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The concept of maintenance sustainability performance assessment by integrating balanced scorecard with non-additive fuzzy integral

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Warianty tytułu
PL
Koncepcja oceny zrównoważonego utrzymania ruchu z zastosowaniem zrównoważonej karty wyników i nie-addytywnej całki rozmytej
Języki publikacji
EN
Abstrakty
EN
In response to the growing sustainability concerns, manufacturing companies have to formulate measures to assess sustainable manufacturing performance, aiming at integration of sustainability aspects. Although various models and methods to assess the sustainability of production processes, and point the role of maintenance have been developed in recent years, contribution of all the elements of the maintenance to the results of sustainable production has not been comprehensively considered, since mostly financial aspects were analyzed. Taking into account this research gap, the article presents the concept of a model and procedure for assessing maintenance from the perspective of sustainable manufacturing requirements. Authors integrate three sustainability dimensions (economic, social and environmental) with Kaplan and Norton’s balance scorecard perspectives as a basis to develop the model of maintenance sustainability performance assessment. For the model developed, the assessment procedure based on the paradigm of aggregate assessment was designed. The Choquet integral, based on the so-called λ – measure, was implemented to aggregate the measures. Then, the results of research on determining the importance and interactions between the perspectives and criteria for assessing sustainable maintenance in enterprises representing the automotive and food industries are presented.
PL
W odpowiedzi na wyzwania zrównoważonego rozwoju (SD), przedsiębiorstwa produkcyjne włączają ekonomiczne, środowiskowe i społeczne wymagania SD do swoich praktyk produkcyjnych i formułują miary do oceny skuteczności podjętych działań. Pomimo, iż w ostatnich latach opracowano wiele modeli i metod oceny zrównoważonej produkcji i wskazywano w nich na rolę utrzymania ruchu, to jednak poza aspektem finansowym nie rozważano w sposób kompleksowy wszystkich elementów wkładu utrzymania ruchu w wyniki zrównoważonej produkcji. Biorąc pod uwagę tę lukę badawczą, w artykule przedstawiono koncepcję modelu i procedury oceny utrzymania ruchu z perspektywy wymagań zrównoważonej produkcji. Autorzy integrują trzy wymiary zrównoważonego rozwoju (ekonomiczny, społeczny i środowiskowy) z perspektywami zrównoważonej karty wyników Kaplana i Nortona, jako podstawę do skonstruowania modelu oceny wyników zrównoważonego utrzymania ruchu. Dla tak opracowanego modelu zaprojektowano opartą na paradygmacie oceny agregatowej procedurę oceniania. Do agregacji składników oceny zastosowano całkę Choqueta, opartą na tzw. mierze λ. Następnie przedstawiono wyniki badań pilotażowych dotyczących określenia ważności i interakcji między perspektywami i kryteriami oceny zrównoważonego utrzymania ruchu w przedsiębiorstwach branży motoryzacyjnej i spożywczej.
Rocznik
Strony
650--661
Opis fizyczny
Bibliogr. 55 poz., rys.
Twórcy
  • Chair of Ergonomics and Quality Management Faculty of Management Engineering Poznan University of Technology ul. 11 Strzelecka, 60-965 Poznań, Poland
autor
  • Department of Imprecise Information Processing Methods Faculty of Mathematics and Computer Science Adam Mickiewicz University in Poznań 87 Umultowska Str. 61-614 Poznań, Poland
Bibliografia
  • 1. Ajukumar V N, Gandhi O P. Evaluation of green maintenance initiatives in design and development of mechanical systems using an integrated approach. Journal of Cleaner Production 2013; 1-13, https://doi.org/10.1016/j.jclepro.2013.01.010.
  • 2. Alsyouf I. Measuring maintenance performance using a balanced scorecard approach. Journal of Quality in Maintenance Engineering 2006; 12(2): 133-149, https://doi.org/10.1108/13552510610667165.
  • 3. Al-Turki U M, Ayar T, Yilbas B S, Sahin A Z. Integrated Maintenance Planning in Manufacturing Systems. Springer Briefs in Manufacturing and Surface Engineering 2014; 25-57, https://doi.org/10.1007/978-3-319-06290-7_3.
  • 4. Antosz K. Maintenance - identification and analysis of the competency gap. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018; 20 (3): 484-494, https://doi.org/10.17531/ein.2018.3.19.
  • 5. Antosz K, Ratnayake R M C. Classification of spare parts as the element of a proper realization of the machine maintenance process and logistics - case study. IFAC-PapersOnLine 2016; 49(12): 1389-1393, https://doi.org/10.1016/j.ifacol.2016.07.760.
  • 6. Bokrantz J, Skoogh A, Berlin C, Stahre J. Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030. International Journal of Production Economics 2017; 191: 154-169, https://doi.org/10.1016/j.ijpe.2017.06.010.
  • 7. Chen S J, Chen S M. A new method for handling multi-criteria fuzzy decision making problems using FN-IOWA operators. Cybernetics and Systems 2003; 34: 109-137, https://doi.org/10.1080/01969720302866.
  • 8. Chiou H K, Tzeng G H. Fuzzy multicriteria decision-making approach to analysis and evaluation of green engineering for industry. Environmental Management 2002; 30(6): 816-830, https://doi.org/10.1007/s00267-002-2673-z.
  • 9. Chopu-inwai R, Diaotrakun R, Thaiupathump T. Key indicators for maintenance performance measurement: The aircraft galley and associated equipment manufacturer case study. in: 10th International conference Service systems and service management (ICSSSM), 17-19 July, 2013; 844-849.
  • 10. EN 13306 : 2018-01 Maintenance - Maintenance terminology
  • 11. Galante G M, Inghilleri R, La Fata C M. A hierarchical framework for the measurement of maintenance efficacy and efficiency using performance indicators, in: Proceedings of the European Safety and Reliability Conference, ESREL 2014, 2015; 1109-1117.
  • 12. Galar D, Parida A, Kumar U, Stenström C, Berger L. Maintenance metrics: a hierarchical model of balanced scorecard. IEEE International Conference on Quality and Reliability (IEEQR), Piscataway 2011; 67-74, https://doi.org/10.1109/ICQR.2011.6031683.
  • 13. Galar D, Berges L, Sandborn P, Kumar U. The need for aggregated indicators in performance asset management. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2014; 16 (1): 120-127.
  • 14. Gan X, Fernandez I C, Guo J, Wilson M, Zhao Y, Zhou B, Wu J. When to use what: Methods for weighting and aggregating sustainability indicators. Ecological Indicators 2017; 81: 491-502, https://doi.org/10.1016/j.ecolind.2017.05.068.
  • 15. Garetti M, Taisch M. Sustainable manufacturing: trends and research challenges. Production Planning and Control 2012; 23(2-3): 83-104, https://doi.org/10.1080/09537287.2011.591619.
  • 16. Grabisch M. The Application of Fuzzy Integrals in Multicriteria Decision Making. European Journal of Operational Research 1996; 89: 445- 456, https://doi.org/10.1016/0377-2217(95)00176-X.
  • 17. Grabisch M. K-order additive discrete fuzzy measures and their representation. Fuzzy Sets and Systems 1997; 92(2): 167-189, https://doi.org/10.1016/S0165-0114(97)00168-1.
  • 18. Grabisch M, Labreuche C A. Decade of Application of the Choquet and Sugeno Integrals in Multicriteria Decision Making. Annals of Operations Research 2010; 175: 247-286, https://doi.org/10.1007/s10479-009-0655-8.
  • 19. Grabisch M. Fuzzy Measures and Integrals: Recent Developments. Fifty years of fuzzy logic and its applications 2015; 125-151.
  • 20. Gupta S, Gupta P, Parida A. Modeling lean maintenance metric using incidence matrix approach. International Journal of Systems Assurance Engineering and Management 2017; 8(4): 799-816, https://doi.org/10.1007/s13198-017-0671-z.
  • 21. Gürbüz T, Alptekin S E, Alptekin G I. A hybrid MCDM methodology for ERP selection problem with interacting criteria. Decision Support Systems 2012; 54: 206-214, https://doi.org/10.1016/j.dss.2012.05.006.
  • 22. Hu Y C, Chen H. Choquet Integral-based hierarchical networks for evaluating customer service perceptions on fast food stores. Expert Systems with Applications 2010; 37: 7880-7887, https://doi.org/10.1016/j.eswa.2010.04.049.
  • 23. Ighravwe D E, Oke S A. Ranking maintenance strategies for sustainable maintenance plan in manufacturing systems using fuzzy axiomatic design principle and fuzzy-TOPSIS. Journal of Manufacturing Technology Management 2017; 28(7): 961-992, https://doi.org/10.1108/JMTM-01-2017-0007.
  • 24. Iung B, Levrat E. Advanced Maintenance Services for Promoting Sustainability. Procedia CIRP 2014; 22: 15-22, https://doi.org/10.1016/j.procir.2014.07.018.
  • 25. Jasiulewicz-Kaczmarek M. Role and contribution of maintenance in sustainable manufacturing. Manufacturing Modelling, Management, and Control 2013; 7(1): 1146-1151, https://doi.org/10.3182/20130619-3-RU-3018.00511.
  • 26. Kaplan R S, Norton D P. The balanced scorecard: translating strategy into action. Harvard Business Press 1996.
  • 27. Kłosowski G, Kozłowski E, Gola A. Integer linear programming in optimization of waste after cutting in the furniture manufacturing. Advances in Intelligent Systems and Computing 2018; 637: 260-270, https://doi.org/10.1007/978-3-319-64465-3_26.
  • 28. Kosicka E, Kozłowski E, Mazurkiewicz D. The use of stationary tests for analysis of monitored residual processes. Eksploatacja I Niezawodnosc - Maintenance and Reliability 2015; 17 (4): 604-609, https://doi.org/10.17531/ein.2015.4.17.
  • 29. Kozłowski E, Optimal route determining for LQ problem with optimally stopped horizon, In 20th International Conference on Methods and Models in Automation and Robotics (MMAR 2015), Międzyzdroje, Poland, 24-27 August 2015; 553-557, https://doi.org/10.1109/MMAR.2015.7283935.
  • 30. Kozłowski E, Optimal stopping of controlled linear stochastic systems, In 21th International Conference on Methods and Models in Automation and Robotics (MMAR 2016), Międzyzdroje, Poland, 29 August - 1 September 2016; 272-277, https://doi.org/10.1109/MMAR.2016.7575146.
  • 31. Kumar U, Ellingsen H P. Design and development of maintenance performance indicators for the Norwegian oil and gas industry. Proceedings of the 15th European Maintenance Congress: Euromaintenance 2000, Gothenburg, Sweden 2000: 224-228.
  • 32. Liyanage J P. Operations and maintenance performance in production and manufacturing assets: The sustainability perspective. Journal of Manufacturing Technology Management 2007; 18(3): 304-314, https://doi.org/10.1108/17410380710730639.
  • 33. Magdi A M, Xiao W. Q-measures: an efficient extension of the Sugeno lambda-measure. IEEE Transactions on Fuzzy Systems 2003; 11(3): 419-426, https://doi.org/10.1109/TFUZZ.2003.812701.
  • 34. Mather D. The Maintenance Scorecard: Creating Strategic Advantage. Industrial Press: New York 2005.
  • 35. Moldavska A. Model-based Sustainability Assessment - an enabler for Transition to Sustainable Manufacturing. Procedia CIRP 2016; 48: 413-418, https://doi.org/10.1016/j.procir.2016.04.059.
  • 36. Muchiri P, Pintelon L, Gelders L, Martin H. Development of maintenance function performance measurement framework and indicators. International Journal of Production Economics 2011; 131: 295-302, https://doi.org/10.1016/j.ijpe.2010.04.039.
  • 37. Murofushi T. Techniques for reading fuzzy measures (I): The Shapley value with respect to a fuzzy measure. in: 2nd Fuzzy Workshop, Nagaoka, Japan, 1992; 39-48, in Japanese.
  • 38. Murofushi T, Soneda S. Techniques for reading fuzzy measures (III): interaction index. in: 9th Fuzzy System Symposium, 693-696, Sapporo, Japan, May 1993. In Japanese
  • 39. Nikolaou I E, Tsalis T. Development of a sustainable balanced scorecard framework. Ecological Indicators 2013; 34: 76-86, https://doi.org/10.1016/j.ecolind.2013.04.005.
  • 40. Nooteboom S. Impact assessment procedures for sustainable development: A complexity theory perspective. Environmental Impact Assessment Review 2007; 27(7): 645-65, https://doi.org/10.1016/j.eiar.2007.05.006.
  • 41. Parida A, Chattopadhyay G. Development of multi-criteria hierarchical framework for maintenance performance measurement (MPM). Journal of Quality in Maintenance Engineering 2007; 13(3): 241-258, https://doi.org/10.1108/13552510710780276.
  • 42. Pires S P, Sénéchal O, Loures E F R, Jimenez J F. An approach to the prioritization of sustainable maintenance drivers in the TBL framework. IFAC-PapersOnLine 2016; 49-28: 150-155.
  • 43. Raouf A. Productivity enhancement using safety and maintenance integration. An overview. Kybernetes 2004; 33(7): 1116-1126, https://doi.org/10.1108/03684920410534452.
  • 44. Roy R, Stark R, Tracht K, Takata S, Mori M. Continuous maintenance and the future - Foundations and technological challenges. CIRP Annals - Manufacturing Technology 2016; 65: 667-688, https://doi.org/10.1016/j.cirp.2016.06.006.
  • 45. Sadiq R, Tesfamariam S. Developing environmental indices using fuzzy numbers ordered weighted averaging (FN-OWA) operators, Stochastic Environmental Research & Risk Assessment 2008; 22(1): 494-505, https://doi.org/10.1007/s00477-007-0151-0.
  • 46. Saniuk A, Saniuk S, Caganova D, Cambal M. Control of strategy realization in metallurgical production. in: 23rd International Conference on Metallurgy and Materials - Metals 2014; 1876-1881.
  • 47. Sari E, Shaharoun A M, Maaram A, Yazid A M. Sustainable maintenance performance measures: a pilot survey in Malaysian automotive companies. Procedia CIRP 2015; 26: 443-448, https://doi.org/10.1016/j.procir.2014.07.16.
  • 48. Savino M M, Macchi M, Mazza A. Investigating the impact of social sustainability within maintenance operations: An action research in heavy industry, Journal of Quality in Maintenance Engineering 2015; 21(3): 310-331, https://doi.org/10.1108/JQME-06-2014-0038.
  • 49. Sobaszek Ł, Gola A, Kozłowski E, Application of survival function in robust scheduling of production jobs, Proceedings of the 2017 Federated Conference on Computer Science and Information Systems (FEDCSIS 2017); 575-578, https://doi.org/10.15439/2017F276.
  • 50. Sugeno M. Theory of fuzzy integrals and its applications, Ph.D. theses, Tokyo Institute of Technology, Japan 1974.
  • 51. Sugeno M, Terano T. A model of learning based on fuzzy information. Kybernetes (1997); 6(2): 157-166.
  • 52. Takata S, Kimura F, van Houten F J A M, Westkämper E, Shpitalni M, Ceglarek D, Lee J. Maintenance: Changing role in life cycle management. Annals of the CIRP 2004; 53(2): 643-656, https://doi.org/10.1016/S0007-8506(07)60033-X.
  • 53. Tsai H H, Lu I Y. The evaluation of service quality using generalized Choquet integral. Information Sciences 2006; 176(6): 640-663, https://doi.org/10.1016/j.ins.2005.01.015.
  • 54. Tsang A H C. A strategic approach to managing maintenance performance. Journal of Quality in Maintenance Engineering 1998; 4(2): 87-94, https://doi.org/10.1108/13552519810213581.
  • 55. Van Horenbeek A, Kellens K, Pintelon L, Duflou J R. Economic and environmental aware maintenance optimization. Procedia CIRP 2014; 5: 343-348, https://doi.org/10.1016/j.procir.2014.06.048.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5f45f568-4510-453e-b07d-b73e65b19a33
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