AI Approaches to Automate Bill of Materials Validation

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 process
Define the required knowledge and methods to make decisions during validation
Perform a literature study on the current research landscape surrounding BoM validation and AI methods
Experiment with AI methods to support/ automate an existing validation process to understand potential impact

The 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 error
Its 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.

Impact Summary

This proposal has been developed from the ground up to guarantee the highest level of impact. The two principal routes towards impact are via the graduates that we train and by the embedding of the research that is undertaken into commercial activity. The impact will have a significant commercial value through addressing skills requirements and providing technical solutions for the automotive industry - a key sector for the UK economy.

The graduates that emerge from our CDT (at least 84 people) will be transformative in two distinct ways. The first is a technical route and the second is cultural.

In a technical role, their deep subject matter expertise across all of the key topics needed as the industry transitions to a more sustainable future. This expertise is made much more accessible and applicable by their broad understanding of the engineering and commercial context in which they work. They will have all of the right competencies to ensure that they can achieve a very significant contribution to technologies and processes within the sector from the start of their careers, an impact that will grow over time. Importantly, this CDT is producing graduates in a highly skilled sector of the economy, leading to jobs that are £50,000 more productive per employee than average (i.e. more GVA). These graduates are in demand, as there are a lack of highly skilled engineers to undertake specialist automotive propulsion research and fill the estimated 5,000 job vacancies in the UK due to these skills shortages. Ultimately, the CDT will create a highly specialised and productive talent pipeline for the UK economy.

The route to impact through cultural change is perhaps of even more significance in the long term. Our cohort will be highly diverse, an outcome driven by our wide catchment in terms of academic background, giving them a 'diversity edge'. The cultural change that is enabled by this powerful cohort will have a profound impact, facilitating a move away from 'business as usual'.

The research outputs of the CDT will have impact in two important fields - the products produced and processes used within the indsutry. The academic team leading and operating this CDT have a long track record of generating impact through the application of their research outputs to industrially relevant problems. This understanding is embodied in the design of our CDT and has already begun in the definition of the training programmes and research themes that will meet the future needs of our industry and international partners. Exchange of people is the surest way to achieve lasting and deep exchange of expertise and ideas. The students will undertake placements at the collaborating companies and will lead to employment of the graduates in partner companies.

The CDT is an integral part of the IAAPS initiative. The IAAPS Business Case highlights the need to develop and train suitably skilled and qualified engineers in order to achieve, over the first five years of IAAPS' operations, an additional £70 million research and innovation expenditure, creating an additional turnover of £800 million for the automotive sector, £221 million in GVA and 1,900 new highly productive jobs.

The CDT is designed to deliver transformational impact for our industrial partners and the automotive sector in general. The impact is wider than this, since the products and services that our partners produce have a fundamental part to play in the way we organise our lives in a modern society. The impact on the developing world is even more profound. The rush to mobility across the developing world, the increasing spending power of a growing global middle class, the move to more urban living and the increasingly urgent threat of climate change combine to make the impact of the work we do directly relevant to more people than ever before. This CDT can help change the world by effecting the change that needs to happen in our industry.