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The purpose of this paper is to generalize the traditional risk evaluation methods and to specify a multi-level risk evaluation framework, in order to prepare customized risk evaluation and to enable effectively integrating the elements of risk evaluation.

A real case study of an electric motor manufacturing company is presented to illustrate the advantages of this new framework compared to the traditional and fuzzy failure mode and effect analysis (FMEA) approaches.

The essence of the proposed total risk evaluation framework (TREF) is its flexible approach that enables the effective integration of firms’ individual requirements by developing tailor-made organizational risk evaluation.

Increasing product/service complexity has led to increasingly complex yet unique organizational operations; as a result, their risk evaluation is a very challenging task. Distinct structures, characteristics and processes within and between organizations require a flexible yet robust approach of evaluating risks efficiently. Most recent risk evaluation approaches are considered to be inadequate due to the lack of flexibility and an inappropriate structure for addressing the unique organizational demands and contextual factors. To address this challenge effectively, taking a crucial step toward customization of risk evaluation.

To strengthen the correlation analysis on risk factors of drug production safety and reduce the influence due to fuzzy judgments, a safety risk assessment method based on Grey-Analytic Network Process (G-ANP) is proposed.

First, an index system evaluating drug production safety risk is constructed according to the “Good Manufacture Practice of Medical Products,” next the influence weight of each risk index is derived by using the Analytic Network Process, then the grey number of each risk index is determined by further utilizing the grey statistical theory, and finally the risk level of drug production is obtained.

An empirical study is conducted and the results support the feasibility and practicability to use G-ANP method for drug production safety risk evaluation. The results of the case show that it is feasible and practical to use G-ANP method for drug production safety risk evaluation.

The innovation lies in the use of G-ANP method to fully consider the interdependence and interaction between the risk factors of drug production safety, which improves the objectivity in judging the risk level of drug production and provides a scientific basis for pharmaceutical manufacturers to formulate further decisions and management in the case of insufficient quantification of risk factors. Based on the findings, more targeted suggestions are made to reduce the production risk of pharmaceutical enterprises.

The purpose of this paper is to investigate the balanced role of internal and external compliance in risk evaluation process of specialized agriculture financing. The authors examine the adaptive behavior of risk managers to determine the role of proposed transformation for risk monitoring (RM) and control process in risk mitigation and avoidance of agriculture credit failure.

A self-administered survey was conducted to collect data from 353 risk-related officers and managers in Zarai Taraqiati Bank Limited (ZTBL) Pakistan. The authors used a previously tested scale for the main constructs. The descriptive analyses were used to gauge the model capacity for determining the strength of proposed risk patterns in agriculture risk management.

The results reveal that risk evaluation process in ZTBL is reasonably efficient in mitigating risks. Given the sensitive nature of farm credit, there is a need of fundamental reforms in risk policy manuals in line with central bank’s agriculture prudential regulations and Basel-III standards. The results fully support H1 and H2, while H3 is partially validated. The result patterns indicate serious issues in risk evaluation process in agriculture finance that is causing higher delinquency in farm credit.

Based on highlighted issues, the authors recommend valuable guidelines in the RM review system for agriculture financing products at ZTBL.

The authors propose remodeling of agriculture risk management and offer valuable insights to the agriculture financial regulators and government in taking policy initiatives in the pre-and-post agriculture risk evaluation process. The proposed model enables RM process to improve farm credit delinquency, particularly in ZTBL and other agriculture banking networks in commercial banks.

This is the first study to empirically investigate RM evaluation process in agriculture risk management of ZTBL in Pakistan, thus, offers new horizon of farm credit regulatory compliance in agricultural sector of Pakistan.

The purpose of this research is to provide a theoretical framework for complex equipment quality risk evaluation. The primary aim of the framework is to enhance the ability to identify risks and improve risk control efficiency during the development phase.

A novel framework for quality risk evaluation in complex equipment is proposed, which integrates probabilistic hesitant fuzzy set-quality function deployment (PHFS-QFD) and grey clustering. PHFS-QFD is applied to identify the quality risk factors, and grey clustering is used to evaluate quality risks in cases of poor quality information during the development stage. The unfolding function of QFD is applied to simplify complex evaluation problems.

The methodology presents an innovative approach to quality risk evaluation for complex equipment development. The case analysis demonstrates that this method can efficiently evaluate the quality risks for aircraft development and systematically trace back the risk factors through hierarchical relationships. In comparison to traditional failure mode and effects analysis methods for quality risk assessment, this approach exhibits superior effectiveness and reliability in managing quality risks for complex equipment development.

This study contributes to the field by introducing a novel theoretical framework that combines PHFS-QFD and grey clustering. The integration of these approaches significantly improves the quality risk evaluation process for complex equipment development, overcoming challenges related to data scarcity and simplifying the assessment of intricate systems.

Reports on the authors' experience with a patient safety quality improvement program, intended to reduce the incidence and severity of adverse outcomes for emergency department (ED) patients aged ≥75. The Identification of Seniors at Risk scale was used for screening, and those at high risk were referred for appropriate intervention. The plan‐do‐study‐act improvement cycle was followed, conducting process evaluation to diagnose and correct implementation difficulties. Reports that: implementing an ED screening and referral program is deceptively difficult; process evaluation multidisciplinary working group meetings are an essential improvement tool; screening inclusion criteria had to be adapted to the subject population in order to make efficient use of staff time; the screening questions and process required ongoing assessment, revision, and local adaptation in order to be useful; and high‐risk screening in the ED is critical to a hospital system's ability to anticipate clinical problems; the plan‐do‐study‐act improvement cycle is a practical and useful tool for improving quality and systems in a real care setting.

Explores the thesis that the techniques of risk analysis are necessary but in themselves insufficient components of the management of risk process. Begins by exploring the debate about risk. Proposes a definition of risk analysis, suggesting that the term refers to the sum of risk identification, estimation and evaluation. Takes the three component headings as the frameworks. Within each framework, gives an overview followed by an outline of the more common and important techniques. Discusses the issues of sufficiency and draws conclusions. Refers to the decision making techniques of risk management and sets out the overall conclusions substantiating the thesis.

The aim of the present study is to overcome some of the limitations of the FMEA method by presenting a theoretical base for considering risk evaluation into its assessment methodology and proposing an approach for its implementation.

Fuzzy AHP is used to calculate the weights of the likelihood of occurrence (O), severity (S) and difficulty of detection (D). Additionally, the prospect-theory-based TODIM method was integrated with fuzzy logic. Thus, fuzzy TODIM was employed to calculate the ranking of potential failure modes according to their risk priority numbers (RPNs). In order to verify the results of the study, in-depth interviews were conducted with the participation of industry experts.

The results are very much in line with prospect theory. Therefore, practitioners may apply the proposed method to FMEA. The most crucial failure mode for a firm's attention is furnace failure followed by generator failure, crane failure, tank failure, kettle failure, dryer failure and operator failure, respectively.

The originality of this paper consists in integrating prospect theory with the FMEA method in order to overcome the limitations naturally inherent in the calculation of the FMEA's RPNs.

To show necessity of risk evaluation during modelling and simulation of production systems. To show an approach to risk evaluation of manufacturing system which has serial reliability structure.

Modelling and simulation of a manufacturing system allows one to conduct verification of different solutions in the area of production planning, before they are started. This is impossible with traditional methods.

Reliable results of simulation research can be obtained only if they are based on an integrated model of the company, that covers all components of the manufacturing process including also its organization and risk in production processes.

The paper describes the stages and results of a project carried out in 2002 in an international company. The proposed method of risk evaluation may be helpful to determine the risk level in the chosen production line and eventually for the whole enterprise manufacturing systems.

The risk concept was treated as a synonym of unreliability. This kind of approach enabled decomposition of the production system into several areas and determination of the reliability structure.

The hefty fines levied on credit institutions in recent years for cases of misconduct, including poor behavioural standards, operational control deficiencies and regulatory breaches more broadly, has been defined by regulatory authorities and the financial sector more broadly as ‘conduct risk’. There is no official definition of conduct risk, as conduct risk profiles are unique to each firm and, therefore, there can never be a one-size-fits-all framework in place. Conceptually, conduct risk is a broad notion that touches every part of an enterprise framework, including culture, customer contact, corporate governance, ethics and integrity, conflicts of interest and compliance, amongst others. As a result, credit institutions tend to associate conduct risk with regulatory censure, financial detriment, poor customer outcomes, and, importantly, reputational damage. In light of the significant consequences of misconduct, recent regulatory measures have sought to specifically target these drivers. In this chapter the author discussed the regulatory spotlight on conduct risk, which continues to top the regulators’ agenda in view of its seriousness and considered the role of the board in managing conduct risk, whilst elaborating on the importance of board evaluations in this respect.

This paper aims to propose a technique based on cognitive assessments to quantify identified operational risks from the perspective of container shipping or logistics system administrators. The results derived from the risk quantification could be used to prioritize risks as well as support the decision-making process in risk prevention and mitigation.

This paper identified container shipping operational risks (CSORs) from a logistics perspective. A multivariate risk evaluation mechanism by fuzzy rules Bayesian network (FRBN) was established. An improved two-level parameter set based on the failure mode and effects analysis (FMEA) was used to support the input extraction process. By feeding cognitive assessments into the model, the identified risks are evaluated based on their utility values. An illustration example and a sensitivity analysis were carried out to justify and validate the proposed model.

The highest positions in the prioritized list of CSORs in the case study are dominated by risks in the physical flow with the first three are piracy and terrorism, force majeure and port congestion. The results derived from the case study with the satisfaction of all pre-defined axioms proved the feasibility and illustrated the functionality of the proposed risk assessment and prioritization technique.

Controlling risk is irrefutably a significant issue of container shipping and logistics management because of the inconsistency of risk definitions and the involvement of uncertainties. The proposed risk evaluation mechanism and the identified list of CSORs could be beneficial in system management, decision-making and reliability performance.