Abstract Recent business environment development forces companies to speed up their new product development (NPD) cycle. The three-dimensional concurrent engineering (3DCE) offers chances to accelerate the process of new product development. However, to date there has been no formal guidelines for 3DCE NPD. This study tries to relate the imperative of concurrency into the stage-gate NPD, especially at the detailed design phase, in which the NPD team is presumed to conduct the decisions on product detailed specifications and production process design simultaneously. A mixed-integer non-linear programming (MINLP) mathematical model was developed to optimize operational manufacturing profit. The main features of the model are to select the best components from available alternative components, to take make-or-buy decision of the components, and to select its suppliers. In addition, the total quality of the components was evaluated and then compared to an expected total quality obtained from the competitors’ product or ideal expectation of the NPD team. A case from a leather bag company was used to demonstrate the model. In this case, sixteen components from 24 alternative components and seven suppliers for a new leather bag product case had to be decided and it was solved by the model. The sensitivity analysis provided evidence of model validity. Finally, a conclusion with several potential future works is provided.

中文翻译：

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3DCE新产品开发详细设计阶段的数学模型

摘要 最近的商业环境发展迫使公司加快新产品开发 (NPD) 周期。三维并行工程 (3DCE) 提供了加速新产品开发过程的机会。但是，迄今为止，还没有针对 3DCE NPD 的正式指南。本研究试图将并发的必要性与阶段门 NPD 联系起来，尤其是在详细设计阶段，假设 NPD 团队同时进行产品详细规格和生产过程设计的决策。开发了混合整数非线性规划 (MINLP) 数学模型来优化运营制造利润。该模型的主要特点是从可用的替代组件中选择最好的组件，对组件进行制造或购买决策，并选择其供应商。此外，还评估了组件的总体质量，然后与从竞争对手的产品中获得的预期总体质量或 NPD 团队的理想期望进行比较。一个皮包公司的案例被用来展示这个模型。在这种情况下，必须确定来自 24 个替代组件的 16 个组件和新皮包产品案例的 7 个供应商，并通过模型解决。敏感性分析提供了模型有效性的证据。最后，提供了几个潜在的未来工作的结论。一个皮包公司的案例被用来展示这个模型。在这种情况下，必须确定来自 24 个替代组件的 16 个组件和新皮包产品案例的 7 个供应商，并通过模型求解。敏感性分析提供了模型有效性的证据。最后，提供了几个潜在的未来工作的结论。一个皮包公司的案例被用来展示这个模型。在这种情况下，必须确定来自 24 个替代组件的 16 个组件和新皮包产品案例的 7 个供应商，并通过模型求解。敏感性分析提供了模型有效性的证据。最后，提供了几个潜在的未来工作的结论。