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Optimization-Based Available-To-Promise with Multi-Stage Resource Availability

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  • Zhenying Zhao
  • Michael Ball
  • Masahiro Kotake

Abstract

Increasingly, customer service, rapid response to customer requirements, and flexibility to handle uncertainties in both demand and supply are becoming strategic differentiators in the marketplace. Organizations that want to achieve these benchmarks require sophisticated approaches to conduct order promising and fulfillment, especially in today’s high-mix low-volume production environment. Motivated by these challenges, the Available-to-Promise (ATP) function has migrated from a set of availability records in a Master Production Schedule (MPS) toward an advanced real-time decision support system to enhance decision responsiveness and quality in Assembly To Order (ATO) or Configuration To Order (CTO) environments. Advanced ATP models and systems must directly link customer orders with various forms of available resources, including both material and production capacity. In this paper, we describe a set of enhancements carried out to adapt previously published mixed-integer-programming (MIP) models to the specific requirements posed by an electronic product supply chain within Toshiba Corporation. This model can provide individual order delivery quantities and due dates, together with production schedules, for a batch of customer orders that arrive within a predefined batching interval. The model considers multi-resource availability including manufacturing orders, production capability and production capacity. In addition, the model also takes into account a variety of realistic order promising issues such as order splitting, model decomposition and resource expediting and de-expediting. We conclude this paper with comparison of our model execution results vs. actual historical performance of systems currently in place. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • Zhenying Zhao & Michael Ball & Masahiro Kotake, 2005. "Optimization-Based Available-To-Promise with Multi-Stage Resource Availability," Annals of Operations Research, Springer, vol. 135(1), pages 65-85, March.
    • Handle: RePEc:spr:annopr:v:135:y:2005:i:1:p:65-85:10.1007/s10479-005-6235-7
    DOI: 10.1007/s10479-005-6235-7
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    References listed on IDEAS

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    Cited by:

    1. Gössinger, Ralf & Kalkowski, Sonja, 2015. "Robust order promising with anticipated customer response," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 529-542.
    2. Sameer Prasad & Jason Woldt & Harish Borra & Nezih Altay, 2022. "Migrant supply chain networks: an empirically based typology," Annals of Operations Research, Springer, vol. 319(1), pages 1331-1358, December.
    3. Seitz, Alexander & Grunow, Martin & Akkerman, Renzo, 2020. "Data driven supply allocation to individual customers considering forecast bias," International Journal of Production Economics, Elsevier, vol. 227(C).
    4. Foad Mahdavi Pajouh & Dahai Xing & Yingjue Zhou & Sharethram Hariharan & Balabhaskar Balasundaram & Tieming Liu & Ramesh Sharda, 2013. "A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates," Interfaces, INFORMS, vol. 43(6), pages 503-517, December.
    5. Alexander Seitz & Martin Grunow, 2017. "Increasing accuracy and robustness of order promises," International Journal of Production Research, Taylor & Francis Journals, vol. 55(3), pages 656-670, February.
    6. Alexander Seitz & Hans Ehm & Renzo Akkerman & Sarah Osman, 2016. "A robust supply chain planning framework for revenue management in the semiconductor industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(6), pages 523-533, December.

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