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As a traditional area, engineering management has always been at the core of the research study in academia. In the current uncertain world, more and more attention is paid to risk management. New risks in many practical engineering management problems require new computational methods and tools. The purpose of this paper is to introduce the state‐of‐the‐art research in engineering management and risk management in China.

The collected papers are briefly reviewed and positioned in existing work.

The special issue includes a variety of perspectives on engineering management and risk management from the Great China area.

The paper shows that all the papers address interesting research problems.

Online banking has attracted a great deal of attention from various bank stakeholders such as bankers, financial service participants, and regulators. The purpose of this paper is to analyze the online banking service performance of giant US and UK banks. Risk analysis is also conducted.

This paper connects the principal component analysis (PCA) method with the data envelopment analysis (DEA) method to estimate the online banking performance. Data are collected from 2007 annual reports of giant banks in the USA and the UK including both financial and non‐financial variables.

Most giant banks are performing well based on DEA analysis. Employees turn out to be a key variable that contribute most to banks' revenue. Different DEA models can be classified into cost‐ and online‐oriented models, which is consistent with existing work based on data from other nations.

This paper presents a unique demonstration of using PCA and DEA for evaluation of giant banks with online banking service.

The paper aims to explore the optimal strategies of inventory financing when the risk-averse retailer has different objectives, in the presence of multi-risk, i.e. demand risk, non-operational risk and retailer's strategic default risk.

This paper develops an inventory financing model consisting of a bank and a risk-averse retailer with strategic default. This paper considers two scenarios, i.e. the capital-constrained retailer cares about its profit or firm value. In the first scenario, the bank acts as a Stackelberg leader determining its interest rate, and the retailer acts as a follower determining its pledged quantity. In the second one, the bank capital market is perfectly competitive. Lagrange multiplier method is adopted to solve the optimization.

The optimal strategies in inventory financing scheme in two scenarios are derived. Only when the initial stock is relatively high, the retailer pledges part of the initial stock. Retailer's risk aversion reduces its pledged quantity and performance. The strategic default reduces its profit. When it is relatively high, the bank refuses to offer the loan.

Analytical inventory and financing strategies are specified to help retailers and banks to better understand the interaction of finance and operations management and to better respond to multi-risk.

New results and managerial insights are derived by incorporating partially endogenous strategic default and risk aversion into inventory financing, which enriches the interfaces of operations management and finance.

The purpose of this paper is to review published approaches to supply chain risk management, to include identification and classification of types of risks, cases, and models. Specific aspects of risk in supply chains involving China are also addressed.

Literature review provides sources which are synthesized.

A generic framework is identified, then categorizations of supply chain risks are compared. Cases and models applied to the study of supply chain risk are reviewed briefly. A review of Chinese risk in the supply chain context is provided.

This review includes many current studies, and is a source of useful references for those examining supply chain risk.

The purpose of this paper is to deal with the different phases of volatility behavior and the dependence of the variability of the time series on its own past, models allowing for heteroscedasticity like autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), or regime‐switching models have been suggested by reserachers. Both types of models are widely used in practice.

Both regime‐switching models and GARCH are used in this paper to model and explain the behavior of crude oil prices in order to forecast their volatility. In regime‐switching models, the oil return volatility has a dynamic process whose mean is subject to shifts, which is governed by a two‐state first‐order Markov process.

The GARCH models are found to be very useful in modeling a unique stochastic process with conditional variance; regime‐switching models have the advantage of dividing the observed stochastic behavior of a time series into several separate phases with different underlying stochastic processes.

The regime‐switching models show similar goodness‐of‐fit result to GARCH modeling, while has the advantage of capturing major events affecting the oil market. Daily data of crude oil prices are used from NYMEX Crude Oil market for the period 13 February 2006 up to 21 July 2009.

Construction projects involve with various risks during all phases of project lifecycle. Failure mode and effective analysis (FMEA) is a useful tool for identifying and eliminating possible risk of failure modes (FMs) and improving the reliability and safety of systems in a broad range of industries. The traditional FMEA method applies risk priority number method (RPN) to calculate risk of FMs. RPN method cannot consider the direct and indirect interdependencies between the FMs and is not appropriate for complex system with numerous components. The purpose of this study is to propose an approach to consider interdependencies between FMs and also using fuzzy theory to consider uncertainties in experts' judgments.

The proposed approach consist of three stages: the first stage of hybrid model used fuzzy FMEA method to identify the failure mode risks and derive the RPN values. The second stage applied Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) method to determine the interdependencies between the FMs which are defined through fuzzy FMEA. Then, analytic network process (ANP) is applied in the third stage to calculate the weights of FMs based on the interdependencies that are generated through FDEMATEL method. Finally, weight of FMs through fuzzy FMEA and FDEMATEL–ANP are multiplied to generate the final weights for prioritization. Afterward, a case study for a commercial building project is introduced to illustrate proficiency of model.

The results showed that the suggested approach could reveal the important FMs and specify the interdependencies between them successfully. Overall, the suggested model can be considered as an efficient hybrid FMEA approach for risk prioritization.

The originality of approach comes from its ability to consider interdependencies between FMs and uncertainties of experts' judgments.

Non-financial corporate debt is one of the important sources of systematic risk in the real economy. Assessing a measure of systematic risk in corporation debt is currently a key challenge. In this regard, we propose a two-tier risk contagion networks model.

Assessing a measure of systematic risk in corporation debt is currently a key challenge. In this regard, we propose a two-tier risk contagion networks model based on four dimensions: concept definition, data structure, risk contagion network construction, and risk measurement indicators construction. We take the Jiangsu bond issuer guarantee network as a sample area.

Taking the Jiangsu bond issuer guarantee network as a sample area, we find that there is a strong correlation between the debts of non-financial corporation in China, and it is easy to become a potential regional systematic risk source. In addition, our empirical research also reveals that external risk exposure and node degree of network are two key indicators when identifying key risk-contagion enterprises.

The main contributions of this study are two-fold. First, this article proposes a two-tier risk contagion networks model to measure systematic risk in non-financial corporation. Second, this article describes the structure of the corporate risk contagion network.

The role of a government investment project (GIP) management supervisor is to improve supervision performance and the Chinese government has issued a series of polices, displaying the determination to solve supervision problems. The purpose of this paper is to assist the government to play its role of supervisor and to realize progress supervision performance for GIP in practice with the Agent Construction System (ACS).

Taking consideration of the essence of ACS, the paper analyzes the system supported theory, which is a supervision mechanism, with contract management as the core and goals as orientation. The authors design the system frame and its composing modules according to the main function of this information system.

The research builds an expected supervision information system with four functional modules, which is designed according to logical sequence of supervision, based on a theoretical supervision mechanism.

The system is, to the best of the authors' knowledge, the most advanced and practical information system for GIP supervision published in the literature.