New model diagnostics for spatio-temporal systems in epidemiology and ecology

A cardinal challenge in epidemiological and ecological modelling is to develop effective and easily deployed tools for model assessment. The availability of such methods would greatly improve understanding, prediction and management of disease and ecosystems. Conventional Bayesian model assessment tools such as Bayes factors and the deviance information criterion (DIC) are natural candidates but suffer from important limitations because of their sensitivity and complexity. Posterior predictive checks, which use summary statistics of the observed process simulated from competing models, can provide a measure of model fit but appropriate statistics can be difficult to identify. Here, we develop a novel approach for diagnosing mis-specifications of a general spatio-temporal transmission model by embedding classical ideas within a Bayesian analysis. Specifically, by proposing suitably designed non-centred parametrization schemes, we construct latent residuals whose sampling properties are known given the model specification and which can be used to measure overall fit and to elicit evidence of the nature of mis-specifications of spatial and temporal processes included in the model. This model assessment approach can readily be implemented as an addendum to standard estimation algorithms for sampling from the posterior distributions, for example Markov chain Monte Carlo. The proposed methodology is first tested using simulated data and subsequently applied to data describing the spread of Heracleum mantegazzianum (giant hogweed) across Great Britain over a 30-year period. The proposed methods are compared with alternative techniques including posterior predictive checking and the DIC. Results show that the proposed diagnostic tools are effective in assessing competing stochastic spatio-temporal transmission models and may offer improvements in power to detect model mis-specifications. Moreover, the latent-residual framework introduced here extends readily to a broad range of ecological and epidemiological models.     

Development of a dynamical systems model of plant programmatic performance on nuclear power plant safety risk

Application of probabilistic risk assessment (PRA) techniques to model nuclear power plant accident sequences has provided a significant contribution to understanding the potential initiating events, equipment failures and operator errors that can lead to core damage accidents. Application of the lessons learned from these analyses has resulted in significant improvements in plant operation and safety. However, this approach has not been nearly as successful in addressing the impact of plant processes and management effectiveness on the risks of plant operation. The research described in this paper presents an alternative approach to addressing this issue. In this paper we propose a dynamical systems model that describes the interaction of important plant processes on nuclear safety risk. We discuss development of the mathematical model including the identification and interpretation of significant inter-process interactions. Next, we review the techniques applicable to analysis of nonlinear dynamical systems that are utilized in the characterization of the model. This is followed by a preliminary analysis of the model that demonstrates that its dynamical evolution displays features that have been observed at commercially operating plants. From this analysis, several significant insights are presented with respect to the effective control of nuclear safety risk. As an important example, analysis of the model dynamics indicates that significant benefits in effectively managing risk are obtained by integrating the plant operation and work management processes such that decisions are made utilizing a multidisciplinary and collaborative approach. We note that although the model was developed specifically to be applicable to nuclear power plants, many of the insights and conclusions obtained are likely applicable to other process industries.     

Process-oriented risk assessment methodology for manufacturing process evaluation

A process-oriented quantitative risk assessment methodology is proposed to evaluate risk associated with processes using modelling, simulation and decision-making approaches. For this purpose, risks involved in a process and the corresponding risk factors are identified through an objective-oriented risk identification approach. The identified risks are first analysed qualitatively in the failure mode effect and critical analysis process and then evaluated quantitatively in a simulation environment employing a process-based risk measurement model. To ease the decision-making process in case of multiple but heterogeneous risk measures, a global risk indicator is developed using the normalisation and aggregation techniques of the decision theory. Using the proposed methodology as a decision-making tool, alternative manufacturing scenarios (i.e. manufacturing process plans) are developed and ranked on the basis of desirability. Although the methodology is illustrated with a case study issued from the part manufacturing, it is also applicable to a wide range of other processes.     

Uncertainty quantification in dynamic system risk assessment: a new approach with randomness and fuzzy theory

Quantifying uncertainty during risk analysis has become an important part of effective decision-making and health risk assessment. However, most risk assessment studies struggle with uncertainty analysis and yet uncertainty with respect to model parameter values is of primary importance. Capturing uncertainty in risk assessment is vital in order to perform a sound risk analysis. In this paper, an approach to uncertainty analysis based on the fuzzy set theory and the Monte Carlo simulation is proposed. The question then arises as to how these two modes of representation of uncertainty can be combined for the purpose of estimating risk. The proposed method is applied to a propylene oxide polymerisation reactor. It takes into account both stochastic and epistemic uncertainties in the risk calculation. This study explores areas where random and fuzzy logic models may be applied to improve risk assessment in industrial plants with a dynamic system (change over time). It discusses the methodology and the process involved when using random and fuzzy logic systems for risk management.     

Determining the Critical Factors of Radical Innovation Using an Integrated Model of Fuzzy Analytic Hierarchy Process-Fuzzy Kano With a Case Study in Mobarakeh Steel Company

Understanding which radical innovation factor is of higher priority enables engineering managers to allocate the appropriate resources to innovation processes. The aim of this article is to develop a systematic approach for determining the most necessary factors affecting radical innovation. For this purpose, a model was proposed with the following three steps: (i) a comprehensive set of factors was extracted from the literature and customized for a case study company; (ii) quality attributes of the Kano model were applied and weighted as attributes for a fuzzy analytic hierarchy process; and (iii) the factors derived from the second step were weighted using a fuzzy Kano approach and then the critical or must-be factors were identified and weighted. All three steps have been examined at Mobarakeh Steel Company. Findings indicate that factors such as leadership and knowledge management are among the critical factors of radical innovation and must be improved to positively impact radical innovation. Prioritizing radical innovation factors enables engineering managers to allocate the appropriate resources to innovation processes.     

Multistage risk assessment of direct delivery business from local oil refineries in Sinopec Group based on normal cloud model

This paper studies the risk assessment problem of the direct delivery business from local oil refineries in Sinopec Group. A total of 23 risk factors associated with four segments of the direct delivery business are first identified. Through explaining the respective characteristics, the connotation of each risk factor is analysed in depth. Next, on the basis of the severity and possibility of each risk factor, a multistage risk assessment method for normal cloud model rooted in the extended TOPSIS approach is developed, and then applied to a real-world case. From the investigation, the weaknesses of the present risk assessment process are addressed from various aspects, including the risk factors, segments, and alternatives. Moreover, considering the possible correlation among risk factors, the proposed method is further extended by using the approach of Choquet integral. Additional discussions and recommendations are provided for improving the risk management process of the direct delivery business from local oil refineries.     

Consensus building in linguistic failure mode and effect analysis: A perspective based on prospect theory

As one of many scientific and efficient risk assessment approaches, failure mode and effect analysis (FMEA) has been widely applied across various fields. There are two core issues in the FMEA approach: identifying the latent failure modes of the systems, products, processes and services and the risk assessment and the prioritization of those failure modes. Then, corrective measures must be taken in a timely and accessible manner to prevent the occurrence of failure modes with higher risk levels. In practice, several FMEA members from different fields are usually involved in the FMEA implementation process; the risk assessment information given by them may vary greatly. Therefore, it is necessary to integrate a consensus-building process into FMEA. Meanwhile, the psychological behaviours of FMEA members have had a great impact on the final prioritization of failure modes. Prospect theory is an effective approach for describing individual psychological behaviours. Therefore, this paper presents a novel linguistic FMEA approach to address the consensus issue from the perspective of prospect theory. In the proposed linguistic FMEA approach, a consensus measurement approach based on prospect theory is constructed. Then, a novel feedback adjustment mechanism is designed in which FMEA members can adjust not only their assessment information but also their reference points to achieve an acceptable consensus degree. Eventually, a practical application is used to show the validity and applicability of the proposed linguistic FMEA approach.     

A distinct decision model for the evaluation and selection of a supplier for a chemical processing industry

The supplier selection process has gained importance recently due to the considerable amount of revenue spent on purchasing. The intention of this work is to develop an appropriate hybrid model by integrating the analytical hierarchy process (AHP) and grey relational analysis (GRA) for supplier evaluation and selection, which comprises three stages. In Stage I, the most influential criteria are selected by mutual-information-based feature selection. Stage II focuses on the determination of the weights of the attributes using AHP, while Stage III is used for the determination of the best supplier using GRA. The proposed model is illustrated using the case study of an electroplating industry to highlight the effectiveness and flexibility of the model. The model effectively combines specialised knowledge, experience and quantitative data to select the best suppliers. This paper presents the model development, solution and application processes of the proposed hybrid model for supplier selection. The decision support software was implemented in Excel to automate supplier selection. The proposed hybrid model is applied to enhance the decision-making process in supplier selection and also helps decision makers to effectively select suppliers.     

Systematic approach for the gap analysis of business processes

Process benchmarking and business process re-engineering (BPR) have become two main methodologies used to implement business process improvement and have attracted much attention in a current fast-changing environment. Leveraging best practice has become the important factor of process benchmarking and BPR. Accurate knowledge of the gaps between best practice processes and a company's business processes is essential for the redesign of business processes. This paper proposes a systematic enterprise model comparison approach to assist a project team in business process gap analysis (BPGA). A three-phased framework including business process modelling, object semantic analysis phase and process gap analysis phase was developed. An approach of applying the concepts of semantic similarity analysis to find the semantic-related objects in different processes was also proposed. In addition, a set of process path patterns and an alternative process path analysis method was developed to facilitate the processed data and process logic gap analysis between two processes. With the proposed BPGA approach, project teams will understand and redesign their processes more effectively.     

The role of risk perception for risk management

Are risks social constructions of different societal actors that can be checked at best against standards of consistency, cohesion and internal conventions of deduction, but cannot claim any validity outside of the actor's logical framework? Or are technical estimates of risk representations of real hazards that can and will affect people as predicted by the statistical values, regardless of the beliefs or convictions of those who conduct the assessments? Which of the two sides one takes determines the legitimate function of risk perception for management purposes. The paper argues that both extremes, the constructivist and the realist perspective, miss the point, as risks are always mental representations of threats that are capable of claiming real losses. Over the last two decades, risk analysts have dealt with both sides of risk in an additive fashion. In times in which risk management has been under serious pressure to demonstrate effectiveness and cost-efficiency, the parallel approach of pleasing the technical elite and the public alike has lost legitimacy. In order to integrate risk assessment and perception, the paper analyses the strengths and weaknesses of each approach to risk analysis and highlights the potential contributions that the technical sciences and the social sciences can offer to risk management. Technical assessments provide the best estimate for judging the average probability of an adverse effect linked to an object or activity. First, public perception should govern the selection of criteria on which acceptability or tolerability are to be judged. Second, public input is needed to determine the trade-offs between criteria. Third, public preferences are needed to design resilient strategies for coping with remaining uncertainties. A public participation model is introduced that promises an integration of analytic knowledge and deliberative process involving those who will be affected by the respective risk.     

Importance sampling for reliability assessment of dynamic systems under general random process excitation

This paper develops a reliability assessment method for dynamic systems subjected to a general random process excitation. Safety assessment using direct Monte Carlo simulation is computationally expensive, particularly when estimating low probabilities of failure. The Girsanov transformation-based reliability assessment method is a computationally efficient approach intended for dynamic systems driven by Gaussian white noise, and this approach can be extended to random process inputs that can be represented as transformations of Gaussian white noise. In practice, dynamic systems may be subjected to inputs that may be better modeled as non-Gaussian and/or non-stationary random processes, which are not easily transformable to Gaussian white noise. We propose a computationally efficient scheme, based on importance sampling, which can be implemented directly on a general class of random processes — both Gaussian and non-Gaussian, and stationary and non-stationary. We demonstrate that this approach is in fact equivalent to Girsanov transformation when the uncertain inputs are Gaussian white noise processes. The proposed approach is demonstrated on a linear dynamic system driven by Gaussian white noise and Brownian bridge processes, a multi-physics aero-thermo-elastic model of a flexible panel subjected to hypersonic flow, and a nonlinear building frame subjected to non-stationary non-Gaussian random process excitation.     

Development of a hybrid fresh food supply chain risk assessment model

Supply chain managers and scholars recognise the importance of managing supply chain risk, especially in fresh food supply chain due to the perishable nature and short life cycle of products. Supply chain risk management consists of supply chain risk assessment, risk evaluation and formulation and implementation of effective risk response strategies. The commonly adopted qualitative methods such as risk assessment matrix to determine the level of risk have limitations. This paper proposes a hybrid model comprising both fuzzy logic (FL) and hierarchical holographic modelling (HHM) techniques where risk is first identified by the HHM method and then assessed using both qualitative risk assessment model (named risk filtering, ranking and management Framework) and fuzzy-based risk assessment method (named FL approach). The risk assessment results by the two different approaches are compared, and the overall risk level of each risk is calculated using the Root Mean Square calculation before identifying response strategies. This novel approach takes advantage of the benefits of both techniques and offsets their drawbacks in certain aspects. A case study in a fresh food supply chain company has been conducted in order to validate the proposed integrated approach on the feasibility of its functionality in a real environment.     

A fuzzy-logic-based approach to qualitative safety modelling for marine systems

Safety assessment based on conventional tools (e.g. probability risk assessment (PRA)) may not be well suited for dealing with systems having a high level of uncertainty, particularly in the feasibility and concept design stages of a maritime or offshore system. By contrast, a safety model using fuzzy logic approach employing fuzzy IF–THEN rules can model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analyses. A fuzzy-logic-based approach may be more appropriately used to carry out risk analysis in the initial design stages. This provides a tool for working directly with the linguistic terms commonly used in carrying out safety assessment. This research focuses on the development and representation of linguistic variables to model risk levels subjectively. These variables are then quantified using fuzzy sets. In this paper, the development of a safety model using fuzzy logic approach for modelling various design variables for maritime and offshore safety based decision making in the concept design stage is presented. An example is used to illustrate the proposed approach.     

New Failure Mode and Effects Analysis: An Evidential Downscaling Method

Failure mode and effects analysis (FMEA) is an engineering and management technique, which is widely used to define, identify, and eliminate known or potential failures, problems, errors, and risk from the design, process, service, and so on. In a typical FMEA, the risk evaluation is determined by using the risk priority number (RPN), which is obtained by multiplying the scores of the occurrence, severity, and detection. However, because of the uncertainty in FMEA, the traditional RPN has been criticized because of several shortcomings. In this paper, an evidential downscaling method for risk evaluation in FMEA is proposed. In FMEA model, we utilize evidential reasoning approach to express the assessment from different experts. Multi‐expert assessments are transformed to a crisp value with weighted average method. Then, Euclidean distance from multi‐scale is applied to construct the basic belief assignments in Dempster–Shafer evidence theory application. According to the proposed method, the number of ratings is decreased from 10 to 3, and the frame of discernment is decreased from 2¹⁰ to 2³, which greatly decreases the computational complexity. Dempster's combination rule is utilized to aggregate the assessment of risk factors. We illustrate a numerical example and use the proposed method to deal with the risk priority evaluation in FMEA. The results and comparison show that the proposed method is more flexible and reasonable for real applications. Copyright © 2014 John Wiley & Sons, Ltd.     

The development of a decision support framework for a quantitative risk assessment in multimodal green logistics: an empirical study

The objective of this research was to develop a decision support framework (DSF) to assess quantitative risk in multimodal green logistics. This risk assessment is the combination of a number of models, the failure mode and effects analysis, the risk contour plot, the quantitative risk assessment, the analytic hierarchy process and the data envelopment analysis which can support a user to perform risk assessment in various decisions. The contribution of this research is that the risk assessment model can generate an optimal green logistics route in accordance with weight from the user. The highlight of this DSF is that the quantitative assessment model can reduce bias on risk assessment of logistics route. An in-depth case study, recommendations, limitations and further research are also provided.     

Clinical performance improvement series. Classic CQI integrated with comprehensive disease management as a model for performance improvement.

BACKGROUND: In recent years, health and disease management has emerged as an effective means of delivering, integrating, and improving care through a population-based approach. Since 1997 the University of Pennsylvania Health System (UPHS) has utilized the key principles and components of continuous quality improvement (CQI) and disease management to form a model for health care improvement that focuses on designing best practices, using best practices to influence clinical decision making, changing processes and systems to deploy and deliver best practices, and measuring outcomes to improve the process. Experience with 28 programs and more than 14,000 patients indicates significant improvement in outcomes, including high physician satisfaction, increased patient satisfaction, reduced costs, and improved clinical process and outcome measures across multiple diseases. DIABETES DISEASE MANAGEMENT: In three months a UPHS multidisciplinary diabetes disease management team developed a best practice approach for the treatment of all patients with diabetes in the UPHS. After the program was pilot tested in three primary care physician sites, it was then introduced progressively to additional practice sites throughout the health system. The establishment of the role of the diabetes nurse care managers (certified diabetes educators) was central to successful program deployment. Office-based coordinators ensure incorporation of the best practice protocols into routine flow processes. A disease management intranet disseminates programs electronically. Outcomes of the UPHS health and disease management programs so far demonstrate success across multiple dimensions of performance-service, clinical quality, access, and value. DISCUSSION: The task of health care leadership today is to remove barriers and enable effective implementation of key strategies, such as health and disease management. Substantial effort and resources must be dedicated to gain physician buy-in and achieve compliance. The challenge is to provide leadership support, to reward and recognize best practice performers, and to emphasize the use of data for feedback and improvement. As these processes are implemented successfully, and evidence of improved outcomes is documented, it is likely that this approach will be more widely embraced and that organizationwide performance improvement will increase significantly. CONCLUSIONS: Health care has traditionally invested extraordinary resources in developing best practice approaches, including guidelines, education programs, or other tangible products and services. Comparatively little time, effort, and resources have been targeted to implementation and use, the stage at which most efforts fail. CQI's emphasis on data, rapid diffusion of innovative programs, and rapid cycle improvements enhance the implementation and effectiveness of disease management.     

MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

Federated learning has been used extensively in business innovation scenarios in various industries. This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario. First, this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises (MSEs) using multi-dimensional enterprise data and multi-perspective enterprise information. The proposed model includes four main processes: namely encrypted entity alignment, hybrid feature selection, secure multi-party computation, and global model updating. Secondly, a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data, which can provide excellent accuracy and interpretability. In addition, a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model. The results of the study show that the model error rate is reduced by 6.22% and the recall rate is improved by 11.03% compared to the algorithms commonly used in credit risk research, significantly improving the ability to identify defaulters. Finally, the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.     

Quantitative model for evaluating the quality of an automotive business process

One of the key issues to business process control is the identification of measurable process attributes. For manufacturing processes these are typically physical parameters of the process (e.g. temperature, set points) or physical attributes of the manufactured product (e.g. dimension, functional performance). However, for business processes the metrics are more abstract. The challenge has been to develop metrics that capture the contributing subtle and hard to measure factors for business process control. This paper presents an analytical model that uses the weights-of-evidence concept to convert answers to audit or self-assessment questions into a single numerical process quality index. This index is used to forecast process success or failure and monitor its performance from start to end. The application of the approach is illustrated with an automotive industry product development sub-process where the process performance metric is the field warranty data, i.e. incidents per thousand vehicles (IPTV). The analytical model converts process self-assessment (failure mode and effect analysis) questions into a single numeric process quality index. The validity of the model is reflected in the strength of the correlation between the index and the IPTV results. Also, in this paper a measure is developed for identifying critical process quality assessment questions. This measure quantifies the deviation in the automotive business process that should have more focus. The significance of the analytical model proposed in this research is that the project managers or quality assurance auditors may be able to use the metric to predict product quality at any point in the product development process.     

Acceptance control and guardbanding for error‐prone individual measurements

The concept of fractional nonconformance was recently proposed to assess the probability of conformance when measurements are error‐prone. Applications of fractional nonconformance assessment include acceptance sampling inspection and short‐run process control. In this study, we introduce a fractional nonconformance‐based one‐sided acceptance control chart to monitor a short‐run process as well as decide the acceptability of products manufactured from the process. The guardband technique is also incorporated in the proposed approach to reduce the impact of measurement errors. Guardband selection is investigated for both independent and autocorrelated processes. Our analysis shows that guardbanding is beneficial for short‐run production environments. The optimum guardbands obtained under risk and cost models are also found to be consistent.