Data-driven product optimization capabilities to enhance sustainability and environmental compliance in a marine manufacturing context

E. L. Synnes, T. Welo
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Abstract

This paper investigates concerns related to product data and digital data flow when aiming to automate company processes. Accurate data is necessary to create value by enabling improved decision-making in product development, including sustainability capabilities. The case analyzed is an engineer-to-order (ETO) company operating in a low-volume marine manufacturing context. A participatory research approach is used to study two projects that are part of the company’s digital business transformation, aiming to digitalize information and autogenerate downstream processes. Building on the strengths promised by digitalization requires precise and extensive product and process information. An important facilitation capability is to create a digital thread from design to finished product, including product documentation. This is necessary to establish capabilities both to autogenerate appropriate compliance reporting as part of the product development process and to conduct virtual testing and validation before the physical equipment is acquired, resulting in a manufacturing process that is ‘right first time’. In addition, data capabilities guide and enable sound-decision making for improved sustainable practices in the early phase of product development. It is found that the data quality required to utilize tools within the context of Industry 4.0 demands changes to existing product design practices and focus on the three pillars harmonization, integration and automation of data and systems.
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以数据为驱动的产品优化能力,在海洋制造业中提高可持续性和环境合规性
本文探讨了在实现公司流程自动化时与产品数据和数字数据流相关的问题。准确的数据是通过改进产品开发决策(包括可持续发展能力)来创造价值的必要条件。所分析的案例是一家在小批量船舶制造领域运营的按订单生产(ETO)公司。采用参与式研究方法对该公司数字化业务转型的两个项目进行了研究,旨在实现信息数字化和下游流程的自动生成。要发挥数字化带来的优势,需要精确而广泛的产品和流程信息。一项重要的促进能力是创建从设计到成品的数字化流程,包括产品文档。这对于在产品开发过程中建立自动生成适当的合规报告的能力,以及在购置物理设备之前进行虚拟测试和验证的能力都是必要的,从而实现 "一次成功 "的生产流程。此外,数据功能还能在产品开发的早期阶段为改进可持续发展实践提供指导和合理决策。研究发现,在工业 4.0 背景下使用工具所需的数据质量要求改变现有的产品设计实践,并重点关注数据和系统的协调、集成和自动化三大支柱。
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