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Accurate forecasting of intermittent demand is very important since parts with intermittent demand characteristics are very common. The purpose of this paper is to bring an easier way of handling the hard work of intermittent demand forecasting by using commonly used Excel spreadsheet and also performing parameter optimization.

Smoothing parameters of the forecasting methods are optimized dynamically by Excel Solver in order to achieve the best performance. Application is done on real data of Turkish Airlines’ spare parts comprising 262 weekly periods from January 2009 to December 2013. The data set are composed of 500 stock-keeping units, so there are 131,000 data points in total.

From the results of implementation, it is shown that using the optimum parameter values yields better performance for each of the methods.

Although it is an intensive study, this research has some limitations. Since only real data are considered, this research is limited to the aviation industry.

This study guides market players by explaining the features of intermittent demand. With the help of the study, decision makers dealing with intermittent demand are capable of applying specialized intermittent demand forecasting methods.

The study brings simplicity to intermittent demand forecasting work by using commonly used spreadsheet software. The study is valuable for giving insights to market players dealing with items having intermittent demand characteristics, and it is one of the first study which is optimizing the smoothing parameters of the forecasting methods by using spreadsheet in the area of intermittent demand forecasting.

Previous researches have proven that customer satisfaction and loyalty are affected by complicated relationships and are challenging to European customer satisfaction index (ECSI) model. Existing approaches mostly limit their hypotheses to linear relationships, which hinder much information that would lead to better modeling and understanding the relationship between customer satisfaction and loyalty. The purpose of this paper is to reveal potential nonlinear and interaction effects that might be embedded in antecedents of ECSI by exemplifying it in Turkish telecommunications sector.

This papar has justified the validity and reliability of the ECSI model implementation in Turk Telekom Company. The path models are tested via conventional structural equation modeling (SEM) and using a novel method, i.e. universal structure modeling with Bayesian neural networks.

The findings of this study reveal that quality has the most important impact on customer satisfaction. The next important construct was found to be the company image. The relationship between customer expectation and customer satisfaction was revealed to be insignificant. This study reveals the fact that while using the ECSI model more attention must be paid to the consideration of potential nonlinear relationships that might be available among model constructs.

This research presents uniqueness in that it reveals significant nonlinear relationships between the model constructs of the ECSI model. Previous studies have identified purely linear relationships, which may not hold true in reality. However, in this study it is revealed that improving one determinant of customer satisfaction may not be as worthy as it is assumed to be in theory, which refers to a nonlinear relationship.