Analyzing Process Quality Control Variables Using Fuzzy Cognitive Maps

• Autorzy: Cogollo-Florez Juan , Valencia-Mena Orfani

Identyfikatory

DOI

10.24425/mper.2022.142386

• Języki publikacji: EN

Abstrakty

EN

Abstract Meeting quality characteristics of products and processes is an important issue for customer satisfaction and business competitiveness. It is necessary to integrate new techniques and tools that improve and complement traditional process variables analysis. This paper proposes a new methodological approach to analyze process quality control variables using Fuzzy Cognitive Maps. Application of the methodology in the production process of carbonated beverages allowed identifying process variables with the greatest influence on finished product quality. The process variables with the greatest impact on carbon dioxide content in the beverage were the beverage temperature in the filler, the carbo-cooler pressure, and the filler pressure.

Słowa kluczowe

EN

fuzzy cognitive map; process management; process improvement; process variables control; quality control; soft computing applications

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2022
• Tom: Vol. 13, No. 3
• Strony: 94--101
• Opis fizyczny: Bibliogr. 29 poz., rys., tab., wykr.

Twórcy

autor

Cogollo-Florez Juan

• Department of Quality and Production, Instituto Tecnológico Metropolitano – ITM 050034, Medellín, Colombia

• https://orcid.org/0000-0002-6101-3134

autor

Valencia-Mena Orfani

• Department of Quality and Production, Instituto Tecnológico Metropolitano – ITM, Colombia

• https://orcid.org/0000-0001-5967-3558

Bibliografia

• Ahn S., Chettupuzha A.J., Ekyalimpa R., Hague S., AbouRizk S.M., and Stylios C.D. (2015), Fuzzy Cognitive Maps as a tool for modeling construction labor productivity, Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 1–7, IEEE. DOI: 10.1109/NAFIPSWConSC.2015.7284150.
• Al-Gunaid M.A., Salygina I.I., Shcherbakov M.V., Trubitsin V.N., and Groumpos P.P. (2021), Forecasting potential yields under uncertainty using fuzzy cognitive maps, Agriculture & Food Security, No. 1, Vol. 10, pp. 1–10. DOI: 10.1186/s40066-021-00314-9.
• Alibage A.A. (2020), Achieving High Reliability Organizations Using Fuzzy Cognitive Maps-The Case of Offshore Oil and Gas (Doctoral dissertation, Portland State University).
• Berenguer M. and Bernal F. (2000), NTP 549: Carbon dioxide in indoor air quality assessment, Madrid, Spain: Instituto Nacional de Seguridad e Higiene en el Trabajo.
• Bevilacqua M., Ciarapica F.E., Marcucci G., and Mazzuto G. (2020), Fuzzy cognitive maps approach for analysing the domino effect of factors affecting supply chain resilience: a fashion industry case study, International Journal of Production Research, No. 20, Vol. 58, pp. 6370–6398. DOI: 10.1080/00207543.2019.1680893.
• Bourgani E., Stylios C., Maniz G., and Georgopoulos V. (2013), Fuzzy Cognitive Maps Modeling and Simulation, In 25th European Modeling and Simulation Symposium, EMSS, pp. 561–570.
• Christova N., Groumpos P., and Stylios C. (2003), Production planning for complex plants using fuzzy cognitive maps, IFAC Proceedings Volumes, No. 3, Vol. 36, pp. 81–86. DOI: 10.1016/S1474-6670(17)37739-X.
• Cogollo J. and Correa A. (2018), Rule-based modeling of supply chain quality management, In 17th International Conference on Modeling and Applied Simulation, MAS 2018, pp. 120–125. University of Calabria.
• Dickerson J.A. and Kosko B. (1994), Virtual Worlds as Fuzzy Cognitive Maps, Presence: Teleoperators and Virtual Environments, No. 2, Vol. 3, pp. 173–189. DOI: 10.1109/VRAIS.1993.380742.
• Eweis D.S. and Stiban J. (2017), Carbon dioxide in carbonated beverages induces ghrelin release and increased food consumption in male rats: Implications on the onset of obesity, Obesity Research & Clinical Practice, No. 5, Vol. 11, pp. 534–543. DOI: 10.1016/j.orcp.2017.02.001.
• Flynn B. and Zhao X. (2015), Global Supply Chain Quality Management: Product Recalls and Their Impact; Boca Raton, FL, USA: CRC Press.
• Hawer S., Braun N., and Reinhart G. (2016), Analyzing Interdependencies between Factory Change Enablers Applying Fuzzy Cognitive Maps, Procedia CIRP, Vol. 52, pp. 151–156. DOI: 10.1016/j.procir.2016.07.015.
• ICONTEC (2020), NTC 2740:2020 Non-alcoholic beverages. Carbonated or soft drinks. Bogotá, Colombia.
• Islas K., Gutiérrez A., Soto A., and Aguillón K. (2015), Carbonated drinks, PÄDI Basic Sciences and Engineering Scientific Bulletin ICBI, No. 4, Vol. 2, pp. 1-7. DOI: 10.29057/icbi.v2i4.545.
• Jamshidi A., Ait-kadi D., Ruiz A., and Rebaiaia M.L. (2018), Dynamic risk assessment of complex systems using FCM, International Journal Production Research, No. 3, Vol. 56, pp. 1070–1088. DOI: 10.1080/00207543.2017.1370148.
• Kahraman C. and Yavuz M. (2010), Production Engineering and Management under Fuzziness, Berlin, Germany: Springer, pp. 417–430.
• Konti A. and Damigos D. (2018), Exploring strengths and weaknesses of bioethanol production from biowaste in Greece using Fuzzy Cognitive Maps, Energy Policy, Vol. 112, pp. 4–11. DOI: 10.1016/j.enpol.2017.09.053.
• Kosko B. (1986), Fuzzy cognitive maps, International Journal of Man Machine Studies, No. 1, Vol. 24, pp. 65–75. DOI: 10.1016/S0020-7373(86)80040-2.
• Mls K., Cimler R., Vaščák J., and Puheim M. (2017), Interactive evolutionary optimization of fuzzy cognitive maps, Neurocomputing, Vol. 232, pp. 58–68. DOI: 10.1016/j.neucom.2016.10.068.
• Montgomery D. (2019), Introduction to Statistical Quality Control. 8th ed. Hoboken, NJ, USA: Wiley, pp. 175–216.
• Papageorgiou E. (2014), Fuzzy Cognitive Maps for Applied Sciences and Engineering, Berlin, Germany: Springer, pp. 221–315. DOI: 10.1007/978-3-642-39739-4.
• Pelta D. and Cruz C. (2018), Soft Computing Based Optimization and Decision Models, Berlin, Germany: Springer, pp. 45–60.
• Peter G., Antigoni A., and Vasileios G. (2015), A New Mathematical Modelling Approach for Viticulture and Winemaking Using Fuzzy Cognitive Maps, IFAC-PapersOnLine, No. 24, Vol. 48, pp. 15–20. DOI: 10.1016/j.ifacol.2015.12.049.
• Rezaee M.J., Yousefi S., Baghery M., and Chakrabortty R.K. (2021), An intelligent strategy map to evaluate improvement projects of auto industry using fuzzy cognitive map and fuzzy slack-based efficiency model, Computers & Industrial Engineering, Vol. 151, 106920. DOI: 10.1016/j.cie.2020.106920.
• Vidal R., Salmeron J.L., Mena A., and Chulvi V. (2015), Fuzzy Cognitive Map-based selection of TRIZ (Theory of Inventive Problem Solving) trends for ecoinnovation of ceramic industry products, Journal Cleaner Production, Vol. 107, pp. 202–214. DOI: 10.1016/j.jclepro.2015.04.131.
• Yousefi S. and Tosarkani B.M. (2022), An analytical approach for evaluating the impact of blockchain technology on sustainable supply chain performance, International Journal of Production Economics, Vol. 246. DOI: 10.1016/j.ijpe.2022.108429.
• Yu V.F. and Hu K.J. (2010), An integrated fuzzy multicriteria approach for the performance evaluation of multiple manufacturing plants, Computers & Industrial Engineering, No. 2, Vol. 58, pp. 269–277. DOI: 10.1016/j.cie.2009.10.005.
• Yuan K., Liu J., Yang S., Wu K., and Shen F. (2020), Time series forecasting based on kernel mapping and high-order fuzzy cognitive maps, Knowledge-Based Systems, Vol. 206. DOI: 10.1016/j.knosys.2020.106359.
• Zanon L.G., Marcelloni F., Gerolamo M.C., and Carpinetti L.C.R. (2021), Exploring the relations between supply chain performance and organizational culture: A fuzzy grey group decision model, International Journal of Production Economics, Vol. 233. DOI: 10.1016/j.ijpe.2020.108023.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)