The need for healthcare equipment has increased due to the COVID-19 outbreak. Forecasting of these demands allows states to use their resources effectively. Artificial intelligence-based forecasting models play an important role in the forecasting of medical equipment demand during infectious disease periods. In this study, a deep model approach is presented, which is based on a multilayer long short-term memory network for forecasting of medical equipment demand and outbreak spreading, during the coronavirus outbreak (COVID-19). The proposed model consists of stages: normalization, deep LSTM networks and dropout-dense-regression layers, in order of process. Firstly, the daily input data were subjected to a normalization process. Afterward, the multilayer LSTM network model, which was a deep learning approach, was created and then fed into a dropout layer and a fully connected layer. Finally, the weights of the trained model were used to predict medical equipment demand and outbreak spreading in the following days. In experimental studies, 77-day COVID-19 data collected from the statistics data put together in Turkey were used. In order to test the proposed system, the data belonging to last 9 days of this data set were used and the performance of the proposed system was calculated using statistical algorithms, MAPE and R 2 . As a result of the experiments carried out, it was observed that the proposed model could be used to estimate the number of cases and medical equipment demand in the future in relation to COVID-19 disease.

In this study, the direct effect of environmental uncertainty on competitive advantage and its indirect effect through the sequential mediator variables of supply chain integration and supply chain agility were investigated. The sample of the study consists of company managers operating in the manufacturing sector in Turkey. An online survey was sent to company managers through connections established on LinkedIn and an analysis was carried out with the data collected from 414 participants. As a result of the analysis, it has been determined that environmental uncertainty has a direct, significant and positive effect on competitive advantage. In addition, the results of the research show that supply chain integration and supply chain agility have a partial mediating role in the relationship between environmental uncertainty and competitive advantage. According to the results of this study, in conditions of high environmental uncertainty, companies can increase their supply chain agility capabilities by establishing a more integrated structure with their supply chain partners, and thus gain a unique competitive advantage over their competitors. It has been observed that the relationships between the concepts, which are the subject of the study, have been investigated separately in different studies in the literature. This study will contribute to the literature by investigating the relationships between concepts in a holistic way.

Tedarik zincirinin etkin yönetilmesi işletmelerin faaliyetlerinin sürdürebilmesi açısından önemli bir rol oynamaktadır. Bu nedenle tedarik zinciri performansının belirlenebilmesi için tedarik zinciri entegrasyonu ve boyutlarından biri olan içsel entegrasyonun birlikte değerlendirilmesi önem kazanmaktadır. Bu çalışmanın amacı, işletme içi ortak sorumluluk alanları bulunan lojistik, pazarlama ve üretim arasındaki içsel entegrasyon ile tedarik zinciri performansı arasındaki ilişkide güven faktörü aracılık rolünün incelenmesi ve öneminin belirlenmesidir. Bu amaçla imalat sektöründe Ankara ve İstanbul’da faaliyette bulunan orta ve büyük ölçekli işletmeler üzerinde ampirik bir çalışma yapılmıştır. Oluşturulan teorik model, güvenirlik ve geçerlik testlerine tabi tutulduktan sonra bootstrap yöntemi ile analiz edilmiştir. Araştırma sonuçlarına göre; güvenin aracı değişken olarak kullanılması lojistik, pazarlama ve üretim arasındaki içsel entegrasyonun, tedarik zincirinin süre odaklı performansına anlamlı ve olumlu yönde etki ettiği tespit edilmiştir. Yapılan bu çalışma, araştırmacılara tedarik zinciri yönetimi çalışmalarında içsel entegrasyon ve tedarik zinciri performansı ilişkisinin güven aracı değişkeni ile olan bağlantısını göstermesi açısından önemlidir.

Blokzincirin günden güne artan farkındalık düzeyiyle birlikte farklı alanlarda kullanılabilirliği hem akademisyenler hem de uygulayıcılar tarafından çalışılmaktadır. Ancak blokzincir teknolojisinin tedarik zinciri yönetimi alanında kullanımı ile ilgili henüz az sayıda çalışma bulunmaktadır. Farklı çalışmalarda ayrı ayrı ele alınan tedarik zinciri izlenebilirliği ve tedarik zinciri sürdürülebilirliği bu çalışmada blokzincir teknolojik altyapısıyla değerlendirilmektedir. Çalışmada yöntem olarak örnek olay yöntemi benimsenerek blokzincir projelerinin örnek olayları paylaşılmıştır. Blokzincir teknolojik altyapısının kullanımının tedarik zinciri şeffaflığını; izlenebilirlik ve sürdürülebilirlik açısından olumlu yönde etkileyeceği düşünülmektedir.

It has become obligatory for businesses to carry out recycling activities in the face of increasing environmental pollution and the danger of depletion of natural resources. The waste collection phase of the recycling process requires interactive transportation that uses a reverse logistics flow from customers to recycling facilities. Businesses need to create appropriate network structures to carry out these activities at minimum cost. This study has developed a model, based on reverse logistics, of collecting products from customers and sending them to warehouses and then to recycling facilities. The chance-constrained programming (CCP) approach was used to regulate the constraints involving stochastic demand in the model. Linearization was performed using the linear approximation method. The cost of transportation from Initial Collection Points (ICP) warehouses to recycling facilities is the most influential component on the objective function. This linearized model was solved by creating different scenarios by changing the standard deviation ratio, reliability level, and warehouse capacities within the scope of sensitivity analysis. In the sensitivity analysis, it was determined that the increase in confidence level and variance negatively affected the objective function. In addition, it has been concluded that the increase in demand has no effect on costs as long as the capacity of the facility is not exceeded.