AI Approaches to Automate Bill of Materials Validation

物料清单（BoM）是一种结构化文档，其中包含构建产品所需的所有组件和资源的信息。BoM的验证是建立产品信息的准确性和完整性的必要过程。该文档不仅是确定正确产品组成的重要事实来源，而且对于依赖于此信息的制造商内的多个业务操作（例如库存管理和服务）也是如此。此任务的复杂程度取决于BoM中所记录的项目和信息的数量和质量。考虑到潜在的产品变化和客户可获得的定制选项，这可以是广泛的，这决定了验证中包含的唯一组合的程度。验证过程需要具有产品设计知识（组成部件和系统，它们的采购和各自组装中的交互）的专家手动审查BoM中的每个项目以进行批准或更正。支持此验证过程的计算工具已经存在，尽管仍然严重依赖产品知识专家的资源来审核BoM。一种尚未被广泛探索的技术是应用人工智能（AI）来提高流程的效率。人工智能提供了了解每种可构建组合的不同配置的可能性，从而消除了遗漏的构建，并为制造商提供了整个产品线的可靠信息，从而可以在组装和财务控制方面进行更准确的规划。该研究的目的是确定使用人工智能方法提高BoM验证过程效率的能力。为了实现这一目标，将设定以下目标：研究BoM验证的行业实践；定义在验证过程中做出决策所需的知识和方法。对当前围绕BoM验证和人工智能方法的研究现状进行文献研究。用人工智能方法进行实验，以支持/自动化现有验证过程，以了解潜在的影响。由此产生的研究可能会为开发更智能的系统提供信息，以更有效地执行BoM验证过程。在减少资源分配方面（例如，时间、人力和财力）。这将提供额外的好处：减少由于不正确的零件交付到生产线而导致的错过构建或停止生产的风险。减少不必要的资源和精力的浪费，储存，采购和报废不需要构建产品的部件。通过提高验证全系列产品BoM的能力和降低错误风险，提高对数据驱动的制造过程和计划的信心。与EPSRC研究委员会的相关性：我的研究主题与EPSRC在人工智能和制造技术两个研究领域的兴趣和投资一致。我的工作将有助于开发智能系统的研究成果，这将解决制造业的一个重要挑战。

A Bill of Materials (BoM) is a structured document that contains the information of all components and resources needed to build a product.The validation of the BoM is an essential process performed to establish the accuracy and completeness of product information. This document acts as a vital source of truth not only to determine the correct product composition, but also for multiple business operations within a manufacturer that rely on this information, such as inventory management and servicing.The complexity of this task is dependent on the quantity and quality of items and information recorded in the BoM. This can be extensive considering the potential product variations and customisation options available to the customer which determine the extent of unique combinations to be included in the validation.The validation process requires experts with knowledge of the product design (the constituent components and systems, their procurement and interaction within their respective assemblies) to manually review each item in the BoM for approval or correction. Computational tools that support this validation process exist, although there is still a heavy reliance on the resource of product knowledge experts to audit the BoM.One technique which has not be explored extensively is the application of artificial intelligence (AI) to improve the efficiency of the process. AI offers the possibility to understand the variant configuration of each buildable combination and thus eradicate miss-builds and provide manufacturers with reliable information across the whole product line-up which will allow for more accurate planning in terms of assembly as well as financial control.The aim of the research is to determine the ability to improve the efficiency of the BoM validation process using AI methods. To meet this aim the following objectives will be set:Research industry practices for BoM validation, and existing systems that are utilised to support the processDefine the required knowledge and methods to make decisions during validationPerform a literature study on the current research landscape surrounding BoM validation and AI methodsExperiment with AI methods to support/ automate an existing validation process to understand potential impactThe resulting research can potentially inform the development of more intelligent systems to perform the BoM validation process more efficiently, in terms of reduced resource allocation (e.g. time, human effort, and financial resources). This will provide additional benefits:Reduced risk of miss builds or stops to production from incorrect part delivery to the production line.Reduced waste of unnecessary resources and effort for storing, procuring, and scrapping parts which were not required to build the product.Improved confidence in data driven manufacturing processes and planning, through increased ability to validate the full range of product BoM's and reduced risks of errorIts relevance to the EPSRC research council:My topic of study aligns with the EPSRC's interests and investment in the two research areas of AI and manufacturing technologies. My work will contribute to the outcome of research towards developing intelligent systems that will address an important challenge in manufacturing.