Mujaj, Leukel An Agent-Based Reverse Pricing Model An Agent-Based Reverse Pricing Model for Reducing Bullwhip Effect in Supply Chains

This paper addresses the problem of increasing order variances in multi-tier supply chains. The majority of current approaches for reducing this problem, namely the bullwhip effect, rely on information sharing and/or cooperative planning in inter-organizational systems. Due to multiple barriers in implementing these approaches, we maintain the local autonomy of the participants in the supply chain and provide a multiagent-oriented solution to the problem. In particular, we design an agent-based reverse pricing model for matching supply and demand between independent agents. We adopt reverse pricing for operational procurement decisions and matchmaking that can be automated to a large extent. We evaluate our proposal by conducting a simulation study using a multiagent-based simulation system, and show that the novel approach results in a significant reduction of the bullwhip effect

A Reverse Pricing Model for Multi-Tier Supply Chains

In recent years, reverse pricing has been marketed as a new pricing mechanism in e-commerce. Several emarkets such as priceline.com are being based on reverse pricing, thus on bids by potential customers instead of static prices by vendors. In this paper, we investigate whether it can also be used in business-tobusiness scenarios. In particular, we look at its potential contribution to reducing the bullwhip effect in multi-tier supply chains with local information and stochastic demand. The bullwhip effect suggests an increasing order variability as one moves down a supply chain. We propose to adopt reverse pricing for operational procurement decisions. We evaluate our proposal by conducting a simulation study using an agent-based simulation system, and can show that the novel approach results in a significant reduction of the bullwhip effect. 1