THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

A variety of mathematical forms have been developed to characterize demand functions which depend on a firm's operational and marketing activities. Such demand functions are being increasingly used by researchers in economics and different functional areas of business. We provide a comprehensive survey of commonly used demand models which depend on (i) price, (ii) rebate, (iii) lead time, (iv) space, (v) quality, and (vi) advertising. Our survey includes single firm–demand models in each category, as well as game theoretic multifirm models involving strategic interaction among the firms. We observe that certain types of functional forms, such as linear, power/iso-elastic, multinomial logit, and multiplicative competitive interaction, have been widely used to construct various demand models in all six categories, but that a large majority of publications deal with categories (i) and (v) of demand models. For each of the six categories, we survey relevant functional forms in the representative papers, and discuss the main properties, the advantages, the disadvantages, and comment on possible future research directions. We also present discussions of the applications of these analytical demand models in empirical studies. The article ends with a summary of our major findings.

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Demand functions in decision modeling : a comprehensive survey and research directions

A survey of game-theoretic models of cooperative advertising ☆

Pricing and return product collection decisions in a closed-loop supply chain with dual-channel in both forward and reverse logistics

Vertical cooperative advertising and pricing decisions in a manufacturer–retailer supply chain: A game-theoretic approach

A game theoretic approach to coordinate pricing and vertical co-op advertising in manufacturer–retailer supply chains

In this paper we consider the production control of a failure prone manufacturing network using the Hedging Point Policy (HPP) This system consist of a network of machines with relationship constraints that can be in one of four states: operational, in repair, starved and blocked Broken machines are subject to a repair process, and up time and repair time in each phase for each machines is assumed to be exponentially distributed The demand for the product produced by the final machine is assumed to be a Poisson process Unmet demand is either backlogged or lost The objective of this paper is to find the optimal production rates of each machine so as to minimize the long run average inventory and backlog cost In order to solve this problem we use a simulation based optimization method that combines stochastic optimal control theory, discrete event simulation, experimental design and Automated Response Surface Methodology (RSM) We include a numerical example to illustrate the effectiveness of the proposed methodology

Mir Mehdi Seyedesfahani

Papers

A game theoretic approach to coordinate pricing and vertical co-op advertising in manufacturer–retailer supply chains

Production control in a network-failure prone manufacturing system with stochastic demand using improved response surface methodology