Creating knowledge environment during lean product development process of jet engine

Organizations invest intense resources in their product development processes. This paper aims to create a knowledge environment using trade-off curves during the early stages of the set-based concurrent engineering (SBCE) process of an aircraft jet engine for a reduced noise level at takeoff. Data is collected from a range of products in the same family as the jet engine. Knowledge-based trade-off curves are used as a methodology to create and visualize knowledge from the collected data. Findings showed that this method provides designers with enough confidence to identify a set of design solutions during the SBCE application

A Review of Risk Matrices Used in Acute Hospitals in England.

In healthcare, patient safety has received substantial attention and, in turn, a number of approaches to managing safety have been adopted from other high-risk industries. One of these has been risk assessment, predominantly through the use of risk matrices. However, while other industries have criticized the design and use of these risk matrices, the applicability of such criticism has not been investigated formally in healthcare. This study examines risk matrices as used in acute hospitals in England and the guidance provided for their use. It investigates the applicability of criticisms of risk matrices from outside healthcare through a document analysis of the risk assessment policies, procedures, and strategies used in English hospitals. The findings reveal that there is a large variety of risk matrices used, where the design of some might increase the chance of risk misprioritization. Additionally, findings show that hospitals may provide insufficient guidance on how to use risk matrices as well as what to do in response to the existing criticisms of risk matrices. Consequently, this is likely to lead to variation in the quality of risk assessment and in the subsequent deployment of resources to manage the assessed risk. Finally, the article outlines ways in which hospitals could use risk matrices more effectively

Prioritizing Multidimensional Interdependent Factors Influencing COVID‐19 Risk

COVID-19 has significantly affected various industries and domains worldwide. Since such pandemics are considered as rare events, risks associated with pandemics are generally managed through reactive approaches, which involve seeking more information about the severity of the pandemic over time and adopting suitable strategies accordingly. However, policy-makers at a national level must devise proactive strategies to minimize the harmful impacts of such pandemics. In this article, we use a country-level data-set related to humanitarian crises and disasters to explore critical factors influencing COVID-19 related hazard and exposure, vulnerability, lack of coping capacity, and the overall risk for individual countries. The main contribution is to establish the relative importance of multidimensional factors associated with COVID-19 risk in a probabilistic network setting. This study provides unique insights to policy-makers regarding the identification of critical factors influencing COVID-19 risk and their relative importance in a network setting

Use of the Generating Options for Active Risk Control (GO-ARC) Technique can lead to more robust risk control options

BACKGROUND: Risk assessment is widely used to improve patient safety, but healthcare workers are not trained to design robust solutions to the risks they uncover. This leads to an overreliance on the weakest category of risk control recommendations: administrative controls. Increasing the proportion of non-Administrative risk control options (NARCOs) generated would enable (though not ensure) the adoption of more robust solutions. OBJECTIVES: Experimentally assess a method for generating stronger risk controls: The Generating Options for Active Risk Control (GO-ARC) Technique. METHODS: Participants generated risk control options in response to two patient safety scenarios. Scenario 1 (baseline): All participants used current practice (unstructured brainstorming). Scenario 2: Control group used current practice; intervention group used the GO-ARC Technique. To control for individual differences between participants, analysis focused on the change in the proportion of NARCOs for each group. RESULTS: Control group: Proportion of NARCOs decreased from 0.18 at baseline to 0.12. Intervention group: Proportion increased from 0.10 at baseline to 0.29 using the GO-ARC Technique. Results were statistically significant. There was no decrease in the number of administrative controls generated by the intervention group. CONCLUSION: The Generating Options for Active Risk Control (GO-ARC) Technique appears to lead to more robust risk control options

Bayesian network and structural equation modeling of dependencies between country-level sustainability risks and logistics performance

The 17 Sustainable Development Goals (SDGs), introduced by the United Nations in 2015, provide a framework for individual countries to track their performance on social, economic, and environmental dimensions of sustainable development. Logistics is considered the backbone of an economy that can influence the development and growth of a country. Using a dataset of 157 countries, this study aims to explore dependencies between the 17 SDG-related risks and logistics performance in a probabilistic network setting. A hybrid methodology, combining Bayesian Belief Networks and Structural Equation Modeling, is used to develop and quantify a network structure. Logistics performance is significantly influenced by four SDGs, namely ‘industry, innovation and infrastructure’, ‘climate action’, ‘partnerships for the goals’, and ‘gender equality’. On the other hand, another cluster of SDGs is observed surrounding the ‘no poverty’ goal, including ‘industry, innovation and infrastructure’, ‘decent work and economic growth’, ‘reduced inequality’, ‘zero hunger’, ‘sustainable cities and communities’, ‘affordable and clean energy’, and ‘clean water and sanitation’. This paper proposes and operationalizes a new hybrid methodology to explore dependencies between the SDGs and logistics performance, thereby providing valuable insights to policymakers and researchers about the association between sustainable development and logistics performance.</p

Design for patient safety: a systems-based risk identification framework

Current risk identification practices applied to patient safety in healthcare are insufficient. The situation can be improved, however, by studying systems approaches broadly and successfully utilised in other safety-critical industries, such as aviation and chemical industries. To illustrate this, this paper first investigates current risk identification practices in the healthcare field, and then examines the potential of systems approaches. A systems-based approach, called the Risk Identification Framework (RID Framework), is then developed to enhance improvement in risk identification. Demonstrating the strengths of using multiple inputs and methods, the RID Framework helps to facilitate the proactive identification of new risks. In this study, the potential value of the RID Framework is discussed by examining its application and evaluation, as conducted in a real-world healthcare setting. Both the application and evaluation of the RID Framework indicate positive results, as well as the need for further research.This research was partly funded by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) East of England, at Cambridgeshire and Peterborough National Health Service Foundation Trust