This paper presents a novel approach for accurately modeling and ultimately predicting wind speed for selected sites when incomplete data sets are available. The application of a seasonal simulation for the synthetic generation of wind speed data is achieved using the Markov chain Monte Carlo technique with only one month of data from each season. This limited data model was used to produce synthesized data that sufficiently captured the seasonal variations of wind characteristics. The model was validated by comparing wind characteristics obtained from time series wind tower data from two countries with Markov chain Monte Carlo simulations, demonstrating that one month of wind speed data from each season was sufficient to generate synthetic wind speed data for the related season.

The lack of a proper communication link between supply chain operations and financial performance seems to be due to the difficulty of using the operational metrics of supply chain measurement to reach the financial goals. This study is an attempt to find the effect of supply chain management (SCM) implementation on the financial success of the firms. The study considers the effects of Revenue, Prime Cost, Cash-to-cash cycle period and Return on working capital on the financial performance of the selected firms listed in Istanbul stock exchange from 2012 to 2017. The study divides the sample size based on their financial growth into two groups and using some statistical test measures the difference between two groups, statistically. The results show that there was a significant difference between the mean of high-growth and lowgrowth companies in terms of SCM implementation. However, the effects of cost, cash cycle and working capital on financial performance were not confirmed.