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Supply chain configuration for diffusion of new products: An integrated optimization approach

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  • Amini, Mehdi
  • Li, Haitao

Abstract

We develop an integrated/hybrid optimization model for configuring new products' supply chains while explicitly considering the impact of demand dynamics during new products' diffusion. The hybrid model simultaneously determines optimal production/sales plan and supply chain configuration. The production and sales plan provides decisions on the optimal timing to launch a new product, as well as the production and sales quantity in each planning period. The supply chain configuration provides optimal selection of options and safety stock level kept at each supply chain function. Extensive computational experiments on randomly generated testbed problems indicate that the hybrid modeling and solution approach significantly outperforms non-hybrid alternative modeling and solution approaches under various diffusion and supply chain topologies. We provide insights on optimal production/sales plan and supply chain configuration for new products during their diffusion process. Also, managerial implications relevant to effectiveness of the hybrid approach are discussed.

Suggested Citation

  • Amini, Mehdi & Li, Haitao, 2011. "Supply chain configuration for diffusion of new products: An integrated optimization approach," Omega, Elsevier, vol. 39(3), pages 313-322, June.
  • Handle: RePEc:eee:jomega:v:39:y:2011:i:3:p:313-322
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    Cited by:

    1. Zhang, Juan & Gou, Qinglong & Liang, Liang & Huang, Zhimin, 2013. "Supply chain coordination through cooperative advertising with reference price effect," Omega, Elsevier, vol. 41(2), pages 345-353.
    2. Negahban, Ashkan & Dehghanimohammadabadi, Mohammad, 2018. "Optimizing the supply chain configuration and production-sales policies for new products over multiple planning horizons," International Journal of Production Economics, Elsevier, vol. 196(C), pages 150-162.
    3. Tsai-Chi Kuo & Ming-Lang Tseng & Hsiao-Min Chen & Ping-Shun Chen & Po-Chen Chang, 2018. "Design and Analysis of Supply Chain Networks with Low Carbon Emissions," Computational Economics, Springer;Society for Computational Economics, vol. 52(4), pages 1353-1374, December.
    4. Lukas, Elmar & Welling, Andreas, 2017. "Efficient non-cooperative bargaining despite keeping strategic information private," Journal of Corporate Finance, Elsevier, vol. 42(C), pages 287-294.
    5. Li, Haitao & Womer, Keith, 2012. "Optimizing the supply chain configuration for make-to-order manufacturing," European Journal of Operational Research, Elsevier, vol. 221(1), pages 118-128.
    6. Garcia, C.A. & Ibeas, A. & Herrera, J. & Vilanova, R., 2012. "Inventory control for the supply chain: An adaptive control approach based on the identification of the lead-time," Omega, Elsevier, vol. 40(3), pages 314-327.
    7. Ashkan Negahban & Jeffrey S. Smith, 2018. "A joint analysis of production and seeding strategies for new products: an agent-based simulation approach," Annals of Operations Research, Springer, vol. 268(1), pages 41-62, September.
    8. Brandenburg, Marcus, 2017. "A hybrid approach to configure eco-efficient supply chains under consideration of performance and risk aspects," Omega, Elsevier, vol. 70(C), pages 58-76.
    9. Sabri, Yasmine & Nuur, Cali & Micheli, Guido J.L., 2015. "Exploring the configuration of innovation-based supply chains," INDEK Working Paper Series 2015/12, Royal Institute of Technology, Department of Industrial Economics and Management.
    10. Moncayo-Martínez, Luis A. & Zhang, David Z., 2013. "Optimising safety stock placement and lead time in an assembly supply chain using bi-objective MAX–MIN ant system," International Journal of Production Economics, Elsevier, vol. 145(1), pages 18-28.
    11. Garcia Salcedo, Carlos Andres & Ibeas Hernandez, Asier & Vilanova, Ramón & Herrera Cuartas, Jorge, 2013. "Inventory control of supply chains: Mitigating the bullwhip effect by centralized and decentralized Internal Model Control approaches," European Journal of Operational Research, Elsevier, vol. 224(2), pages 261-272.
    12. Liu, Songsong & Papageorgiou, Lazaros G., 2013. "Multiobjective optimisation of production, distribution and capacity planning of global supply chains in the process industry," Omega, Elsevier, vol. 41(2), pages 369-382.
    13. A. Negahban & J.S. Smith, 2016. "The effect of supply and demand uncertainties on the optimal production and sales plans for new products," International Journal of Production Research, Taylor & Francis Journals, vol. 54(13), pages 3852-3869, July.
    14. Li, Xiaohong & Yang, Dong & Hu, Mengqi, 2018. "A scenario-based stochastic programming approach for the product configuration problem under uncertainties and carbon emission regulations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 126-146.
    15. Formaneck, Steven D. & Cozzarin, Brian P., 2013. "Technology adoption and training practices as a constrained shortest path problem," Omega, Elsevier, vol. 41(2), pages 459-472.
    16. Gaur, Jighyasu & Amini, Mehdi & Rao, A.K., 2017. "Closed-loop supply chain configuration for new and reconditioned products: An integrated optimization model," Omega, Elsevier, vol. 66(PB), pages 212-223.
    17. K. Katsaliaki & P. Galetsi & S. Kumar, 2022. "Supply chain disruptions and resilience: a major review and future research agenda," Annals of Operations Research, Springer, vol. 319(1), pages 965-1002, December.
    18. Nihan Kabadayi & Mohammad Dehghanimohammadabadi, 2022. "Multi-objective supplier selection process: a simulation–optimization framework integrated with MCDM," Annals of Operations Research, Springer, vol. 319(2), pages 1607-1629, December.
    19. György Kovács & Béla Illés, 2019. "Development of an Optimization Method and Software for Optimizing Global Supply Chains for Increased Efficiency, Competitiveness, and Sustainability," Sustainability, MDPI, vol. 11(6), pages 1-28, March.

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