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Master production scheduling policy and rolling schedules in a two-stage make-to-order supply chain

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  • Sahin, Funda
  • Powell Robinson, E.
  • Gao, Li-Lian

Abstract

This research studies master production schedule (MPS) and advanced order commitment (AOC) in two-stage supply chains. Experimental analysis, based on computer simulation, evaluates the impact of environmental and MPS design factors on optimal policy design as measured by schedule cost and stability. The vendor's order-size flexibility is the major factor impacting system performance. The manufacturer's optimal MPS policy is often inefficient for the vendor, resulting in total costs significantly greater than the optimal system policy. The research provides insight into the dynamic nature of scheduling and procurement systems and how they can be better coordinated through the MPS/AOC policy design.

Suggested Citation

  • Sahin, Funda & Powell Robinson, E. & Gao, Li-Lian, 2008. "Master production scheduling policy and rolling schedules in a two-stage make-to-order supply chain," International Journal of Production Economics, Elsevier, vol. 115(2), pages 528-541, October.
  • Handle: RePEc:eee:proeco:v:115:y:2008:i:2:p:528-541
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Gansterer, Margaretha, 2015. "Aggregate planning and forecasting in make-to-order production systems," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 521-528.
    2. Wong, W.K. & Guo, Z.X. & Leung, S.Y.S, 2014. "Intelligent multi-objective decision-making model with RFID technology for production planning," International Journal of Production Economics, Elsevier, vol. 147(PC), pages 647-658.
    3. Yao, Jianming & Liu, Liwen, 2009. "Optimization analysis of supply chain scheduling in mass customization," International Journal of Production Economics, Elsevier, vol. 117(1), pages 197-211, January.
    4. Toy, Ayhan Özgür & Berk, Emre, 2013. "Dynamic lot sizing for a warm/cold process: Heuristics and insights," International Journal of Production Economics, Elsevier, vol. 145(1), pages 53-66.
    5. Kastsian, Darya & Mönnigmann, Martin, 2011. "Optimization of a vendor managed inventory supply chain with guaranteed stability and robustness," International Journal of Production Economics, Elsevier, vol. 131(2), pages 727-735, June.
    6. Jairo R. Montoya-Torres & Diego A. Ortiz-Vargas, 2014. "Collaboration and information sharing in dyadic supply chains: A literature review over the period 2000–2012," ESTUDIOS GERENCIALES, UNIVERSIDAD ICESI, November.
    7. Afshin Mansouri, S. & Gallear, David & Askariazad, Mohammad H., 2012. "Decision support for build-to-order supply chain management through multiobjective optimization," International Journal of Production Economics, Elsevier, vol. 135(1), pages 24-36.
    8. As'ad, Rami & Demirli, Kudret, 2010. "Production scheduling in steel rolling mills with demand substitution: Rolling horizon implementation and approximations," International Journal of Production Economics, Elsevier, vol. 126(2), pages 361-369, August.
    9. Horng-Huei Wu & Chih-Hung Tsai & Liang-Ying Wei & Min-Jer Lu & Tzu-Fang Hsu, 2016. "A Requirement-Dependent Inventory Allocation Model for Dynamic Allocation Process in LED Chip Manufacturing Plants," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 6(3), pages 177-189, July.
    10. Hill, Alex & Doran, Des & Stratton, Roy, 2012. "How should you stabilise your supply chains?," International Journal of Production Economics, Elsevier, vol. 135(2), pages 870-881.
    11. Narayanan, Arunachalam & Robinson, Powell, 2010. "Evaluation of joint replenishment lot-sizing procedures in rolling horizon planning systems," International Journal of Production Economics, Elsevier, vol. 127(1), pages 85-94, September.

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