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The purpose of this paper is to report results from a survey on emerging climate changes and the risks to the operation of building assets in the UK. The property sector is facing major challenges as a result of projected climate change scenarios. Predictions concerning future climate change and the subsequent impact on building operations are still subject to a high degree of uncertainty. However, it is important that building stockholders consider a range of possible future risks that may influence the operation of their assets.

The literature review and questionnaire are used to elicit and assess the likelihood of occurrence of climate change risks impacting building operations. The survey was carried out among building stockowners and professionals in the UK. Statistical methods were used to rank and compare the findings.

The majority of the respondents strongly agreed that the list of risks that were elicited from the literature will have an impact on their building assets within a 0-5 years’ time horizon. It was found that the professionals were most concerned about higher energy prices and an increase in operation costs in general; they were least concerned about an electricity blackout.

This paper is limited to the UK, and regional variations are not explored. Nevertheless, the buildings’ operation risk study provides a starting point for further investigations into the emerging risks from climate change, and their impact on the operation of building stock. Future work could investigate direct mapping between climate risks and the financial value of properties.

Findings of this paper can help professionals and building stockowners improve their understanding of climate change risks and the impact on their assets. This paper could also help these individuals to formulate appropriate adaptation and mitigation strategies.

Cross operation is a common operation method in the building construction process nowadays. Due to the crossover, each other's operations are disturbed, and risks also interact. This superimposed relationship of risks is worthy of attention. The study aims to develop a model for analyzing cross-working risks. This model can quantify the correlation of various risk factors.

The concept of cross operation and the cross types involved are clarified. The risk factors were extracted from cross-operation accidents. The association rule mining (ARM) was used to analyze the results of various cross-types accidents. With the help of visualization tools, the intensity distribution and correlation path of the relationship between each factor were obtained. A complete cross-operation risk analysis model was established.

The application of ARM method proves that there are obvious risk correlation deviations in different types of cross operations. A high-frequency risk common to all cross operations is on-site safety inspection and process supervision, but the subsequent problems are different. Cutting off the high-lift risk chain timely according to the results obtained by ARM can reduce or eliminate the danger of high-frequency risk factors.

This is the first systematic analysis of cross-work risk in the construction. The study determined the priority of risk management. The results contribute to targeted cross-work control to reduce accidents caused by cross-work.

The basis of safe flight is the management of risks. This paper aims to present a new process-based risk assessment model, with an approach to calculate the risk score.

Since thousands of minor changes occur within ground operations, it is difficult to calculate how much risk these variations will pose. This paper proposes a risk assessment model fed from analysis of ground operation processes using functional resonance analysis method (FRAM) and fuzzy logic.

FRAM is used to detect variations in ground operation. Using the FRAM analysis, it has been revealed how much risk the process steps described in the procedures involve. The risk score was calculated by combining the probability value obtained from the airline’s database and the severity assessment of the expert group in fuzzy logic. The risk level can be monitored dynamically with the transfer of events in the airline’s database to the process-based risk assessment model.

FRAM analysis, which is used to detect function variations before undesirable risk occurs, has brought a proactive approach to risk assessment. The process-based risk assessment model allows the creation of new safety parameter indicators to be followed to reduce the risk level of the function with a high-risk level. The proposed approach can be used for other operational areas in aviation as well.

Firms are increasingly being pressured by the public, regulators and customers to ensure that their suppliers behave in a socially and ecologically sound manner. Yet, the complexity and risks embedded in many supply chains makes this challenging, with monitoring practices offering one means to attenuate supply sustainability risk. Drawing on agency theory, the purpose of this paper is to examine the relationship between sustainability and operations risk, supplier sustainability monitoring practices, supply improvement initiatives and firm performance.

This research uses data from a survey and archival sources from a sample of large US firms to empirically examine the relationship between sustainability and operations risk, supplier sustainability monitoring practices, supply improvement initiatives and firm performance.

Findings indicate that higher levels of perceived sustainability risk is related to greater monitoring of supplier sustainability practices by focal firms. Perceptions of higher operations risk are indirectly related to greater social monitoring through investment in supply improvement initiatives. Monitoring of supplier sustainability practices is also found to have a positive effect on focal firm performance.

Findings suggest that managers process operations risks and sustainability risks independently. Greater sustainability risk leads to increased sustainability monitoring, while greater operations risk leads to increased investment in supply improvement initiatives, which in turn leads to increased social monitoring. The research also indicates that behavior-oriented approaches, such as monitoring of supplier environmental and social practices, are an effective approach to improving firm sustainability performance. However, due to resource constraints, a challenge for supply chain managers is where and when to invest in behavior-oriented approaches for suppliers.

This research advances supply risk literature by exploring the effects of supply sustainability risk on the use of monitoring practices to manage supplier environmental and social behavior. Using a combination of survey and archival data to independently assess the implications of sustainability monitoring practices on firm sustainability performance, this study provides a methodology for evaluating the impact of sustainability monitoring practices on the triple bottom line in supply chain management.

The purpose of this paper is to focus on flight exceedances in pilots' operations. With some bad conditions, such as a bad weather, flight exceedances might lead to serious consequences. They are significant hidden dangers of aviation. Risk analysis is carried out to identify pilots' high‐risk or low‐risk operations.

A multi‐objective optimization model is proposed for risk analysis of flight operations. An evolutionary algorithm is designed to divide flight operation state‐space into some high‐risk and low‐risk sub‐spaces.

Through the empirical study of a certain flight exceedance with the analysis model, the authors discover some high‐risk flight operations, which indicate coordination problems in coordinate control of airplane's speed, rate of descent, heading, roll and pitch, etc.

This paper employs a quantitative model to carry out risk analysis of flight operations. The results are useful to pilots' training and may improve flight safety fundamentally. The risk analysis of flight exceedance is one specific case in airlines safety risk management. Some other problems, such as cause analysis of flight delay, aircraft faults diagnosis, can be addressed in the same way and dealt with by specific model adjustments and algorithm designs.

The success of public–private partnership (PPP) projects is highly correlated to the successful management of risks encountered during the operation phase. PPP projects are especially exposed to risk due to the long operation period over which revenues need to be generated to recoup substantial initial investment and operational running costs. Despite the critical impact of risk exposure, limited research has been specifically undertaken on the matter of operational risk management. This study seeks to address this oversight by identifying and evaluating operational risk management strategies for PPPs.

Vulnerability theory is the theoretical lens used, with context drawn from Chinese PPP projects. Based on the data collected from expert interviews and questionnaires, 28 operational risk management strategies are identified. A fuzzy synthetic method is employed to analyze the effectiveness of the 28 strategies.

The findings reveal that providing an exit mechanism clause into the contract, establishing a comprehensive performance evaluation mechanism and developing a clear compensation mechanism are the top three effective strategies. This study also reveals that risk mitigation approaches that reduce vulnerability prove more effective than attempts to reduce external threats. Specifically, strategies aimed at managing contract, political, technical and financial risk are the most effective.

The findings of this study extend current knowledge regarding the risk management of PPP projects. They also offer a reference by which practitioners may select effective operational risk management pathways and thereby, galvanize the sustainable development of PPPs.

Risk control programs of modern complex organizations make extensive use of stochastic models. The purpose of this paper is to consider a class of stochastic models in severity and risk duration reduction operations.

A new stochastic model is formulated which is shown to be of some importance in fundamental risk management operations. The investigation of such a model is based on classical methods of characteristic functions theory.

A stochastic model having the form of the product of two non‐negative and independent random variables is formulated. A characterization of the distribution of such a model is established. Moreover, applications of the proposed stochastic model in risk control programs of organizations are provided.

The difficulty of evaluating the corresponding distribution function, which extends the practical applicability of the proposed stochastic model still remains.

The formulated stochastic model consists of a strong analytical tool for investigating operations of risk control programs.

This research has the potential to contribute to the understanding of the sustainable ground handling operations framework. Ramp operations as the main system of ground handling include critical services for aircraft/airlines. The purpose of this study is to identify the risk factors in ramp operations for all related stakeholders’ awareness to enhance flight safety. Classifying risk factors, the four main performance fields under risk taxonomy is determined. Thus, managers may allocate resources effectively to handle related threats for corporate sustainability.

New taxonomy with human performance value indicators, which sources from the environment is developed. New developed taxonomy is entitled as “environmental value approach,” which represents environmental value-based approach. The developed new risk factors taxonomy is divided into groups such as ramp personnel, organizational, sustainability-based risk factors: triple view and ergonomics obtained from an extensive literature review and experts’ opinions in the field of human performance.

The findings of this research show that managers need a risk management-oriented approach to manage the human factor affecting performance and sustainability. The newly developed taxonomy offers not only identifying the sources of unsafe operational risk factors but also using as a decision-support tool to manage risks for achieving their sustainability goals. When managerial decisions are made according to risk taxonomy and managing these risks, then corporate performance and individual performance may improve.

The new taxonomy presents the performance-based management of the human factor with a holistic and systematic risk management-based approach. There is no risk taxonomy study designed considering ramp operations and sustainability-based human factor performance.

To remedy the inherent defect in current research that focuses only on a single type of participants, this paper endeavors to look into the situation as an evolutionary game between a representative Logistics Service Integrator (LSI) and a representative Functional Logistics Service Provider (FLSP) in an environment with sudden crisis and tries to analyze how LSI supervises FLSP's operations and how FLSP responds in a recurrent pattern with different interruption probabilities.

Regarding the risks of supply chain interruption in emergencies, this paper develops a two-level model of single LSI and single FLSP, using Evolutionary Game theory to analyze their optimal decision-making, as well as their strategic behaviors on different risk levels regarding the interruption probability to achieve the optimal return with bounded rationality.

The results show that on a low-risk level, if LSI increases the degree of punishment, it will fail to enhance FLSP's operational activeness in the long term; when the risk rises to an intermediate level, a circular game occurs between LSI and FLSP; and on a high level of risk, FLSP will actively take actions, and its functional probability further impacts LSI's strategic choices. Finally, this paper analyzes the moderating impact of punishment intensity and social reputation loss on the evolutionary model in emergencies and provides relevant managerial implications.

First, by taking both interruption probability and emergencies into consideration, this paper explores the interactions among the factors relevant to LSI's and FLSP's optimal decision-making. Second, this paper analyzes the optimal evolutionary game strategies of LSI and FLSP with different interruption probability and the range of their optimal strategies. Third, the findings of this paper provide valuable implications for relevant practices, such that the punishment intensity and social reputation loss determine the optimal strategies of LSI and FLSP, and thus it is an effective vehicle for LSSC system administrator to achieve the maximum efficiency of the system.

The purpose of this paper is to describe new methods to manage variation in complex manufacturing process chains and to show synergies between the variation risk management (VRM) and six‐sigma approaches.

The research methodology was experimental prototyping conducted in collaboration with industry partners. A prototype IT system was developed and tested to implement the approach. A quality cost‐based system was used to assess variation at each operation stage, for every product characteristic.

A comprehensive approach to the management of manufacturing variation is introduced, based on a new process risk matrix which can be used to specify an individual variation risk for every manufactured characteristic, throughout a manufacturing process chain. The approach has been implemented in a prototype software system and is aimed at the complex products such as those manufactured by the aerospace industry.

The IT approach described was developed during the research and is not commercially available.

Manufacturing industry should be able to use this approach, in particular the process risk matrix concept, to develop more effective management of product variation and resultant cost, in complex process chains.

The paper describes a novel approach to combine VRM and six‐sigma concepts, and introduces the process risk matrix as a structure to understand process variation.