In supply chain literature, supplier evaluation and selection problem is one of the most studied subjects because of the significant roles of suppliers in terms of the chain’s sustainability and profitability. Therefore, it is important for organizations to adopt a systematic way to evaluate and select the best supplier according to their respective criteria in today’s competitive environment. Multicriteria decision-making methods provide for this need of organizations because determination of an appropriate supplier selection is a multicriteria decision-making (MCDM) problem essentially. Although a lot of applications of these methods for supplier evaluation and selection can be seen in the literature, studies in the health-care sector are insufficient. Hospitals in the health-care sector also have to consider their supplier-related decisions to decrease risks and threads which affect their effectiveness. The aim of this study was to fill this gap by providing different hybrid models for selecting the best supplier for hospitals. Supplier evaluation and selection process start with recognizing the related criteria according to the studies in the literature. Analytic hierarchy process (AHP) method is deployed to weight the criteria, and suppliers are listed via technique for order preference by similarity to ideal solution (TOPSIS), elimination and choice translating reality English (ELECTRE), grey relational analysis (GRA), and simple additive weighting (SAW) methods. The main aim of this study was to present different hybrid MCDM methods and show their efficiency and consistency with each other. In this study, hybrid multicriteria decision-making models (AHP-TOPSIS, AHP-ELECTRE, AHP-GRA, and AHP-SAW) are presented and compared. The results show that the presented hybrid methods in this study are consistent with each other and give the same ranking for the selection of the best supplier. It can be considered as a useful guideline for hospitals.

Due to the increasing global warming in the world, analyzing greenhouse gas emissions is a crucial issue. This study has examined greenhouse gas emissions in Turkey according to energy sector, industrial processes sector, agriculture sector and waste sector. Then, time series analysis models are used to estimate greenhouse gas emissions based on sectors. Models' performances are tested using mean error, mean absolute error and root mean square error. The results show that forecasting models have a good potential to estimate the national greenhouse gas emissions for different sector within a reasonable error. The study results will help organize and estimate the national greenhouse gas emissions inventory.

Business intelligence is the process and methods that enable businesses to effectively analyze large amounts of data and transform it into meaningful information, helping to increase efficiency and productivity in businesses, thus enabling businesses to gain competitive advantage. In this context, business intelligence improves data management and decision-making processes and plays a critical role in strategic management. The main purpose of this study is to analyze the transition process of business intelligence solutions in financial institutions in detail, to increase efficiency in reporting processes, and to optimize decision-making processes. The study examines the ‘Cheque Report’, which reports the status of cheques in XY Financial Institution. Within the scope of the study, the transition process to business intelligence in the financial institution examined the ‘Cheque Report’ in three stages: in the first stage, reports were prepared manually; in the second stage, they were prepared with PL/SQL, and in the last stage, they were prepared with a business intelligence solution, and their outputs were compared. As a result, it was observed that with the use of business intelligence, fast and direct access to reports, data security, freedom from person dependency, and efficiency in internal information sharing are provided.

Examination of greenhouse gas emissions is crucial for understanding the global warming. For this reason, identification of the sources of greenhouse gas emissions is crucial. This paper presents a hybrid multi criteria decision making method, which combines analytic hierarchy process and technique for order preference by similarity to ideal solution and compares this method with actual data for identifying the risk priority of sources of greenhouse gas emissions. For this purpose, the historical data of 25-years, for six-greenhouse gas sources and three-greenhouse gases (CO₂, CH_4, N₂O) are considered. Consequently, it was found that while incineration of wastes caused the minimum GHG emissions, energy sector caused the maximum GHG emissions. The results of this paper show that use of this hybrid method is easy and intelligible, and has a good potential for sorting the risk priority of sources of greenhouse gas emissions.