Rishav Ranjan’s Time-Saving Capstone Project Slashes Time for PPAP Document Completion, Improving Manufacturing Efficiency

It’s a GAME changer.
A groundbreaking capstone project may transform automotive manufacturing after tackling the complexities and time-consuming nature of the Production Part Approval Process (PPAP) documentation. PPAP is essential for ensuring automotive parts meet all required specifications before production begins.
Spearheaded by Rishav Ranjan, a Global Automotive and Manufacturing Engineering (GAME) student in the University of Michigan Mechanical Engineering Department, this innovative project introduces an advanced automation system that dramatically shrinks documentation time frame by approximately 60 percent.
“Traditional methods of managing this process are manual, error-prone, and labor-intensive, often taking 10-24 hours for suppliers and 2-6 hours for Supplier Industrialization Engineers,” Ranjan said.
The core of his project is a set of macro codes and Excel templates that streamline the PPAP documentation workflow. The tool automates the extraction of dimensional data from CAD drawings, ensuring that all measurements are recorded accurately and consistently. These dimensions are integral to creating various PPAP reports, including the Inspection Standard, Dimensional Analysis, Measurement System Analysis (MSA) Proposal, and Validation documents.
“One of the project’s key achievements is integrating VBA code within the CATIA environment to automate dimension checks and generate inspection standards,” he said. “This automation curtails the tedious manual verification process, significantly reducing the scope for human error.”
The tool’s efficiency is evident in the time study conducted on 10 different parts, where the tool reduced the time required for PPAP document completion from 70 minutes to just 30 minutes per part.
Ranjan’s innovative approach extends to creating comprehensive MSA Acceptance templates, which tabulate calibration certificates and Gage R&R study results in a unified dashboard. This ensures all measuring instruments used adhere to the proposed specifications, minimizing discrepancies and enhancing data accuracy. Standardized Gage R&R templates for variable and attribute data streamline the study format, ensuring consistency and precision.
“This project doesn’t just stop at immediate applications,” he said. “It paves the way for future advancements.”
Recommendations for enhancing the tool with machine learning capabilities and Optical Character Recognition (OCR) systems promise to further automate document verification processes and expand the tool’s utility across various stages of manufacturing. Guided by Patrick Hammett, Ph.D., and supported by a prominent electric vehicle manufacturer, this capstone project represents a significant leap toward smarter, more efficient manufacturing processes in the automotive industry.
“This project not only showcases the power of automation in automotive engineering, but also sets a benchmark for future engineering solutions,” Ranjan said.