We performed business process modernization for a top-tier automotive OEM using Generative AI to eliminate their supply chain management inefficiencies, improve client partner relationships and revenue growth.
Business Goals
The client is an automotive OEM major that supplies components to an impressive global portfolio of leading automotive brands. With an outstanding manufacturing infrastructure and high-quality products, the client was progressively gaining its presence in the automotive OEM market share. However, the prevalent supply chain inefficiencies were substantially clipping its growth potential.
- The OEM faced challenges in optimizing supply chain operations owing to its complex network of suppliers and logistics facilitators globally.
- Procurement and manufacturing processes were plagued with longer lead times, denting customer satisfaction and market competitive edge.
- Overstocking/understocking critical components due to inefficient demand planning and management.
Solution
We analyzed and comprehended how the existing systems support their supply chain operations. We found that the systems are operating in silos creating a big void in terms of collaboration and data interoperability amongst the external stakeholders in the supply chain network. To overcome this, we infused a tailor-made Gen AI platform into their application ecosystem. This integration enabled them to experience seamless communication and collaboration with external stakeholders and facilitate real-time updates on order statuses, material availability, and potential disruptions. We also moderated a data management platform encouraging data sharing among suppliers and logistics providers to improve overall supply chain visibility and coordination.
Key Highlights
- Integrated real-time market feedback mechanisms to adjust forecasts dynamically, improving responsiveness to the changing market and demand trends.
- Introduced bespoke Gen AI platforms to simulate the entire supply chain network to proactively identify potential bottlenecks and inefficiencies. This greatly enabled the stakeholders to decide strategically on supplier selection, inventory levels, and logistics planning.
- Developed route optimization algorithms to determine the most efficient routes and transportation modes for logistics, reducing delivery times and logistics costs.
- Created specialized AI tools for dynamic inventory management, automatically adjusting stock levels based on the latest demand forecasts and supply chain conditions.
Outcomes
- 15% increase in annual revenue as a result of the improvements in demand forecasting and supply chain efficiency.
- 5% increase in market share, attributed to enhanced service levels and product availability.
- 20% increase in customer retention rate through reliable and timely order fulfillment records.
Technologies Used: Apache Spark, PostgreSQL, Scikit-learn, ERP integrations, Docker, Jenkins and OAuth 2.0
