AI Approaches to Automate Bill of Materials Validation

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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.