Alexander Chakhunashvili

Variation Mode and Effect Analysis: a Practical Tool for Quality Improvement

This paper describes a statistically based engineering method, variation mode and effect analysis (VMEA), that facilitates an understanding of variation and highlights the product/process areas in which improvement efforts should be targeted. An industrial application is also described to illustrate how the VMEA can be used for quality improvement purposes. Copyright

Variation mode and effect analysis

In this paper, we introduce an engineering method, variation mode and effect analysis (VMEA) developed to systematically look for noise factors affecting key product characteristics (KPCs) early in product development. Conducted on a systematic basis, the goal of VMEA is to identify and prioritize noise factors that significantly contribute to the variability of KPCs and might yield unwanted consequences with respect to safety, compliance with governmental regulations, and functional requirements. As a result of the analysis, a variation risk priority number (VRPN) is calculated which directs the attention to areas where reasonably anticipated variation might be detrimental.

An EWMA Solution to Detect Shifts in a Bernoulli Process in an Out-of-Control Environment

The Exponentially Weighted Moving Average (EWMA) control chart has mainly been used to monitor continuous data, usually under the normality assumption. In addition, a number of EWMA control charts have been proposed for Poisson data. Here, however, we suggest applying the EWMA to hypergeometric data originating from a multivariate Bernoulli process. The problem studied in this paper concerns the wear-out of electronics testers resulting in unnecessary and costly reparations of electronic units. Assuming that the testing process is in statistical control, although the quality of the tested units is not, we can detect the wear-out of a tester by finding assignable causes of variation in that tester. This reasoning forms the basis of a new EWMA procedure designed to detect shifts in a Bernoulli process in an out-of-control environment. Copyright

Community collaboration to increase foreign-born women's participation in a cervical cancer screening program in Sweden: a quality improvement project

IntroductionThe prevailing inequities in healthcare have been well addressed in previous research, especially screening program participation, but less attention has been paid to how to overcome these inequities. This paper explores a key factor of a successful improvement project: collaboration with local doulas to raise cervical cancer screening participation by more than 40 percent in an area with a large number of foreign-born residents.MethodsData was collected through two focus group discussions with the doulas in order to design interventions and debrief after interventions had been carried out in the community. Various tools were used to analyze the verbal data and monitor the progress of the project.ResultsThree major themes emerged from the focus group discussions: barriers that prevent women from participating in the cervical cancer screening program, interventions to increase participation, and the role of the doulas in the interventions.ConclusionsThis paper suggests that several barriers make participation in cervical cancer screening program more difficult for foreign-born women in Sweden. Specifically, these barriers include lack of knowledge concerning cancer and the importance of preventive healthcare services and practical obstacles such as unavailable child care and language skills. The overarching approach to surmount these barriers was to engage persons with a shared cultural background and mother tongue as the target audience to verbally communicate information. The doulas who helped to identify barriers and plan and execute interventions gained increased confidence and a sense of pride in assisting to bridge the gap between healthcare providers and users.