Failure Modes and Effects Analysis: An Evaluation of Group versus Individual Performance

Failure modes and effects analysis (FMEA) is one of the most frequently used tools in process and product design: it is used in quality and reliability planning, and event and failure mode analysis. It has a long history of use and is a formally prescribed procedure by a number of prominent standards organizations. In addition, it's popular use has evolved as a less formal and widely interpreted tool in the area of Lean/Six Sigma (LSS) process improvement. This paper investigates one of the most important issues related to FMEA practice—the quality of individual vs. group performance in ranking failure modes. In particular, we compare FMEA rankings generated by: (i) individuals, (ii) group consensus, and (iii) non‐collaborative aggregation of group input (a synthesized group ranking). We find that groups outperform individuals and that synthetic groups perform as well as group consensus. We explain the implications of this result on the coordination of the design of products and processes amongst distributed organizations. The increasing distribution of product design efforts, both in terms of geography and different organizations, presents an opportunity to improve coordination using distributed synthetic group‐based FMEA.     

A Common Situation Conducive to Bizarre Distribution Shapes

Conditions conducive to bizarre-shaped distributions are fairly common in certain areas of research where, for perfectly valid reasons, an important causal variable is left uncontrolled. Theoretical rationale and actual examples are given to show how such distributions are generated, to exemplify their shapes, and to indicate their prevalence in practice. An L-shaped type tends to occur when time scores are recorded for a task subject to infrequent but time-consuming errors; other types occur when measuring behavior influenced by social conformity, or under other circumstances. The L shape appears to be far more conducive to nonrobustness than are previously investigated shapes.     

IT Governance Characteristics,Electronic Medical Records Sophistication,and Financial Performance in U.S. Hospitals: An Empirical Investigation

As a result of a recent federal government mandate, an increasing number of hospitals have decided to adopt electronic medical record (EMR) systems. This initiative is expected to lead toward more efficient and higher quality health care; however, little is known about governance characteristics and organizational performance for EMR adopters. Our goal is to inform theory and practice by examining hospitals with a sophisticated EMR and comparing those hospitals to similar hospitals (with a less sophisticated EMR) to understand the association between information technology (IT) governance characteristics and the implications on financial performance. Leveraging elements of the upper echelon theory, we posit that hospitals in which the chief information officer (CIO) reports to the chief executive officer, CIO turnover is low, and an IT steering committee is present are more likely to have a sophisticated EMR. We argue that EMR sophistication leads to improved financial performance. Our results underscore the importance of continuity in the CIO position on successful EMR implementations. Results also show that hospital size and financial performance are strongly associated with EMR sophistication. In addition, we find that a sophisticated EMR appears to be a fundamental element in improving hospitals’ revenue cycle management. Moreover, we find that hospitals with a sophisticated EMR appear to be more profitable. Finally, we observe that total payroll expense adjusted by total discharges drops among the sophisticated hospitals, potentially due to an increase in employee productivity. These insights can serve as a basis for tempering expectations relative to the financial impact of EMR adoption.     

Unpacking Team Familiarity: The Effects of Geographic Location and Hierarchical Role

Examination of team productivity finds that team familiarity, i.e., individuals' prior shared work experience, can positively impact the efficiency and quality of team output. Despite the attention given to team familiarity and its contingencies, prior work has focused on whether team members have worked together, not on which team members have worked together, and under what conditions. In this paper, I parse overall team familiarity to consider effects of geographic location and the hierarchical roles of team members. Using data on all software‐development projects completed over 3 years at a large Indian firm in the global outsourced software services industry, I find that team familiarity gained when team members work together in the same location has a significantly more positive effect on team performance compared with team familiarity gained while members were collaborating in different locations. Additionally, I find that hierarchical team familiarity (a manager's experience with front‐line team members) and horizontal team familiarity (front‐line team members' experience gained with one another) have differential effects on project team performance. These findings provide insight into the relationship between team experience and team performance.     

How parents' income,time and job quality affect children's health and development

The advent of the Global Financial Crisis reminds us that modern epidemiological research has consistently demonstrated links between the socio‐economic circumstances of families and children's health and development. Drawing on data from the Longitudinal Study of Australian Children, this article firstly examines the evidence for intergenerational transmission of socio‐economic disadvantage from parents to young children. It then examines parents' jobs as another source of social inequality. Results confirm that children's healthy development is affected by family income, by parents' hours of work and by the quality of parents' jobs. Job combinations that include long work hours of mothers and fathers and poorer quality jobs are associated with elevated rates of parental mental health problems, less time spent in developmentally important activities with children, and socio‐emotional developmental difficulties for children. The evidence suggests that these effects are greater within low income families. These findings highlight the need for social and economic policies to move beyond simplistic notions of promoting parental workforce participation as a way of reducing the adverse effects of social disadvantage. A more nuanced approach is required that considers the additional impacts of the quality and characteristics of jobs, especially for the parents of young children.     

A developmental, biopsychosocial model for the treatment of children with gender identity disorder

This article provides a summary of the therapeutic model and approach used in the Gender Identity Service at the Centre for Addiction and Mental Health in Toronto. The authors describe their assessment protocol, describe their current multifactorial case formulation model, including a strong emphasis on developmental factors, and provide clinical examples of how the model is used in the treatment.     

Generalized gamma parameter estimation and moment evaluation

The generalized gamma distribution includes the exponential distribution, the gamma distribution, and the Weibull distribution as special cases. It also includes the log-normal distribution in the limit as one of its parameters goes to infinity. Prentice (1974) developed an estimation method that is effective even when the underlying distribution is nearly log-normal. He reparameterized the density function so that it achieved the limiting case in a smooth fashion relative to the new parameters. He also gave formulas for the second partial derivatives of the log-density function to be used in the nearly log-normal case. His formulas included infinite summations, and he did not estimate the error in approximating these summations.

We derive approximations for the log-density function and moments of the generalized gamma distribution that are smooth in the nearly log-normal case and involve only finite summations. Absolute error bounds for these approximations are included. The approximation for the first moment is applied to the problem of estimating the parameters of a generalized gamma distribution under the constraint that the distribution have mean one. This enables the development of a correspondence between the parameters in a mean one generalized gamma distribution and certain parameters in acoustic scattering theory.     

Testing for empty sets when misclassifications may be present

A test for empty sets is described. This new test is suitable for testing for mutual exclusivity, sets being nested, and many other configurations. It might be hypothesized that two medical conditions do not both occur in individuals. This can be expressed as an empty intersection of the two sets of people with each condition. An important feature of the test is the incorporation of misclassification rates into the analysis. The test utilizes the potential misclassifications for false designations in the sets that are hypothesized to be empty. The test is quite powerful. When the null hypothesis is rejected, follow-up tests on one or more sets that are included in the null hypothesis can be performed using the same new test. MatLab code is supplied.     

Exploring Risk Judgments in a Trade Dispute Using Bayesian Networks

Bayesian networks (BNs) are graphical modeling tools that are generally recommended for exploring what‐if scenarios, visualizing systems and problems, and for communication between stakeholders during decision making. In this article, we investigate their potential for exploring different perspectives in trade disputes. To do so, we draw on a specific case study that was arbitrated by the World Trade Organization (WTO): the Australia‐New Zealand apples dispute. The dispute centered on disagreement about judgments contained within Australia's 2006 import risk analysis (IRA). We built a range of BNs of increasing complexity that modeled various approaches to undertaking IRAs, from the basic qualitative and semi‐quantitative risk analyses routinely performed in government agencies, to the more complex quantitative simulation undertaken by Australia in the apples dispute. We found the BNs useful for exploring disagreements under uncertainty because they are probabilistic and transparently represent steps in the analysis. Different scenarios and evidence can easily be entered. Specifically, we explore the sensitivity of the risk output to different judgments (particularly volume of trade). Thus, we explore how BNs could usefully aid WTO dispute settlement. We conclude that BNs are preferable to basic qualitative and semi‐quantitative risk analyses because they offer an accessible interface and are mathematically sound. However, most current BN modeling tools are limited compared with complex simulations, as was used in the 2006 apples IRA. Although complex simulations may be more accurate, they are a black box for stakeholders. BNs have the potential to be a transparent aid to complex decision making, but they are currently computationally limited. Recent technological software developments are promising.