Applications of flexible pricing in business-to-business electronic commerce. IBM Syst J

IBM Research Division, Thomas J. Watson Research Center, P.O. Box 218, Yorktown Heights, New York 10598, USA
Ibm Systems Journal (Impact Factor: 1.79). 02/2002; 41(2):287 - 302. DOI: 10.1147/sj.412.0287
Source: IEEE Xplore


The increasingly dynamic nature of business-to-business electronic commerce has produced a recent shift away from fixed pricing and toward flexible pricing. Flexible pricing, as defined here, includes both differential pricing, in which different buyers may receive different prices based on expected valuations, and dynamic-pricing mechanisms, such as auctions, where prices and conditions are based on bids by market participants. In this paper we survey ongoing work in flexible pricing in the context of the supply chain, including revenue management, procurement, and supply-chain coordination. We review negotiation mechanisms for procurement, including optimization approaches to the evaluation of complex, multidimensional bids. We also discuss several applications of flexible pricing on the sell side, including pricing strategies for response to requests for quotes, dynamic pricing in a reverse logistics application, and pricing in the emerging area of hosted applications services. We conclude with a discu ssion of future research directions in this rapidly growing area.

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Available from: Yingdong lu, Oct 30, 2014
    • "As pointed out in [19], there are three main classes of pricing problems, namely retailing pricing, bid/auction pricing and negotiation pricing. Many studies have been carried out in retail and auction/bid pricing decision support and have been successfully employed to a wide range of application domains [3] [4] [11]. However, negotiation pricing support is still at its very early stage. "
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    ABSTRACT: With the emergence of customisation services, business-to-business price negotiation plays an increasingly important role in economic and management science. Negotiation pricing aims to provide different customers with products/services that perfectly meet their requirements, with the "right" price. In general, pricing managers are responsible for identifying the "right" negotiation price with the goal of maintaining good customer relationship, while maximising profits for companies. However, efficiently and effectively determining the "right" negotiation price boundary is not a simple task; it is often complicated, time-consuming and costly to reach a consensus as the task needs to take a wide variety of pricing factors into consideration, ranging from operation costs, customers' needs to negotiation behaviours. This paper proposes a systematic fuzzy system (FS) approach, for the first time, to provide negotiation price boundary by learning from available historical records, with a goal to release the burden of pricing managers. In addition, when the number of involved influencing factors increases, conventional FS approach easily suffers from the curse of dimensionality. To combat this problem, a novel method, simplified FS with single input and single output modules (SFS-SISOM), is also introduced in this paper to handle high-dimensional negotiation pricing problems. The utility and applicability of this research is illustrated by three experimental datasets that vary from both data dimensionality and the number of training records. The experimental results obtained from two approaches have been compared and analysed based on different aspects, including interpretability, accuracy, generality and applicability.
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    • "These approaches have been illustrated through a hypothetical case study, which has a very strong resemblance to an actual business problem of pricing IaaS for a specific engagement. Global Journal of Flexible Systems Management This proposed hybrid methodology would also address the need of flexibility literature in pricing schemas, while addressing the requirements surrounding greater customer participation and orientation in the business strategy formulation thereby facilitating greater openness to the customer's uniqueness of needs, deeper understanding of the variation in needs, improved accommodation of the fluctuation in needs and therefore greater relationship orientation of the organization (Sushil 1997; Bichler et al. 2002; Zhang et al. 2010). Further, the hybrid methodology also addresses the need of literature focusing on the systematic approaches to accommodate the diversity in customer's requirements rather than on the core offering while pricing such services (Sushil 2012c; Wu and Banker 2010). "
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    ABSTRACT: Today, cloud computing is transforming the consumption of IT/ITeS. Numerous vendors are offering services where computing, storage and application resources, can be dynamically provisioned on a pay per use basis, purely based on the user’s need. However, the demands and requirements of different users vary significantly. In order to maximize the revenue, a flexible pricing approach is required, which can address these diverse requirements systematically. These systemic approaches need to estimate the potential value of such services to specific users for a specific context. The tradeoffs from potential value drivers also need to be accounted for while prioritizing the value drivers. In these lines, the current study proposes a flexible pricing approach for Infrastructure as a Service (IaaS), one of the important delivery models, based on its perceived value to multiple key stake-holders. The proposed approach prioritizes and aggregates the key features of IaaS for the migration to cloud, from multiple key users’ perspective by integrating fuzzy set theory and analytic hierarchy process for group decision making under consensus. Subsequently, the prioritization is mapped with a utility function to estimate the trade-offs from each value driver. The performance of the proposed approach has also been compared with that of another flexible pricing model through a case study.
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    • "The pricing technique has a wide range of applications such as marketing (e.g. Bertsimas and Perakis, 2001; Bichler et al., 2002), supply chain management (e.g. Jia and Hu, 2011), inventory management (e.g. "
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