Supply chain coordination using revenue-dependent revenue sharing contracts

A typical single period revenue sharing contract specifies a priori a fixed fraction for the supply chain revenue to be shared among the supply chain players. Over the years, supply chains, especially in the movie industry, have adopted multi-period revenue sharing contracts that specify one fraction for each contract period. These revenue sharing contracts are of revenue-independent type such that the revenue sharing fractions are independent of the quantum of revenue generated. Motivated by the recent events in Bollywood – one of the popular arms of the Indian movie industry – in this paper we develop and analyze a game theoretic model for revenue-dependent revenue sharing contracts wherein the actual proportion in which the supply chain revenue is shared among the players depends on the quantum of revenue generated. Our aim is to understand why revenue-dependent revenue sharing contracts are (or not) preferred over revenue-independent contracts. We also examine if supply chains can be coordinated over multiple periods using both types of revenue sharing contracts. We build a two-period model characterizing supply chains in the movie industry and highlight the implications of the multi-period contractual setting for the supply chain coordinating revenue sharing contracts. We show that supply chains can be perfectly coordinated using both types of revenue sharing contracts; however, there exist situations in which revenue-dependent contracts outperform revenue-independent contracts. Using revenue-dependent revenue sharing contracts supply chains can be coordinated while providing positive surplus to the supply chain players that is otherwise not possible under certain situations in revenue-independent contracts. We also demonstrate how revenue-dependent contracts enhance supply chain coordination and highlight their significance when the drop in the revenue potential from one period to another is moderate.     

Bayesian estimation of mean and square of mean of normal distribution using linex loss function

In this paper, the Bayes estimators for mean and square of mean ol a normal distribution with mean μ and vaiiance σ r2 (known), relative to LINEX loss function are obtained Comparisons in terms of risk functions and Bayes risks of those under LINEX loss and squared error loss functions with their respective alternative estimators viz, UMVUE and Bayes estimators relative to squared error loss function, are made. It is found that Bayes estimators relative to LINEX loss function dominate the alternative estimators m terms of risk function snd Bayes risk. It is also found that if t2 is unknown the Bayes estimators are still preferable over alternative estimators.