Many companies embrace Industry 4.0 technologies to enable operational sustainability against increasing climate change effects, decreasing natural resources, and raising consumer awareness of environmental issues. Even though readiness and maturity assessment of smartness and sustainability concepts are nested, no study simultaneously focuses on these concepts. As pioneering research, we propose a novel model titled “Smart and Sustainable Supply chain Readiness and Maturity model (S3RM)” and validate it by conducting a case study in the automotive industry. We design our model upon the triple-bottom-line (TBL) approach consisting of smartness and sustainability dime5nsions. Our study introduces the TBL of smartness covering availability, integrity, and adaptability sub-dimension. TBL of sustainability includes social, environmental, and economic sub-dimensions. The proposed model calculates the Smart and Sustainable Readiness and Maturity Index by averaging sustainability scores’ summation and smartness scores’ multiplication. Each sub-dimension consists of items measured by a readiness and maturity scale. The findings suggest how smartness and sustainability items create strengths, weaknesses, opportunities, and threats for the supply chain operations. Our model provides managerial implications in assessing the readiness and maturity of Industry 4.0 tools and sustainability indicators. This study offers a road map to managers on smart and sustainable supply chains’ defined target areas.

As financial markets worldwide continue to evolve, the importance of predictive models for investors and analysts is growing. This paper presents a comparative analysis of metaheuristic-based Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models for predicting stock prices in the Borsa Istanbul 100 (BIST 100) index. Using a comprehensive dataset of weekly data (2010–2023), researchers tested different model configurations, and the ANFIS model was improved with Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Model performance was evaluated using performance criteria such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R²). The results showed that, in particular, the ANFIS-PSO and ANN-based approaches exhibited higher accuracy compared to traditional regression methods. The study also analyzed the impact of various macroeconomic and financial indicators, including the gold/TL exchange rate, VIX index, and government bond interest rates, on the BIST 100. The findings reveal that artificial intelligence-based modeling significantly improves stock price forecasts thanks to its adaptability to dynamic market conditions. In this respect, the study makes theoretical contributions and highlights practical implications for investment strategies and risk management. Future research can further enrich the financial modeling literature by testing the validity of similar approaches in other emerging or developed markets, as well as by incorporating additional macroeconomic factors and different meta-heuristic algorithms into the model.

Industry 4.0 is a recent revolution that influences many aspects of society today. The sustainable and gradual development of technology stimulated the spread of the fourth industrial revolution into different industries, ranging from low-tech manufacturing to highly advanced service sectors. The aviation industry is one of the largest and the most strategic industries due to economic, political, and military considerations. The growth of this industry is essential, and Industry 4.0 supports its this through the use of effective technological aids, such as the Internet of Things (IoT) and Cyber-Physical Systems (CPS). These are considered prime examples of Industry 4.0 technologies. IoT connects the physical objects in an environment to collect extensive data that can be analyzed for meticulous planning. In the aviation industry, all this precise information is used to offer customers more qualified service, produce better weather forecasts, improve real-time flight tracking service, and provide excellent aids that help flight crews make safe decisions. In addition, CPS integrates hardware and software systems to conduct operations safer, more economical, and more effectively. In the aviation industry, CPS is mainly employed to improve the autonomy of the aircraft and the air traffic management systems. Autonomous ability leads to intelligent and sustainable systems that can reduce air traffic congestion, lighten the heavy workload of air traffic controllers and pilots, and minimize the likelihood of accidents. The future generation of air traffic management systems will have a high level of autonomy and be capable of making decisions closer to those of humans. To that end, this chapter introduces IoT and CPS technologies and their applications in the aviation industry. This chapter explores the application of IoT and CPS technologies in the aviation industry, focusing on the smart baggage handling system. It provides a brief overview of Industry 4.0, IoT, and CPS. Then, it presents a case study of how an airline company implemented an IoT-based baggage handling system that reduced mishandled bags, improved customer satisfaction, and increased operational efficiency. The chapter also discusses the implications of IoT and CPS for aviation operations, safety, and overall efficiency and suggests future directions and considerations for further research and development. The chapter aims to provide a comprehensive understanding of the present and future of Industry 4.0 in the aviation sector. <!----> <!----> <!----> <!---->

Artan küresel nüfus, azalan tarım alanları, iklim değişikliği ve jeopolitik riskler gibi faktörlerin tarımsal emtia fiyatlarını artırması ve cari açık sorununa rasyonel çözüm arayışları, tarım makineleri sektöründe Ar-Ge ve inovasyonun önemini ekonomik ve stratejik bir zorunluluk olarak rasyonel bir şekilde ön plana çıkarmaktadır. Bu çalışma, Konya bölgesinde faaliyet gösteren tarım makineleri üreticisi firmaların (NACE 28.30) Ar-Ge ve inovasyon faaliyetlerinin, ihracat performansı üzerindeki etkisini araştırmaktadır. Elde edilen bulgular, ihracat cirosu ile Ar-Ge kapasitesi arasında anlamlı bir ilişki olduğunu göstermektedir. Özellikle, ihracat hacmi 1 milyon ABD Doları'nın üzerinde olan firmaların %82.9'unda bağımsız bir Ar-Ge biriminin mevcut olması ve bu firmaların prototip geliştirme ve patent başvuru süreçlerinde diğer firmalara kıyasla daha aktif rol almaları dikkat çekmektedir. Mühendis istihdamının kritik bir faktör olduğu belirlenmiş; 10 ve üzeri sayıda mühendis istihdam eden firmaların tamamının Ar-Ge birimine sahip olduğu ve %88.8'inin patent başvurusunda bulunduğu gözlemlenmiştir. Buna karşın, firmaların %72.9'unun 3 veya daha az sayıda mühendis çalıştırması, %29.4'ünün Ar-Ge faaliyetleri için bütçe ayırmaması ve %45.3'ünün herhangi bir patent başvurusunda bulunmaması, sektördeki Ar-Ge ve inovasyon eksikliklerini ortaya koymaktadır. Genç firmaların %41,7'sinin Ar-Ge'ye kaynak tahsis etmemesi, orta vadede rekabet dirençliliğini aşındıracak ve sürdürülebilir büyümeyi tehlikeye atacak önemli bir husus olarak öne çıkmaktadır.