Kartikeya S. Puranam

On bidding for a fixed number of items in a sequence of auctions

We consider the problem of a firm (“the buyer”) that must acquire a fixed number (L) of items. The buyer can acquire these items either at a fixed buy-it-now price in the open market or by participating in a sequence of N>L auctions. The objective of the buyer is to minimize his expected total cost for acquiring all L items. We model this problem as a Markov Decision Process and establish monotonicity properties for the optimal value function and the optimal bidding strategies.

Managing blood inventory with multiple independent sources of supply

This paper focuses on the management of red blood cells (RBCs) by a large medical center within a regional blood exchange network. We provide both a theoretical and managerial contribution to the periodic-review fixed lifetime perishable inventory literature by considering multiple independent sources of supply. One source supplies blood via a typical standing order process. The other sources are smaller lower usage hospitals that randomly transfer blood to the medical center. Transferred blood is characterized by a much shorter average lifetime than blood supplied via standing order and introduces additional uncertainty into the inventory management process. We propose a solution approach that can be readily applied in practice and solve the multi-period cost minimization problem using a dynamic program. We provide numerical examples and demonstrate that our solution approach outperforms a corresponding base stock policy as well as the ordering policy that was actually used by the medical center.

On optimal bidding in sequential procurement auctions

Abstract We investigate the problem of optimal bidding for a firm that in each period procures items to meet a random demand by participating in a finite sequence of auctions. We develop a new model for a firm where its item valuation derives from the sale of the acquired items via their demand distribution, sale price, acquisition cost, salvage value and lost sales. We establish monotonicity properties for the value function and the optimal dynamic bid strategy and we present computations.

On optimal bidding and inventory control in sequential procurement auctions: the multi period case

We consider the problem of a firm that in each cycle of a planning horizon builds inventory of identical items that it acquires by participating in auctions in order to satisfy its own market demand. The firm’s objective is to have a procurement strategy that maximizes the expected present value of the profit for an infinite planning horizon of identical cycles. We formulate this problem as a Markov decision process. We establish monotonicity properties of the value function and of the optimal bidding rule.

Handbook of Research on Organizational Transformations through Big Data Analytics

Big data analytics utilizes a wide range of software and analytical tools to provide immediate, relevant information for efficient decision-making. Companies are recognizing the immense potential of BDA, but ensuring the data is appropriate and error-free is the largest hurdle in implementing BDA applications. The Handbook of Research on Organizational Transformations through Big Data Analytics not only catalogues the existing platforms and technologies, it explores new trends within the field of big data analytics (BDA). Containing new and existing research materials and insights on the various approaches to BDA; this publication is intended for researchers, IT professionals, and CIOs interested in the best ways to implement BDA applications and technologies. Market: This premier publication is essential for all academic and research library reference collections. It is a crucial tool for academicians, researchers, and practitioners. Ideal for classroom use.

Consumer evaluation of ingredient branding strategy

Purpose Prior research in ingredient branding (IB) has identified several important decision variables consumers use when evaluating IB alliances. This exploratory research aims to investigate the relationship between these variables and consumers’ buying likelihood of the IB alliance and the relative importance of these variables for low- vs high-involvement product categories. Design/methodology/approach A study with the participation of 458 mTurkers was conducted and the data were analyzed using random forests. Findings Findings reveal relative importance of different variables for an IB alliance and that these differ for low- vs high-involvement categories. Research limitations/implications Being exploratory in nature, this research has several limitations, such as using only one high- and one low-involvement categories. Practical implications Results of this research will help brand managers as they make decisions entering an IB alliance as well as with investing their budget on different aspects of their brand, and tailoring their marketing activities for low- vs high-involvement product categories. Originality/value To the best of authors’ knowledge, this paper is the first to discuss the relative importance of different decision variables in an IB context empirically.

On optimising taboo criteria in Markov decision processes

The standard approach when using a Markov decision process to find an optimal policy is to assume a fixed profit or cost structure. However, in many applied problems it may be not possible to determine profits or costs associated with all states or actions. In such cases we propose the use of taboo first passage reward and taboo first passage time as objectives. In this paper, we investigate problems related to optimising aforementioned taboo measures and we provide two examples.