AI Approaches to Automate Bill of Materials Validation

物料清单（BOM）是一个结构化的文档，其中包含构建产品所需的所有组件和资源的信息。物料清单的验证是确定产品信息准确性和完整性的重要过程。此文档不仅是确定正确产品组成的重要信息来源，而且对于依赖此信息的制造商内部的多项业务操作（如库存管理和服务）也是如此。此任务的复杂性取决于物料清单中记录的物料和信息的数量和质量。考虑到潜在的产品变化和客户可用的定制选项，这可能是广泛的，这些选项决定了要包括在验证中的独特组合的程度。验证过程需要具有产品设计知识的专家（组成部件和系统，它们的采购及其各自装配中的相互作用）手动审查BOM中的每个项目，以进行批准或更正。计算工具，支持这一验证过程中存在，虽然仍然是一个严重依赖的资源，产品知识专家审计的物料清单。一种技术，尚未被广泛探讨的是应用人工智能（AI），以提高效率的过程。人工智能提供了了解每个可构建组合的变体配置的可能性，从而消除错误构建，并为制造商提供整个产品线的可靠信息，这将允许在装配和财务控制方面进行更准确的规划。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：研究物料清单验证的行业实践，以及用于支持该过程的现有系统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物料清单验证和人工智能方法的研究现状进行文献研究使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，以更有效地执行物料清单验证过程，减少资源分配（例如时间，人力和财务资源）。这将提供额外的好处：减少错误的生产或停止生产的风险，因为不正确的部件交付到生产线减少不必要的资源浪费和存储，采购和报废不需要的部件来构建产品，提高数据驱动的制造流程和规划的信心，通过提高验证全方位产品BOM的能力和降低错误的风险它与EPSRC研究理事会的相关性：我的研究课题与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，以应对制造业的重要挑战。

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.