This paper is extensions of the Tesfay(2014) paper on the Bullwhip effects and Tesfay(2015) paper on the foundations of the Bullwhip effect and its implications on the theory of organizational coordination.The major o...This paper is extensions of the Tesfay(2014) paper on the Bullwhip effects and Tesfay(2015) paper on the foundations of the Bullwhip effect and its implications on the theory of organizational coordination.The major outcome of the Tesfay(2014) paper suggests that one way or another Bullwhip effect attacks all the business collaborates in supply chain.The major outcome of the Tesfay(2015) paper suggests that the solutions from the transaction cost economics are still insufficient to produce the most efficient organizational coordination.This paper extends stochastic analysis by applying various panel data regression models and structural equations of seemingly unrelated regression(SUR)models on the experimental data from Beer game.Recursive autoregressive-SUR model estimation result confirmed that the Bullwhip effects can be cause by both intra-organizational coordination and inter-organizational coordination of the business collaborates of supply chain.In order to control the effect of intra-organizational coordination on the Bullwhip effects,the author outline a new coordination type known as the hyper-hybrid coordination.As a final point,the author has shown important applications of the hyperhybrid coordination in the airline industry.展开更多
This article conducts a stochastic analysis on the passenger load factor of the airline in- dustry. Used to measure competence and performance of the airline, load factor is the percentage of seats filled by revenue p...This article conducts a stochastic analysis on the passenger load factor of the airline in- dustry. Used to measure competence and performance of the airline, load factor is the percentage of seats filled by revenue passengers. It is considered a complex metric in the airline industry. Thus, it is affected by several dynamic factors. This paper applies advanced stochastic models to obtain the best fitted trend of load factor for Europe＇s North Atlantic （NA） and Mid Atlantic （MA） flights in the Association of European Airlines. The stochastic model＇s fit helps to forecast the load factor of flights within these geographical regions and evaluate the airline＇s demand and capacity management. The paper applies spectral density estimation and dynamic time effects panel data regression models on the monthly load factor flights of NA and MA from 1991 to 2013. The results show that the load factor has both periodic and serial correlations. Consequently, the author acknowledges that the use of an ordinal panel data model is inappropriate for a realistic econometric model of load factor. Therefore, to control the periodic correlation structure, the author modified the existing model was modified by introducing dynamic time effects. Moreover, to eradicate serial correlation, the author applied the Prais-Winsten methodology was applied to fit the model. In this econometric analysis, the study finds that AEA airlines have greater demand and capacity management for both NA and MA flights. In conclusion, this study prosperous in finding an effective and efficient dynamic time effects panel data regression model fit, which empowers engineers to forecast the load factor off AEA airlines.展开更多
文摘This paper is extensions of the Tesfay(2014) paper on the Bullwhip effects and Tesfay(2015) paper on the foundations of the Bullwhip effect and its implications on the theory of organizational coordination.The major outcome of the Tesfay(2014) paper suggests that one way or another Bullwhip effect attacks all the business collaborates in supply chain.The major outcome of the Tesfay(2015) paper suggests that the solutions from the transaction cost economics are still insufficient to produce the most efficient organizational coordination.This paper extends stochastic analysis by applying various panel data regression models and structural equations of seemingly unrelated regression(SUR)models on the experimental data from Beer game.Recursive autoregressive-SUR model estimation result confirmed that the Bullwhip effects can be cause by both intra-organizational coordination and inter-organizational coordination of the business collaborates of supply chain.In order to control the effect of intra-organizational coordination on the Bullwhip effects,the author outline a new coordination type known as the hyper-hybrid coordination.As a final point,the author has shown important applications of the hyperhybrid coordination in the airline industry.
文摘This article conducts a stochastic analysis on the passenger load factor of the airline in- dustry. Used to measure competence and performance of the airline, load factor is the percentage of seats filled by revenue passengers. It is considered a complex metric in the airline industry. Thus, it is affected by several dynamic factors. This paper applies advanced stochastic models to obtain the best fitted trend of load factor for Europe＇s North Atlantic （NA） and Mid Atlantic （MA） flights in the Association of European Airlines. The stochastic model＇s fit helps to forecast the load factor of flights within these geographical regions and evaluate the airline＇s demand and capacity management. The paper applies spectral density estimation and dynamic time effects panel data regression models on the monthly load factor flights of NA and MA from 1991 to 2013. The results show that the load factor has both periodic and serial correlations. Consequently, the author acknowledges that the use of an ordinal panel data model is inappropriate for a realistic econometric model of load factor. Therefore, to control the periodic correlation structure, the author modified the existing model was modified by introducing dynamic time effects. Moreover, to eradicate serial correlation, the author applied the Prais-Winsten methodology was applied to fit the model. In this econometric analysis, the study finds that AEA airlines have greater demand and capacity management for both NA and MA flights. In conclusion, this study prosperous in finding an effective and efficient dynamic time effects panel data regression model fit, which empowers engineers to forecast the load factor off AEA airlines.
