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Öğe Bir otomotiv yan sanayisinde IATF 16949: 2016 kalite yönetim sistemi standardının balanced scorecard modeli ile performans değerlendirmesi(2022) Yıldız, Mehmet Selami; Kesici, BüşraBu araştırmanın temel amacı otomotiv sektörüne özgü olan Kalite Yönetim Sistemi standardının eski versiyonu ISO/TS 16949: 2009’dan, son versiyon IATF 16949: 2016’a geçiş sırasında eklenen gerekliliklerinin ve bunların işletmeye etkilerinin analiz edilmesidir. Ek olarak geçiş sonrasında işletmenin performans göstergelerindeki değişimin analiz edilmesi amaçlanmıştır. Performans takibi Balanced Scorecard modeli ile yapılmıştır. Nitel araştırma deseninin kullanıldığı araştırmada, analiz ve veri toplama süreci doküman inceleme ve görüşme yöntemleri ile gerçekleştirilmiştir. Şirketin üst düzey üç yöneticisi ile yapılan 14 adet görüşme, yarı yapılandırılmış görüşme yön- temi ile gerçekleştirilmiştir. Elde edilen veriler betimsel analiz tekniği ile analiz edilmiştir. Analiz sonucunda, yeni versiyon standartlara geçiş için kullanılması gereken tüm uygulamalar belirle- nerek otomotiv sektörüne özgü olarak örneklendirilmiştir. GAP Analizi, SWOT Analizi, PESTEL ve PRIMO-F, Risk Analizi, İç Denetim ve Yönetim Gözden Geçirme Toplantıları hakkındaki gereklilikler işletmeye uygulanmıştır. Bu uygulamaların, işletme performansının artışı ile pozitif yönlü bir iliş- kisi olduğu belirlenmiştir. Takip edilen 24 adet performans göstergesi, Balanced Scorecard boyut- larına göre sınıflandırılmıştır. Çalışma sonuçlarına göre 22 göstergede pozitif yönlü iyileşme tespit edilmiştir. Tedarikçi PPM ve Numune gönderim performansı göstergelerinde ise çalışma öncesi ve sonrasında anlamlı bir değişiklik olmadığı saptanmıştır.Öğe A Decision-Making Framework for Struggling with Digital Supply Chain Barriers through Industry 5.0 Technologies(Research Expansion Alliance (REA), 2025) Kesici, Büşra; Coban, AbdulkadirPurpose: The objective of this research is to determine and rank methods for overcoming implementation hurdles for Digital Supply Chains (DSCs) in the context of the Industry 5.0 (I5.0) framework. Methodology: First, twenty-three DSC barriers hindering the digitalization of the manufacturing supply chain and nine Industry 5.0 technologies to overcome them were defined with an extensive literature review and expert opinions. The importance weights of the identified criteria were calculated using the Full Consistency Method (FUCOM). Multi-Attribute Ideal-Real Comparative Analysis (MAIRCA), Multi-Attributive Border Approximation Area Comparison (MABAC) and Additive Ratio Assessment (ARAS) method were used to rank the Industry 5.0 (I5.0) technologies. The results were compared with each other using different ranking methods. Sensitivity analysis was performed for all ranking methods to monitor the consistency of the results. Findings: The results showed that “high initial investment”, “issues with data” and “complexity in integrating systems” are the most important DSC barriers to overcome first. To overcome these barriers, “virtual training” and “blockchain” technologies seem to be the most important I5.0 innovations that should be addressed primarily. Originality/Value: By identifying the critical barriers to DSC and prioritizing the I5.0 technologies needed to overcome them, this study provides a solid framework for future research and practical applications in the Digital Transformation (DT) journey of companies. © 2025 Elsevier B.V., All rights reserved.Öğe Lean Digital Transformation in Health Supply Chain: Identifying Barriers to Machine Learning and AI Applications and Strategies to Overcome(IGI Global, 2025) Kesici, Büşra; Duzdar, IremOne of the important sectors that need to keep up with digital transformation quickly is healthcare. Given its direct impact on human lives, it is crucial to conduct studies to improve service quality in this sector. Considering the effects of the lean management approach in the healthcare sector on digital transformation in the supply chain is the first important research question for this study. Another research question is the contribution of artificial intelligence (AI) and machine learning (ML) approaches developed in recent years to digital transformation in the healthcare sector. The necessity of digital transformation tools, including artificial intelligence and machine learning, in healthcare services and the challenges faced during their implementation were identified as criteria and a weighting analysis was conducted with the help of multi-criteria decision-making (MCDM) analysis. Step-Wise Weight Assessment Ratio Analysis (SWARA) was used to determine the importance weights of the evaluation criteria. Strategies were defined with Enterprise Resource Planning (ERP) as a solution to the important weights of the challenges. The aim is to reduce costs while increasing supply chain efficiency and effectiveness by identifying practices and strategies that incorporate expert feedback from the industry. © 2025 Elsevier B.V., All rights reserved.Öğe Mapping the Future: Prioritizing Selection Factors for Tomorrow’s Professions(Springer Science and Business Media Deutschland GmbH, 2025) Ayyildiz, Ertugrul; Erdogan, Melike; Kesici, Büşra; Aydin, NezirCareer selection significantly impacts an individual's financial stability, job satisfaction, and overall quality of life. As industries evolve with technological advancements and digital transformation, understanding the key factors influencing career choices has become increasingly important. This study aims to prioritize the factors affecting future career decisions using a structured multi-criteria decision-making approach. A framework comprising six main criteria and 24 sub-criteria was developed through expert opinions and a literature review. The Spherical Fuzzy Step-wise Weight Assessment Ratio Analysis (SF-SWARA) is applied to assess and rank these factors while accounting for uncertainty. The results indicate that Starting Salary is the most influential factor, followed by Alignment with Personal Goals, emphasizing the role of financial security and personal motivation in career decisions. Conversely, Remote Work Potential was ranked as the least important factor, suggesting that direct workplace interaction and industry-specific requirements remain crucial. © 2025 Elsevier B.V., All rights reserved.
