İstatistiksel Süreç Kontrolün Kalite Açısından Değerlendirilmesi: Tekstil Sektöründeki Bir İşletmede Uygulaması

Kalite, rekabetçi mal ve hizmetlerin karşılaştırılmasında müşteriler için en önemli karar vermekriterlerinden biridir. Kalite olgusu, müşterinin bir kişi, bir sanayi şirketi, bir perakende mağaza veya bir askerikoruma programı olup olmadığına bakılmaksızın evrenseldir. Sonuç olarak, farkındalık ve kalite yönetimi,pazar performansına, gelişmeye ve rekabetçi konumun iyileştirilmesine katkıda bulunan kritik bir itici güçtür.Artan verimlilik ve verimliliğin genel pazarlama planının önemli bir parçası olarak etkin bir şekildeuygulanması, önemli bir yatırım getirisine yol açmaktadır. İstatistiksel süreç kontrolü (İSK), bir üretimsürecinden yüksek kaliteli ürünler elde etmek için en yaygın tekniktir. ISK, istatistiksel kontrolü elde etmek vekorumak için etkili adımlar atmak için bir yöntemi ve/veya performansını değerlendirmek için yönetimharitaları gibi istatistiksel yöntemlerin kullanılmasını içerir. Bu araştırma, ABC Tekstil İşletmesinin günümüz endüstrisinin kalite kriterlerini yerine getirmesini desteklemek için matematiksel süreç yönetim sisteminintanıtılması için hazırlanmıştır. Bu araştırmanın amacı, ISK 'nın temel unsurlarını ve önemli kavramlarınıayrıntılı bir literatür taraması ile analiz ettikten sonra, bir tekstil işletmesinin bu kavramları daha iyiuygulamasını mümkün kılmaktır. Bu araştırma makalesi, performans ölçümünün temel kavramlarınaodaklanmaktadır İstatistiksel süreç kontrolü (İSK) ve Pareto Analizi (PA), temel olarak bu tür tekstilaraştırmaları için çalışan ve üretim hatalarıyla ilgilenir. Bu nedenle, bu çalışmanın konusu ile ilişkili olarakkullanılan İSK ve PA yöntemleri kullanılarak organizasyon, kurumsal ve üretimin verimliliğine bağlı olaraktartışılmaktadır. Bu konuya yönelik çalışmalar, bu sektörde faaliyet gösteren bu şirket ve diğer şirketler içintemel yapı taşı ve yol gösterici olacaktır.

Evaluation of Statistical Process Control In Terms of Quality: Application In A Business inthe Textile Sector

Quality is one of the most significant decision-making criteria for customers in the comparison of competitive goods and services. The phenomenon of quality is universal, irrespective of whether the customer is a person, an industrial corporation, a retail store, or a military protection program. As a consequence, awareness and quality management are critical drivers that contribute to market performance, development, and an improvement in competitive position. Increased efficiency and efficient application of efficiency as an important part of the overall marketing plan lead to a significant return on investment. Statistical process control (SPC) is the most common technique for obtaining high quality products from a manufacturing process. SPC involves the use of statistical methods, such as management maps, to evaluate a method and/or its performance in order to take effective steps to obtain and retain statistical control. fter analyzing the major elements and crucial concepts of SPC through a detailed literature review, the objective of this study is to make it possible for a textile business to better implement these concepts. This research study was prepared for the introduction of the mathematical process management system to support ABC Textile Company in its aim to fulfill the quality criteria of today’s textile industry. This research paper focuses on the main concepts of the performance Measurement SPC mainly addresses human mistakes that can occur in this type of textile research. Therefore, the topic of this study is discussed in accordance with aspects of organizational efficiency. This research studies will act as a building stone for the following studies in ABC Company as well as for those in the industry.

___

  • Antony, J. (2014). Readiness factors for the lean six sigma journey in the higher education sector. International Journal of Productivity and Performance Management, 63(2), 257-264. http://dx.doi.org/10.1108/IJPPM-04-2013-0077/full/html
  • Azizi, A. (2015). Evaluation ımprovement of production productivity performance using statistical process control, overall equipment efficiency, and autonomous maintenance. Procedia Manufacturing, 2, 186–190. http://dx.doi.org/10.1016/j.promfg.2015.07.032
  • Benneyan, J. (1998). Use and interpretation of statistical quality control charts, International Journal for Quality in Health Care, 10(1), pp. 69-73. http://intqhc.oxfordjournals.org/content/10/1/69.full.pdf.
  • Boudreaux-Kelly, M., Wilson, M., & Bokhari, M. (2015). Statistical methods of risk-adjusted statistical process control charts to assess surgical performance in consecutive colorectal operations at a single institution. JAMA Surgery, 150 (3), 271-272, https://doi.org/10.1001/jamasurg.2014.1773
  • Cassady, R., Bowden, R., Liew, L. & Pohl, E. (2000). Combining preventive maintenance and statistical process control: a preliminary investigation. IIE Transactions, 32, 471-478, https://doi.org/10.1080/07408170008963924
  • Chen, Y. & Durango-Cohen, P. L. (2015). Development and field application of a multivariate statistical process control framework for health-monitoring of transportation infrastructure. Transportation Research Part B: Methodological, 81, 78– 102. http://dx.doi.org/10.1016/j.trb.2015.08.012
  • Davenport, DL., Bowe EA., Henderson, WG., Khuri, S.F. & Mentzer, RMJ. (2006). National surgical quality improvement program (NSQIP) risk factors can be used to validate American society of anesthesiologists physical status classification (ASA PS) levels. Annals of Surgery. 243(5), 636-644, https://doi.org/10.1097/01.sla.0000216508.95556.cc
  • De Boeck, E., Jacxsens, L., Bollaerts, M. & Vlerick, P. (2015). Food safety climate in food processing organizations: Development and validation of a self-assessment tool. Trends in Food Science & Technology, https://doi.org/10.1016/j.tifs.2015.09.006
  • Dutoit, C., Dehombreux, P., Lorphèvre, E. R. & Equeter, L. (2019). Statistical process control and maintenance policies for continuous production systems subjected to different failure ımpact models: literature review. Procedia CIRP, 86, 55– 60. https://doi.org/10.1016/j.procir.2020.01.050
  • Evans, J. (2008). Statistical process control for quality ımprovement: a training guide to caring spc, prentice hall, cincinnati, Pearson Technology Group; 1st Edition, 204 p., ISBN-13: 978- 0135589908 Fishbone diagram-Ishikawa cause and effect, https://whatis.techtarget.com/definition/fishbonediagram) (Access Date: 12.05.2020).
  • Gejdoš, P. (2015). Continuous quality ımprovement by statistical process control. Procedia Economics and Finance, 34, 565–572. https://doi.org/ 10.1016/s2212-5671(15)01669-x
  • Grigg, N. P. & Walls, L. (2007). Developing statistical thinking for performance improvement in the food industry. International Journal of Quality & Reliability Management, 24(4), 347-369. http://dx.doi.org/10.1108/02656710710740536
  • Harris T. J. & W. H. Ross. (1991). Statistical process control procedures for correlated observations. The Canadian Journal of Chemical Engineering, 69, 48-57.
  • Hawkins, D. M. and Zamba, K.D. (2003). On small shifts in quality control, Quality Engineering, 16, 143-149, https://doi.org/10.1081/QEN-120020780 Hdz – Jasso, A. M., Contreras – Valenzuela, M. R., Rodríguez – Martínez, A., Romero, R. J. &
  • Venegas, M. (2015). Experimental heat transformer monitoring based on linear modelling and statistical control process. Applied Thermal Engineering, 75, 1271– 1286. http://dx.doi.org/10.1016/j.applthermaleng.2014.09.013
  • Ho, C. & Case, K. E. (1994). Economic design of control charts: A literature review for 1981–1991. Journal of Quality Technology, 26, 39–53. https://doi.org/10.1080/00224065.1994.11979497
  • How a Cause & Effect Diagram Helped Reduce Defects by 19% https://goleansixsigma.com/achieving-a-19-improvement-in-response-time-using-a-causeand-effect-diagram/ (Access Date: 10.09.2020).
  • How to Do Pareto Chart Analysis, https://tallyfy.com/pareto-chart-analysis/, (Access Date: 10.09.2020).
  • Hu, K. & Yuan, J. (2008). Multivariate statistical process control based on multiway locality preserving projections. Journal of Process Control, 18(7-8), 797– 807. http://dx.doi.org/10.1016/j.jprocont.2007.11.002
  • Interpreting Control Charts, https://www.spcforexcel.com/knowledge/control-chart basics/interpreting-control-charts (Access Date: 10.09.2020).
  • Kano, M., Sakata, T. & Hasebe, S. (2011). Just-ın-time statistical process control: Adaptive monitoring of vinyl acetate monomer process. IFAC Proceedings Volumes, 44(1), 13157– 13162. http://dx.doi.org/10.3182/20110828-6-it-1002.01756
  • Kafetzopoulos, D. P., Gotzamani, K. D. & Psomas, E. L. (2014). The impact of employees’ attributes on the quality of food products, International Journal of Quality & Reliability Management, 31(5), 500-521. https://doi.org/10.1108/IJQRM-05-2012-0057
  • Kaskavelis, E., Martin, E. B., Morris, A. J. & Jonathan, P. (2000). On the statistical process control of multivariate autocorrelated processes. IFAC Proceedings, 33(10), 165– 170. https://doi.org/10.1016/s1474-6670(17)38536-1
  • Keller, D. S., Reif de Paula, T., Yu, G., Zhang, H., Al-Mazrou, A. & Kiran, R. P. (2019). Statistical process control (SPC) to drive improvement in length of stay after colorectal surgery. The American Journal of Surgery. https://doi.org/ 10.1016/j.amjsurg.2019.08.029
  • King, M. (2011). Process control: A practical approach, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom, p.1-28, ISBN 978-0- 470-97587-9.
  • Kharbach, M., Cherrah, Y., Vander Heyden, Y., & Bouklouze, A. (2017). Multivariate statistical process control in product quality review assessment – A case study. Annales Pharmaceutiques Françaises, 75(6), 446–454, http://dx.doi.org/10.1016/j.pharma.2017.07.003
  • Kourti T. (2009). Multivariate statistical process control and process control, using latent variables. In: Brown SD, Tauler R, Walczak B, editors. Comprehensive chemometrics. Oxford: Elsevier; 2009. p. 21-54.
  • Kumar, R., Garg, D. & Garg, T.K. (2011), TQM success factors in north Indian manufacturing and service industries, The TQM Journal, 23(1), 36-46, https://doi.org/ 10.1108/17542731111097470
  • Lameei, A. (2005). Assessment of organization readiness for TQM implementation. Iranian Journal of Public Health, 34(2), 58-63.
  • Laursen, K., Frederiksen, S.S., Leuenhagen, C. & Bro, R. (2010) Chemometric quality control of chromatographic purity. Journal Chromatogr A, 1217, 6503—6510, http://dx.doi.org/10.1016/ j.chroma.2010.08.040
  • Levinson, W. A. (2011). Statistical process control for real-world applications, Taylor and Francis Group, LLC, p.1-56. ISBN 13: 978-1-4398-2001-8.
  • Lim, S. A. H. & Antony, J. (2016). Statistical process control readiness in the food industry: Development of a self-assessment tool. Trends in Food Science & Technology, 58, 133– 139. https://doi.org/10.1016/j.tifs.2016.10.025
  • Lim, S. A. H., Antony, J. & Albliwi, S. (2014). Statistical process control (SPC) in the food industry– A systematic review and future research agenda. Trends in food science & technology, 37(2), 137-151. http://dx.doi.org/10.1016/j.tifs.2014.03.010
  • Linderman, K., McKone-Sweet, K. E. & Anderson, J. C. (2005). An integrated systems approach to process control and maintenance. European Journal of Operation Research, 164, 324–340. https://doi.org/10.1016/j.ejor.2003.11.026
  • Matos, A. S., Requeijo, J. G. & Pereira, Z. L. (2008). Integration of engineering process control and statistical control in pulp and paper industry. 18th European Symposium on Computer Aided Process Engineering, 399–404. http://dx.doi.org/10.1016/s1570-7946(08)80071-5.
  • Maskin, E. & Sjöström, T. (2002). Implementation theory, Chapter 5, Handbook of social Choice and Welfare, 1, 237-288.
  • Mirko, S., Jelena, J., Zdravko, K. & Aleksandar, V.(2009). Basic quality tools in continuous ımprovement process. Journal of Mechanical Engineering, 55(5), 1-4.
  • Mehrafrooz, Z. & Noorossana, R. (2011). An integrated model based on statistical process control and maintenance, Computers and Industrial Engineering, 61(4), 1245– 1255. https://doi.org/10.1016/j.cie.2011.07.017
  • Montgomery D. C. & C. M. Mastrangelo. (1991). Some statistical process control procedures for autocorrelated data and discussion. Journal of Quality Technology, 23(3), 179-204.
  • Montgomery, D. C. (2001). Introduction to statistical quality control (fourth ed.). Wiley
  • Montgomery, D. C. (1980). Economic design of control charts: A review of literature survey. Journal of Quality Technology, 12, 40–43.
  • Montgomery, D. C. (2013). Introduction to statistical quality control, seventh edition, John Wiley & Sons, Inc, p.235-279, ISBN: 978-1-118-14681-1.
  • Oakland, J.S. (2008). Statistical process control, Sixth Edition p.1-20., p.90-159, ISBN–13: 978-0- 7506-6962-7.
  • Pignatiello, JJ. & Runger, GC.(1990). Comparisons of multivariate CUSUM charts. Journal of Quality Technology, 22, 173-186. https://doi.org/10.1080/00224065.1990.11979237
  • Psomas, E. & Fotopoulos, C. (2010), Total quality management practices and results in food companies. International Journal of Productivity and Performance Management. 59(7), pp. 668-687, https://doi.org/10.1108/17410401011075657
  • Porteus, E. L. & Angelus, A. (1997). Opportunities for improved statistical process control. Management Science, 43, 1214–1228. https://www.jstor.org/stable/2634634?seq=1&cid=pdf-reference.
  • Panagiotidou, S. & Nenes, G. (2009). An economically designed, integrated quality and maintenance model using an adaptive Shewhart chart. Reliability Engineering and System Safety, 94, 732– 741. https://doi.org/10.1016/j.ress.2008.07.003
  • Pinto, J. K. & Prescott, J. E. (1988). Variations in critical success factors over the stages in the project life cycle. Journal of management, 14(1), 5-18, https://doi.org/10.1177/014920638801400102
  • Radnor, Z. (2011). Implementing lean in health care: Making the link between the approach, readiness and sustainability. International Journal of Industrial Engineering and Management, 2(1), 1-12. http://www.ftn.uns.ac.rs/ijiem/.
  • Quality Improvement, https://qi.elft.nhs.uk/resource/cause-and-effect-diagram-fish-bone/ (Acces Date: 12.05.2020).
  • Raguram, R. (2014). Implementation of overall equipment effectiveness(OEE). Middle-EastJournal Science Research, 20(5), 567-576.
  • Ramírez, H., Mendoza, E., Mendoza, M. & González, E. (2015). Application of augmented reality in statistical process control, to ıncrement the productivity in manufacture. Procedia Computer Science, 75, 213–220. http://dx.doi.org/10.1016/j.procs.2015.12.240
  • Stapenhurst, T. (2005). Mastering statistical process control a handbook for performance ımprovement using cases, Elsevier Butterworth-Heinemann, 1-20, ISBN 0 7506 6529 7 p.1-10 /chart types 10-20.
  • Surak, J. G. (1999). Quality in commercial food process. Quality progress, February, 25-29.
  • Shamsuzzaman, M. and Wu, Z.(2006). Control chart design for minimizing the proportion of defective units. Journal of Manufacturing Systems, 25(4), 269-278.
  • Sánchez-Fernández, A,. Baldán, F. J., Sainz-Palmero, G. I., Benítez, J. M. & Fuente, M. J. (2018). Fault detection based on time series modeling and multivariate statistical process control. Chemometrics and Intelligent Laboratory Systems, https://doi.org/10.1016/j.chemolab.2018.08.003
  • Sedlack, JD. (2010). The utilization of six sigma and statistical process control techniques in surgical quality improvement. Journal for Healthcare Quality, 32, 18-26, https://doi.org/10.1111/j.1945-1474.2010.00102.x
  • Silva, A. F., Sarraguça, M. C., Fonteyne, M., Vercruysse, J., De Leersnyder, F., Vanhoorne, V. &
  • Lopes, J. A. (2017). Multivariate statistical process control of a continuous pharmaceutical twin-screw granulation and fluid bed drying process. International Journal of Pharmaceutics, 528(1-2), 242–252. https://doi.org/10.1016/j.ijpharm.2017.05.075
  • Sujová, A. & Marcineková, K. (2015). Modern methods of process management used in slovak enterprises. Procedia-Economics and Finance Journal, vol. 23/2015, Elsevier Ltd., 889 - 893. ISSN 2212-5671, https://doi.org/10.1016/S2212-5671(15)00381-0 Prístupné online: http://ac.elscdn.com/S2212567115003810/1-s2.0-S2212567115003810- main.pdf?_tid=f5abbcea-295b-11e5- 9d7c00000aacb362&acdnat=1436791244_e20038b90e4840c6597448c04b90d27d
  • Smith, I. (2005). Achieving readiness for organisational change. Library Management, 26(6/7), 408- 412, http://dx.doi.org/10.1108/01435120510623764
  • Spedding, T. A. & Chandrashekar, M. (2005). A component-based simulation environment for statistical process control systems analysis. Robotics and Computer-Integrated Manufacturing, 21(2), 99–107, http://dx.doi.org/10.1016/j.rcim.2002.11.001
  • Srikaeo, K., Furst, J. E., & Ashton, J. (2005). Characterization of wheat-based biscuit cooking process by statistical process control techniques. Food Control, 16(4), 309-317. http://dx.doi.org/ 10.1016/j.foodcont.2004.03.010
  • Thompson, J.R. & Koronacki, J. (2002), Statistical process control: the deming paradigm and beyond, Chapman and Hall/CRC, 2nd edition, ISBN: 1-58488-242-5.
  • Trietsch, D. (1998). Statistical quality control A loss minimization approach, World Scientific publishing Co. Pte Ltd., p.1-16, p.113-165, ISBN 9810230311.
  • Tsacle, E.G. & Aly, N. A. (1996). An expert system model for ımplementing statistical process control in the health care ındustry. Computers industry engineering, 1(1), 447-450.
  • Terek, M. & Hrnčiarová, Ľ. (2014). Štatistické riadenie kvality. Bratislava: IURA EDITION s.r.o. Bratislava 2004, 234s, ISBN 80-89047-97-1.
  • Teixeira, H. N., Lopes, I., Braga, A. C., Delgado, P. and Martins, C. (2019). Screwing process analysis using multivariate statistical process control. Procedia Manufacturing, 38, 932– 939. https://doi.org/10.1016/j.promfg.2020.01.176
  • Vetter, TR. & Morrice, D. (2019). Statistical process control: No hits, No runs, No errors. Anesthesia & Analgesia, 128, 374-382, https://doi.org/10.1213/ANE.0000000000003977
  • Wang, P., Zhang, D., LI, S., & Chen, B. (2012). Machining error control by ıntegrating multivariate statistical process control and stream of variations methodology. Chinese Journal of Aeronautics, 25(6), 937–947. http://dx.doi.org/10.1016/s1000-9361(11)60465-2.
  • Yin, H., Zhang, G., Zhu, H., Deng, Y. & He, F. (2015). An integrated model of statistical process control and maintenance based on the delayed monitoring. Reliability Engineering and System Safety, 133, 323–333. http://dx.doi.org/10.1016/j.ress.2014.09.020
  • Zhou, W. H. & Zhu, G. L. (2008). Economic design of integrated model of control chart and maintenance management. Mathematical and computer Modelling, 47, 1389–1395