按订单生产:基于文献综述的关键挑战和解决方案的重要性分析

C.S. Fortes, A.B. Tenera, P.F. Cunha, J.P. Teixeira
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引用次数: 0

摘要

按订单生产企业(ETO)根据客户的具体要求定制产品,面临着巨大的挑战。其中一些挑战与这类公司在生产调度、计划和控制、提高效率和缩短交货时间等方面的活动有关。本研究通过系统的文献回顾和对 ETO 企业的调查,找出了最常见和最关键的问题。其中最关键的问题是难以优化生产绩效 (P3),其 GCI 值为 16,这意味着时间和成本的关键程度相同。随后,利用建议的关键度矩阵进行了分析,使企业能够对决策和资源分配进行优先排序。结果凸显了采用大规模定制战略、创新方法和工作流程优化的重要性。对关键度水平的持续监测和分析也有助于电子贸易机会公司识别新出现的问题,并改进知情决策。利益相关者之间的有效沟通与合作也被认为是至关重要的。未来的研究可以进一步扩大研究样本,并为 ETO 制造公司开发决策支持工具。本研究为 ETO 公司提供了一个新的关键度矩阵,使其更好地了解和应对生产挑战,帮助决策和资源分配,从而为该领域做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Engineering-to-order manufacturing: A criticality analysis of key challenges and solutions based on literature review
Engineer-to-Order (ETO) manufacturing companies involve customised production products based on specific customer requirements, and face a significant challenge. Some of those challenges are related with this type of company’s activities in production scheduling, planning and control, efficiency improvement and lead time reduction. The present study was conducted with a systematic literature review and a survey from ETO firms to identify the most frequent and critical problems. Among the most critical issues identified is the difficulty in optimising production performance (P3), with a GCI value of 16, implying that both time and cost share the same critical level. An analysis using a proposed Criticality Matrix was then performed enabling companies to prioritise decision-making and resource allocation. The results highlight the importance of adopting mass customisation strategies, innovative approaches and workflow optimisation. Continuous monitoring and analysis of criticality levels can also help ETO companies identify emerging issues and improve informed decisions. Effective communication and collaboration among stakeholders were also identified as vital. Future research could be done expanding further the study sample and developing decision-support tools for ETO manufacturing companies. This study contributes to the field by providing a new criticality matrix for ETO companies to understand better and address their production challenges, aiding in decision-making and resource allocation.
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