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Implementation of Statistical Process Control (SPC) in the Sewing Section of Garment Industry for Quality Improvement

-Khabeery M. M., “Statistical Process Control Charts Applied to Steelmaking Quality Improvement”, Quality Technology & Quantitative Management, vol.3 (4), pp.473-491, 2006. [13] Omar, M. H., “Statistical Process Control Charts for Measuring and Monitoring Temporal Consistency of Ratings”, Journal of Educational Measurement, vol. 47(1), pp.18-35, 2010. [14] Victor, E. S., Jaideep G. M. and Michael J. S. (1994), “δ Charts for Short Run Statistical Process Control,” International Journal of Quality & Reliability Management, vol.11 (6), pp.50

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Quality Improvement in the Production Company

Abstract

The article discusses the quality improvement concept in terms of companies’ competitiveness and its operations. The research object is a production company located in Silesia province. We analyzed the quality problems occurring in the company based on the selected quality management tools: Ishikawa diagram and the Pareto-Lorenz. It was presented and discussed basic types of waste occurring in the enterprise. It was proposed improvement actions and highlights the importance of immaterial resources - the company's workers.

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Self-assessment questionnaire for family doctors’ assessment of quality improvement competencies: a cross-cultural adaptation in Slovenia

et al. Development of a aompetency framework for quality improvement in family medicine: a qualitative study. J Contin Educ Health Prof 2012; 32: 174-80. 10. Klemenc-Ketis Z, Vanden Bussche P, Rochfort A, Emaus C, Eriksson T, Kersnik J. Teaching quality improvement in family medicine. Educ Prim Care 2012; 23: 378-81. 11. Gordon P, Tomasa L, Kerwin J. ACGME outcomes project: selling our expertise. Fam Med 2004; 36: 164-7. 12. Tomolo AM, Lawrence RH, Watts B, Augustine S, Aron DC, Singh MK. Pilot study evaluating a

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Quality Registers in Professional Health Care Educations; Knowledge Gaps and Proposed Actions

References Aagard Nielsen, K. A., & Svensson, L. (2006). Action research and interaction research:beyond practice and theory. Maastricht: Shaker Publishing. Batalden, P. B., & Davidoff, F. (2007). What is quality improvement and how can it transform health care? Quality and Safety in Health Care, 16(1), 2-3, http:// dx.doi.org/10.1136/qshc.2006.022046 Baumgart, A., & Neuhauser, D. (2009). Frank and Lillian Gilbrecht; scientific management in the operating room. Quality and Safety in Health Care, 18(5), 413

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Keeping Higher Education Aligned with the Requirements and Expectations of the Knowledge-Based Economy

. in Proc. IAJC-ASEE, Joint International Committee. Cave, M., Hanney, S., Henkel, M. Kogan, M. 2000. The Use of Performance Indicators in Higher Education – The Challenge of the Quality Movement . Higher Education Policy, 50, London, Jessica Kingsley Publishers. Kahloun, F., Ghannouchi, S.A., 2017. Evaluating the Quality of Business Process Models Based on Measures and Criteria in Higher Education : Developing a Framework for Continuous Quality Improvement, in: Madureira, AM and Abraham, A and Gamboa, D and Novais, P (Ed.), INTELLIGENT SYSTEMS DESIGN

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Measures for Improving the Quality of Health Care

: A Practical Approach to Enhancing Organizational Performance (2 nd Edition). Jossey Bass, San Francisco, 2009. Deming WE. The New Economics for Industry, Government, and Education. Cambridge, MA: The MIT Press; 2000. Jam Mainz Defining and classifying clinical indicators for quality improvement. International Journal for Quality in Health Care 2003; 15(6): 523-30.

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Quality Improvement in Production Process

Abstract

Quality is a certain degree of excellence and is one of the important factor in realization of the production process. Evaluation of the quality excellence in production project management is connected with a process of making changes in particular phases of project realization. The thesis proposed in the article is: the effective quality improvement based on implementation of the quality management method contributes to achieve the planned quality in the production project.

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Keeping Technical Education Aligned to the Needs and Expectations of Industry

Abstract

Paper describes the continuous quality improvement procedures used by the author to keep the General Engineering program at Penn State Hazleton aligned to the needs and expectations of industry. The paper addresses the procedure for the development of program educational objectives which are the goals of the program. The goals of the program are defined jointly by industry leaders and former Engineering graduates as well as the Engineering faculty. The paper also focuses on the development of the student outcomes which is the body of knowledge that every graduate should have at the day of graduation. The paper also explains the process for collecting data for the purpose of assessing the level of attainment of program objectives and student outcomes. Based on the data which is being collected on an ongoing basis, the necessary corrective actions are implemented. This is happening on the program level as well as the course level. The necessary changes on the course level are happening every semester. The adjustments to the Engineering curriculum are being made yearly usually during the summer months. The paper focuses on the constant need for revising the curriculum in order to stay current with technology.

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Approaches to Evaluating Efficiency and Quality of Integrated Health Care: State of the Evidence

prevention programmes in coronary heart disease: systematic review. British Medical Journal, 323(7319), 957-962. McDonald, K. M, Vandana Sundaram, M. M., Dena, M. P., Bravata, M. et al. (2007). Closing the Quality Gap: a critical analysis of quality improvement strategies, AHRQ Publication No. 04 (07)-0051-7. Standford: Stanford University, Evidence-based Practice Center. Moser, D. K. (2000). Heart failure management: optimal health care delivery programs. Annual Review of Nursing Research, 18(1), 91-126. Mur-Veeman, I., Van

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Statistical Modeling of Low SNR Magnetic Resonance Images in Wavelet Domain Using Laplacian Prior and Two-Sided Rayleigh Noise for Visual Quality Improvement

phase. IEEE Trans. on Acoustic, Speech, and Signal Processing , 24 (1), 58-63. Rabbani, H. (2008). Statistical modeling of low SNR magnetic resonance images in wavelet domain using Laplacian prior and two-sided Rayleigh noise for visual quality improvement. In International Conference on Information Technology and Applications in Biomedicine (ITAB 2008) , May 30-31, 2008. IEEE, 116-119. Rabbani, H., Vafadust, M. (2008). Image/video denoising based on a mixture of Laplace distributions with local parameters in

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