  The main research areas are engineering risk analytics, risk management of the energy sector and the financial engineering risk (Wu and Olson 2017).  In order to reduce risk and improve decision making, data intelligence is also widely used for patent claim analysis (Lee et al. 2013) , driver safety risk prediction (Ou et al. 2013) , customer relationship management (Krishna and Ravi 2016) , customer credit card loss (He et al. 2016)  and so on. However, such massive and invaluable data from risk analytics may bring new challenges such as data processing, data visualization, data-driven decision models, risk decision support systems, etc. in the era of big data.

This special issue addresses the following key topics:

•    Cross-functional approaches to managing enterprise risks
•    Data-driven methodologies for finance and operations
•    Joint financing and capacity-investment choices
•    Integrated measures of risk and performance evaluation
•    System risk management of global supply chains
•    Insurance and supply chain risks
•    Trade credit in operations
•    Operational risk management of the service sector (banking, entertainment, travel, etc.)
•    Credit risks in operations management
•    Risk aversion in supply chain management
•    Data mining and sentiment analysis in managing market risks in enterprises
•    Intelligence multi-criteria decision making in financial enterprise services
•    Financial implications of capacity-planning and dynamic pricing strategies
•    Other risk management models of operations problem

Submission

•    Please read the publication style guidelines before submitting your paper
•    Go here to register/or log on to submit your paper to ScholarOne
•    Full papers should follow the Industrial Management & Data Systems guidelines and clearly indicate the “Special issue on Data intelligence and risk analytics”. For further enquiries, please contact the managerial guest editor Prof. Desheng Dash Wu at dash@risklab.ca .

Deadlines

Submission deadline: 31 March, 2019

Papers reviewed: 30 May, 2019

Revised papers reviewed and accepted: 30 August, 2019

Final versions of accepted papers delivered: 31 October, 2019