challenge
A leading conveyor belt system manufacturer was facing increasingly tight timelines from their clients, which include some of the largest ecommerce brands in the world. They employ over 50 sales engineers who manually review order accuracy and are responsible for generating adjusted shop orders, which is also a manual process. Engineering lead times are currently up to six weeks, so our client needed a solution to significantly reduce timelines while improving engineering quality. With millions of files and configurations on decades of custom-built systems, our client believed AI would be the key to utilizing this data to shorten the timeline leading up to production and ensure design correctness. This would free up valuable engineering resources whose expertise could be used elsewhere within the organization. Our client sought a partner with a background developing AI-infused products who could create a solution that would not only decrease their lead time, but also make their order inventory replenishment easier. In short, they needed a solution that would give them the ability to reach their goals to be a leading global provider of conveyor systems.
solution
We used a number of AI technologies for this project. To get started, we used generative AI to translate legacy code into newer technology for implementation purposes. This created a foundation from which we could use AI Search and semantic queries to develop data models for machine learning processes.
For the initial phase, we used a large language model (LLM) which created a consistent data taxonomy across 20 years of information. The resulting data model was used by machine learning processes to help identify similar conveyor components and make parts recommendations. The output was integrated with their system to validate the Bill of Materials (BOM) for each assembly. Additionally, we built AI indexes that are accessible through APIs which provide additional functionality for integrated systems. All of these features helped optimize the evaluation of orders and improve inventory management, while cutting engineering lead time and providing more accurate build sheets for the manufacturing process.
Phase II incorporates complete vectorization of their master inventory parts list which will allow the client to develop in-line validation to standardize BOMs for common components and conveyor systems. Over time, with AI training and new machine learning models, the AI will be able to make more accurate suggestions and eventually develop complete BOMs autonomously.
results
Implementation of the AI-powered solution resulted in a substantial reduction in the time required to transition from order intake to manufacturing. With this solution, our client expects to cut lead times to a couple of days including inventory replenishment. The resulting efficiency will ultimately improve customer satisfaction and strengthen our client’s competitive position in the market.
By providing intelligent suggestions based on previous build-outs and future inventory integration, our client will achieve faster project turnaround times, optimized resource utilization and enhanced overall operational efficiency. Sales engineering staff will be reallocated to other responsibilities as the AI becomes more accurate, eventually reducing the sales engineering element to as few as 6-8 resources. Pre-manufacturing process times will continue to shrink and will be measured in minutes as the AI matures.
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