Avoiding Risks Related with Strategic Pricing in Virtual Enterprise Networks: An Agent Based Approach

Avoiding Risks Related with Strategic Pricing in Virtual Enterprise Networks: An Agent Based Approach

Christos Manolarakis (Athens Information Technology, Greece), Ioannis T. Christou (Athens Information Technology, Greece) and Gregory Yovanof (Athens Information Technology, Greece)
DOI: 10.4018/978-1-61520-607-0.ch014
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In this chapter we describe an effort to capture market dynamics of consumer products via Agent-based Models and apply the findings in the context of risk management focused in Virtual Enterprise Networks. The developed tool can help significantly leverage risks associated with price wars, or other aggressive strategies the players in a given market may choose to follow. It can also significantly improve purchasing/procurement and production planning decisions by better estimating (for the short-term of course) the variability in demand that will be experienced by a new price for a given product, thus optimizing supply chain operations and minimizing the logistics risks often associated with marketing campaigns. Finally, the tool can also be used to estimate the short-term profitability and break-even point for a proposed new product introduced by a Virtual Enterprise in an existing market, information that greatly minimizes risks related to the introduction of new products in the market, and then to estimate the optimal introductory price for the new product.
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Analysis of complex systems and decision making in such complex environments as today’s volatile markets is an extremely demanding challenge in terms of data required, processes and personnel, with results often really founded on little more than the top decision maker’s instincts. Tools for monitoring and studying interacting behaviors of system components could, therefore, possibly provide a better insight into the market dynamics at least for the short-term (Gilbert, 2008). The aim of the research described in this chapter is to support enterprises participating in a Virtual Enterprise venture (or a Supply Chain network) in assessing their strategic pricing process and thus relax all (or some of) the risks inherent in the pricing function. Virtual Enterprises (VE) arose as a prominent trend in the notion of cooperative synergies among businesses. A business formation may be outsourcing cooperative schemes leveraging the power of supply chains or even business consortiums sharing common vision or market targets (Camarinha et al., 2007). The use of Agent Based Modeling Systems (ABMS) techniques to represent such formations is widely used in the literature as a means of absorption of the inhering complexity which includes knowledge transfer, cooperative strategic decisions and information sharing.

ABMS is essentially a tool for managing complex systems by using advanced techniques such as mathematical modeling and discrete-event simulation to produce a new way to discover strategic, tactical and operational business solutions (North et. al., 2007). The basic advantage of Agent Based Modeling in comparison to the existing traditional modeling techniques such as statistical modeling, risk analysis, optimization, or system dynamics, is that it can capture non linear interactions that are common in the problems addressed in the industries and markets without having to explicitly consider the underlying mathematical structure that governs the system behavior. Emergent behavior is after all, an aggregate result of usually non-linear interactions. Another major advantage of ABMS is that it can be used to investigate not only what happened in a given business situation but also what might have happened. Once an Agent Based Model has been tuned to capture the real–life events that occurred in the immediate past, it can be used for sensitivity analysis by running what–if scenarios with slight perturbations of one or more of the world parameters of the model. The ability to build what-if scenarios is of fundamental importance to making business decisions both in the short and in the medium term. Uncovering unexpected issues through a trial and error process in real life may be extremely costly or simply infeasible. Agent models on the other hand, can be used to quickly explore many different possibilities in order to select the “best” one found.

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