KOBİ'ler için KOSGEB destek modellerinin veri zarflama analizi ve çok ölçütlü karar verme yöntemleri ile belirlenmesi
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Dosyalar
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Kırıkkale Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Küçük ve Orta Ölçekli İşletmeler (KOBİ), oluşturdukları katma değer itibariyle genel ekonomide katalizör görevi yapmaktadır. Üretim, pazarlama, ihracat ve istihdama sağladığı katkı tartışmasız çok ciddi boyutlara ulaşmış, ekonomi yönetimlerinin vazgeçemediği bir kesim olmuştur. KOBİ'ler içerisinde otomotiv yan sanayi önemli bir yer tutmaktadır. Mart 2017 ihracat verilerine göre otomotiv endüstrisinin % 19'luk payı da, bu sektörün ekonomideki önemini göstermektedir. KOBİ'lerin ekonomide çok önemli bir yeri bulunmasına rağmen; verimlilik, kurumsallaşma, markalaşma, üretim, pazarlama ve finansman alanlarında ciddi problemlerle karşılaşmaktadırlar. Devlet bu tür problemleri çözmek için KOSGEB aracılığıyla destek vermektedir. KOSGEB 2016 yılı destek bütçesi yaklaşık 1 milyar TL dir. KOSGEB tarafından KOBİ'lerin problemlerini çözmek için mevcut 27 adet destek modeli bulunmaktadır. Firmalar bu destek modellerinin çoğuna başvurabilmektedir. KOBİ'lerin öncelikli problemler ile ilgili destek modellerine başvurması gerekirken öncelikli olmayan modellere başvurması ciddi kaynak israfına ve zaman kaybına neden olmaktadır. Bu tezde KOBİ'lerin öncelikli sorunlarına göre hangi destek modellerine başvurması problemi ele alınmıştır. Uygulama yeri olarak Konya Sanayi Odasına kayıtlı 82 adet KOBİ ölçekli otomotiv yan sanayi ile ilgili firma seçilmiştir. İlk olarak firmaların verimlilik değerlendirilmesi veri zarflama analizi ile yapılmıştır. Firmalar etkinlik skorlarına göre A, B1, B2, C1 ve C2 şeklinde gruplandırılmıştır. Her grubun ortak sorunları dikkate alınarak hangi destek modellerine başvurabileceği çok ölçütlü karar verme yöntemleriyle belirlenmiştir. Burada AHP ile kriterlerin ağırlıkları hesaplanmış, TOPSIS yöntemi ile de KOSGEB desteklerinin sıralaması bulunmuştur. Veri zarflama analizi BCC ve CCR modellerine göre yapılmıştır. A, B1, B2, C1, C2 sayıları BCC'ye göre sırasıyla 31, 6, 5, 15 ve 25 iken CCR'da 16, 4, 7, 18 ve 37 olarak bulunmuştur. Çok ölçütlü karar verme yöntemleri sonucunda gruplarda ilk sırayı A'da KOBİGEL, B1'de lojistik, B2'de KOBİGEL, C1 ve C2'de test, analiz ve kalibrasyon destek modeli yer almıştır. Diğer sıralamalar ise farklılık göstermektedir. Bu tez, KOBİ'lerin verimlilik durumu ve öncelikli sorunlarına göre hangi destek modeline başvurması konusunda yol gösterici olacaktır. Böylece firmalar öncelikli problemlerine odaklanabileceği gibi, kaynakların etkin kullanılmasına da imkân sağlayacaktır. Anahtar Sözcükler; Veri Zarflama Analizi, KOBİ, Verimlilik, AHP, TOPSIS, ÇKKV.
Small and Medium Scale Enterprises (SMEs) act as catalysts in the general economy with their added value. The contribution of production, marketing, exports and employment has reached a very serious dimension without any doubt, and it has become a sector that economic administrations can not give up. Among the SMEs, the automotive supplier industry has an important place. According to the export figures of March 2017, 19% of the automotive industry shows the importance of this sector in the economy. Although SMEs have a very important place in the economy; Productivity, institutionalization, branding, production, marketing and financing. The state supports by KOSGEB to solve such problems. KOSGEB 2016 support budget is approximately 1 billion TL. KOSGEB has 27 support models available to solve the problems of SMEs. Firms can apply a lot of these support models. While SMEs need to apply to support models related to priority problems, applying to non-priority models causes serious resource waste and time loss. This thesis deals with the problem of applying to support models according to the priority problems of SMEs. As a place of application, 82 firms registered with Konya Chamber of Industry were selected for the automotive supplier industry. Firstly, data envelopment analysis of companies productivity evaluation was done. Firms are grouped into A, B1, B2, C1 and C2 according to their activity scores. Considering the common problems of each group, multi-criteria decision-making methods are available which support models can be applied. Weights of criteria were calculated with AHP and the order of KOSGEB support was found by TOPSIS method. Data envelopment analysis was done according to BCC and CCR models. The numbers of A, B1, B2, C1 and C2 were found to be 16, 4, 7, 18 and 37 in CCR, respectively, while BCC was 31, 6, 5, 15 and 25 respectively. As a result of multi-criteria decision making methods, the first order in the groups was KOBIGEL in A, logistic in B1, KOBIGEL in B2, test, analysis and calibration support model in C1 and C2. Other orders are different. This thesis will guide the support model of SMEs according to their productivity status and priority problems. Thus, firms can focus on their priority problems and will be able to use resources efficiently. Key Words; Data Envolpment Analysis, SME, Productivity, AHP, TOPSIS, MCDA.
Small and Medium Scale Enterprises (SMEs) act as catalysts in the general economy with their added value. The contribution of production, marketing, exports and employment has reached a very serious dimension without any doubt, and it has become a sector that economic administrations can not give up. Among the SMEs, the automotive supplier industry has an important place. According to the export figures of March 2017, 19% of the automotive industry shows the importance of this sector in the economy. Although SMEs have a very important place in the economy; Productivity, institutionalization, branding, production, marketing and financing. The state supports by KOSGEB to solve such problems. KOSGEB 2016 support budget is approximately 1 billion TL. KOSGEB has 27 support models available to solve the problems of SMEs. Firms can apply a lot of these support models. While SMEs need to apply to support models related to priority problems, applying to non-priority models causes serious resource waste and time loss. This thesis deals with the problem of applying to support models according to the priority problems of SMEs. As a place of application, 82 firms registered with Konya Chamber of Industry were selected for the automotive supplier industry. Firstly, data envelopment analysis of companies productivity evaluation was done. Firms are grouped into A, B1, B2, C1 and C2 according to their activity scores. Considering the common problems of each group, multi-criteria decision-making methods are available which support models can be applied. Weights of criteria were calculated with AHP and the order of KOSGEB support was found by TOPSIS method. Data envelopment analysis was done according to BCC and CCR models. The numbers of A, B1, B2, C1 and C2 were found to be 16, 4, 7, 18 and 37 in CCR, respectively, while BCC was 31, 6, 5, 15 and 25 respectively. As a result of multi-criteria decision making methods, the first order in the groups was KOBIGEL in A, logistic in B1, KOBIGEL in B2, test, analysis and calibration support model in C1 and C2. Other orders are different. This thesis will guide the support model of SMEs according to their productivity status and priority problems. Thus, firms can focus on their priority problems and will be able to use resources efficiently. Key Words; Data Envolpment Analysis, SME, Productivity, AHP, TOPSIS, MCDA.
Açıklama
Anahtar Kelimeler
İşletme, Business Administration, , , , , , , ,
