Setting tolerance limits for statistical project control using earned value management

Project control has been a research topic since decades that attracts both academics and practitioners. Project control systems indicate the direction of change in preliminary planning variables compared with actual performance. In case their current project performance deviates from the planned performance, a warning is indicated by the system in order to take corrective actions.Earned value management/earned schedule (EVM/ES) systems have played a central role in project control, and provide straightforward key performance metrics that measure the deviations between planned and actual performance in terms of time and cost. In this paper, a new statistical project control procedure sets tolerance limits to improve the discriminative power between progress situations that are either statistically likely or less likely to occur under the project baseline schedule. In this research, the tolerance limits are derived from subjective estimates for the activity durations of the project. Using the existing and commonly known EVM/ES metrics, the resulting project control charts will have an improved ability to trigger actions when variation in a project׳s progress exceeds certain predefined thresholdsA computational experiment has been set up to test the ability of these statistical project control charts to discriminate between variations that are either acceptable or unacceptable in the duration of the individual activities. The computational experiments compare the use of statistical tolerance limits with traditional earned value management thresholds and validate their power to report warning signals when projects tend to deviate significantly from the baseline schedule.     

Quality and Reliability in Top-event Estimation: Quantitative Fault Tree Analysis in Case of Dependent Events

This article deals with the quantitative Fault Tree Analysis (FTA) and the estimation method of the top-event in case of dependent events. It aims at addressing two main issues: (1) the decomposition of variability for the top-event according to several error components linked to the estimation of the top-event and sources of internal and external variations for a complex system; and (2) the definition of a Performance Measure Independent of Adjustment in order to set the quality of the top-event as a complex measure of the system failure. A simulated study applied to the health system is also carried out.     

A Tiered Framework for Risk‐Relevant Characterization and Ranking of Chemical Exposures: Applications to the National Children's Study (NCS)

A challenge for large‐scale environmental health investigations such as the National Children's Study (NCS), is characterizing exposures to multiple, co‐occurring chemical agents with varying spatiotemporal concentrations and consequences modulated by biochemical, physiological, behavioral, socioeconomic, and environmental factors. Such investigations can benefit from systematic retrieval, analysis, and integration of diverse extant information on both contaminant patterns and exposure‐relevant factors. This requires development, evaluation, and deployment of informatics methods that support flexible access and analysis of multiattribute data across multiple spatiotemporal scales. A new “Tiered Exposure Ranking” (TiER) framework, developed to support various aspects of risk‐relevant exposure characterization, is described here, with examples demonstrating its application to the NCS. TiER utilizes advances in informatics computational methods, extant database content and availability, and integrative environmental/exposure/biological modeling to support both “discovery‐driven” and “hypothesis‐driven” analyses. “Tier 1” applications focus on “exposomic” pattern recognition for extracting information from multidimensional data sets, whereas second and higher tier applications utilize mechanistic models to develop risk‐relevant exposure metrics for populations and individuals. In this article, “tier 1” applications of TiER explore identification of potentially causative associations among risk factors, for prioritizing further studies, by considering publicly available demographic/socioeconomic, behavioral, and environmental data in relation to two health endpoints (preterm birth and low birth weight). A “tier 2” application develops estimates of pollutant mixture inhalation exposure indices for NCS counties, formulated to support risk characterization for these endpoints. Applications of TiER demonstrate the feasibility of developing risk‐relevant exposure characterizations for pollutants using extant environmental and demographic/socioeconomic data.     

Replication: Revisiting Tversky and Shafir’s (1992) Disjunction Effect with an extension comparing between and within subject designs

Does uncertainty about an outcome influence decisions? The sure-thing principle (Savage, 1954) posits that it should not, but Tversky and Shafir (1992) found that people regularly violate it in hypothetical gambling and vacation decisions, a phenomenon they termed “disjunction effect”. Very close replications and extensions of Tversky and Shafir (1992) were conducted in this paper (N = 890, MTurk). The target article demonstrated the effect using two paradigms in a between-subject design: here, an extension also testing a within-subject design, with design being randomly assigned was added. These results were consistent with the original findings for the “paying to know“ problem (original: Cramer’s V = 0.22, 95% (CI) [0.14, 0.32]; replication: Cramer’s V = 0.30, 95% CI [0.24, 0.37]), yet not for the “choice under risk” problem (original: Cramer’s V = 0.26, 95% CI [0.14, 0.39]; replication: Cramer’s V = 0.11, 95% CI [−0.07, 0.20]). The within-subject extension showed very similar results. Implications for the disjunction effect and judgment and decision-making theory are discussed, and a call for improvements on the statistical understanding of comparisons of between-subject and within-subject designs is introduced. All materials, data, and code are available on https://osf.io/gu58m/.     

Prevalence of and risk factors associated with cesarean section in Lebanon — A retrospective study based on a sample of 29,270 women

Background

During the last decades, there has been an alarming and dramatic increase in the number of cesarean births in both developed and undeveloped countries. This increase has not been clinically justified but, nevertheless, has raised an important number of issues.

Trust and the Dilemmas of Suicide Risk Assessment in Non-government Mental Health Services

Reducing suicide rates is a national mental health priority as over 2,200 people die from suicide each year in Australia. Increasingly, nongovernment organisations (NGOs) provide services to people experiencing severe and persistent mental illnesses—a significant cohort at risk of suicide. While clinical services are generally seen as the arbiters of risk, little is known of how suicide risk assessments are undertaken in NGOs. This article reports the findings of a survey-based pilot study of 44 frontline workers in mental health-focused NGOs in Tasmania, Australia, with the aim of sketching a preliminary picture of this under-studied terrain. We identified the assessment practices utilised by workers, and the challenges and dilemmas they experienced in navigating issues of trust in suicide risk assessment in contexts where they often felt vulnerable and under-prepared. We argue that these early findings demonstrate the need for organisations to foster cultures of trust to facilitate both the activities of relationship building between practitioners and clients, and those of monitoring risk.     

信用风险视角下的公共危机及其应对论略——基于社会伦理与经济伦理的分析

在发达的现代性中,财富的社会生产被认为系统地伴随着风险的社会生产。当我们就信用风险及其在工业化时代的扩张,结合现代化的进程尝试进行深入的原因探析时,可以发现对于总体上呈现多样性和复杂性的公共危机,不论是经济危机、社会危机,还是政治性危机等等,经由社会成员间的信用关系的视角来予以把握和考量,显然会非常有益于加深我们对公共危机的理解。     

Building up Resilience in a Pharmaceutical Supply Chain through Inventory,Dual Sourcing and Agility Capacity

This paper is inspired by a risk management problem faced by a leading pharmaceutical company. Key operational risk mitigation measures include Risk Mitigation Inventory (RMI), Dual Sourcing and Agility Capacity. We study the relationship between these three measures by modeling the drug manufacturing firm that is exposed to supply chain disruption risk. The firm determines optimal RMI levels for assumed Dual Sourcing and Agility Capacity. We quantify the decrease in RMI levels in the presence of Dual Sourcing and Agility Capacity. Furthermore, using an example, we analyze RMI, Dual Sourcing and Agility Capacity decisions jointly. It turns out that RMI and Agility Capacity can be substitutes as long as no Dual Source is available. Once the Dual Source is available, Agility Capacity and Dual Sourcing appear to be substitutes. We further show that for long disruption times, the optimal Dual Source production rate may decrease in the disruption time. Within our modeling framework, we introduce an operational metric that quantifies Supply Chain Resilience. Supply chain disruptions can have a severe business impact and need to be managed appropriately.