An Application on The Evaluation And Monitoring of

HONORARY CO-PRESIDENTS OF THE CONGRESS / KONGRE ONURSAL EŞ BAŞKANLARI

Prof. Dr. Ahmet Kemal ÇELEBİ (President of Manisa Celal Bayar University) Prof. Dr. Muzaffer ELMAS (President of Sakarya University) Prof. Dr. Fadıl HOCA (President of International Vision University) Prof. Dr. Hayati AKTAŞ (President of AKEV University) Prof. Dr. Alexandar ANDREVIC (President of Educons University) COORDINATORS OF THE CONGRESS / KONGRE KOORDİNATÖRLERİ

Prof. Dr. Mustafa MIYNAT (Manisa Celal Bayar University) Prof. Dr. Ahmet Vecdi CAN (Sakarya University) Assoc. Dr. Abdülmecid NUREDİN (International Vision University) Prof. Dr. Rıdvan KARLUK (AKEV University) Prof. Dr. Nenad PENEZIC (Educons University)

ORGANISATION COMMITTEE / ORGANİZASYON KOMİTESİ

Yrd. Doç. Dr. Coşkun ÇILBANT (Manisa Celal Bayar University), Dr. Sloban RAKIC (Educons University), Assist.Prof.Dr. Kamil Taşkın (Sakarya University), Yrd. Doç. Dr. Burak ÖZDOĞAN (Manisa Celal Bayar University), Yrd. Doç. Dr. Pelin MASTAR ÖZCAN (Manisa Celal Bayar University), Dr. Aysun KAHRAMAN (Manisa Celal Bayar University), Milena GALETIN (Educons University), Assoc. Prof. Umut Önal (AKEV University), Jelena VIKTOROVA (Educons University)

SCIENTIFIC COMITTEE / BİLİM KURULU ÜYELERİ Prof.Dr. Ahmet Kemal ÇELEBİ (Manisa Celal Bayar University), Prof.Dr. Ahmet Vecdi CAN (Sakarya University), Prof.Dr. Aleksandar Andrejević (Educons University), Prof.Dr. Alptekin ERKOLLAR (Sakarya University), Prof.Dr. Birol KOVANCILAR (Manisa Celal Bayar University) Prof.Dr. Hüseyin AKTAŞ (Manisa Celal Bayar University), Prof.Dr. Cengiz YILMAZ (Manisa Celal Bayar University), Prof.Dr. Doğan UYSAL (Manisa Celal Bayar University), Prof.Dr. Erman COŞKUN (Sakarya University), Prof.Dr. Fadıl HOCA (International Vision University), Prof.Dr. Florya MİFTARİ (FON University), Prof.Dr. Haluk BENGÜ (Ömer Halisdemir University), Prof.Dr. İbrahim EROL (Manisa Celal Bayar University), Prof.Dr. Kadir ARDIÇ (Sakarya University), Prof.Dr. Mehmet SARIIŞIK (Sakarya University), Prof.Dr. Mehmet YÜCE (Uludağ University), Prof.Dr. Musa EKEN (Sakarya University), Prof.Dr. Mustafa MIYNAT (Manisa Celal Bayar University), Prof.Dr. Orhan BATMAN (Sakarya University), Prof.Dr. Ramazan GÖKBUNAR (Manisa Celal Bayar University), Prof.Dr. Semra ÖNCÜ (Manisa Celal Bayar University), Prof. Dr. Sibel SELİM (Manisa Celal Bayar University), Prof.Dr. Nenad PENEZIC (Educons University), Prof. Dr. Sasho KOZUHAROV (University of Tourism and Management), Prof. Dr. Asılbek KULMIRZAEV (Kyrgyz Turkish Manas University), Prof. Tülin CANBAY (Celal Bayar University) Prof. Dr. Mahabbet MAMMEDOV (Azerbaijan University of Architecture and Construction), Prof. Dr. Selahattin KARABINAR (İstanbul University), Prof. Dr. Anarkul URDALETOVA (Kyrgyz Turkish Manas University), Prof. Dr. Halit YANIKKAYA(Technical University of Gebze), Prof. Dr. Zoran FILIPOVSKI (International Vision University), Prof. Dr. Şakir SAKARYA (Balıkesir University), Prof. Dr. Remzi ALTUNISIK (Sakarya Üniversitesi), Prof. Dr. Hayati Aktaş (AKEV University) EDITORIAL BOARD / EDİTÖR KURULU Prof. Dr. Mustafa MIYNAT Prof.Dr. Ahmet Vecdi CAN Yrd. Doç. Dr. Coşkun ÇILBANT Yrd. Doç. Dr. Burak ÖZDOĞAN Yrd. Doç. Dr. Kamil TAŞKIN Araş. Gör. Dr. Anıl GACAR Araş. Gör. Osman GÜLDEN DİZGİ

Araş. Gör. Osman GÜLDEN (Manisa Celal Bayar University) CONGRESS SECRETARY / KONGRE SEKRETERYASI

Araş. Gör. Tuna Can GÜLEÇ (Manisa Celal Bayar University) E – ISBN: 978­975­8628­62­9

Yayın Tarihi: 21.12.2017

Yayımcı: Manisa Celal Bayar Üniversitesi Basım Yeri: Manisa Celal Bayar Üniversitesi Matbaası

ÖNSÖZ İlki 2015 yılında düzenlenen kongremizi bu yıl da geleneksel olmasını kararlaştırdığımız "yeni ekonomik trendler ve iş fırsatları teması" ile üçüncü kez düzenlemenin gururunu yaşadık. Diğer yıllara nazaran katılımın artması bu kongrenin önemini bir kez daha ortaya çıkarmaktadır. İletişim ve bilgi teknolojilerindeki gelişim sayesinde artık tüm dünya rekabet halindedir. Bu kapsamda şirketler faaliyetlerini planlarken bir yandan tüketici tercihlerindeki değişimleri dikkate almak, bir yandan da iç ve dış piyasalarda meydan gelen değişimleri yakından izlemek zorundadırlar. Değişen ekonomik düzen; işletmeleri, en iyi olunan alanlara odaklanma, ekonomik dalgalanmalara karşı esnek planlar yapma ve maliyetleri etkili bir şekilde düşürme yönünde tercihe zorlamaktadır. Elektronik iletişim ve sanal yapılar, tüm ekonomi oyuncularını yeniden şekillendirmektedir. Teknoloji globalleşmeyi hızlandırırken, rekabetin kaynağı verimliliğe bağlanmaktadır. Bu dönemde daha düşük maliyetli işgücünün, daha iyi bir finansal ortamın ve yurt içi üretim inovasyonlarının hızlı yükselmesi, yerli üretimin canlanması için yapılacak çalışmalar ülkeler için hayati öneme sahiptir. Diğer taraftan yaşanan bu gelişim ve değişim süreci bir takım olumsuzlukları da beraberinde getirmektedir. Bu kapsamda; başta sosyal bilimler alanında olmak üzere, bilimin her alanında faaliyet gösteren bilim insanarına büyük görevler düşmektedir. Önemli olan sadece bilimsel ve teknolojik ilerlemenin sunduğu imkanlardan kâr maksimizasyonu amacıyla yararlanmak değil, diğer insanları ve yaşadığımız çevreyi de düşünerek çalışmalarını yapmalarıdır. Dolayısıyla farklı disiplinlerde çalışan bilim insanlarının çalışmaları paylaşma ve fikir alışverişinde bulunmalarına olanak sağlayan kongrelerin de önemi giderek artmaktadır. Bu yıl üçüncüsü düzenlenen Uluslararası İşletme ve Ekonomi Kongresi ICEB'17, üniversitemizin ana koordinatörlüğünde; Sakarya Üniversitesi, International Vision University, Educons University ve Antalya AKEV Üniversitesi işbirliğiyle Sırbistan'ın Novi Sad şehrinde başarıyla gerçekleştirildi. Kongrede 28 üniversiteden ve TÜBİTAK'tan bilim insanlarının katılımıyla 107 bildiri sunuldu. Bu yıl açılış teması "Endüstri 4.0" olarak tespit edilen kongrede, Türkçe ve İngilizce olarak ekonomi ve işletme alanından çalışmalar yer almaktadır. Prof. Dr. Mustafa MIYNAT (Manisa Celal Bayar University) Prof. Dr. Ahmet Vecdi CAN (Sakarya University) Assoc. Dr. Abdülmecid NUREDİN (International Vision University) Prof. Dr. Rıdvan KARLUK (AKEV University) Prof. Dr. Nenad PENEZIC (Educons University) Coordinators of The Congress / Kongre Koordinatörleri Manisa Celal Bayar University

İÇİNDEKİLER Türkiye’de İşgücü Piyasası ve İşsizlik; İşsizlik Üzerine Spesifik Bir Çalışma ………………..….1 Yrd. Doç. Dr. Hakan Aracı , Öğr.Gör. Uğur Bilgen, Öğr. Gör. Bahadır Bilge Aycan Manisa Celal Bayar Üniversitesi Çalışma Psikolojisi ve Günümüz Çalışma Yaşamındaki Psikolojik Sorunlar-Makedonya Örneği.. ……………………………………………………………………………………………10 Doç. Dr. Osman Emin Uluslararası Vizyon Üniversitesi Mesleki Farkındalık Üzerine Bir Çalışma: Gördes Anadolu Lisesi Örneği …………………......19 Öğr. Gör. Bahadır Bilge Aycan, Öğr. Gör. Uğur Bilgen, Öğr.Gör. Ali Taha İnce, Yeliz Aycan Manisa Celal Bayar Üniversitesi Üniversite Eğitiminde Hizmet Kalitesinin Ölçülmesi: Manisa Celal Bayar Üniversitesi Ahmetli Meslek Yüksekokulu'nda Bir Uygulama………………………………………………………....31 Öğr. Gör. Emine Ayvaz Güven, Öğr. Gör. İlham Yılmaz, Uzm. Hüseyin Güven Manisa Celal Bayar Üniversitesi Altın Riskten Korunma Aracı Mı Güvenli Liman Mı? Geleneksel ve İslami Hisse Senedi Piyasalarında Uygulama…………………….……………………………………………………43 Prof. Dr. Ahmet Vecdi Can, Doç. Dr. Gülfen Tuna, Dr. Vedat Ender Tuna (YMM) Sakarya Üniversitesi Borsa İstanbul’da Üç Aylar Etkisinin İncelenmesi: Turizm Ve Gıda Sektöründe Ampirik Uygulama………………………………………………………………………………………….50 Prof. Dr. Ahmet Vecdi Can, Doç. Dr. Gülfen Tuna, Dr. Vedat Ender Tuna (YMM) Sakarya Üniversitesi Üniversite Öğrencilerinin Finansal Okuryazarlık Düzeyleri Üzerine Bir İnceleme …………....59 Arş. Gör. Dr. Çağatay Orçun, Doç. Dr. Sevinç Güler Özçalık Dokuz Eylül Üniversitesi Altman Z Skor Modellerindeki Değişkenlerin Alternatif Sınıflandırma Yöntemlerindeki Başarımı…………………………………………………………………………………………...70 Yrd. Doç. Dr. Elif Bulut, Doç. Dr. Fevzi Serkan Özdemir Ondokuz Mayıs Üniversitesi Spot Fiyatlar Vadeli Fiyatları Etkiler Mi? Borsa İstanbul A.Ş.’de Bir Uygulama……………...93 Doç. Dr. Sevinç Güler Özçalık, Arş. Gör. Dr. Çağatay Orçun, Arş. Gör. Dr. Ayşegül Çimen Dokuz Eylül Üniversitesi

Türkiye’de Yerel Gazetecilik Üzerine Bir Çalışma: Gördes Bölge Gazetesi Örneği ………..…101 Öğr. Gör. Ali Taha İnce, Öğr. Gör. Bahadır Bilge Aycan, Öğr. Gör. Uğur Bilgen Manisa Celal Bayar Üniversitesi Enerji Tüketimi, Dış Ticaret ve Ekonomik Büyüme İlişkisi: Panel Veri Analizi ……………...114 Arş. Gör. Can Karabıyık, Yrd. Doç. Dr. Coşkun Çılbant Manisa Celal Bayar Üniversitesi Doğrudan Yabancı Sermaye Yatırımlarının Ekonomik Büyümeye Katkısı………….………..127 Yrd. Doç. Dr. Deniz Züngün, Yrd. Doç. Dr. Ahmet Okur, Öğr. Gör. Tekmez Kulu, Doç. Dr. Mustafa Kırlı Manisa Celal Bayar Üniversitesi Köyden Kente Göçün Azaltılması Noktasında Tarımsal Kredilerin Önemi…………………....137 Arş. Gör. Dr. Selim DURAMAZ Manisa Celal Bayar Üniversitesi Tarımda Ar-Ge Harcamalarının Etkinliği………………………………………………………146 Doç. Dr. İlkay Dilber, Öğr. Gör. Dr. Lale Demirlioğlu, Nihan Şeker Manisa Celal Bayar Üniversitesi Yeşil İnovasyonda Yönetsel Rol ve Davranışlar…………………………………………………155 Doç. Dr. Aylin Ünal, Soner Alaca, Uzm. Gül Binboğa Manisa Celal Bayar Üniversitesi Türkiye’de Genç Nüfusun İstihdam Sorunları………………………………………………….161 Yrd. Doç. Dr. Ahmet Okur, Yrd. Doç. Dr. Deniz Züngün, Doç. Dr. Mustafa Kırlı, Öğr. Gör. Tekmez Kulu Manisa Celal Bayar Üniversitesi Regional Differences And Development Agencies……………………………………………..167 Arş. Gör. İsmet Güneş, Öğr.Gör. Ayşe Güneş Manisa Celal Bayar Üniversitesi Bölgesel Enflasyon ve Bölgesel Nüfus İlişkisi: Türkiye Örneği………………………………..186 Arş.Gör. İsmet Güneş, Öğr.Gör. Ayşe Güneş Manisa Celal Bayar Üniversitesi, Dumlupınar Üniversitesi Türkiye Ekonomisinde Bireysel Tüketici Kredileri ve Cari İşlemler Dengesi İlişkisi…………202 Doç. Dr. Melih Özçalık, Yrd. Doç. Dr. Ece Erol Manisa Celal Bayar Üniversitesi

Yapısal Kırılmalar Altında Satın Alma Gücü Paritesinin Geçerliliği: Türkiye Örneği……….213 Araş. Gör. Dr. Taner Taş, Araş. Gör. Dr. Selim Duramaz, Araş. Gör. Dr. Kubilay Ç. Yılmaz Manisa Celal Bayar Üniversitesi Kırgızistan Ve Türkiye’deki Üniversite Öğrencilerinin Girişimcilik Eğilimlerinin Öğrencilik Döneminde Çalışma Kriterine Göre İncelenmesi 30……………………………………….…...220 Cengiz Yılmaz, Tuncer Özdil, Beran Gülçiçek Tolun Manisa Celal Bayar Üniversitesi Kadın Girişimciler ve Mikro Kredi Uygulaması…………………………………………….…..235 Öğr. Gör. Dr. Esra Güven Manisa Celal Bayar Üniversitesi Applied analysis of Electronic Market Preferences of the Students of International Vision University………………………………………………………………………………………....244 M. Sc. Aybeyan Selimi, Prof. Dr. Suad Bećirović Uluslararası Vizyon Üniversitesi An Application on The Evaluation And Monitoring of Supply Risks In The Transportation Sector……………………………………………………………………………………………..255 Doç. Dr. Çiğdem Sofyalıoğlu, Doç. Dr. Burak Kartal, Arş. Gör. Ebru Sürücü Manisa Celal Bayar Üniversitesi Adil Ticaretin Pazarlamadaki Bazı Kavramlarla İlişkisine Yönelik Bir Literatür Araştırması.278 Arş. Gör. Ebru Sürücü, Doç. Dr. Burak Kartal, Doç. Dr. Çiğdem Sofyalıoğlu Manisa Celal Bayar Üniversitesi Lojistik Sektörü Üzerine Bir İnceleme: Mersin ve İskenderun Limanı Örneği ………...……...287 Öğr. Gör. Furkan Göksu, Öğr. Gör. Mustafa Yasin Sırça Manisa Celal Bayar Üniversitesi Ekonomilerde Artan Rekabet ve İnovasyonun Lojistik Üzerine Etkisi: Türkiye………………296 Öğr. Gör. Mümine Karadağ, Yrd .Doç. Dr. Hakan Yalçınkaya, Yrd. Doç. Dr. Neslihan Yalçınkaya Manisa Celal Bayar Üniversitesi Rekabetçi Dijital Piyasaların ve Pazarlama Uygulamalarının Ekonomiye Katkısı ………...….311 Yrd .Doç. Dr. Hakan Yalçınkaya, Yrd. Doç. Dr. Neslihan Yalçınkaya Manisa Celal Bayar Üniversitesi Marka Kişiliği Algısı: Sosyal Medya Markalarının Demografik Özellikler Açısından Değerlendirilmesi……...………...………...………...………...………...………...……….....…330 Yrd .Doç. Dr. Nurcan Yücel, Öğr. Gör. Melike Halifeoğlu Fırat Üniversitesi

Dijital Pazarlama Stratejisi Aracı Emojiler: Türk Tüketicilerin Üzerinde Yarattığı Marka Çağrışımına Yönelik Bir Araştırma...………...………...………...………...………...…………346 Tuğba Ulaştıran, Ayşegül Elmin Manisa Celal Bayar Üniversitesi Türkiye’de Kamu Kurumlarında İç Denetim Süreci Ve İç Denetim Koordinasyon Kurulu’nun Sorumluluklarının Değerlendirilmesi…...………...………...………...………...………...…….355 Doç. Dr. Ahmet Özen, Yrd. Doç. Dr. Serkan Cura Dokuz Eylül Üniversitesi, Manisa Celal Bayar Üniversitesi Removal of Fossil Fuel Subsidies for Protection of Environment as a Global Public Goods and Turkey…………………………………………………………………………………………….365 Öğr. Gör. Esra Hanbay Kahriman, Öğr. Gör. Hilal Cura Adnan Menderes Üniversitesi, Celal Bayar Üniversitesi Balkan Ülkelerinde Vergi Reformu Uygulamaları ve Potansiyel Etkileri……………………...385 Doç. Dr. Mine Biniş, Yrd. Doç. Dr. Neslihan Yılmaz Balıkesir Üniversitesi, Uşak Üniversitesi Yatırımlarda Devlet Desteği: Proje Bazlı Yatırım Teşvik Sistemi……………………………....397 Prof. Dr. Mustafa Mıynat, Yrd. Doç. Dr. Pelin Mastar Özcan Manisa Celal Bayar Üniversitesi Yeşil Bütçe Yaklaşımı ve Türkiye’de Yeşil Bütçe Uygulamaları ………………………………..407 Öğr. Gör. Mustafa Yasin Sırça, Öğr. Gör. Furkan Göksu Manisa Celal Bayar Üniversitesi Obezite ile Mücadelede Vergi Politikaları……………………………………………………….420 Taha Artuç, Bahar Örs, Sema Çakır, Ünal Özkaya Manisa Celal Bayar Üniversitesi Türkiye’de Katma Değer Vergisinde Oran Yapısı ve Uygulama Sonuçları ………………..….430 Prof.Dr. Zeynep Arıkan Dokuz Eylül Üniversitesi Türkiye’de Muhasebe Yükseköğretimi ve Aktif Programlar Üzerine Bir Değerlendirme ……..441 Doç.Dr. Fevzi Serkan Özdemir, Prof.Dr. Ahmet Vecdi Can, Prof.Dr.Haluk Bengü Ondokuz Mayıs Üniversitesi, Sakarya Üniversitesi, Ömer Halisdemir Üniversitesi Kredi Değerlendirmesinde Yaratıcı Muhasebe Tekniklerine Karşı Bankaların Yaptığı Düzeltmeler Üzerine Bir Değerlendirme………………………………………………………..455 Yrd.Doç. Dr. Hakan Aracı, Öğr. Gör. Ergun Metin Manisa Celal Bayar Üniversitesi

An Application on The Evaluation And Monitoring of Supply Risks In The Transportation Sector Çiğdem SOFYALIOĞLU1

Burak KARTAL2

Ebru SÜRÜCÜ3

ABSTRACT In today’s business life, which is characterized by a high level of complexity and uncertainty, businesses have realized that to be successful, they need to be distinct from their competitors not only on the basis of price and quality but also of flexibility and speed. To draw this distinction, management strategies like outsourcing, lean philosophy and Just in Time Production have increasingly been adopted. Moreover, many businesses have decided to harmonize and combine all their internal and external operations to realize the economy of scale. Globalization and extended supply chains offer many advantages for realizing the efficiency and effectiveness of operations. On the other hand, they make supply chains more fragile and increase the risk of experiencing failures. Negative experiences in this respect have necessitated understanding and decreasing the risks in a supply chain. Parallel to all these developments, supply chain risk management, which includes describing, evaluating, controlling and monitoring the risks in a supply chain, has gained importance in recent years. Continuous implementation of risk management is highly significant to reach success in the supply chain. Various methods have been proposed in the literature about risk identification and assessment. However, these methods must be dynamic, easy to understand, and practical so that they can be adopted by the practitioners. A risk assessment method that could meet these needs was developed by Tummala and Schoenhern (2011). In this study, the risk assessment methodology developed by Tummala and Schoenhern (2011) was used to assess the procurement risks of a firm operating in the transport sector, revised in line with the recommendations we have made, taking into account the implementation difficulties encountered. INTRODUCTION Today, both globalization and the developments in communication technologies have strengthened the interaction between markets and significantly increased world trade. Furthermore, the effects of globalization have been observed not only in international trade but also in reaching resources and production. The number of multi-national companies, which shift their production activities to the countries where labor force and other raw material sources are cheaper and more abundant, is increasing day by day. International marketing and access to resources are getting easier for firms, and competition strategies which are necessary for the firms operating in these markets to survive vary as well. Nowadays, firms need to differ from their competitors in terms of not only price and quality but also flexibility and speed. This may be possible only if firms adopt strategies like outsourcing and lean management as well as harmonizing all internal and external operations. Meeting these conditions depends on businesses’ handling all their activities from the supply chain perspective. With this perspective, different geographies may become a source of supply for businesses in terms of production input, and purchasing and marketing activities may be shaped by international dynamics, and production headquarters of the same company may be located in different countries. Thus, readdressing the purchasing, production and marketing functions within the scope of a holistic supply chain management system has become mandatory (www.lojiblog.com, 25.05.2017). While globalization, extended supply chains and supplier consolidation offer many advantages for realizing effectiveness and efficiency, they may also make supply chains more fragile and may increase the risk of supply chain disruptions (Supply Chain Risk Leadership 1

Assoc.Prof.Dr. Manisa Celal Bayar University, Faculty of Economics and Administrative Sciences, Production Management and Marketing, [email protected] 2 Assoc.Prof.Dr.Manisa Celal Bayar University, Faculty of Economics and Administrative Sciences, Production Management and Marketing, [email protected] 3 Research Asst. Manisa Celal Bayar University, Faculty of Business, Logistics Management, [email protected] (Corresponding Author)

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Council, 2011). Experiences in the last 20 years have shown that supply chain risks must be understood and reduced. For example, in the year 2000, due to the fire that erupted in the manufacturing facilities of Ericsson’s suppliers, production was disrupted, which led to a loss of $400 million for Ericsson (Chopra&Sodhi, 2004). Even a single case like the Tohoku earthquake that hit Japan in 2011 and the subsequent tsunami showed that many elements of global supply chain like supply, distribution and communication may be disrupted (Park et al., 2013). Following this disaster, Toyota decreased its production to 40,000 cars and its daily loss reached $72 million (Ho et al., 2015). Ford and Toyota stopped production in their facilities in the US due to the delays in the delivery of parts coming from other countries. The flood that occurred in Thailand in October 2011 affected the supply chain of computer manufacturers and led to delays in the supply chains of Japanese car manufacturers due to the problem in factories located in Thailand (Chopra&Sodhi, 2014). In Turkey, some car companies had to stop production in 2015 due to the strikes in their own companies and in their suppliers’ (Demirkol et al., 2015). As external factors lead to disruptions in supply chains and thus cause damage to the image of firms and significantly increase costs as well, risks in supply chains have attracted the attention of many researchers. Based on these developments, Supply Chain Risk Management system, which requires describing, evaluating, controlling and monitoring the risks in the supply chain, has recently gained considerable importance (Wagner &Bode, 2008). 1. Supply Chain Risk Management Through a systematic perspective, Supply Chain Risk Management deals with the disruptions that hinder the smooth flow of supply chain as cases whose time, frequency and impact level cannot be estimated and for which firms must be ready rather than seeing them as problems that must be solved as they emerge. Supply Chain Risk Management is a management process which enables companies to predict the risks they may encounter in their own supply chains and to take the necessary measures before being exposed to risks so as to minimize their impact. The first stage of this process is describing and understanding the risks that may be encountered. The second stage is taking measures by adopting one of the appropriate risk mitigation strategies to minimize the probability of exposure to risks. The third stage is dealing with the situation quickly and effectively when an unexpected problem occurs so that the supply chain suffers minimal damage (http://www.lojisturk.net/lojistik/haberler/supply-zincirinde-risk-yonetimi1291109159hhtml). As companies become more dependent on supply chain networks, they are now more sensitive to supply chain risks. The risk with regards to supply chains is defined as cases which lead to disruptions in the whole supply chain performance. In this respect, identifying the supply risks specific to businesses and determining supplier risk profiles are the most significant steps of supply chain management. Supplier risk profiles are composed of risky situations that may affect businesses negatively (Lockamy III, 2014). Although it is not very possible to predict such risks, the occurrence probability may be assessed by constructing a supplier risk profile. Thus, it is important that purchasing organizations have the ability to internally compare the risk levels of their suppliers. Also, organizations should have the tools that could assess the risk levels of the potential members of supply networks. In the literature, some studies focus on the assessment of supply risks. In their study, Zsidin et al. (2004) examined the risk assessment techniques that concentrate on the quality problems of suppliers, improving the processes and minimizing the possibility of disruptions in supply. To define supply chain risks and to determine their level of importance for businesses, Gaudenzi and Borghesi (2006) used AHP, which is one of the multi-criteria decision-making techniques. In addition to the multi-criteria decision making techniques like AHP, ANP, TOPSIS, and VIKOR, which are used to choose and monitor suppliers based on risks, there are many studies that have used SWOT analysis, multi-purpose programming, and linear programming methods (Chan & Kumar, 2007; Xiao et al., 2012; Wu et al., 2010; Shemsadi et al., 2011; Amin et al., 2011). In risk assessment based on all these and other multi-criteria decision making techniques, the aim is to determine the risk factors that must be dealt with first(ly) based on their severity. However, since these methods have a complex structure, new risk assessment techniques that are easy to understand and adopt and that can be implemented practically and quickly by practitioners are needed. In Supply Chain Risk Management literature, there are some studies which have

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examined and developed methods that could meet these needs at the academic level. Some of these studies are summarized below. The supplier risk assessment methodology proposed by Blackhurst et al. (2008) enables assessing, monitoring and controlling the suppliers that provide the critical parts of a product in terms of supply risks. The methodology calculates the risk indexes specific to critical parts and suppliers. The results may also be used to develop risk mitigation strategies before the occurrence of disruptions in a supply chain. Tuncel and Alpan (2010) used Failure Mode Effects and Criticality Analysis to determine and assess supply chain risks, and they helped apply risk management to the design, planning and performance management processes of the supply chain by modeling with Petri Networks. Chen and Wu (2013), on the other hand, proposed the HTEA method extended with AHP for the selection of suppliers through supply chain risk perspective. Tummala and Schoenherr (2011) maintained that when the method they developed is applied to the supply chain risk management, supply chain risks may be managed more effectively. The proposed methodology is composed of stages of risk identification, risk measurement, risk assessment, risk mitigation and contingency plans, controlling and monitoring risks with data management systems. The methodology suggests some techniques to be able to conduct the process. Within the scope of our study, we mainly used the model proposed by Tummala and Schoenherr (2011) to carry out the supply chain risk management process. And we proposed some revisions in this model so as to ease the implementation. In the following parts, the revized method is explained in detail through a company case. 2. Supply Chain Risks The concept of risk has many definitions in the literature. Sitkin and Pablo (1992) define risk as the important and/or disappointing consequences of the decisions made. On the other hand, Tummala and Schoenher (2011) define risk as the severity of the situations that lead to risk, and the probability or frequency of the occurrence of identified hazards. According to Harland et al. (2003), a risk is the occurrence probability of undesired consequences like danger, loss, and damage. In their study, Manuj and Mentzer (2008) maintain that a risk leads to different types of losses and it emerges from the combination of the probability of occurrence of loss and its importance for the individuals and the organization (cited in Mitchell, 1995). For the researchers, the conceptualization of a risk includes three factors: 1. What are the possible losses when a risk occurs? What will be the consequences of these losses? 2. What is the probability of these losses (like the probability of occurrence of the situation that leads to risk)? 3. What is the importance of the consequences of the loss? Lockamy III (2014) emphasizes the existence of an uncertainty about a certain outcome and argues that uncertainty that could be reduced through risk prevention steps is the key determinant of risk (Yates & Stone, 1992; cited in Slack&Lewis, 2001). Risk is difficult to measure and interpret. An extensive literature exists regarding risk in the fields of finance, marketing, and management. Many researchers have defined supply chain risk in the field of Supply Chain Management. The internal and external uncertainties constitute the sources of supply chain risk. The changes in capacity existence, breakdown in information flow, and reduction of operational effectiveness can be listed as the sources of internal uncertainties. The external sources of uncertainties, on the other hand, lead to an increase in supply chain risks including the activities of competitors, price fluctuations, changes in political environment, and instability in the quality of suppliers. The internal and external sources of uncertainty can be considered as risks leading to disruptions in the supply chain that could damage the performance (Cucchiella & Gastaldi, 2006, cited in Lockamy III, 2014). Thus, before attempting to reduce supply chain risks, it is of great importance for managers to understand the risk categories and the cases and the conditions that lead to risks. Supply chain risks are classified in different ways by different researchers. The supply chain risk categories obtained as a result of a comprehensive literature review are summarized in Table 1:

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Table 1. Supply Chain Risk Categories Risk Categories Author Risks based on internal and external factors Blackhurst et al., 2008; Trkman and Tormack, 2009; Kumar et al., 2010; Lin and Zhou, 2011 Controllable or partially controllable risks concerning supply Wu et al., 2006a Supply risks, operational risks, demand risks, safety risks, Manuj and Mentzer, 2008 macro risks, political risks, competition risks, and resource risks Supply risks, operational risks and demand risks Sosa et al., 2014 Disruption risks, delay risks, system risks, risks concerning Chopra and Sodhi, 2004 intellectual property right, purchasing risks, operational risks, demand risks and macro risks Risks concerning intellectual property right, behavioral risks, Tang and Tomlin, 2008 supply risks, operational risks, demand risks and political risks Interaction risks, supply risks, operational risks and demand Kumar et al., 2010 risks Information flow risks, financial flow risks, material flow risks Tang and Musa, 2011 Transportation risks, managerial risks, risks concerning the Tummala and Schoenherr, capacity of physical facilities, risks concerning the 2011 manufacturing process, inventory risks, delay risks, disruption risks, system risks, demand risks Disruption risks and operational risks Tang, 2006a Demand risks, supply risks, regulatory, legal and bureaucratic Wagner and Bode, 2008 risks, infrastructure risks, and disruptive risks Source: Compiled by the authors Each of the supply chain risks classified in different categories in the literature has been examined closely by the researchers and it was agreed that risks should better be categorized under the following risk categories: supply-related risks, operational risks, demand risks, regulatory, legal and bureaucratic risks, system risks, logistics risks, financial risks, and risks leading to disaster. These risks are briefly explained below. Supply-related risks: These risks are defined as the probability of experiencing situations which are related to inbound logistics, which negatively affect the ability of the firm to meet the customer needs and which threaten the life and safety of customers. Supply risks are related to disruptions that may occur during the material flow from suppliers to the parent company. Reliability of suppliers, use of multiple sources or a single source, decision to manufacture or purchase, centralized/decentralized purchasing, and safety problems are among the most frequently encountered supply risks.  The risk of supplier reliability stems from selecting a wrong supplier (supplier does not act in accordance with the stated abilities) and the goal conflicts between the parties (Manuj&Mentzer, 2008; Levary, 2007).  To decrease the administrative costs resulting from the use of multiple sources and to establish better supplier relationships, many manufacturing businesses have decreased the number of direct suppliers and some companies have even opted for single-source use. While fewer suppliers can be managed more effectively, they can increase supply risks. The use of a single source may cause the firm to have very little control over the supply costs and also leads to commitment risk regarding supply. For example, Canon is the only supplier for the motors in HP laser jet printer. Because of the change in customer demand, HP wanted to delay the orders, but HP was not allowed to make changes in the amount of orders due to the contract it had signed with the supplier firm. This contract constrained the ability of HP to respond to the changes in demand (Tang & Tomlin, 2008). Furthermore, the use of a single source may lead to disruptions in the supply process (intentional or unintentional situations like fire or bankruptcy, which may also occur due to opportunism), in inventory and production plans, and in access to technology (Manuj&Mentzer, 2008).

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

Risks entailed by manufacturing and purchasing decisions include technological uncertainties, product complexity (the number of product parts, the level of product originality) and the number of material design changes (Manuj&Mentzer, 2008).  The centralized purchasing strategy, which is mainly seen in group companies, involves undertaking and managing the material demands in different units from the centre. Centralized purchasing brings advantages like working with big suppliers, and having better prices and purchasing conditions; however, it is not an appropriate strategy in situations like frequent material design change when interaction with suppliers is critical. Operational risks: The ability of the firm to develop products and services is defined as the quality and promptness of production and the probability of occurrence of situations related to the parent business which can affect the firm’s profitability. Operational risk sources are found within the firm and may result in disruptions in basic operations, insufficient manufacturing or process capacity, high process variability, and technological changes that could render existing facilities invalid (Manuj and Mentzer, 2008; Kumar et al., 2010). Although firms have been more interested in programs like TQM, Lean Production, and Six Sigma in the last 30 years to improve their internal quality and process competencies, internal operations are still sensitive to the problems that could lead to capacity and quality fluctuations (Tang and Tomlin, 2008). Demand risks: They are defined as the probability of occurrence of situations that could affect the ability of the firm to meet orders on time at the desired amount, volume and variety. Demand risks emerge during the process of flow of products from the firm to the customers of the customers. Demand risks may set back the introduction of the products into the market or may lead to the launch of unsuitable products into the market. Causes like incorrect demand predictions, seasonality, and the introduction of new products into the market by competitors lead to changes in demand. Furthermore, information breakdown that occurs in the supply chains from customers to suppliers (whip effect, sales promotions, etc.) leads to unnecessary inventories and complexity in the system. Also, demand risks differ based on product structure. They are fewer in functional products compared to the innovative ones (Manuj & Mentzer, 2008). Other demand risks are extended time of delivery due to incorrect predictions, short product life cycle, invisibility of the supply chain, and over growing demand during product scarcity (Tummala & Schoenherr, 2011). Regulatory, legal and bureaucratic risks: These risks are related to the degree and frequency of the changes that occur in the laws and policies regarding the supply chain (like trade and transportation) and the feasibility and implementation of these laws. These risks involve obtaining the necessary approvals for supply chain operations. Administrative obstacles like customs and trade arrangements may restrain the operational activities of the supply chain. Legal changes mostly occur instantly and it is difficult to predict them. In European countries, new price tariffs for transportation vehicles considerably increase transportation costs. Environmental regulations lead to needs like product traceability and the establishment of reverse logistics systems. Firms adopt more complex supply chains to address such environmental obligations, and consequently, cause supply chain costs to increase (Wagner &Bode, 2008). Information (System) risks: They are defined as the probability of occurrence of losses due to access to information through incorrect, imperfect or illegal ways (Faisal et al., 2007). The effects of risks related to information can be grouped into four categories: information security and disruption risks, prediction risks, risks concerning intellectual capital rights, and risks related to the use of foreign sources in information technologies. Logistics (Transport) risks: Supply chains and transport networks have become more effective compared to the past due to globalization, lean processes, geographical distribution of production and some other factors. That may make logistics structure more complex and bring some certain risks with itself as well (Wright et al., 2012). Transport risks may be defined as the probability of occurrence of situations that may result from causes like bureaucracy, insufficient port capacity, ineffective customs procedures, and labor disputes in transport (Tummala and Schoenherr, 2011). Financial risks: These risks include lack of ability to make payments and unsuitable investments. Due to badly-managed supply chains, inventory levels may be excessive and unsuitable, which may lead to big financial risks. Financial risks may also include paying a fine for reworking capital or for the products which are not delivered on time (Christopher & Lee, 2004). Fluctuations in exchange rate, price cost risk, financial power of the supply chain partners, financial transactions and practices, letter of credit, paying bills on time, bankruptcy, payment plans, credit conditions, and supplier contracts are among the financial risks (Tang and Musa, 2011).

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Disruptive risks: These risks include the cases which lead to crucial consequences in the field they occur. Socio-political instability, civil war, terrorist attacks, epidemics, and natural disasters are among these risks. In many parts of the world, the probability of occurrence of disasters like tsunami, hurricane, earthquake, and flood threaten societies and firms. As manufacturing facilities and transportation systems are extremely vulnerable to such disasters, supply chains are adversely affected as well. Global effects of local disasters have been gradually increasing since manufacturing and marketing have become global (Wagner and Bode, 2008; Larsson and Jager, 2012) 3. Supply Chain RiskManagement Model The phases of the supply chain risk management model which was proposed by Tummala and Schoenherr (2011) and which was taken as the basis of this study are summarized in Figure 1.

SUPPLY CHAIN RISK MANAGEMENT MODEL

Figure 1. Supply Chain Risk Management Model (adapted from Tummala & Schoenherr, 2011) PHASE I Step 1: Risk Identification The first step of the risk management process is risk identification. Risk identification requires listing the potential situations that could harm the performance of supply chain in any way. It also helps organizations realize the risks that could lead to disruptions and develop plans that prevent the emergence of risks (Norrman and Jannson, 2004; Faizal and Palaniappan, 2014). However, it is almost impossible to list all the risks and this process only involves identifying the risks that have the most significant effect on the supply chain (Vilko, 2012). To evaluate the risks they are exposed to, firms should identify not only the risks that are directly related with their operations but also the potential reasons behind risks or the sources of risks at every stage of the supply chain. Kern et al. (2012) maintains that risk identification quality is extremely important for the risk management process through the whole supply chain and that risk evaluation phase depends mainly on the process and findings of the first phase since only the risks that are identified as risk and that could damage the supply chain of the organization are evaluated in the second phase. Giannakis and Louis (2011) argue that monitoring various key performance criteria regarding the supply chain partners (suppliers, logistics service providers, etc.) is the backbone of the risk identification process. Key performance indicators like inventory levels, production outcomes, capacity use, and delivery time can be used to identify the extraordinary cases involving risks. The identified performance indicators are monitored for a certain period and they are compared with the values that have been identified before through official/unofficial agreements between supply

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chain partners. If an important deviation is detected in the situation, it is reported to the stakeholders. Different methods are proposed in the literature for the identification of risks. The literature review about the techniques used at this stage by Ho et al. (2015) revealed that both qualitative and quantitative methods like AHP, supply chain risk mapping, conceptual model, hazard and operability analysis, and value-driven process engineering are proposed. In addition to these methods, Tummala and Schoenherr (2011) mentioned that methods like control lists/charts, event/error tree analysis, Failure Mode Effect Analysis (FMEA), and Cause- and Effect Analysis (fishbone diagram) may also be used. Step 2: Risk Measurement and Analysis Risk measurement and analysis, the second step of the first phase, requires understanding of the general characteristics of risk. Each risk factor has three key features: occurrence probability, effect and criticality. The third feature is a function of effect (severity) and occurrence probability. In other words, criticality is related to the severity of the event if it occurs (Ouabouch and Amri, 2013). Other variables like the frequency of being exposed to risk, time of exposure, vulnerability, detectability of the error, reliability of control, and the efficiency of preventive measures could be added to this function (Popov et al., 2016). Crockford (1986) categorized the risks into four groups based on the occurence probability, severity and its predictability of risk: Negligible, low, average, high (Tummala and Schoenherr, 2011; Sarakinis and Carlsson, 2013). Negligible risks are the most frequent risks with the effects that could be considered unimportant. These could be related to the situations that could easily be predicted in daily work activities and that could sometimes be neglected. The risks with considerable results have the most profound effect. These are mainly the lowfrequency risks like natural disasters and terrorist attacks. Thus, it is hard to predict these risks and get ready for them. On the other hand, the occurrence probability of risks with small consequences is higher than that of the risks with big consequences. Therefore, it is easier to predict and take measures against such risks. In practice, qualitative scales with different categories are frequently used to determine the impact and occurrence probability of risk consequences (Pujawan and Geraldin, 2009; Thun and Hoenig, 2011; Tummala and Schoenher, 2011). At the beginning of our study, we considered how the model proposed by Tummala and Schoenherr (2011) could be used in a firm; however, the problems and limitations encountered during the practice stage have become the source of motivation for the researchers to overcome them, and thus the model by Tummala and Schoenherr (2011) was revised. The revisions that, we believe, contribute to the model are summarized below:  In the model proposed by Tummala and Schoenherr (2011), the cost of risk prevention actions is evaluated on a scale including statements from very high to very insignificant (1-4), while a nominal value representing the cost of each action is needed. However, the difficulty of predicting the nominal value for the costs was encountered in practice, and thus, firms were asked to evaluate the costs of their activities on a scale from 0 to 100 at the cost evaluation stage.  Furthermore, to determine the primary risk prevention actions with the HTP analysis, evaluations regarding the consequence severity of the risk, occurrence probability, and the cost of risk prevention action were made. In addition, firms were asked to evaluate the potential for risk prevention for each action and the feasibility of each action on a scale from 0 to 100. Since two more factors are evaluated, the HTP codes pertaining to all these factor evaluations were revised. The application phases of the Supply Chain Risk Model developed by Tummala and Schoenherr (2011) and improved with our suggestions mentioned above are summarized in the following sections. Tummala and Schoenherr (2011) suggested using ABD 882C Military Standard in evaluating the impact of risk consequences qualitatively and made the relevant risk effect index suitable for the delivery risk (Table 2). The definitions of the risk effect index may be changed in a way to make them suitable for a particular case. Table 2 includes the HTP codes, which correspond to the risk index values and which are explained in the following sections, as well.

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Table 2. Risk Effects and Index Values (adapted to delivery risk) Description Risk HTP Consequence Code Index Plant shut down for more than a month due to 4 A Catastrophic lack of components with zero safety stock levels. Slow down of process flows or plant shut down 3 B Critical for one week due to lack of components with zero safety stock levels. Decreased service levels with depleting safety 2 C Marginal stocks Service levels are not impacted due to sufficient 1 D Negligible safety stock levels. (Source: Tummala and Schoenherr, 2011) In the second step, HTP codes corresponding to the risk probability evaluation scale developed by Tummala and Schoenherr (2011) were changed. Qualitative descriptions, probability index values, and the HTP codes for the risk probability categories are given in Table 3. Consequence severity level

Risk Probability Category Often Infrequent Rare Extremely rare

Table 3. Probability Categories and Index Values Qualitative Description Probability The identified risk factor could occur on an average Index of.................... Once per week 4 Once per month 3 Once per year 2 Once per decade 1

HTP Code E F G H

PHASE II This phase includes the steps of risk evaluation, risk mitigation and contingency plans. Both steps draw on evaluation criteria for suppliers and logistics activities (management capability, management of suppliers/3PL method, management of warehouse capacity, transportation alternatives, management/contribution of ICT, innovation potential, technical competence, financial conditions, ability to work with the customer) and performance evaluations (price/cost competitiveness, product quality, reliability, delivery performance, value-added services). Step 1: Risk Evaluation One of the most frequently used approaches in risk evaluation step is the risk matrix. Based on this approach, risks are compared with each other and the risks which require immediate action are determined. In the risk matrix, risks can be shown on a graph in which their probability and the impact of consequences engender the dimensions with data stemming from a Likert scale questionnaire.(Wittmann, 2000, as cited in Thun and Hoenig, 2011). Although probability and impact evaluation is not complex, they may not be useful if not implemented properly (Zsidin, 2003).

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Figure 2. Probability-impact-matrix (Thun, J.H. and Hoenig, D., 2011) In the risk matrix approach, risks are identified in different colors according to their severity. In another approach, multiplication of probability and impact factors are preferred in determining the criticality of a risk. No matter which method is used, it is very important to differentiate between risks and prioritize the important ones in order to achieve organizational and economic productivity in supply chain risk management (Sarakinis and Carlsson, 2013). As proposed by Tummala and Schoenherr (2011) in their model, risk consequence index and risk probability index are multiplied to calculate the value of each risk. The risk values that could be obtained according to all possible combinations of risk consequence and probability indexes for supply chain risks are given in Table 4. Table 4. Risk Values Probability Infrequent Rare (2) Extremely (3) Rare (1) Catastrophic (4) 16 12 8 4 Critical (3) 12 9 6 3 Marginal (2) 8 6 4 2 Negligible (1) 4 3 2 1 (Source: Tummala & Schoenherr, 2011) According to the proposed model, risks are categorized into four groups based on the values they have for an easier comprehension and interpretation (Table 5). Table 5. Risk Categorization based on Values Risk Range Category 1-5 Negligible risks (late, incomplete or defective deliveries from suppliers) 6-10 Critical risks (strike risk at a supply chain partner, risk of breakdown or malfunction in the machines used by suppliers, delays at customs etc.) 11-16 Most critical risks (Supplier bankruptcy, fire in the warehose, risk of shipment being stolen or lost during transfer etc.) (Source: Tummala and Schoenherr, 2011) Severity

Often (4)

In the risk acceptance step of the proposed model, risks are categorized according to their acceptability levels.

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Since it is not possible to avoid all the risks in business life, some risks are accepted, and thus the continuity of the business is ensured. The concept of “acceptable risk” which first emerged in business life in the field of occupational health and safety is now applied in every field of business life. Within the framework of this approach, the most important aspect is either preventing the risk or keeping it under control. At this stage, various methods have been developed to determine which risk is at acceptable level. However, it was mentioned that ALARP (As Low as Reasonability Practicable) principle in the model by Tummala and Schoenherr (2011) can be drawn on. According to this principle, when the cost of risk prevention activities significantly exceeds the possible based on a cost-benefit analysis, it may not be necessary to take any more safety precautions. In the relevant method, tolerable and acceptable risks point to different situations. For example, input from an Asian supplier involves a tolerable risk because of the cost advantage it provides. Furthermore, signing a contract with the Asian supplier about delivery and quality conformity may have lowered the risk of working with an Asian supplier to the acceptable level. In determining the level of acceptance, first, risk value is calculated, and then, where the risk falls into among the unacceptable, tolerable and acceptable risk groups is determined.

Figure 3. ALARP Acceptable Risk Levels (Mercan, 2013) When Figure 3 is examined, it is seen that risk level increases towards the base of the triangle. Risks that need to be definitely mitigated by taking measures are in the unacceptable area in the figure. Unacceptable risks usually affect the smooth functioning of the firm operations negatively and thus require immediate action. For the risks in the tolerable area, an evaluation based on ALARP principle must be made before making a decision. In other words, when risks cannot be mitigated or when disproportion occurs based on the results of the cost-benefit analysis, risks may be tolerated. Risks which do not hinder business activities and which businesses do not need to spend time and resources to mitigate are found in the acceptable area (Mercan, 2013). Although no information on the limit values that separate the categories from each other is provided regarding the implementation of ALARP principle in the proposed model, Table 5 may be a guide in this respect. Step 2: Risk Mitigation and Contingency Plans This step involves developing risk response action plans to control the risks. In this step, Hazard Totem Pole (HTP) Analysis, which integrates risk severity, occurrence probability and the cost of preventive measures, may be used to evaluate risk factors in a systematic manner (Tummala and Shoenherr, 2011). In our study, since it is difficult to predict the monetary value of risk prevention actions, it is suggested to evaluate the cost of each action on a scale from 0 to 100 and to categorize the costs under four groups according to the evaluation scores. The four-level cost category system given in Table 6 may be used to facilitate the selection of best course of action in the context of HTP analysis. Each category is related to cost index and an HTP code. Risk mitigation actions can be compared with each other in terms of cost. Table 6. Implementation of Cost Categories for Risk-Response Action Plans Cost Categories Cost Evaluation Score Cost HTP Range Index Code

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Trivial Low High Substantial

0-25 26-50 51-75 76-100

4 3 2 1

L K J I

In addition to the costs of risk prevention actions, risk prevention potential and feasibility of each action should be evaluated, which increases the probability of making realistic selections. Thus, it is suggested to evaluate the risk prevention potential and feasibility of the relevant action on a scale from 0 to 100 and to categorize the actions into four main groups based on the results of the evaluation (Table 7 and Table 8). Table 7. Categorization of Preventive Actions According to Risk Prevention Potential Risk Prevention Score Range Risk Prevention HTP Potential Potential Index Code Trivial 76-100 4 M Low 51-75 3 N High 26-50 2 O Substantial 0-25 1 P Table 8. Categorization of Preventive Actions According to Feasibility Level Feasibility Level Risk Prevention Potential Feasibility HTP Score Range Index Code Very easy 76-100 4 U Easy 51-75 3 T Difficult 26-50 2 S Very Difficult 0-25 1 R HTP Analysis is a systematic risk evaluation method based on the integration of risk severity, occurrence probability, the cost of risk prevention action, risk prevention potential and feasibility given in Table 3, Table 4, Table 6, Table 7 and Table 8, respectively. HTP diagram is designed in a way to provide a single ordering for supply chain risks with the integration of these five risk categories. Combining the codes in each table and the numerical values corresponding to these codes, different risk category levels are obtained. During the analysis, based on the five different coding levels pertaining to risk severity, occurrence probability, the cost of risk prevention action, risk prevention potential and feasibility level, each risk factor is assigned a five-letter code. For example, AELMU code (4,4,4,4,4) indicates;  a catastrophic effect,  high occurrence probability,  trivial preventive action cost,  very high risk prevention potential and  very good feasibility Total HTP risk index is obtained by adding the values pertaining to these five categories (Total HTP risk index=4+4+4+4+4=20). Risks with higher values must primarily be dealt with by the management based on risk severity, probability, the cost of preventive actions, risk prevention potential and feasibility.

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Figure 4: HTP Diagram In HTP diagram, first, risks are ranked in descending order based on the total HTP index values. Secondly, five-letter codes corresponding to each risk are added to gain more information about a particular risk. If necessary, the cost of preventive action, risk prevention potential and action feasibility columns may be added and evaluation scores for these factors may be written. The most important risks (those that require immediate management action) are at the top, while less significant risks are at the bottom of the diagram (Grose, 1987). The risk factors at the top of the HTP diagram show catastrophic risks that could be eliminated or mitigated for a small amount of control cost. The impact of the risk factors diminishes as we go down the HTP. PHASE 3 In the last phase of the supply chain risk management process which includes risk control and risk monitoring, improvements made, as a result of the implementation of risk response action plans, may be examined. Afterwards, corrective measures can be taken if there are deviations in achieving the desired supply chain performance. This process can be considered as a tool to determine the possible preventive measures and to provide guidelines for further improvements. Deviations from the desired outcomes, abnormal cases and supply chain disruptions are reported. Data management systems may support this task. For example, by following a modular structure, identified supply chain risk factors, risk severity, risk probabilities, HTP analysis, government regulations and policies, tariff and customs policies, transport schedules and supply chain risk triggers may be cataloged; and related risk information may be stored and updated when needed. Information management systems may be used not only in monitoring risks effectively but also in taking corrective measures and improving risk evaluation and management continuously. SCRM software that provides commercial solutions is also available. With the implementation of these three phases, supply chain decisions may be made. However, this process must be sustained. Management must continuously highlight that changes may occur in business environment and that risk tolerances, prevention costs and severity levels may change (Tummala and Schoenherr, 2011). The proposed supply chain risk management process is a tool that provides useful and strategic information regarding the supply chain risk profiles of a particular case. In contrast to the traditional approach, it relies on single point prediction (Tummala and Schoenherr, 2011). Supply chain risk management process helps supply chain managers in strategic thinking and decision making to improve the performance of a supply chain. The analysis enables not only to evaluate

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improvement but also to select the best alternative action plans and to make the best decisions based on the supply chain risk profiles. It is a comprehensive and practitioner-oriented approach to manage the risks and uncertainties about a problem. Supply chain managers may use this method for inspection purposes in order to deal with risks and uncertainties and to reach the desired supply chain performance level. It is a method that does not make decisions for managers but that helps managers make decisions. APPLICATION In the application phase of the current study, the model by Tummala and Schoenherr (2011) was applied to a firm that operates in the transportation sector, and a road map was developed to enable the firm to monitor and manage the supply chain risks in a systematic way. In this process, first, the general manager, assistant general manager, supply chain department manager and the employees in the department were informed about the application model and an agreement was reached to apply the proposed model in the firm. Secondly, a list of supply chain risks obtained by the authors through a comprehensive literature review comprising the risks that could be common in many sectors was given to the firm as a guide. The working team that came together for this study examined the risks in the list and made the necessary additions and removals to determine the supply chain risks specific to the firm. As a result, a total of 57 supply chain risks, which were categorized under the sub-groups of supply, operational, demand, regulatory, legal and bureaucratic, system, logistics, financial and disasterrelated risks, emerged. To analyze the risks that could particularly be related to supply, firm authorities identified four critical suppliers, two of which operate at home and two of which operate in different countries. These four suppliers with which problems may be experienced had high purchase costs. In the third phase, with the evaluation form, the identified supply chain risks were evaluated with respect to their adverse effects on the firm for each supplier separately considering their occurrence probability and realization. During evaluation, the evaluation scales in Table 2 and Table 3 were utilized. However, firm authorities made a revision in Table 2 regarding the evaluation of the occurrence probability of the risks and decided to evaluate the risks that could occur "once in two weeks" in the "often" category. In this study, supply chain risks were evaluated with respect to four critical suppliers (two local, two foreign) and as a result of this evaluation, risk mitigation action plans were developed for the most critical and significant risks considering the cost. The firm evaluated the supply, operational, demand, legal and bureaucratic, system, logistics, financial and disaster-related risks considering the supply chain structure between the firm and the supplier. Given that a risk with critical importance even for a supplier may have severe negative effects on a firm, risk exposure values were identified based on the weakest link approach. Based on the information in Table 5, the critical and most critical risks in the 6-16 risk exposure values range were considered for evaluation in the HTP analysis. “RiskExposure Values” for the risks that were considered for evaluation were given in Table 9.

267

Table 9. Risk Exposure Values Calculated Based on the Occurrence Probability and Severity Levels of Supply Chain Risks

LEGAL AND BUREAUCRATIC RISKS

DEMANDRISKS

OPERATIONAL RISKS

SUPPLY RISKS

RISK CODE

OCCURRENCE PROBABILITY

SEVERITY LEVEL

RISK EXPOSURE VALUE

4

3

12

3

4

12

3

3

9

3

3

9

3

3

9

2

3

6

Incomplete and incorrect delivery of orders Not being able to change the demand amount due to the contracts signed with the suppliers and loss of flexibility

SUP_R1

Not delivering the orders on time

SUP_R3

Communication problems with the suppliers Dependence on few or single supply source Suppliers’ not being able to respond to the changes in product mix and production volume

SUP_R4

Not delivering the orders on time

SUP_R7

2

3

6

Shipment-related damage in the product

SUP_R8

2

3

6

Problems due to bad packaging

SUP_R9

2

3

6

Supply of wrong materials due to product tree and wrong product descriptions Lack of ability to implement the information technology systems like MRP/ERP Insufficient logistics activities

OP1 2

4

9

2

4

8

2

4

8

Lack of ability to respond to the changes in production volume

OP4 2

3

6

Prolonging supply process due to the seasonal fluctuation in demand

DEM_R1 3

3

9

Short life cycles of products

DEM_R2

3

3

9

Exaggeration of demand during product shortage

DEM_R3 3

3

9

Not being able to decrease or change the products due to late order changes by customers Customers’ not informing the firm about the product demand (Demand uncertainty)

DEM_R4 3

3

9

3

2

6

Unknown product demand by the firm and the suppliers

DEM_R6 3

2

6

Changes in the existing laws and obligations due to the changes in political environment

LEG_R1 2

4

8

Practices of national governments like quota restrictions and sanctions

LEG_R2 2

4

8

2

4

8

2

3

6

SUP_R2

SUP_R5 SUP_R6

OP2

OP3

DEM_R5

LEG_R3 Regional instability Violation of intellectual property rights and lack of control (e.g., leaking trade secrets)

LEG_R4

268

CATASTRO PHIC RISKS

FINANCIAL RISKS

LOGISTICS RISKS

SYSTEM RISKS

Information infrastructure breakdowns

SYS_R1

2

3

6

2

3

6

Lack of compatibility among supply chain partners in IT platforms

SYS_R2

Scheduling

LOG_R1

4

3

12

Late deliveries

LOG_R2

4

3

12

Delay at ports due to port capacity Bureaucracy (Documentation) Delays due to bureaucratic transactions and the added costs Problems with the supplier due to the agreed on delivery type (long-distance transport, added costs and uncertainties)

LOG_R3

3

3

9

3

3

9

3

3

9

Controls at border or excessive handling due to different transportation modes

LOG_R6 2

3

6

Lengtening customs and inspection procedures at ports Transportation breakdowns Exchange rate risk (Exchange rate has a significant impact on the decisions of firms like post-tax profit, supplier selection, market growth and other operations within supplychain) Price and cost risk (It can be seriously affected by the fluctuations in exchange rate, which leads to raw material shortage) Inflation rates and changes in price indexes Financial power of supply chain partners (It is the increasing equity risk, financial leverage and asset risk. Financial flow fragility of supply chain partners may be easily affected by the whole supply chain network.)

LOG_R7

2

3

6

2

3

6

2

3

8

2

3

8

4

2

8

2

3

6

2

3

6

LOG_R4

LOG_R5

LOG_R8 FIN_R1

FIN_R2

FIN_R3 FIN_R4

DIS_R1 Natural disasters (Hurricane, earthquake, flood, tsunami etc.)

HTP (HAZARD TOTEM POLE) ANALYSIS Upon identifying the risks to be analyzed, a second team in the firm identified the actions that could eliminate or reduce the risks. At this point, it was seen that there is mostly more than one risk prevention action for each risk. However, in Tummala and Schoenher’s (2011) model, there is one risk prevention action for each risk. After determining the preventive actions for all the risks, the application team was asked to evaluate each preventive action with the evaluation form on a scale from 0 to 100 with respect to cost, potential to eliminate the risk and feasibility. Based on these evaluations, the actions with the lowest cost and the highest risk prevention potential and feasibility were selected. The implementation of the process is explained through one risk based on the information in Table 10.

269

Feasibility of Action (0 – 100)

Cost (0 – 100)

4

Risk Prevention Potential of the Action (0 -100)

SUP_R1

Possible Effect on the Firm

Probability

Table 10: Evaluation of Preventive Action Alternatives Based on their Risk Prevention Potential, Feasibility and Cost

ACTION 1

80

90

5

ACTION 2

80

20

50

ACTION 3

90

10

85

ACTION 4

60

90

60

ACTION 5

40

95

10

ACTION 6

80

10

90

Risk Mitigation Actions

3

When Table 9 is examined, it is seen that 6 different preventive actions were considered for SUP_R1, and among these actions, Action 1 was found to be the best action due to its high potential of risk prevention, quite good feasibility level and low cost. The method also allows for considering more than one action with the desired features. In the following stage of the analysis, based on the score ranges described in Table 6, 7 and 8, each action was assigned a risk prevention potential, feasibility and prevention cost index value and an HTP code corresponding to this value. As previously mentioned, in the application steps so far, the following indexes and codes have been identified for each risk: a. Impact severity index and HTP code b. Occurrence probability index and HTP Code c. Risk prevention potential index of the preventive action and HTP Code d. Feasibility index of the preventive action and HTP Code e. Prevention cost and HTP Code When the index values for these five evaluation factors are added, the Total HTP Index Value is obtained. Values of relevant five factors for each risk are given in Table 11.

270

4 A 3 F 12

SUP_R 3

3 B 3 F

9

SUP_R 4

3 B 3 F

9

SUP_R 4

3

SUP_R 5 SUP_R 6 SUP_R 7

3 B 3 F

OP_R 1 OP_R 2

3 B 3 F

OP_R 3 DEM_ R1

3 B 2 G 6

B

F 3

9

9

3 B 2 G 6 3 B 2 G 6

9

3 B 2 G 6

3 B 3 F

9

Sharing the product details with the supplier firms in a standard format simultaneously and in a systematic manner when a new product is described in ERP systems Bringing the time of ordering forward, revision opportunity regarding amount and mode of transport 30 days before shipment Switching from sea freight to air freight mode in case of delay and make the supplier responsible for the added transport costs Sending the performance evaluation reports to the key suppliers with strategic importance regularly. Sharing the problems and breakdowns through immediate feedback Sharing information with the suppliers often but in a controlled manner (in a way not to create information pollution) Considering candidate suppliers to improve supplier portfolio Selecting the supplier with amount flexibility Requesting the necessary packaging changes from the supplier Creating a product tree database Working with more than one supplier as far as particular products are concerned Use of external source in logistics activities Considering the impact of seasonal fluctuations in demand predictions

5

4

M

90

4

Total HTP Index

80

Feasibility Index / HTP Code

4 L

Feasibility of the Action (0 – 100)

Risk Prevention Potential Index / HTP Code

SUP_R 2

Risk Prevention Potential of the Action (0 -100)

3 B 4 E 12

Cost Index / HTP Code

SUP_R 1

Risk Mitigation Actions

Cost (0 – 100 Arası)

Risk Exposure Value

Impact / HTP Code

Risks

Probability / HTP Code

Table 11. Final Evalution Schedule for Supply Chain Risks

U 19

0

4 L

80

4

M

5

1

R

16

0

4 L

90

4

M

95

4

U

18

10

4 L

80

4

M

90

4

U

18

10

4 L

80

4

M

70

3

T

17

45

3 K

50

2

O

1

1

R

12

70

2 J

55

3

N

45

2

S

12

5

4 L

2

O

40

2

S

13

50 60

2 J

75

3

N

65

3

T

14

55

2 J

75

3

N

55

3

T

13

80

1 I

80

4

M

10

1

R

11

70

2 J

45

2

O

80

4

U

14

271

LOG_ R6

3 B 2 G 6

FIN_R 1

3 B 2 G 6

FIN_R 2

3 B 2 G 6

DIS_R 1

3 B 2 G 6

Getting in touch with the customs administration about this issue and as to how the procedures could be made more effective Employing a finance specialist who could implement international financial techniques Depositing some of firm’s income in foreign exchange account Adding the risk monitoring of weather and route safety in the supply process

30

3 K

70

3

N

10

1

R 12

40

3 K

70

3

N

70

3

T

14

40

3 K

80

4

M

80

4

U

16

40

3 K

70

3

N

50

2

S

13

Finally, to obtain the HTP diagram in Table 12, risks were listed in descending order based on the "Total HTP Index", and in order to obtain more detailed information, five-letter HTP codes corresponding to each risk and the evaluation scores derived from the scale with regard to risk prevention cost, risk prevention potential and feasibility were added into the columns. This ranking indicates the most urgent risks that the management should deal with first based on the factors of risk severity, risk probability and risk prevention potential of the preventive action, feasibility and prevention cost. The risks that require immediate action are at the top of the pyramid, while the less important risks are towards the bottom.

272

Table 12. Supply chain risks HTP diagram

According to Table 11 and Table 12, the following evaluations can be made for the firm:  The risk which the firm should urgently deal with is “Incomplete and incorrect delivery of orders (SUP_R 1)". As the evaluations show, the occurrence probability of this risk is quite high (once in two weeks) and when it occurs, its impact on the firm is so critical that "it can lead to a slow down in manufacturing or can even lead to a halt in manufacturing for a week ". To prevent this risk, the firm identified the preventive action of "sharing the product details with the supplier firms in a standard format simultaneously and in a systematic manner when a new product is described in ERP systems". It is seen that risk prevention cost evaluation score of this action is quite low (5P), whereas its risk prevention potential (80 P) and feasibility (90 P) are quite high.  The second most urgent risk is "not delivering the orders on time (SUP_R 3)". The application team found that the occurrence probability of this risk is often (once a month) and when it occurs, its impact on the firm is critical.To avoid the delays in delivery, the preventive action of "switching from sea freight to air freight mode in case of delay and make the supplier responsible for the added transport costs" was determined. It is seen that this action bears no cost for the firm and its risk prevention potential and feasibility is very high.

273

 As seen in the HTP diagram, the risk with the lowest value and thus which appears at the bottom of the pyramid is “practices of national governments like quota restrictions and sanctions (LEG_R2)”. If this risk occurs, it has a very low negative impact on the firm, but its occurrence probability is very high. To prevent this risk, the application team developed the action of "supplying products with the help of an affiliated firm or a facility in a nearby country and thus avoiding the related restrictions and sanctions". This action has high prevention cost and its risk prevention potential and feasibility is very low. DISCUSSION AND CONCLUSION As mentioned before, in our study, the Supply Chain Risk Management Model by Tummala and Schoenherr (2011) was improved through suggestions and was applied to a firm operating in the transportation sector. Our contributions to the model can be listed as follows: In the original model, total risk value is obtained based on risk impact severity, occurrence probability, and risk prevention cost. Since it is thought that it may not be appropriate to select the actions that could prevent the risks with high values based merely on cost, the actions were evaluated with respect to their risk prevention potentials and feasibility as well. Eventually, based on five factors, total risk index for each risk was calculated. Supply Chain RiskManagement Model can be used as a means of decision support; however, the subjective nature of evaluation and ranking poses the risk of changes in risk ranking according to different users. Furthermore, while the original model focused on developing a single preventive action for each risk, it was realized that in practice this is not the case. Thus, it was recommended that in cases when more than one preventive action is developed for a risk, actions may be evaluated with respect to their cost, risk prevention potential and feasibility level, and a selection can be made based on this evaluation. In our opinion, although improved, two limitations of the model proposed by Tummala and Schoenherr (2011) are that the model does not consider the total impact of more than one risk prevention action and it does not evaluate the possible effect of the preventive actions developed for each risk on other risks. To overcome these limitations, Quality Function Deployment (QFD) method, which is also based on subjective evaluations, but which takes interactions into account, could be used. Furthermore, it is of great significance to apply the Risk Management Model and to follow the consequences of the measures regularly and continuously. For instance, following the use of a source for a particular preventive action, the risk may not have been eliminated entirely and its impact severity may have decreased from catastrophic to critical, or the risk may re-emerge if the risk prevention actions are not implemented sufficiently. It must be emphasized that the evaluations made today may not be valid tomorrow or in the future due to their subjective nature. Thus, a new approach to be proposed taking these caveats into account may help conceptualize and understand the problem in a more structured manner.

References Amin, S. H. Razmi, J. and Zhang, G. (2011)," Supplier Selection and Order Allocation Based on Fuzzy SWOT Analysis and Fuzzy Linear Programming", Expert Systems with Applications Vol. 38, pp. 334–342 Atadeniz, Ş. (2010), "Tedarik Zincirinde Risk Yönetimi", www.lojisturk.net (Erişim tarihi: 20.02.2017) Blackhurst, J. V.,Scheibe, K.P. ve Johnson, D.J. (2008), "Supplier risk assessment and monitoring for the automotive industry", International Journal of Physical Distribution &Logistics Management, Vol. 38, Issue 2, pp. 143 – 165 Chan, F. T.S. and Kumar, N. (2007), "Global Supplier Development Considering Risk Factors Using Fuzzy Extended AHP-Based Approach", Omega: The International Journal of Management Science, Vol. 35, pp. 417-321

274

Chen P.S and Wu, M.T (2013); “A Modified Failure Mode and Effect Analysis for Supplier Selection Problems in the Supply Chain Risk Environment: A Case Study”, Computers & Industrial Engineering, Vol. 66, Iss. 4, pp. 634-642 Chopra, S. ve Sodhi, S. (2004); “Managing Risk to Avoid Supply - Chain Breakdown”, MIT Sloan Management Review, Vol.46, No.1, pp.53-61 Crockford, N. (1986), An Introduction to Risk Management, 2nd ed., Woodhead-Faulkner. Demirkol, İ., Ünğan, M.C. and Ayanoğlu, M. (2015), " Tedarik Zinciri Risklerinin İşletme Performansı Üzerindeki Etkisi: Otomotiv Sektöründe Bir Uygulama ", The Journal of Business Science, Vol. 3, No.1, pp.20-37 Faisal, M. N., Banwet, D.K and Shankar, R. (2007), "Information Risks Management in Supply Chains: an Assessment and Mitigation Framework", Journal of Enterprise Information Management, Vol. 20 Iss: 6, pp.677 - 699 Faizal. K and Palaniappan, PL. K. (2014), "Risk Assessment and Management in Supply Chain", Global Journal of Researches in Engineering: G Industrial Engineering, Volume 14 Issue 2 Gaudenzi, B. and Borghesi A. (2006),"Managing risks in the supply chain using the AHP method", The International Journal of Logistics Management, Vol. 17 Iss 1 pp. 114 - 136 Giannakis M. and Louis M. (2011), "A multi-Agent Based Framework for Supply Chain Risk Management", Journal of Purchasing & Supply Management, Vol. 17, pp. 23–31 Harland, C., Brenchley, R. and Walker, H. (2003), "Risk in supply networks", Journal of Purchasing & Supply Management, Vol. 9, pp. 51–62 Ho, W., Zheng, T., Yıldız, H. and Talluri, S. (2015), “Supply Chain Risk Management: A Literature Review”, International Journal of Production Research, Vol.53, No.16, pp. 5031-5069 Karagülle, A. Ö. (2016); “Gelişen Dünya Ekonomisinde İki İnteraktif Kavram: Küreselleşme ve Lojistik”, http://www.lojiblog.com (Erişim Tarihi: 17.02.2017) Kern, D., Moser, R., Hartmann, E. and Moder M. (2012), "Supply Risk Management: Model Development and Empirical Analysis", International Journal of Physical Distribution & Logistics Management, Vol. 42 Iss 1 pp. 60 - 82 Kumar, S.K., Tiwari, M.K. and Babiceanu, R.F. (2010), "Minimization of supply chain cost with embedded risk using computational intelligence approaches", International Journal of Production Research, Vol. 48, No.13, pp. 3717-3739 Larsson, E.A. and Jager, M.J. (2012), "Supply Chain Disruptions Caused by Natural Catastrophes: an Explorative Study of 83 International Organizations" (Supervisor: Pogrebnyakov, N.), Copenhagen Business School, Master of Economics and Business Administration Master Thesis) Levary, R., R. (2007),"Ranking Foreign Suppliers Based on Supply Risk", Supply Chain Management: An International Journal, Vol. 12, Iss. 6, pp. 392 - 394 Lockamy III, A. (2011),"Benchmarking Supplier Risks Using Bayesian Benchmarking: An International Journal, Vol. 18 Iss 3 pp. 409 – 427

Networks",

275

Manuj, I. and Mentzer, J. T. (2008), "Global Supply Chain Risk Management", Journal of Business Logistics, Vol.29, No.1, pp.133-155 Mercan, Z.B. (2013), "SEVESO II Kapsamında Kabul Edilebilirlik (ALARP) Seviyesi Değerlendirme Metodolojileri", Çukurova Üniversitesi, FBE Yayınlanmamış YL Tezi, Adana Mitchell, A., Madill, J. and Chreim, S. (2015)," Marketing and Social Enterprises: Implications for Social Marketing ", Journal of Social Marketing, Vol. 5 Iss. 4, pp. 285 - 306 Norrman, A. and Jansson, U. (2004) "Ericsson's Proactive Supply Chain Risk Management Approach after a Serious Sub‐Supplier Accident", International Journal of Physical Distribution & Logistics Management, Vol. 34 Issue: 5, pp. 434-456 Park, Y.W., Hong, P. and Roh, J. J. (2013), "Supply Chain Lessons from the Catastrophic Natural Disaster in Japan", Business Horizons, Vol. 56, pp. 75-85 Pujawan, I. N., Geraldin, L. H. (2009) "House of Risk: A Model for Proactive Supply Chain Risk Management", Business Process Management Journal, Vol. 15, Issue: 6, pp.953-967 Quabouch, L. and Amri, M. (2013), "Analyzing Supply Chain Risk Factors: A Probability-Impact Matrix Applied to Pharmaceutical Industry", Journal of Logistics Management, Vol.2, No.2, pp. 35-40 Sarakinis, M. P., Carlsson, R. (2013), "Supply Chain Risk Management in the Swedish Armed Forces: A Case Study", (Supervisor: Jonas Floden), Master Degree Project in Logistics and Transport Management, University of Gothenburg, School of Business, Economics and Law. Slack, N. and Lewis, M. (2001), Operations Strategy, 3rd ed., Prentice-Hall, Harlow. Sosa L.A., Alcaraz, G. and Torres, C. (2014), "The Effects of Some Risk Factors in the Supply Cains Performance: A Case of Study", Journal of Applied Research and Technology, Vol. 12 , pp.958-968 Supply Chain Risk Leadership Council (2011), “Supply Chain Risk Management: A Compilation of Best Practices”, http://www.scrlc.com(Erişim Tarihi: 19.02.2017) Tang, C. and Tomlin, B. (2008), "The Power of Flexibility for Mitigating Supply Chain Risks", International Journal of Production Economics, Vol. 116, pp. 12-27 Tang, C. and Tomlin, B. (2008), "The Power of Flexibility for Mitigating Supply Chain Risks", International Journal of Production Economics, Vol. 116, pp. 12–27 Tang, C.S. (2006a), "Perspectives in Supply Chain Risk Management", International Journal of Production Economics, Vol. 103, pp. 451-488 Tang, O. and Musa, N. (2011), “Identifying risk issues and research advancements in supply chain risk management", International Journal of Production Economics, Vol. 133, pp. 25-34 Thun, J.H. and Hoenig, D. (2011), "An Empirical Analysis of Supply Chain Risks Management in the German Automotive Industry", International Journal of Production Economics, Vol. 131, pp. 242-249

276

Trkman, P. and Mc Cormack, K. (2009), "Supply Chain Risk in Turbulent Environments-A Conceptual Model for Managing Supply Chain Network Risk", International Journal of Production Economics, Vol.119, pp. 247-258 Tummala, R. Ve Schoenherr, T. (2011), "Assessing and managing risks using the Supply Chain Risk Management Process(SCRMP)", Supply Chain Management: An International Journal, Vol. 16 Iss 6 pp. 474 - 483 Tuncel, G. and Alpan, G. (2010), "Risk Assessment and Management for Supply Chain Networks: A Case Study, Computers in Industry, Vol. 61, pp. 250-259 Vilko, J. (2012), "Approaches to Supply Chaın Rısk Management: Identıfıcatıon, Analysıs and Control", Thesis for the degree of Doctor of Science, Lappeenranta University of Technology, Finland. Wagner, S. and Bode, C. (2008), “An Emprical Examination of Supply Chain Performance Along Several Dimensions of Risk”, Journal of Business Logistics, Vol.29, No.1, pp. 307-325 Wittmann, E.,(2000) ”Risikomanagement im internationalen Konzern”.In:Dorner, D., Horvath, P.,Kagermann,H.(Eds.),Praxis des Risikomanagements.Stuttgart, pp. 789–820. Wright, J. and Datskovska, D. (2012), "New Perspectives On", Logistics Management, www.logisticsmgmt.com (Access Date: 03.03.2017) Wu, F., Yeniyurt, S., Kim D. and Çavuşgil, T. (2006), "The impact of information technology on supply chain capabilities and firm performance: A resource-based view", Industrial Marketing Management, Vol. 35, pp. 493 – 504 Wu, T., Blackhurst, J and Chidambaram, V. (2006_a), "A Model for Inbound Supply Risk Analysis", Computers in Industry, Vol. 57, pp. 350-365 Xiao, Z., Weijie, C. and Li, L. (2012), "An Integrated FCM and Fuzzy Soft Set for Supplier Selection Problem based on Risk Evaluation", Applied Mathematical Modelling, Vol. 36, Iss. 4, pp. 1444-1454 Yates, J.F. and Stone, E. (1992), “The risk construct”, in Yates, J.F. (Ed.), Risk-Taking Behaviour, Wiley, Chichester, pp. 1-25. Zhou, Y. ,L.,L (2011),"The impacts of product design changes on supply chain risk: a case study", International Journal of Physical Distribution & Logistics Management, Vol. 41 Iss 2 pp. 162 - 186 Zsidin, G.A. (2003), "A Grounded Definition of Supply Risk", Journal of Purchasing and Supply Management, Vol.9, pp. 217-224 Zsidisin, G.A., Ellram, L. M., Carter, J.R. and Cavinato, J.L. (2004) "An analysis of supply risk assessment techniques", International Journal of Physical Distribution & Logistics Management, Vol. 34, Issue.5, pp.397-413

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