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Towards real-time customized management of supply and demand chains

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Abstract

Our focus herein is on developing an effective taxonomy for the simultaneous and real-time management of supply and demand chains. More specifically, the taxonomy is developed in terms of its underpinning components and its research foci. From a components perspective, we first consider the value chain of supplier, manufacturer, assembler, retailer, and customer, and then develop a consistent set of definitions for supply and demand chains based on the location of the customer order penetration point. From a research perspective, we classify the methods that are employed in the management of these chains, based on whether supply and/or demand are flexible or fixed. Interestingly, our taxonomy highlights a very critical research area at which both supply and demand are flexible, thus manageable. Simultaneous management of supply and demand chains sets the stage for mass customization which is concerned with meeting the needs of an individualized customer market. Simultaneous and real-time management of supply and demand chains set the stage for real-time mass customization (e.g., wherein a tailor first laser scans an individual’s upper torso and then delivers a uniquely fitted jacket within a reasonable period, while the individual is waiting). The benefits of real-time mass customization can not be over-stated as products and services become indistinguishable and are co-produced in real-time, resulting in an overwhelming economic advantage.

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Authors and Affiliations

  1. Department of Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute, 110 Eighth St. Troy, New York, 12180, USA

    James M. Tien, Ananth Krishnamurthy & Ali Yasar

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  1. James M. Tien
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  2. Ananth Krishnamurthy
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Additional information

James M. Tien received the BEE from Rensselaer Polytechnic Institute and the SM, EE and PhD from the Massachusetts Institute of Technology. He has held leadership positions at Bell Telephone Laboratories, at the Rand Corporation, and presently at the Structured Decisions Corporation. He joined the Department of Electrical, Computer and Systems Engineering at Rensselaer in 1977, became Acting Chair of the department (1986–87), joined a unique interdisciplinary Department of Decision Sciences and Engineering Systems as its founding Chair (1988-Present), and twice served as the Acting Dean of Engineering (1992–1994; 1998–1999). He has made significant contributions to the area of computer and systems engineering and to the development of information and decision systems. He has published extensively and been honored with many research and teaching awards, including being elected a Fellow in IEEE, AAAS and INFORMS. He is also an elected member of the prestigious U. S. National Academy of Engineering.

Ananth Krishnamurthy received his Ph.D. in Industrial Engineering from the University of Wisconsin-Madison, in 2002. He is currently an assistant professor in the Department of Decision Sciences and Engineering Systems at Rensselaer Polytechnic Institute, Troy, N.Y. His research focuses on analytical models for performance evaluation of manufacturing systems and supply chains where product variety and customization are emphasized. He is also an expert in the development of efficient models for the design and analysis of hybrid control strategies that integrate push and pull aspects of production control. He has an M.S. from the University of Wisconsin-Madison and an M.Tech from the Indian Institute of Technology, Bombay, India, and he is a member of INFORMS, IIE, SME, and APICS.

Ali Yasar is a PhD student in the Department of Decision Sciences and Engineering Systems at Rensselaer Polytechnic Institute. He received the BS degree in Industrial Engineering from Bilkent University, Turkey in 2001. His research focuses on supply and demand chains and their simultaneous management. He has authored a number of papers in conference proceedings.

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Tien, J.M., Krishnamurthy, A. & Yasar, A. Towards real-time customized management of supply and demand chains. J. Syst. Sci. Syst. Eng. 13, 257–278 (2004). https://doi.org/10.1007/s11518-006-0164-0

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