Sustainable supply chain management (SCM) demands efficiency while minimizing environmental impact, yet conventional automation lacks adaptability. This paper presents SustAI-SCM, an AI-powered framework integrating agentic intelligence to automate supply chain tasks with sustainability in focus. Unlike static rule-based systems, it leverages a transformer model that continuously learns from operations, refining procurement, logistics, and inventory decisions. A diverse dataset comprising procurement records, logistics data, and carbon footprint metrics trains the model, enabling dynamic adjustments. The experimental results show a 28.4% cost reduction, 30.3% lower emissions, and 21.8% improved warehouse efficiency. While computational overhead and real-time adaptability pose challenges, future enhancements will focus on energy-efficient AI, continuous learning, and explainable decision making. The framework advances sustainable automation, balancing operational optimization with environmental responsibility.

Lojistik sektörü dünyada ve Türkiye’de giderek büyümekte ve sektörün potansiyeli zamanla daha iyi anlaşılmaktadır. Lojistik sektörünün gelişime oldukça açık olduğu, teknoloji ile ortaya çıkan yeniliklere ayak uydurmak zorunda olduğu bilinmektedir. İşletmeler bu yeniliklere ayak uydurarak, rekabette başarılı olmaya çalışmaktadır. Endüstri 4.0 özellikle lojistik gibi rekabetin ön planda olduğu sektörleri etkisi altına almıştır. Yapılan son araştırmalarda yapay zeka tekniklerinin kullanımında büyük oranda artış olduğu görülmektedir. Yapay zekanın lojistik sektöründe kullanılması sonucunda özellikle işleyiş ve dinamiklerde değişiklikler oluşmaya başlamıştır. Yapay zeka, insan zekasının fizyolojik ve nörolojik yapısını çeşitli teknolojiler yardımı ile modelleyerek makinelere aktarmaktadır. Yapay zeka ile birlikte ortaya çıkan sürücüsüz araçlar, depolama ve raflarda kullanılan robotlar, sistem içerisinde büyük verilerin rahatlıkla kullanılması gibi seçenekler lojistik sektöründeki hataların en aza indirgenmesini sağlamaktadır. Lojistik sektöründe yapay zeka kullanımı sayesinde işletmeler daha verimli işler ortaya koymaktadır. Yapılan bu çalışmada, lojistik sektöründe kullanılan yapay zeka ve makine öğrenmesi uygulamalarının geniş bir perspektif ile incelenmesi amaçlanmıştır. Çalışmada önce yapay zeka ve makine öğrenimi kavramları açıklanmış ardından endüstri ve lojistik kavramlarına değinilerek lojistikte kullanılan yapay zeka ve makine öğrenmesi uygulamalarına yer verilmiştir. Küresel lojistik ve tedarik zinciri yönetimi konusunda yapay zekanın günden güne kendini geliştirdiği ve lojistik süreçleri kolaylaştırdığı görülmektedir.

Inventory management is crucial for companies to minimize unnecessary costs associated with overstocking or understocking items. Utilizing the economic order quantity (EOQ) to minimize total costs is a key decision in inventory management, particularly in achieving a sustainable supply chain. The classical EOQ formula is rarely applicable in practice. For example, suppliers may enforce a minimum order quantity (MOQ) that is much larger than the EOQ. Some conditions such as imperfect quality and growing items represent variants of EOQ. Moreover, some requirements, such as the reduction of CO₂ emissions, can alter the formula. Moreover, disruptions in the supply chain, such as COVID-19, can affect the formula. This study investigates which requirements must be considered during the calculation of the EOQ. Based on a literature review, 18 requirements that could alter the EOQ formula were identified. The level of coverage for these requirements has been tracked in the literature. Research gaps were presented to be investigated in future research. The analysis revealed that, despite their importance, at least 11 requirements have seldom been explored in the literature. Among these, topics such as EOQ in Industry 4.0, practical EOQ, and resilient EOQ have been identified as promising areas for future research.

Because of continuous competition in the corporate industrial sector, numerous companies are always looking for strategies to ensure timely product delivery to survive against their competitors. For this reason, logistics play a significant role in the warehousing, shipments, and transportation of the products. Therefore, the high utilization of resources can improve the profit margins and reduce unnecessary storage or shipping costs. One significant issue in shipments is the Pallet Loading Problem (PLP) which can generally be solved by seeking to maximize the total number of boxes to be loaded on a pallet. In many previous studies, various solutions for the PLP have been suggested in the context of logistics and shipment delivery systems. In this paper, a novel two-phase approach is presented by utilizing a number of Machine Learning (ML) models to tackle the PLP. The dataset utilized in this study was obtained from the DHL supply chain system. According to the training and testing of various ML models, our results show that a very high (>85%) Pallet Utilization Volume (PUV) was obtained, and an accuracy of >89% was determined to predict an accurate loading arrangement of boxes on a suitable pallet. Furthermore, a comprehensive analysis of all the results on the basis of a comparison of several ML models is provided in order to show the efficacy of the proposed methodology.

Blockchain technologies (BT) began to be used in many areas because of the many advantages they offer. In this respect, BT applications take place in different types of supply chains. One of the areas where blockchain is used is the food supply chain (FSC). FSC is a general concept that also covers different types such as agri-FSC and cold chain. These supply chains need the advantages of modern BT for crucial reasons such as the products’ time-dependent nature and direct impact on human health. We evaluate the use of blockchain applications in the field of FSC in order to provide a deep understanding of problems inherent to the FSC and present insights to companies into developing and implementing their own blockchain-driven solutions to address the FSC performance challenges. As the FSC is a complex and dynamic system, where multi-stakeholders are involved, a systematic approach should be established to reveal the BT integration behavior of the FSC. An extensive literature review was conducted within the scope of the study. A critical review of the studies shows which BT applications are used in the FSC chain to drive supply chain performance, their application areas, and main problems in various aspects in the applications. Using the causal loop diagram (CLD) as a system dynamics approach, the interrelationship of causes and effects is visualized and prescriptive information is generated to guide researchers and practitioners in the field of new technology adoption in FSCs in general and BT integration in particular. This study is intended to encourage the use of BT in FSC applications.