Case Study
Pick Pack: Rolls Royce
Rolls Royce relies on Carsets, organized collections of car parts, to streamline vehicle production. These parts are systematically packed into trays and shelves for efficient assembly. However, the manual process of preparing Carsets was labor-intensive and prone to human error, leading to inefficiencies and increased part damage. To maintain high production standards and reduce operational risks, Rolls Royce needed a smarter, more efficient solution for Carset preparation.
Rolls Royce faced the challenge of developing a digital solution to improve Carset production efficiency. The goal was to significantly reduce labor demands and minimize part damage during assembly. Achieving these objectives required a system that could accurately model and optimize how various car parts are packed and prepared, accounting for design variations and space constraints.
Inmind.ai introduced Pick Pack, an advanced simulation tool that models and optimizes the Carset assembly process. The simulation technology precisely replicates different Carset configurations, ensuring accurate fitting of each component. By analyzing various design layouts, the solution identifies the most efficient packing strategy for each set, reducing errors and improving workflow efficiency. This smart system streamlines production while minimizing damage risks.
The implementation of Pick Pack led to significant improvements in Rolls Royce’s production process. Part damage was substantially reduced, and Carset production capacity increased dramatically—from 2 to 12 sets per day. This transformation resulted in considerable labor savings, enhanced operational efficiency, and higher production output, positioning Rolls Royce for continued success in manufacturing excellence.