Assessment of process capabilities in transition to a data-driven organisation: A multidisciplinary approach

The ability to leverage data science can generate valuable insights and actions in organisations by enhancing data-driven decision-making to find optimal solutions based on complex business parameters and data. However, only a small percentage of the organisations can successfully obtain a business value from their investments due to a lack of organisational management, alignment, and culture. Becoming a data-driven organisation requires an organisational change that should be managed and fostered from a holistic multidisciplinary perspective. Accordingly, this study seeks to address these problems by developing the Data Drivenness Process Capability Determination Model (DDPCDM) based on the ISO/IEC 330xx family of standards. The proposed model enables organisations to determine their current management capabilities, derivation of a gap analysis, and the creation of a comprehensive roadmap for improvement in a structured and standardised way. DDPCDM comprises two main dimensions: process and capability. The process dimension consists of five organisational management processes: change management, skill and talent management, strategic alignment, organisational learning, and sponsorship and portfolio management. The capability dimension embraces six levels, from incomplete to innovating. The applicability and usability of DDPCDM are also evaluated by conducting a multiple-case study in two organisations. The results reveal that the proposed model is able to evaluate the strengths and weaknesses of an organisation in adopting, managing, and fostering the transition to a data-driven organisation and providing a roadmap for continuously improving the data-drivenness of organisations

Blockchain-Based Supply Chain Management: Understanding the Determinants of Adoption in the Context of Organizations

This study investigates the importance of the determinants affecting the adoption and usage of blockchain-based SCM systems in the context of organizations. Hence, an SLR method was followed to uncover critical determinants in the literature. Then, a research model, including 14 key determinants, was developed based on the TOE Framework. Subsequently, the AHP method was applied to rank the adoption determinants. The findings reveal that environment-related determinants are more critical than technology-related or organization-related determinants

VERİ BİLİMİ KABİLİYET OLGUNLUK MODELİ

Today, data science presents immense opportunities in attaining competitive advantage, generating business value, and driving revenue streams for organizations. Data science has also significantly changed our understanding of how businesses should operate. In order to survive, it is now indispensable for a contemporary organization to adopt data science as part of its business processes. However, organizations face difficulties in managing their data science endeavors for reaping these potential benefits. This has led to the need for a comprehensive and structured model to continuously assess and improve the maturity of data science capabilities of organizations. This thesis seeks to address this problem by proposing a theoretically grounded Data Science Capability Maturity Model (DSCMM) for organizations to assess their existing strengths and weaknesses, perform a gap analysis, and draw a roadmap for continuous improvements. DSCMM comprises six maturity levels from “Not Performed” to “Innovating” and twenty-eight data science processes categorized under six headings: Organization, Strategy Management, Data Analytics, Data Governance, Technology Management, and Supporting. The applicability and usefulness of DSCMM are validated through a multiple case study conducted in organizations of various sizes, industries, and countries. The case study results indicate that DSCMM is applicable in different settings, is able to reflect the organizations’ current data science maturity levels and provide significant insights to improve their data science capabilities.Günümüzde veri bilimi, rekabet avantajı elde etme, iş değeri yaratma ve kuruluşlar için gelir akışlarını yönlendirme konusunda önemli fırsatlar sunuyor. Veri bilimi, işletmelerin nasıl çalışması gerektiğine dair anlayışımızı da önemli ölçüde değiştirdi. Çağdaş bir organizasyonun hayatta kalabilmesi için veri bilimini iş süreçlerinin bir parçası olarak benimsemesi artık vazgeçilmezdir. Ancak kuruluşlar, bu potansiyel faydaları elde etmek için yürüttükleri veri bilimi girişimlerini yönetmede zorluklarla karşılaşmaktadır. Bu, kuruluşların veri bilimi yeteneklerinin olgunluğunu sürekli olarak değerlendirmek ve geliştirmek için kapsamlı ve yapılandırılmış bir model ihtiyacı olduğunu göstermektedir. Bu tez, kuruluşların mevcut güçlü ve zayıf yönlerini değerlendirmeleri, fark analizi yapmaları ve ilerlemelerinde sürekli iyileştirmeye yönelik bir yol haritası çizmeleri için teorik olarak temellendirilmiş bir Veri Bilimi Yetenek Olgunluk Modeli (DSCMM) önererek bu sorunu çözmeyi araştırmaktadır. DSCMM, ‘Uygulanmıyor’ ile ‘Yenilikçi’ arasında altı olgunluk seviyesinden ve Organizasyon, Strateji Yönetimi, Veri Analitiği, Veri Yönetimi, Teknoloji Yönetimi ve Destek olarak altı grup altında sınıflandırılan yirmi sekiz veri bilimi sürecinden oluşur. DSCMM'nin uygulanabilirliği ve yararlılığı, çeşitli büyüklük, endüstri ve ülkelerdeki kuruluşlarda yürütülen çoklu vaka çalışmalarıyla geçerlenmiştir. Vaka çalışması sonuçları, DSCMM'nin farklı ortamlarda uygulanabilir olduğunu ve kuruluşların mevcut veri bilimi olgunluk düzeylerini yansıtabildiğini ve veri bilimi yeteneklerini geliştirmek için önemli bilgiler sağlayabildiğini göstermektedir.Ph.D. - Doctoral Progra

Dağıtık Gerçek Zamanlı Sürekli Sorguları İşlemek için Bulut Tabanlı bir Mimari.

The technological advancements in Internet of Things (IoT) domain have enabled us to reshape the physical world through smart devices, sensors and actuators. The data collected by IoT devices has become a valuable asset to extract knowledge about the environment and other nearby devices. Existing IoT applications mostly store collected data in a central server and allow users to query stored data to notice and react to changes in the environment. Usually cloud and big data technologies are utilized in those applications for scalability. Nevertheless, the responsiveness of such IoT applications is limited due to the use of polling based queries. In this thesis, we primarily focus on the problem of specifying a generic and scalable architecture to process a multitude of continuous queries in real time, respond to events and notify users in a timely manner. For this purpose, we propose a data-flow based query definition model to allow users create flexible queries. We devise a centrally managed distributed infrastructure based on the state of the art big data technologies to execute the continuous queries over streaming data rather than storing and frequently querying the data collected. A prototype has been implemented to demonstrate the applicability and to evaluate the scalability of the proposed approach.M.S. - Master of Scienc

Tıbbi görüntüleme araçları için bulut bilişim tabanlı öngörücü bakım uygulama çatısı

Nesnelerin İnterneti ve Bulut Bilişim alanlarındaki son teknolojik gelişmeler, hastanelerde sunulan sağlık hizmetlerinin kalitesinin iyileştirilmesine olanak sağlamaktadır. Bu teknolojilerden biri olan, akıllı sensör ve aktüatör teknolojilerinin hastanelerde yaygın kullanımı ile çeşitli tıbbi cihazlardan toplanan veriler sayesinde, sunulan sağlık hizmetlerinin iyileştirilmesi sağlanmaktadır. Örneğin, cihazlarda oluşacak hataları önceden görerek, bu hataların düzeltilmesini kapsayan öngörücü bakım sistemleri için biyomedikal cihazlardan toplanan veriler önemli bir potansiyele sahiptirler. Ancak, öngörücü bakım sistemlerinden azami fayda elde etmek, bakım maliyetlerini düşürmek ve sağlık hizmetlerini iyileştirilmek için Bulut Bilişim ve Nesnelerin İnterneti teknolojilerinin tıbbi görüntüleme cihazları ile entegrasyonun etkin bir şekilde gerçekleştiği bir çözüme ihtiyaç duyulmaktadır. Literatürde bu sorunu çözmek için umut verici bazı çalışmalar olmasına rağmen, günümüz bilgi çağında kullanılması için henüz yeterli olgunlukta değillerdir. Bu nedenle, bu çalışma kapsamında, temel olarak, tıbbi görüntüleme cihazları için Bulut Bilişim ve Nesnelerin İnterneti teknolojilerine dayanan bir öngörücü bakım uygulama çatısı tanımlanmıştır. Ardından, önerilen bu uygulama çatısının faydaları ve zararları tartışılmıştır.Recent technological advancements in Internet of Things (IoT) and Cloud Computing domains, enable improving quality of health services in hospitals. The widespread use of smart sensor and actuator technologies in hospitals allow us to improve healthcare services by collecting data from various medical devices. Therefore, hospitals grasp noteworthy potential to convert these collected data into valuable information for predictive maintenance of biomedical devices. However, in order to obtain maximum benefit from the predictive maintenance system to reduce maintenance costs and improve healthcare services, a well-integrated solution is needed to combine cloud computing and IoT technologies with medical imaging devices. Despite some promising efforts in this area to solve this problem, they are not sufficient to be used in the information era. Thus, in this study, we primarily focus on the problem of how to define a predictive maintenance framework for medical imaging devices based on cloud computing and IoT technologies. Then, we identify the benefits and challenges of the proposed predictive maintenance framework. Anahtar Kelimele

Hazır Giyim ve Konfeksiyon Sektöründe Endüstri 4.0 Devrimi: Akıllı Konfeksiyon Fabrikası

Together with the latest technological innovations, the vision of the smart factory has been enabled in the light of the last industrial revolution called Industry 4.0. Industry 4.0 has an important potential to change all production processes and business models in the fashion and apparel sector, which is a labor-intensive sector called the locomotive sector of our country. Therefore, in order to survive in a challenging global competitive environment, the transition of the fashion and apparel industry from ordinary production facilities to smart factories is required to invest in Industry 4.0 technologies. However, it has been determined that the studies in the literature on how these relatively new technologies can affect the apparel industry are limited and not adequately covered. In this study, which was prepared in order to address this gap, firstly the current system analysis was carried in the production system of an ordinary garment factory. Then, a smart apparel factory was proposed as a new production system in line with the vision of Industry 4.0. After introducing the technological components of the proposed Smart Apparel Factory, innovative approaches developed based on these technological components are presented in a holistic way. Then, the benefits and challenges of the proposed Smart Apparel Factory were analyzed and the proposed progressive implementation plan for transition from the current situation to the smart apparel factory was given