Application of Principle Component Analysis and logistic regression to support Six Sigma implementation in maintenance

• Autorzy: Antosz Katarzyna , Jasiulewicz-Kaczmarek Małgorzata , Machado Jose , Relich Marcin

Identyfikatory

DOI

10.17531/ein/174603

• Języki publikacji: EN

Abstrakty

EN

Improving the efficiency of maintenance processes is one of the goals of companies. Improvement activities in this area require not only an appropriate maintenance strategy but also the use of a new approach to increase the efficiency of the process. This article focuses on using Six Sigma (SS) to improve maintenance processes. As an introduction, the generations of SS development are identified, and traditional and advanced analytical tools that can be useful in SS projects are reviewed. As part of the research, an example of the implementation of the SS project in the maintenance process using the DMAIC and selected advanced analytical methods, such as PCA and logistic regression, was presented. The PCA results showed that it was enough to have seven main components to keep about 84% of the information on variability. In developed logistic regression explained the impact of the individual factors affecting the availability of the machines. The identified factors and their interactions made it possible to define maintenance activities requiring improvements

Słowa kluczowe

EN

maintenance management; Six Sigma generation; DMAIC; PCA; logistic regression

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2023
• Tom: Vol. 25, no. 4
• Strony: art. no. 174603
• Opis fizyczny: Bibliogr. 112 poz., rys., tab., wykr.

Twórcy

autor

Antosz Katarzyna

• Rzeszow University of Technology, Faculty of Mechanical Engineering and Aeronautics, Powstańców Warszawy 8,35-959 Rzeszów, Poland

• https://orcid.org/0000-0001-6048-5220

autor

Jasiulewicz-Kaczmarek Małgorzata

• Poznan University of Technology, Faculty of Management Engineering, Rychlewskiego 2, 60-965 Poznań, Poland

• https://orcid.org/0000-0002-8332-3192

autor

Machado Jose

• University of Minho,MEtRICs Research Centre, Campus de Azurem, Guimarães,Portugal

• https://orcid.org/0000-0002-4917-2474

autor

Relich Marcin

• University of Zielona Góra,Faculty of Economics and Management, 65-417 Zielona Gora, Poland

• https://orcid.org/0000-0003-1193-432X

Bibliografia

• 1. Abdi H, Williams LJ. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics. 2010; 2:433-459. https://doi.org/10.1002/wics.101
• 2. Ahmed A, Page J, Olsen J. Enhancing Six Sigma methodology using simulation techniques: Literature review and implications forfuture research. International Journal of Lean Six Sigma. 2020; 11(1):211-232. doi:10.1108/IJLSS-03-2018-0033
• 3. Alarcón M, Martínez-García FM, de León Hijes FCG. Energy and maintenance management systems in the context of industry 4.0. Implementation in a real case. Renewable and Sustainable Energy Reviews.2021; 142:110841.https://doi.org/10.1016/j.rser.2021.11084
• 4. Al-Mishari ST, Suliman S. Integrating Six Sigma with other reliability improvement methods in equipment reliability and maintenance applications. Journal of Quality in Maintenance Engineering. 2008; 14(1):59-70. doi:10.1108/13552510810861941
• 5. Alsubaie B, Yang Q. Maintenance process improvement model by integrating LSS and TPM for service organisations. In: Engineering Asset Management 2016: Proceedings of the 11th World Congress on Engineering Asset Management. Springer International Publishing; 2018. p. 13-24. doi:10.1007/978-3-319-62274-3_2
• 6. Alzoubi HM, In'airat M, Ahmed G. Investigating the impact of total quality management practices and Six Sigma processes to enhance the quality and reduce the cost of quality: the case of Dubai. International Journal of Business Excellence.2022; 27(1):94-109.https://doi.org/10.1504/IJBEX.2022.123036
• 7. Andrych-Zalewska M, Chlopek Z, Pielecha J, Merkisz J, Investigation of exhaust emissions from the gasoline engine of a light duty vehicle in the Real Driving Emissions test Eksploatacja i Niezawodnosc –Maintenance and Reliability 2023: 25(2) http://doi.org/10.17531/ein/165880
• 8. Anton J. Is Six Sigma a management fad or fact? Assembly Automation. 2007; 27(1):17-19. https://doi.org/10.1108/01445150710724658
• 9. Antony J, Sony M, Dempsey M, Brennan A, Farrington T, Cudney EA. An evaluation into the limitations and emerging trends of Six Sigma: An empirical study. The TQM Journal. 2019; 31(2):205-221. https://doi.org/10.1108/TQM-12-2018-0191
• 10. Antony J, Sony M. An empirical study into the limitations and emerging trends of Six Sigma in manufacturing and service organisations. International Journal of Quality & Reliability Management. 2020; 37(3):470-493. https://doi.org/10.1108/IJQRM-07-2019-0230
• 11. Antosz K, Jasiulewicz-Kaczmarek M, Waszkowski R, Machado J. Application of Lean Six Sigma for sustainable maintenance: Case study. IFAC-PapersOnLine. 2022; 55(19):181-186. https://doi.org/10.1016/j.ifacol.2022.09.204
• 12. Antosz K, Stadnicka D. Possibilities of maintenance service process analyses and improvement through Six Sigma, lean and Industry 4.0 implementation. In: Product Lifecycle Management to Support Industry 4.0: 15th IFIP WG 5.1 International Conference, PLM 2018, Turin, Italy, July 2-4, 2018, Proceedings. Springer International Publishing; 2018. p. 465-475. https://doi.org/10.1007/978-3-030-01614-2_43
• 13. Belhadi A, Kamble SS, Gunasekassran A, Zkik K, M. DK, Touriki FE. A Big Data Analytics-Driven Lean Six Sigma Framework for Enhanced Green Performance: A Case Study of Chemical Company. Production Planning & Control. 2021. https://doi.org/10.1080/09537287.2021.1964868
• 14. Bhat VS, Bhat S, Gijo EV. Simulation-based lean Six Sigma for Industry 4.0: An action research in the process industry. International Journal of Quality & Reliability Management. 2021; 38(5):1215-1245. https://doi.org/10.1108/IJQRM-05-2020-0167
• 15. Borucka A, Kozłowski E, Antosz K, Parczewski R. A New Approach to Production Process Capability Assessment for Non-Normal Data. Applied Sciences 2023;13:6721. https://doi.org/10.3390/app13116721.
• 16. Borucka A. Logistic regression in modeling and assessment of transport services, Open Engineering, 2020; 10(1,):2020, 26-34, doi:10.1515/eng2020-0029
• 17. Braglia M, Castellano D, Gallo M. A novel operational approach to equipment maintenance: TPM and RCM jointly at work. Journalof Quality in Maintenance Engineering. 2019; 25(4):612-634. https://doi.org/10.1108/JQME-05-2016-0018
• 18. Bro R, Smilde AK. Principal component analysis. Anal Methods. 2014; 6:2812-2831.DOI: 10.1039/C3AY41907J
• 19. Chiarini A, Kumar M. Lean six sigma and industry 4.0 integration for operational excellence: evidence from Italian manufacturing companies, Production Planning and Control. 2020; 32(13):1084-1101. doi: 10.1080/09537287.2020.1784485
• 20. Ciancio V, Homri L, Dantan JY, Siadat A, Convain P. Development of a flexible predictive maintenance system in the context ofIndustry 4.0. IFAC-PapersOnLine. 2022; 55(10):1576-1581. https://doi.org/10.1016/j.ifacol.2022.09.615
• 21. Citybabu G, Yamini S. Lean Six Sigma and Industry 4.0 –a bibliometric analysis and conceptual framework development for future research agenda. International Journal of Product Performance Management. 2023. https://doi.org/10.1108/IJPPM-10-2022-0549
• 22. Connelly L. Logistic regression. Medsurg Nursing 2020; 29(5):353-354. PMID: 33081747.
• 23. Costa LBM, Godinho Filho M, Fredendall LD, Ganga GMD. Lean six sigma in the food industry: Construct development and measurement validation. International Journal of Production Economics. 2021; 231:107843. https://doi.org/10.1016/j.ijpe.2020.107843
• 24. Dalzochio J, Kunst R, Pignaton E, Binotto A, Sanyal S, Favilla J, Barbosa J. Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Computer Industry. 2020; 123:103298. https://doi.org/10.1016/j.compind.2020.103298
• 25. Domagała I, Przystupa K, Firlej M, Pieniak D, Gil L, Borucka A, et al. Analysis of the Statistical Comparability of the Hardness and Wear of Polymeric Materials for Orthodontic Applications. Materials 2021;14:2925. https://doi.org/10.3390/ma14112925.
• 26. Dominic C, Okwu M, Tartibu L, Enarevba D. Systematic literature review of Six Sigma philosophy in manufacturing operations. In: Proceedings of the International Conference on Industrial Engineering and Operations Management Sao Paulo, Brazil; 2021.
• 27. Efimova A, Briš P, Efimov A. A bibliometric analysis of the evolution of Six Sigma in the context of Industry 4.0. InzinerineEkonomika-Engineering Economics. 2021; 32(4):338-349. EN 13306:2017 Maintenance -Maintenance terminology.https://doi.org/10.5755/j01.ee.32.4.28536
• 28. Farrukh A, Mathrani S, Taskin N. Investigating the theoretical constructs of a green lean Six Sigma approach towards environmental sustainability: A systematic literature review and future directions. Sustainability. 2020; 12(19):8247. https://doi.org/10.3390/su12198247
• 29. Ferrer A. Multivariate Six Sigma: A key improvement strategy in Industry 4.0. Qual Eng. 2021; 33(4):758-763. https://doi.org/10.1080/08982112.2021.1957481
• 30. Filz MA, Langner JEB, Herrmann C, Thiede S. Data-driven failure mode and effect analysis (FMEA) to enhance maintenance planning. Computers in Industry. 2021; 129:103451. https://doi.org/10.1016/j.compind.2021.103451
• 31. Franciosi C, Tortora AMR, Miranda S. A Maintenance Maturity and Sustainability Assessment Model for Manufacturing Systems. Management and Production Engineering Review. 2023; 14(1):137-155. https://doi.org/10.24425/mper.2023.145372
• 32. Franciosi C, Voisin A, Miranda S, Riemma S, Iung B. Measuring maintenance impacts on sustainability of manufacturing industries: from a systematic literature review to a framework proposal. Journal of Cleaner Production. 2020; 260:121065. https://doi.org/10.1016/j.jclepro.2020.121065
• 33. Gastelum-Acosta C, Limon-Romero J, Tlapa D, Baez-Lopez Y, Tortorella G, Rodriguez Borbon MI, Navarro-Cota CX. Assessing the adoption of critical success factors for lean six sigma implementation. Journal of Manufacturing Technology Management. 2022;33(1):124-145. http://dx.doi.org/10.1108/jmtm-12-2020-0488
• 34. Ghaleb M, Taghipour S. Assessing the impact of maintenance practices on asset's sustainability. Reliability Engineering & System Safety. 2022; 228:108810. https://doi.org/10.1016/j.ress.2022.108810
• 35. Giannetti C, Ransing RS. Risk Based Uncertainty Quantification to Improve Robustness of Manufacturing Operations. Computers &Industrial Engineering. 2016; 101:70-80. https://doi.org/10.1016/j.cie.2016.09.017
• 36. Gijo EV, Bhat S, Antony J, Park SH. Ten commandments for successful implementation of design for six sigma, The TQM Journal. 2022; 33(8):1666-1682. doi: 10.1108/TQM-01-2021-0014
• 37. Glawar R, Ansari F, Reichsthaler L, Sihn W, Toth D. Maintenance-Free Factory: A Holistic Approach for Enabling Sustainable Production Management. IFAC-PapersOnLine. 2022; 55(10):2318-2323. https://doi.org/10.1016/j.ifacol.2022.10.054
• 38. Gomes CF, Yasin MM, Simões JM. The emerging organizational role of the maintenance function: a strategic perspective. Journalof Quality Maintenance Engineering. 2021; 27(1):144-168. https://doi.org/10.1108/JQME-03-2017-0012
• 39. Gopalakrishnan M, Subramaniyan M, Skoogh A. Data-driven machine criticality assessment–maintenance decision support for increased productivity. Production Planning & Control. 2022; 33(1):1-19. https://doi.org/10.1080/09537287.2020.1817601
• 40. Graafmans T, Turetken O, Poppelaars H, Fahland D. Process mining for six sigma: a guideline and tool support. Business & Information Systems Engineering. 2021; 63:277-300. https://doi.org/10.1007/s12599-020-00649-w
• 41. Gupta S, Modgil S, Gunasekaran A. Big Data in Lean Six Sigma: A Review and Further Research Directions. International Journalof Production Research. 2020; 58(3):947-969. https://doi.org/10.1080/00207543.2019.1598599
• 42. Hallioui A, Herrou B, Katina PF, et al. A Review of Sustainable Total Productive Maintenance (STPM). Sustainability. 2023; 15(12):12362. https://doi.org/10.3390/su151612362
• 43. Hallioui A, Herrou B, Santos R, Katina P, Khadiri H, Egbue O. Systems Theory and Evidence-Based Decision-Making as Keys for Arbitrating between Optimal Production and Efficient Maintenance: A Case Study. Discrete Dynamics in Nature and Society. 2022; 7660082. https://doi.org/10.1155/2022/7660082
• 44. Hosmer Jr DW, Lemeshow S, Sturdivant RX. Applied logistic regression. John Wiley & Sons; 2013.https://doi.org/10.1002/9781118548387
• 45. Howell MT, Alshakhshir F. Energy Centered Maintenance: A Green Maintenance System. CRC Press; 2020.https://doi.org/10.1201/9781003151371
• 46. Huang C, Zhang E, Guo K, Yang Y, Zhang C, Wei J. Potential application of Six Sigma method in operation and maintenance management of UHVDC converter station. International Journal of Emerging Electric Power Systems. 2022; 24(2):151-162. https://doi.org/10.1515/ijeeps-2021-0320
• 47. Imanov T, Yıldız M, Koruyucu E. Lean Six Sigma Application for Aircraft Maintenance Process. In: Progress in Sustainable Aviation. Springer; 2022:137-153. ISO 13053-2:2011 Quantitative methods in process improvement —Six Sigma —Part 2: Tools and techniques.https://doi.org/10.1007/978-3-031-12296-5_8
• 48. Jasiulewicz-Kaczmarek M, Antosz K, Zhang C, Waszkowski R. Assessing the barriers to industry 4.0 implementation from a maintenance management perspective-pilot study results. IFAC-PapersOnLine. 2022; 55(2):223-228. https://doi.org/10.1016/j.ifacol.2022.04.197
• 49. Jou YT, Silitonga RM, Lin MC, Sukwadi R, Rivaldo J. Application of Six Sigma Methodology in an Automotive Manufacturing Company: A Case Study. Sustainability. 2022; 14(21):14497. https://doi.org/10.3390/su142114497
• 50. Kaswan MS, Rathi R, Antony J. Green lean six sigma sustainability–oriented project selection and implementation framework for manufacturing industry. International Journal of Lean Six Sigma. 2023; 14(1):33-71. https://doi.org/10.1108/IJLSS-12-2020-0212
• 51. Kent MD, Costello O, Phelan S, Petrov K. Cost oriented maintenance management systems for manufacturing processes, IFAC-Papers OnLine. 2018; 51(30):48-53. https://doi.org/10.1016/j.ifacol.2018.11.244
• 52. Khan A, Pohl M, Bosse S, Hart S, Turowski K. A Holistic View of the IoT Process from Sensors to the Business Value. In Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security -IoTBDS; SciTePress 2017: 392-399. doi: 10.5220/0006362503920399
• 53. Kolbusz P. The use of the Six Sigma methodology in a project improving the manufacturing process. Technologia i AutomatyzacjaMontażu. 2022; 118(4):8-19. https://doi.org/10.7862/tiam.2022.4.2
• 54. Koppel S, Chang S. MDAIC–a Six Sigma implementation strategy in big data environments. International Journal of Lean Six Sigma. 2021; 12(2):432-449. https://doi.org/10.1108/IJLSS-12-2019-0123
• 55. Kumar P, Bhadu J, Singh D, Bhamu J. Integration between lean, six sigma and industry 4.0 technologies. International Journal of Six Sigma and Competitive Advantage. 2021; 13(1-3):19-37. https://doi.org/10.1080/09537287.2023.2188496
• 56. Lameijer BA, Pereira W, Antony J. The implementation of Lean Six Sigma for operational excellence in digital emerging technology companies. Journal of Manufacturing Technology Management. 2021; 32(9):260-284. https://doi.org/10.1108/JMTM-09-2020-0373
• 57. Lee CKM, Yi Cao, Kam Hung Ng. Big data analysis for predictive maintenance strategies. In: Supply chain management in the eraof Big Data. IGI Global; 2017:50-74. https://doi.org/10.4018/978-1-5225-0956-1.ch004
• 58. Loska A, Paszkowski W. Geometric approach to machine exploitation efficiency : modelling and assessment. Eksploatacja i Niezawodność – Maintenance and Reliability. 2022; 24(1):114-122. https://doi.org/10.17531/ein.2022.1.13
• 59. Maia DDRP, Lizarelli FL, Gambi LDN. Industry 4.0 and Six Sigma: a systematic review of the literature and research agenda proposal. Benchmarking: An International Journal. 2023. https://doi.org/10.1108/BIJ-05-2022-0289
• 60. Maier HT, Schmiedbauer O, Biedermann H. Validation of a lean smart maintenance maturity model. Tehnički glasnik. 2020; 14(3):296-302. https://doi.org/10.31803/tg-20200706131623
• 61. Mjime I, Aoula ES, Achouyab EH. Contribution of machine learning in continuous improvement processes. Journal of Quality Maintenance Engineering. 2023; 29(2):553-567. https://doi.org/10.1108/JQME-03-2022-0019
• 62. Mkaka A, Burduk A. Implementation of the Integrated Lean Six Sigma philosophy in an Angolan manufacturing company –a case study. Technologia i Automatyzacja Montażu. 2022; 115(1):58-6. https://doi.org/10.7862/tiam.2022.1.6
• 63. Mohan J, Rathi R, Kaswan MS, Nain SS. Green lean six sigma journey: Conceptualization and realization. Materials Today: Proceedings. 2022; 50:1991-1998. https://doi.org/10.1016/j.matpr.2021.09.338
• 64. Montero Jiménez JJ, Vingerhoeds R, Grabot B, Schwartz S. An ontology model for maintenance strategy selection and assessment.Journal of Intelligent Manufacturing. 2023; 34(3):1369-1387. https://doi.org/10.1007/s10845-021-01855-3
• 65. Montgomery DC, Woodall WH. An overview of six sigma. International Statistical Review. 2008; 76(3):329-346. https://doi.org/10.1016/j.matpr.2021.09.338
• 66. Nguyen VT, Do P, Vosin A, Iung B. Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems. Reliability Engineering & System Safety. 2022; 228:108757. https://doi.org/10.1016/j.ress.2022.108757
• 67. Nurprihatin F, Ayu YN, Rembulan GD, Andry JF, Lestari TE. Minimizing Product Defects Based on Labor Performance using Linear Regression and Six Sigma Approach. Management and Production Engineering Review. 2023; 14(2):Volu88–98. DOI: 10.24425/mper.2023.146026
• 68. Palací-López D, Borràs-Ferrís J, da Silva de Oliveria LT, Ferrer A. Multivariate six sigma: A case study in industry 4.0. Processes. 2020; 8(9):1119. https://doi.org/10.3390/pr8091119
• 69. Park SH, Dhalgaard-Park SM, Kim DC. New paradigm of lean six sigma in the 4th industrial revolution era. Quality Innovation Prosperity. 2020; 24(1):1-16. doi: 10.12776/QIP.V24I1.1430.
• 70. Patel AS, Patel KM. Critical review of literature on Lean Six Sigma methodology. International Journal of Lean Six Sigma. 2021; 12(3):627-674. https://doi.org/10.1108/IJQRM-04-2017-0081
• 71. Pérez MP, Rodríguez ÁTP, de la Paz Martínez EM. Contribution to the sustainability of the plastics industry in Cuba through lean-green maintenance management. IFAC-PapersOnLine. 2022; 55(10):1918-1923. https://doi.org/10.1016/j.ifacol.2022.09.679
• 72. Pongboonchai-Empl T, Antony J, Garza-Reyes JA, Komkowski T, Tortorella GL. Integration of Industry 4.0 technologies into Lean Six Sigma DMAIC: A systematic review. Production Planning & Control. 2023; 1-26. https://doi.org/10.1080/09537287.2023.2188496
• 73. Pophaley M, Vyas RK. Fortification of plant maintenance management practices: role of six sigma approach. Brazilian Journal of Operations & Production Management. 2015; 12(1):56-64. DOI: 10.14488/BJOPM.2015.v12.n1.a6
• 74. Pugna A, Negrea R, Miclea S. Using Six Sigma methodology to improve the assembly process in an automotive company. Procedia-Social and Behavioral Sciences. 2016; 221:308-316. https://doi.org/10.1016/j.sbspro.2016.05.120
• 75. Purushothaman K, Ahmad R. Integration of Six Sigma methodology of DMADV steps with QFD, DFMEA and TRIZ applications for image-based automated inspection system development: a case study. International Journal of Lean Six Sigma. 2022; 13(6):1239-1276. https://doi.org/10.1108/IJLSS-05-2021-0088
• 76. Ribeiro DRS, Mendes LG, Forcellini FA, Frazzon EM. Maintenance 4.0: a literature review and SWOT analysis. In: International
• 77. Conference on Dynamics in Logistics. Springer; 2022:409-422. https://doi.org/10.1007/978-3-031-05359-7_33
• 78. Rifqi H, Souda SB, Zamma A. Lean Maintenance 4.0: An application of the IoT Technology in the facility management industry. International Journal. 2020; 9(5):9064-9070. https://doi.org/10.30534/ijatcse/2020/311952020.
• 79. Rifqi H, Zamma A, Ben Souda S. Lean 4.0, six Sigma-Big data toward future industrial opportunities and challenges: A literature review. Advances on Smart and Soft Computing: Proceedings of ICACIn 2020. 201-210. doi:10.1007/978-981-15-6048-4_18
• 80. Samadhiya A, Agrawal R, Luthra S, Kumar A, Garza-Reyes JA, Srivastava DK. Total productive maintenance and Industry 4.0 in a sustainability context: exploring the mediating effect of circular economy. The International Journal of Logistics Management. 2022; 34(3):818-846. https://doi.org/10.1108/IJLM-04-2022-0192
• 81. Schäfer F, Mayr A, Hess A, Zeiselmair C, Otten H, Franke J. Six Sigma 4.0 –Data Mining as Supporting Technique within the Zero Defect Management. Zeitschrift Für Wirtschaftlichen Fabrikbetrieb. 2019; 114(3):140-144. https://doi.org/10.3139/104.112047
• 82. Schäfer F, Schwulera E, Otten H, Franke J. From descriptive to predictive six sigma: machine learning for predictive maintenance. In: 2019 Second International Conference on Artificial Intelligence for Industries (AI4I). IEEE; 2019: 35-38. https://doi.org/10.1109/AI4I46381.2019.00017
• 83. Schroeder RG, Linderman K, Liedtke C, Choo AS. Six Sigma: Definition and underlying theory. Journal of Operations Management.2008; 26(4):536-554. https://doi.org/10.1016/j.jom.2007.06.007
• 84. Sharma K, Sharma G. Integrating Six Sigma culture and TPM framework to improve manufacturing performance in SMEs. Quality andReliability Engineering International. 2014; 30(5):745-765. https://doi.org/10.1002/qre.1525
• 85. Sharma R, Gupta P, Saini V. Six sigma DMAIC Methodology Implementation in Automobile industry: A case study. Journal of Manufacturing Engineering. 2018; 13(1):042-050. https://smenec.org/index.php/1/article/view/103
• 86. Shou W, Wang J, Wu P, Wang X. Lean management framework for improving maintenance operation: Development and application in the oil and gas industry. Production Planning & Control. 2021; 32(7):585-602. https://doi.org/10.1080/09537287.2020.1744762
• 87. Sing SK, Saudi MM. The impact of embedded total productive maintenance with six sigma on supply chain performance. Journal ofFundamental and Applied Sciences. 2018; 10(1S):678-688.
• 88. Sordan JE, Oprime PC, Pimenta ML, Silva SLD, Gonz_alez MOA. Contact points between Lean Six Sigma and Industry 4.0: a systematic review and conceptual framework, International Journal of Quality and Reliability Management. 2022; 39:2155-2183, doi: 10.1108/IJQRM-12-2020-0396
• 89. Sperandei S. Understanding logistic regression analysis. Biochemia medica. 2014; 24(1):12-18. https://doi.org/10.11613/BM.2014.003
• 90. Stanivuk T, Gvozdenović T, Žanić Mikuličić J, Lukovac V. Application of six sigma model on efficient use of vehicle fleet. Symmetry. 2020; 12(5):857. https://doi.org/10.3390/sym12050857
• 91. Syan CS, Ramsoobag G. Maintenance applications of multi-criteria optimization: an overview. Reliability Engineering and System Safety. 2019; 190:106520. https://doi.org/10.1016/j.ress.2019.106520
• 92. Tao F, Qi Q, Liu A, Kusiak A. Data-driven smart manufacturing. Journal of Manufacturing Systems. 2018; 48:157-169. https://doi.org/10.1016/j.jmsy.2018.01.006
• 93. Tengtarto MAK, Singgih ML, Siswanto N. From 1904 to 2022: A Comprehensive Review of Six Sigma Methodology. In: International Conference on Intellectuals’ Global Responsibility (ICIGR 2022). Atlantis Press; 2023:629-639. doi:10.2991/978-2-38476-052-7_69
• 94. Theissler A, Pérez-Velázquez J, Kettelgerdes M, Elger G. Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability engineering & system safety. 2021; 215:107864. https://doi.org/10.1016/j.ress.2021.107864
• 95. Thomas A, Barton R, Byard P. Developing a Six Sigma maintenance model. Journal of Quality in Maintenance Engineering. 2008; 14(3):262-271. https://doi.org/10.1108/13552510810899463
• 96. Tissir S, Cherrafi A, Chiarini A, Elfezazi S, Bag S. Lean Six Sigma and Industry 4.0 combination: Scoping review and perspectives. Total Quality Management & Business Excellence. 2023; 34(3-4):261-290. https://doi.org/10.1080/14783363.2022.2043740
• 97. Titmarsh R, Assad F, Harrison R. Contributions of Lean Six Sigma to sustainable manufacturing requirements: an industry 4.0 perspective. Proceed CIRP. 2020; 90:589–593. https://doi.org/10.1016/j.procir.2020.02.044
• 98. Trimarjoko A, Hardi Purba H, Nindiani A. Consistency of DMAIC phases implementation on Six Sigma method in manufacturing and service industry: a literature review. Management and Production Engineering Review. 2020; 11(4):34-45. doi: 10.24425/mper.2020.136118
• 99. Tsarouhas P. Reliability, availability and maintainability analysis of a bag production industry based on the six sigma DMAICapproach. International Journal of Lean Six Sigma. 2021; 12(2):237-263. https://doi.org/10.1108/IJLSS-09-2019-0101
• 100. Uluskan M, Karşı MG. Predictive Six Sigma for Turkish manufacturers: utilization of machine learning tools in DMAIC. International Journal of Lean Six Sigma. 2023; 14(3):630-652. https://doi.org/10.1108/IJLSS-02-2022-0046
• 101. van der Meulen F, Vermaat T, Willems P. Case study: An application of logistic regression in a six sigma project in health care. Quality Engineering. 2011; 23(2):113-124. https://doi.org/10.1080/08982112.2011.553761
• 102. Velmurugan K, Saravanasankar S, Bathrinath S. Smart maintenance management approach: Critical review of present practices andfuture trends in SMEs 4.0. Materials Today: Proceedings. 2022; 62:2988-2995. https://doi.org/10.1016/j.matpr.2022.02.622
• 103. Vrignat P, Kratz F, Avila M. Sustainable manufacturing, maintenance policies, prognostics and health management: A literaturereview. Reliability Engineering & System Safety. 2022; 218:108140. https://doi.org/10.1016/j.ress.2021.108140
• 104. Wen Y, Rahman MF, Xu H, Tseng TLB. Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective. Measurement. 2022; 187:110276. https://doi.org/10.1016/j.measurement.2021.110276
• 105. Wold S, Esbensen K, Geladi P. Principal component analysis. Chemom Intell Lab Syst. 1987; 2:37-52. https://doi.org/10.1016/0169-7439(87)80084-9
• 106. Yadav N, Mathiyazhaga, K, Kumar K. Application of Six Sigma to minimize the defects in glass manufacturing industry: A case study. Journal of Advances in Management Research. 2019, 16(4):594-624. https://doi.org/10.1108/JAMR-11-2018-0102
• 107. Yadav V, Kaswan MS, Gahlot P, Duhan RK, Garza-Reyes JA, Rathi R, Yadav, G. Green Lean Six Sigma for sustainability improvement: A systematic review and future research agenda. International Journal of Lean Six Sigma. 2023; 14(4):759-790. https://doi.org/10.1108/IJLSS-06-2022-0132
• 108. Yanamandra R, Alzoubi HM. Empirical investigation of mediating role of six sigma approach in rationalizing the COQ in serviceorganizations. Operations and Supply Chain Management: An International Journal. 2022; 15(1):122-135. https://doi.org/10.31387/oscm0480335
• 109. Youssouf A, Rachid C, Ion V. Contribution to the optimization of strategy of maintenance by lean six sigma. Physics procedia.2014; 55:512-518. https://doi.org/10.1016/j.phpro.2014.08.001
• 110. Yuniarto HA, Lawlor-Wright T. Root Cause Systemic Analysis: Improving maintenance performance with System Dynamics and Six Sigma. In Prosiding seminar internasional dari The 7th Annual Conference on Systems Engineering Research (CSER 2009). 2009; 20-23.
• 111. Zasadzień M. Application of the Six Sigma method for improving maintenance processes–case study. In: International Conference on Operations Research and Enterprise Systems. SCITEPRESS; 2017:314-320. doi: 10.5220/0006184703140320
• 112. Zhang C, Zhang Y, Dui H, Wang S, Tomovic MM. Importance measure-based maintenance strategy considering maintenance costs. Eksploatacja i Niezawodność – Maintenance and Reliability. 2022; 24(1):15-24. https://doi.org/10.17531/ein.2022.1.12
• 113. Zwetsloot IM, Kiuper A, Akkerhuis TS, Koning HD. Lean six sigma meets data science: integrating two approaches based on threecase-studies. Quality Engineering. 2018; 30:419-431. https://doi.org/10.1080/08982112.2018.1434892