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A hybrid adaptive decision system for supply chain reconfiguration

Author

Listed:
  • Navin K. Dev
  • Ravi Shankar
  • Angappa Gunasekaran
  • Lakshman S. Thakur
Abstract
Due to short product life cycle, it is expedient to reconfiguration an existing supply chain from time to time. Companies need to impose the standards on operational units for finding the best or the near best alternative configuration. Thus, it becomes imperative to effectively adapt various enablers in a supply chain by understanding the dynamics between them that help to reconfigure a supply chain for high levels of performance. This paper presents an integration of agent-based simulation and decision tree learning as the data mining techniques to determine adaptive decisions of operational units of a mobile phone supply chain. Agent-based simulation output is subjected to data mining analysis to understand system behaviour in terms of interactions and the factors influencing the performance. An entropy-based formulation is proposed as the basis for comparing different operational units in the supply chain. The insights obtained are then encapsulated as operational rules and guidelines supporting better decision-making.

Suggested Citation

  • Navin K. Dev & Ravi Shankar & Angappa Gunasekaran & Lakshman S. Thakur, 2016. "A hybrid adaptive decision system for supply chain reconfiguration," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7100-7114, December.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:23:p:7100-7114
    DOI: 10.1080/00207543.2015.1134842
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    References listed on IDEAS

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    1. Glock, C. H., 2012. "Lead time reduction strategies in a single-vendor-single-buyer integrated inventory model with lot size-dependent lead times and stochastic demand," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 57816, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Ivanov, Dmitry & Sokolov, Boris & Kaeschel, Joachim, 2010. "A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations," European Journal of Operational Research, Elsevier, vol. 200(2), pages 409-420, January.
    3. Beamon, Benita M., 1998. "Supply chain design and analysis:: Models and methods," International Journal of Production Economics, Elsevier, vol. 55(3), pages 281-294, August.
    4. Shuiabi, Eyas & Thomson, Vince & Bhuiyan, Nadia, 2005. "Entropy as a measure of operational flexibility," European Journal of Operational Research, Elsevier, vol. 165(3), pages 696-707, September.
    5. Glock, Christoph H., 2012. "Lead time reduction strategies in a single-vendor–single-buyer integrated inventory model with lot size-dependent lead times and stochastic demand," International Journal of Production Economics, Elsevier, vol. 136(1), pages 37-44.
    6. Mak, Brenda & Munakata, Toshinori, 2002. "Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3," European Journal of Operational Research, Elsevier, vol. 136(1), pages 212-229, January.
    7. Navin K. Dev & Ravi Shankar, 2012. "Design of fractal information coordination system in a supply chain network," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 12(1), pages 1-19.
    8. Navin K. Dev & Rahul Caprihan & Sanjeev Swami, 2011. "A case study on redesign of supply chain network of a manufacturing organization," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 8(2), pages 195-212, November.
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    Cited by:

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    2. Dev, Navin K. & Shankar, Ravi & Swami, Sanjeev, 2020. "Diffusion of green products in industry 4.0: Reverse logistics issues during design of inventory and production planning system," International Journal of Production Economics, Elsevier, vol. 223(C).
    3. Mohammad J. Aladaileh & Eva Lahuerta Otero, 2023. "The antecedents of SSCI: Evidence from the textile and fashion industry in Jordan," DOCFRADIS Working Papers 2302, Catedra Fundación Ramón Areces de Distribución Comercial, revised Mar 2023.
    4. Limin Zhang & Fei Gu & Mingke He, 2024. "The Influence of Digital Transformation on the Reconfigurability and Performance of Supply Chains: A Study of the Electronic, Machinery, and Home Appliance Manufacturing Industries in China," Sustainability, MDPI, vol. 16(7), pages 1-22, March.
    5. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
    6. Lechtenberg, Sandra & Hellingrath, Bernd, 2021. "Applications of artificial intelligence in supply chain management: Identification of main research fields and greatest industry interests," ERCIS Working Papers 37, University of Münster, European Research Center for Information Systems (ERCIS).
    7. Anna Trunk & Hendrik Birkel & Evi Hartmann, 2020. "On the current state of combining human and artificial intelligence for strategic organizational decision making," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 875-919, November.
    8. Alba Barrero Caballero, 2023. "Comportamiento de consumo de los millennials y estrategias comunicativas en Facebook e Instagram del sector de la moda 2016-2019," DOCFRADIS Working Papers 2303, Catedra Fundación Ramón Areces de Distribución Comercial, revised Apr 2023.
    9. Devesh Kumar & Gunjan Soni & Rohit Joshi & Vipul Jain & Amrik Sohal, 2022. "Modelling supply chain viability during COVID-19 disruption: A case of an Indian automobile manufacturing supply chain," Operations Management Research, Springer, vol. 15(3), pages 1224-1240, December.
    10. Negar Jalilian & Seyed Mahmoud Zanjirchi & Alireza Naser Sadrabadi & Ahmadreza Asgharpourmasouleh & Mark Goh, 2021. "Agent-Based Approach to Configure Processes in Iran’s Banking Service Supply Chain," Sustainability, MDPI, vol. 13(14), pages 1-23, July.
    11. Lee, Neil Chueh-An, 2021. "Reconciling integration and reconfiguration management approaches in the supply chain," International Journal of Production Economics, Elsevier, vol. 242(C).

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