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Statistical Process Control in Automotive Industry

characteristics of phosphate conversion coating. Ruse : PB at Angel Kanchev University of Ruse, vol. 40, pp. 221-224. KELLER, P. 2011. Statistical process control. 1st ed. The McGraw-Hill Companies. 320 pp. ISBN 978-0-07-174250-4. Krastev , G. - Kangalov , P. 2004. Physico-mechanical and operational characteristics of restoration electrolytic coatings. Ruse : PB at Angel Kanchev University of Ruse, vol. 41, pp. 142-144. Nikolov , m. - Kangalov , P. 2012. Benefits from maintenance and repair in utilization of resources. In

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

References [1] Mason, B. & Antony, J., Is SPC Just about Control Charts. Unpublished work. [2] Montgomery, D. C., Introduction to Statistical Quality Control, 6th Edition. New York: John Wiley and Sons, 2009. [3] Shewhart, W. A, “Statistical Method from the Viewpoint of Quality Control”, Washington DC: The Graduate School of the US Department of Agriculture, 1939. [4] Sultana, F., Islam, N.R., Azeem, A. Implementation of Statistical Process Control (SPC) For Manufacturing Performance Improvement

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Statistical Process Control as a Failure Removal Improvement Tool

. Oakland, J. S. 2007. Statistical Process Control. Oxford: Butterworth-Heinemann, 458 pp. ISBN 9780750669627. Romao, X. - Delgado, R. - Costa, A. 2010. An empirical power comparison of univariate goodness-of-fit tests for normality. In Journal of Statistical Computation and Simulation, vol. 80, pp. 545-591. Šibalija, T. 2004. Attaining process robustness through design of experiment and statistical process control. In Proceedings of the 11th CIRP International Conference on Life Cycle Engineering - LCE 2004 - Quality Management Issues, pp

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Statistical Control Charts: Performances of Short Term Stock Trading in Croatia

and Quantitative Analysis, Vol. 43 No. 1, pp. 191-212. 4. Box, G. E. P., Luceno, A., Paniagua-Quinones, M. D. C., (2009), “Statistical Control by Monitoring and Adjustment”, 2nd ed., New Jersey, John Wiley & Sons. 5. Caporale, G. M., Howells, P. G. A, Soliman, A. M. (2004), “Stock market development and economic growth: The causal linkage”, Journal of Economic Development, Vol. 29 No. 1, pp. 33-50. 6. Chen, T. (2010), “On reducing false alarms in multivariate statistical process control”, Chemical Engineering Research

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Statistical process control and CAQ systems as a tools assuring quality in the automotive industry

REFEENCES Azizi, A. (2015). Evaluation Improvement of Production Productivity Performance using Statistical Process Control, Overall Equipment Efficiency and Autonomous Maintenance. Procedia Manuf., vol. 2, pp. 186-190. Deming W.E. (2000). Out of the crisis. USA: MIT Press Ltd. EN ISO 9001:2015 (2015). Quality Management Systems – Requirements. Brussels: European Committee for Standardization. Hamrol, A. (2005). Quality management with examples, Warszawa: PWN. IATF 16949:2016. 2016. Quality management system requirements for

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The Model of the Production Process for the Quality Management

York: John Wiley & Sons, Inc. [11] Oakland, J.S., 2003. Total Quality Management - text with cases. Third edition. Burlington: Butterworth- Heinemann. [12] Oakland, J.S., 2014. Total Quality Management and Operational Excellence: Text with Cases. Fourth Edition. New York, Abingdon, Oxon: Routledge. [13] Oakland J.S., 2003. Statistical Process Control. Fifth Edition. Oxford - Burlington: Butterworth- Heinemann. [14] PN-EN ISO 9000, 2006. Systemy zarządzania jakością. Podstawy i terminologia (Quality

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Capabilities of Statistical Residual-Based Control Charts in Short- and Long-Term Stock Trading

: Fixed versus variable control limits. Quality Engineering, 4 (2), 167–188. http://dx.doi.org/10.1080/08982119108918904 5. Alwan, L. C., & Roberts, H. V. (1988). Time-series modeling for statistical process control. Journal of Business and Economic Statistics, 6 (1), 87–95. http://dx.doi.org/10.1080/07350015.1988.10509640 6. Benić, V., & Franić, I. (2008). Stock market liquidity: Comparative analysis of Croatian and regional markets. Financial Theory and Practice , 32 (4), 477–498. 7. Best, M., & Neuhauser, D. (2006). Walter A. Shewhart, 1924

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Quality Control in Production Processes

Abstract

The tools for quality management are used for quality improvement throughout the whole Europe and developed countries. Simple statistics are considered one of the most basic methods. The goal was to apply the simple statistical methods to practice and to solve problems by using them. Selected methods are used for processing the list of internal discrepancies within the organization, and for identification of the root cause of the problem and its appropriate solution. Seven basic quality tools are simple graphical tools, but very effective in solving problems related to quality. They are called essential because they are suitable for people with at least basic knowledge in statistics; therefore, they can be used to solve the vast majority of problems.

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The Use of Statistical Process Control Tools for Analysing Financial Statements

Abstract

This article presents the proposed application of one type of the modified Shewhart control charts in the monitoring of changes in the aggregated level of financial ratios. The control chart x̅ has been used as a basis of analysis. The examined variable from the sample in the mentioned chart is the arithmetic mean. The author proposes to substitute it with a synthetic measure that is determined and based on the selected ratios. As the ratios mentioned above, are expressed in different units and characters, the author applies standardisation. The results of selected comparative analyses have been presented for both bankrupts and non-bankrupts. They indicate the possibility of using control charts as an auxiliary tool in financial analyses.

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Intelligent Performance Analysis with a Natural Language Interface

statistical process control (GSPC) in stress monitoring”, IFAC-Papers OnLine, vol. 28, no. 17, pp. 207-212, 2015. [43] E.K. Juuso, “Integration of knowledge-based information in intelligent condition monitoring”, in 9th Int. Conf. on Condition Monitoring and Machinery Failure Prevention Technologies, London, United Kingdom, vol. 1, 2012, pp. 217-228. [44] M. De Cock and E.E. Kerre, “Fuzzy modifiers based on fuzzy relations”, Information Sciences, vol. 160, no. 1-4, pp. 173-199, 2004. [45] R.E. Moore, Interval Analysis

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