(Georgia Institute of Technology, 2010-06-15)
Agrawal, Vishal
With the increased attention of different stakeholders on the environmental performance of businesses, several firms are increasingly focusing on product recovery and reuse activities which are not only profitable but may also help to reduce the environmental impact of their operations. This dissertation focuses on managerial challenges associated with such value-added recovery and reuse activities. The first essay examines how a firm should bring a product to market, in particular, whether to lease or sell products. Motivated by claims that leasing can be an environmentally superior to selling, we analytically investigate if either leasing or selling can be both more profitable for a monopolist and have a lower total environmental impact. The second essay first experimentally examines the effect of remanufactured products on the perceived value of new products. This effect is then incorporated to analytically investigate an OEM's strategy in the presence of competition from third-party remanufacturers. In the third essay, motivated by a major IT company, we investigate the optimal product recovery and remanufacturing strategy for a firm that can offer trade-in rebates to achieve price discrimination. We also consider the effect of potential entry of third-party remanufacturers on the firm's recovery and remanufacturing strategy.
In this paper, we study pricing situations where a firm provides a price quote in the
presence of uncertainty in the preferences of the buyer and the competitive landscape. We introduce two customized-pricing bid-response models used in practice, which can be developed from the historical information available to the firm based on previous bidding opportunities. We show how these models may be used to exploit the differences in the market segments to generate optimal price quotes given the characteristics of the current bid opportunity. We also describe the process of evaluating competing models using an industry dataset as a test bed to measure the model fit. Finally, we test the models on the industry dataset to compare their performance and estimate the percent improvement in expected profits that may be possible from their use.
In this paper, we study pricing situations where a firm provides a price quote in the presence of uncertainty in the competitive landscape and the preferences of the buyer. We review two possible customized-pricing bid-response models used in practice which can be developed from the historical information available to the firm based on previous bidding opportunities. We show how these models may be used to exploit the differences in the market segments to generate optimal price quotes given the characteristics of the current bid opportunity. We also show how the models may be adjusted depending on the amount of historical bid information available to the user. Finally, we test the two methods on two industry datasets to compare their performance and estimate the percent improvement in expected profits that may be possible from their use.