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Analysis of dynamic pricing scenarios for multiple-generation product lines

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Abstract

In technology-intensive markets, it is a common strategy for companies to develop long-term multiple generation product lines instead of releasing consecutive single products. Even though this strategy is more profitable than sequentially introducing single product generations, it can also result in inter-product line cannibalization. Cannibalization of multiple-generation product lines is a complex problem that needs to be taken into account at the early product line planning stage in order to sustain long-term profitability. In this paper, we propose an agent-based model that can simulate the potential cannibalization scenarios within a multiple-generation product line. We view a multiple-generation product line (MGPL) as complex adaptive system where each product generation in the MGPL adjusts its sales price over time based on the shifts in the market demand. The proposed model provides insights into how various pricing strategies impact the overall lifecycle profitability of MGPL and can be used to assist companies in developing appropriate dynamic pricing strategies at the early product line planning stages.

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Author information

Authors and Affiliations

  1. Great Valley School of Graduate Professional Studies, Penn State University, Malvern, PA, 19355, USA

    Nil Kilicay-Ergin

  2. Department of Industrial and Manufacturing Engineering, Penn State University, University Park, PA, 16852, USA

    Chun-yu Lin

  3. School of Engineering Design, Department of Industrial and Manufacturing Engineering, Penn State University, University Park, PA, 16852, USA

    Gul E. Okudan

Authors
  1. Nil Kilicay-Ergin
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  2. Chun-yu Lin
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  3. Gul E. Okudan
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Corresponding author

Correspondence to Nil Kilicay-Ergin.

Additional information

Nil Kilicay-Ergin is Assistant Professor of Systems Engineering at Penn State University’s Great Valley School of Graduate Professional Studies. Prior to joining Penn State University, she worked as a Research Assistant Professor within the Research Institute for Manufacturing and Engineering Systems (RIMES) at the University of Texas at El Paso where she taught for the systems engineering graduate program and served on industry funded research contracts. She was also a Postdoctoral Fellow at Missouri University of Science & Technology. Nil Ergin received her Ph.D. in systems engineering and M.S. in Engineering Management from the Missouri University of Science & Technology. She also holds a B.S. degree in Environmental Engineering from Istanbul Technical University, Turkey. Her research interests include model-based systems engineering, system of systems engineering, complex adaptive systems, and multi-agent systems. She is a member of INCOSE and IEEE.

Chun-yu Lin received his Ph.D. from the Department of Industrial and Manufacturing Engineering at The Pennsylvania State University in 2012. His research interests focus on developing methods to solve product design and development problems. Most recently, he has made significant contributions to multiple generation product line literature by proposing to use dynamic state variable methods in generation introduction timing decisions.

Gul E. Okudan Kremer is a Professor of engineering design and industrial engineering at The Pennsylvania State University. She received her Ph.D. from the Department of Engineering Management and Systems Engineering of Missouri University of Science & Technology in 1997. Her research focuses on development and application of decision making methods and design theory to improve products and systems. She has co-authored over 250 peer-reviewed papers to date and received six best paper awards. She has been also a National Research Council — US AFRL Summer Faculty Fellow for the Human Effectiveness Directorate for 2002, 2003 and 2004, and a Fulbright Scholar (2010–2011). She is a senior member of IIE and a fellow of ASME She is currently serving as a Program Director for Division of Undergraduate Education of the National Science Foundation.

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Kilicay-Ergin, N., Lin, Cy. & Okudan, G.E. Analysis of dynamic pricing scenarios for multiple-generation product lines. J. Syst. Sci. Syst. Eng. 24, 107–129 (2015). https://doi.org/10.1007/s11518-015-5264-2

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  • DOI: https://doi.org/10.1007/s11518-015-5264-2

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