In truck manufacturing, each vehicle requires thousands of small parts across models and variants. Each truck requires 4,000 to 15,000 standard components, with 500 to 1,500 part types that vary slightly. During the design phase, engineers select components from extensive part libraries without complete visibility into assembly complexity, procurement impact, or total lifecycle cost. This results in high part variability on multi-model production lines, increasing operator confusion, assembly effort, rework, tooling requirements, and supply chain inefficiencies.
To streamline this, the authors developed a consolidated small-parts catalogue using a data-driven approach. The existing standards, specifications, usage volumes, and cost data were analyzed, and parts were clustered based on their features. These clusters were mapped to assembly stations using a visualization based digital tool that displays all parts used at each station and highlights functionally similar components that can be used alternatively. The developed tool for optimization quantifies consolidation opportunities, provide before and after part counts, with estimated cost-saving potential to reduce complexity in the design and assembly stations.
For length-based optimization, manual validation of screw lengths across variants was replaced by an automated evaluation. The automated system replaces the proposed parts at existing assembly location across variants, and evaluates collision, interference, thread engagement, and clearance with nearby components. It increases the quality and reduces significant time for each proposal.
Overall, the approach identified analytical consolidation opportunities of 24% in total small-part variant count and delivered a directly measured reduction exceeding 75% per proposal length validation time. Engineering effort reduction (∼50%) and direct procurement cost savings (up to 3%) are indicative projections pending production implementation. This approach improves manufacturing robustness and standardization repeatability for future vehicle programs.