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Determining optimal batch interval and production strategy based on available-to-promise model for order promising

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Abstract

With the intensification of market competition, rapid changes in customer demand in the era of globalization, and uncertainty in the supply chain, manufacturers are facing increasing challenges in responding to customer requests on time. Available-to-promise (ATP) is a system that provides product availability information for promising customer order requests by considering the tradeoff between customer satisfaction and supply chain efficiency. This paper proposes a mixed integer programming model based on ATP theory to obtain the optimal batch interval length and production strategy of bill of material items. The objective of the proposed model is to maximize the manufacturer’s profit. Experimental data is analyzed to investigate the impact of the batch interval length and production strategies adopted by the manufacturer on its profit. The results provide theoretical guidance and implementation methods for manufacturers to maximize profits.

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Funding

This study was funded by National Science and Technology Council, Taiwan (MOST 110-2622-E-025-002-).

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Authors and Affiliations

  1. Department of Intelligent Production Engineering, National Taichung University of Science and Technology, Taichung, 40401, Taiwan, R.O.C.

    Lu-Wen Liao

  2. Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C.

    Yu-Chung Tsao

  3. Artificial Intelligence for Operations Management Research Center, National Taiwan University of Science and Technology, No. 43, Sec.4, Keelung Rd., Taipei 106, Taiwan, R.O.C.

    Yu-Chung Tsao

  4. Strategic Planning Center, Fu Chun Shin Machinery Manufacture Co., Ltd., Tainan, 71841, Taiwan, R.O.C.

    Yu-Han Hung

Authors
  1. Lu-Wen Liao
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Correspondence to Yu-Chung Tsao.

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Liao, LW., Tsao, YC. & Hung, YH. Determining optimal batch interval and production strategy based on available-to-promise model for order promising. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05641-7

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