Dynamic Control Mechanism For Customer Buy Down Behavior
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Revenue Management (RM) has become one of the most successful application areas of Operation Research. What started off as an obscure practice among few airlines in U.S in early seventies, has attained the status of mainstream business practice, thanks to the major success enjoyed by companies applying RM. Over the same period, academic and industrial research on the methodology of RM has also grown rapidly. Despite the vast technical literature on the subject of revenue management, relatively few papers explicitly model the customer’s choice behavior. Such a behavior of customers could have major impact on revenue realized by an organization. Motivated by this, we focus on addressing the problem faced by a seller who serves customers exhibiting buy-down behavior. We address two important problems faced by a seller with few perishable goods. His objective is to obtain maximum revenue possible by sales of his perishable goods. The seller now potentially faces the problem of fixing the price of the products and then control the availability of products so as to maximize his revenue by minimizing the number of customers who buy-down. The first problem is the multi-product pricing problem where we consider a monop- olistic market situation in which a seller has some quantities of perishable goods under his disposal. The seller has the option of adding few additional features to the base product(perishable good) and thereby differentiating the products to cater to different market segments. Adding each additional feature involves certain cost and there are no restrictions on the availability of the features except that a feature can be added to the base product atmost once . The customers are price-sensitive and the seller is aware of the price-demand relationship of the various customer segments. A customer looking for a product buys the product if and only if the price is less than his reservation price. The sellers’ problem is to identify the price and bundling of features for the various customer segments so as to generate maximum possible revenue. We develop a Mixed integer non-linear mathematical programming model for the problem. We then split the problem into pricing problem and bundling problem and solve them sequentially. We finally provide a numeric example to illustrate the solution procedure. Once the prices are fixed, the next problem is to control the availability of products so as to prevent the buy-down behavior of the customers. We deal with the situation of a seller with two substitutable products. The price of both products are fixed over entire selling period. In a traditional control mechanism structure if the sequence of arrival of customers are known, then it becomes trivial to solve the problem of setting control limits which would prevent buy-down behavior. But in reality it never happens that the seller knows the arrival sequence. Hence in this study to isolate the effect of arrival sequence from other complexities like demand variability, we assume a deterministic demand for both the products but the arrival sequence is randomized. We initially analyze the above described problem and develop a static control mech- anism. We show that the static control mechanism is asymptotically equivalent to the traditional selling mechanism. Then we move on to make modification in the static con- trol mechanism and make it a dynamic control mechanism such that it will respond to the buy-down customers. In order to analyze the performance of dynamic control mechanism, we build a simulation model that would compare traditional selling mechanism and dynamic control mechanism. Statistical analysis is then done on the simulation results. It is shown that for all values of buy-down proportion, on an average the dynamic control mechanism outperforms the traditional control mechanism. Further there is a trend in revenues generated depending upon the buy-down proportion which is also explained. The chapter concludes with operating guidelines for better revenue realization. The organization of the thesis is as follows. In chapter 2, we present the literature survey. We start off with the history of RM and proceed to discuss the inventory control problems in RM in detail. Then we discuss literatures dealing with customer choice behavior. In chapter 3, we define and model the multi - product pricing problem. We present a mixed integer non-linear mathematical program to model the pricing problem. The solution to this problem is divided into two sub problems - the pricing problem and the bundling problem. Solution methodologies for both sub - problem are given and the chapter concludes with a numerical illustration for a 3 - product pricing problem. In chapter 4, we define and address the inventory control problem for a two product case when customers exhibit buy-down nature. We develop a static control mechanism and study its properties. Then we move on to the dynamic control mechanism which would suit real - world conditions. Finally we study the quality of developed methodology using statistical testing methods.