AI-Driven Logistics Impact Screening for Engineering Changes in the Automotive Supply Chain Mercedes-Benz Group AG Research and Development

Format:

Book

Conference/Event

Author/Creator:

Surampudi, Tejas, author.

Contributor:

Anandan, Tejaswee

Namdev, Vanshika

Yadav, Vishwas

Conference Name:

Automotive Technical Papers (2025-01-01 : Warrendale, Pennsylvania, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Engineering change (EC) is a complex, manual, and expert-driven topic. It has significant downstream effects on logistic operations and cost structures. The impact of logistics cost is a critical consideration for any profiting company or original equipment manufacturer (OEM). Evaluating the logistics cost impact of an EC is a time-consuming and tedious labor-intensive task. There are multiple steps taken by engineers before making an evaluation of the logistics cost of an EC, these include examining multiple sources, computer-aided design (CAD) drawings, PDF documents, PowerPoint files, and descriptions of the modification. Automation is further complicated by the wide variation of ECs across vehicle model, module group, and product type. To address this, we introduce the logistics impact screening application (LISA), an AI-based system designed to predict logistics cost impacts automatically. LISA pulls together both structured and unstructured data and uses a mix of techniques: machine learning methods (like logistic regression, support vector machine (SVM), random forest, gradient boosting, AdaBoost, XGBoost), deep learning approaches (such as longshort-term memory (LSTM), bi-LSTM, multilayer perceptron (MLP), gated recurrent unit (GRU), and even large language models (OpenAI's GPT-4o)). Each method was evaluated for accuracy, interpretability, and computational efficiency. Results demonstrate that certain configurations achieved 83%85% accuracy and reduced assessment time by up to 70% compared with manual evaluation. This indicates that AI-powered prediction is not merely feasible, but also efficient and cost-effective. By enabling engineers to rapidly comprehend the ECs' logistics effect, LISA is a scalable method for enhancing decisions. Increased application within carmaker OEM processes can enable proactive cost management and logistics planning optimization

Notes:

Vendor supplied data

Publisher Number:

2025-01-5081

Access Restriction:

Restricted for use by site license