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Failure modes and effect analysis (FMEA) is a proactive, highly structured and systematic approach for failure analysis. It has been also applied as a risk assessment tool, by ranking potential risks based on the estimation of risk priority numbers (RPNs). This article develops an improved FMEA methodology for strategic risk analysis. The proposed approach combines the analytic hierarchy process (AHP) technique with the exponential and weighted geometric mean method (EWGM) to support risk analysis. AHP is applied to estimate the weights of three risk factors: Severity (S), Occurrence (O) and Detection (D), which integrate the RPN for each risk. The EWGM method is applied for ranking RPNs. Combining AHP with EWGM allows avoiding repetition of FMEA results. The results of the developed methodology reveal that duplication of RPNs has been decreased, facilitating an effective risk ranking by offering a unique value for each risk. The proposed methodology not only focuses on high severity values for risk ranking but it also considers other risk factors (O and D), resulting in an enhanced risk assessment process. Furthermore, the weights of the three risk factors are considered. In this way, the developed methodology offers unique value for each risk in a simple way which makes the risk assessment results more accurate. This methodology provides a practical and systematic approach to support decision makers in assessing and ranking risks that could affect long-term strategy implementation. The methodology was validated through the case study of a power plant in the Middle East, assessing 84 risks within 9 risk categories. The case study revealed that top management should pay more attention to key risks associated with electricity price, gas emissions, lost-time injuries, bad odour and production.

Every year, approximately 62,000 people with stroke and transient ischemic attack are treated in Canadian hospitals. The 2016 update of the Canadian Stroke Best Practice Recommendations Telestroke guideline is a comprehensive summary of current evidence-based and consensus-based recommendations appropriate for use by all healthcare providers and system planners who organize and provide care to patients following stroke across a broad range of settings. These recommendations focus on the use of telemedicine technologies to rapidly identify and treat appropriate patients with acute thrombolytic therapies in hospitals without stroke specialized expertise; select patients who require to immediate transfer to stroke centers for Endovascular Therapy; and for the patients who remain in community hospitals to facilitate their care on a stroke unit and provide remote access to stroke prevention and rehabilitation services. While these latter areas of Telestroke application are newer, they are rapidly developing, with new opportunities that are yet unrealized. Virtual rehabilitation therapies offer patients the opportunity to participate in rehabilitation therapies, supervised by physical and occupational therapists. While not without its limitations (e.g., access to telecommunications in remote areas, fragmentation of care), the evidence-to-date sets the foundation for improving access to care and management for patients during both the acute phase and now through post stroke recovery.

The importance of maintenance has escalated significantly by the increase in automation in manufacturing processes. This condition changed the perspective of maintenance from being considered as an inevitable cost to being seen as a key business function to drive competitiveness. Consequently, maintenance decisions need to be aligned with the business competitive strategy as well as the requirements of manufacturing/quality functions in order to support manufacturing equipment performance. Therefore, it is required to synchronise the maintenance strategy and operations with business and manufacturing/quality aspects. This article presents the design and development of a Knowledge Based System for Integrated Maintenance Strategy and Operations. The developed framework of the Knowledge Based System for Integrated Maintenance Strategy and Operations is elaborated to show how the Knowledge Based System for Integrated Maintenance Strategy and Operations can be applied to support maintenance decisions. The knowledge-based system integrates the Gauging Absences of Prerequisites methodology in order to deal with different decision-making priorities and to facilitate benchmarking with a target performance state. This is a new contribution to this area. The Knowledge Based System for Integrated Maintenance Strategy and Operations is useful in reviewing the existing maintenance system and provides reasonable recommendations for maintenance decisions with respect to business and manufacturing perspectives. In addition, it indicates the roadmap from the current state to the benchmark goals for the maintenance system.

The type of plant layout has very significant impact on the structure and operation of a manufacturing system. The current common and intuitive methods of selecting a suitable type of layout by using simple graphs of production volume versus number of parts manufactured in the plant, or production volume versus flexibility, can result in inaccurate decisions for such an important issue. This paper presents an alternative approach to the choice of plant layouts, which applies the Analytic Hierarchy Process (AHP). The methodology considers three main objectives for the selection of layouts: increasing flexibility, increasing production volume and reducing manufacturing costs. Each objective has a number of subobjectives. Three main types of layout are considered: functional (process) layout, group technology (cellular) layout and transfer (flow) lines. The AHP methodology has been applied as a knowledge-based system, which is integrated with another three knowledge-based systems for the technical and social conditions required for the successful implementation of each of the above types of layout. An example of the application of the AHP methodology is presented, which represents an important step towards proper validation of the methodology.