16,095 research outputs found

    An Exploratory Study of Patient Falls

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    Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

    Assessing the Efficiency of Mass Transit Systems in the United States

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    Frustrated with increased parking problems, unstable gasoline prices, and stifling traffic congestion, a growing number of metropolitan city dwellers consider utilizing the mass transit system. Reflecting this sentiment, a ridership of the mass transit system across the United States has been on the rise for the past several years. A growing demand for the mass transit system, however, necessitates the expansion of service offerings, the improvement of basic infrastructure/routes, and the additional employment of mass transit workers, including drivers and maintenance crews. Such a need requires the optimal allocation of financial and human resources to the mass transit system in times of shrinking budgets and government downsizing. Thus, the public transit authority is faced with the dilemma of “doing more with less.” That is to say, the public transit authority needs to develop a “lean” strategy which can maximize transit services with the minimum expenses. To help the public transit authority develop such a lean strategy, this report identifies the best-in-class practices in the U.S. transit service sector and proposes transit policy guidelines that can best exploit lean principles built upon best-in-class practices

    Case study in six sigma methadology : manufacturing quality improvement and guidence for managers

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    This article discusses the successful implementation of Six Sigma methodology in a high precision and critical process in the manufacture of automotive products. The Six Sigma define–measure–analyse–improve–control approach resulted in a reduction of tolerance-related problems and improved the first pass yield from 85% to 99.4%. Data were collected on all possible causes and regression analysis, hypothesis testing, Taguchi methods, classification and regression tree, etc. were used to analyse the data and draw conclusions. Implementation of Six Sigma methodology had a significant financial impact on the profitability of the company. An approximate saving of US$70,000 per annum was reported, which is in addition to the customer-facing benefits of improved quality on returns and sales. The project also had the benefit of allowing the company to learn useful messages that will guide future Six Sigma activities

    REGRESSION ANALYSIS OF FACTORS IMPACTING PROBLEM SOLVING ENGAGEMENT WITHIN LEAN SYSTEMS IMPLEMENTATION

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    Organizations around the world have attempted to implement the concepts of the Toyota Production System (TPS), commonly referred to as Lean, with limited sustainable success. The central principles of TPS, continuous improvement and respect for people, are grounded in the Japanese values of Monozukuri and Hitozukuri. Monozukuri deals with creating or making a product, while Hitozukuri conveys the idea of developing people through learning. In order for organizations to adopt these values they must have a system that engages employees at all levels in applying problem solving to improve their work. This research uses organizational assessments obtained from a variety of organizations implementing the lean approach using the Monozukuri and Hitozukuri values, referred to as the True Lean System (TLS). This research uses an inductive research approach to identify and analyze factors that impact the use of problem solving within organizations implementing a TLS. First, the qualitative assessment data is studied using textual analysis to identify themes impacting TLS. This analysis identified three topics as the highest weighted themes: number of problem solving methods, standardization, and employee roles. This qualitative data is then transformed using an integrated design model to systematically code the information into quantitative numerical data. Finally, this data was analyzed statistically by logistic regression to identify the factors impacting the use of problem solving within these organizations. The results from the logistic regression suggest that the most successful problem solving organizations have established standards for work and training employees; as well as, a single problem solving method that all employees use when identifying and implementing continuous improvement ideas. Which leads to the conclusion, in order for an organization to sustain the concepts of TPS, there must be a focus on defining clear standardized work, training, and the implementation of a single problem solving method

    Integration of Industry 4.0 technologies into Lean Six Sigma DMAIC: a systematic review

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    This review examines which Industry 4.0 (I4.0) technologies are suitable for improving Lean Six Sigma (LSS) tasks and the benefits of integrating these technologies into improvement projects. Also, it explores existing integration frameworks and discusses their relevance. A quantitative analysis of 692 papers and an in-depth analysis of 41 papers revealed that “Analyse” is by far the best-supported DMAICs phase through techniques such as Data Mining, Machine Learning, Big Data Analytics, Internet of Things, and Process Mining. This paper also proposes a DMAIC 4.0 framework based on multiple technologies. The mapping of I4.0 related techniques to DMAIC phases and tools is a novelty compared to previous studies regarding the diversity of digital technologies applied. LSS practitioners facing the challenges of increasing complexity and data volumes can benefit from understanding how I4.0 technology can support their DMAIC projects and which of the suggested approaches they can adopt for their context

    Beyond Surveys: Analyzing Software Development Artifacts to Assess Teaching Efforts

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    This Innovative Practice Full Paper presents an approach of using software development artifacts to gauge student behavior and the effectiveness of changes to curriculum design. There is an ongoing need to adapt university courses to changing requirements and shifts in industry. As an educator it is therefore vital to have access to methods, with which to ascertain the effects of curriculum design changes. In this paper, we present our approach of analyzing software repositories in order to gauge student behavior during project work. We evaluate this approach in a case study of a university undergraduate software development course teaching agile development methodologies. Surveys revealed positive attitudes towards the course and the change of employed development methodology from Scrum to Kanban. However, surveys were not usable to ascertain the degree to which students had adapted their workflows and whether they had done so in accordance with course goals. Therefore, we analyzed students' software repository data, which represents information that can be collected by educators to reveal insights into learning successes and detailed student behavior. We analyze the software repositories created during the last five courses, and evaluate differences in workflows between Kanban and Scrum usage

    Using webcrawling of publicly available websites to assess E-commerce relationships

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    We investigate e-commerce success factors concerning their impact on the success of commerce transactions between businesses companies. In scientific literature, many e-commerce success factors are introduced. Most of them are focused on companies' website quality. They are evaluated concerning companies' success in the business-to- consumer (B2C) environment where consumers choose their preferred e-commerce websites based on these success factors e.g. website content quality, website interaction, and website customization. In contrast to previous work, this research focuses on the usage of existing e-commerce success factors for predicting successfulness of business-to-business (B2B) ecommerce. The introduced methodology is based on the identification of semantic textual patterns representing success factors from the websites of B2B companies. The successfulness of the identified success factors in B2B ecommerce is evaluated by regression modeling. As a result, it is shown that some B2C e-commerce success factors also enable the predicting of B2B e-commerce success while others do not. This contributes to the existing literature concerning ecommerce success factors. Further, these findings are valuable for B2B e-commerce websites creation

    Enhanced manufacturing storage management using data mining prediction techniques

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    Performing an efficient storage management is a key issue for reducing costs in the manufacturing process. And the first step to accomplish this task is to have good estimations of the consumption of every storage component. For making accurate consumption estimations two main approaches are possible: using past utilization values (time series); and/or considering other external factors affecting the spending rates. Time series forecasting is the most common approach due to the fact that not always is clear the causes affecting consumption. Several classical methods have extensively been used, mainly ARIMA models. As an alternative, in this paper it is proposed to use prediction techniques based on the data mining realm. The use of consumption prediction algorithms clearly increases the storage management efficiency. The predictors based on data mining can offer enhanced solutions in many cases.Telefónica, through the “Cátedra de Telefónica Inteligencia en la Red”Paloma Luna Garrid
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