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PETRİÇLİ, GÜLCAN

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PETRİÇLİ

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GÜLCAN

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    Publication
    Integrating quality function deployment with fuzzy cognitive maps for resolving correlation issues in the roof matrix
    (Ege Üniversitesi, 2022-02-01) Emel, Gül Gökay; Petriçli, Gülcan; Kayguluoğlu, Cem; Emel, Gül Gökay; PETRİÇLİ, GÜLCAN; Bursa Uludağ Üniversitesi/İktisadi ve İdari Bilimler Fakültesi; Bursa Uludağ Üniversitesi/İnegöl Meslek Yüksekokulu/Uluslararası Ticaret Programı; JCN-8103-2023; AAH-5172-2021
    The roof matrix represents correlations among engineering characteristics (EC) in the house of quality (HoQ) in Quality Functions Deployment (QFD). Correlations are usually measured qualitatively and omitted in the analysis. However, ignoring them may cause duplication of effort, decreased product performance, and unsatisfied customer requirements (CR). Hence, this paper intends to propose an approach to considering the correlations quantitatively. Fuzzy Cognitive Maps (FCM) were used for this purpose. Additionally, Axiomatic Design (AD), for examining relationships between CRs and ECs, and Fuzzy Analytic Hierarchy Process (FAHP) with the Extent Analysis (EA) were used for checking the consistency of the evaluations. The proposed approach was applied in a sheet metal die-making company for ranking CRs and ECs. Results show that FCM enables analysing the quantitative roof matrix practically. The square roof matrix that supports FCM's adjacency matrix structure successfully represents asymmetric relationships among ECs. Integrating the correlations into the analysis resulted in a change in the final ranking. It also helped determine the most manageable ECs, better satisfiable CRs, and most critical/least manageable ECs.
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    Identifying green citizen typologies by mining household-level survey data
    (Elsevier, 2023-10-24) Petriçli, Gülcan; İnkaya, Tülin; Emel, Gül Gökay; PETRİÇLİ, GÜLCAN; İNKAYA, TÜLİN; Emel, Gül Gökay; 0000-0002-6260-0162; AAH-5172-2021; JCN-8103-2023; AAH-2155-2021
    Some impactful but unfavorable results of rapid urbanization are human-nature disconnection, waste of energy/ water resources, and increased greenhouse gas emissions. To save the future of our planet, a transition to a more sustainable urban life is a must. However, there is no single sustainable city model because cities differ in terms of their assets. Hence, locally customized sustainable actions linked to global sustainability should be developed, such as a change in individual behaviors leads to a sustainable society, city, and country. This research investigated green citizen profiles and variables affecting the profiles in the context of environmental behavior and sustainability. For this purpose, survey research was done at the household level in a metropolis in Turkey. Measurement scales about environmental concern, human-nature connections, and sustainable consumption behavior were used for collecting data. A data analysis approach was proposed as the survey dataset contains mixed-type variables. It amalgamates statistics with machine learning algorithms, namely two-stage clustering with multilayered self-organizing maps, k-medoid clustering algorithm, factor analysis, permutational multivariate analysis of variance, principal component analysis and classification and regression trees algorithm. The results reveal that (i) five distinct profiles, namely unconscious greens, risky greens, economic greens, potential greens, and wasters are identified, none of which is entirely green; (ii) district, family life-cycle, household size, number of rooms, altruistic and biocentric environmental concerns are the most critical variables in distinguishing profiles; (iii) the proposed approach enables processing socio-demographic, psychographic, behavioral and consumption variables together.