Supply Chain Management and Analytics - Automotive

Supply Chain Management and Analytics - Automotive

Problem Overview

The automotive supply chain for manufacturing is one of the most complex in the industry, second only to the electronics sector. Supply chain analytics play a major role in routing, inventory management, and optimization.

Our supply chain analytics team has been predominantly focusing on various problems pertaining to inventory management and routing.


With the advent of AI and Intelligent Systems, modern Software and Models have moved towards a more Stochastic based approach in comparison to older Rule Based Methods. Major challenges include:

A leading OEM was leveraging a hybrid logistics comprising of internal and external provider to run their transportation network. The client was interested in identifying opportunities to optimize the transportation of parts and reduce dependencies on external providers. IELEKTRON worked with the client to develop a solution that proposed new routes that resulted in a significant reduction in transportation costs and a 25% reduction in dependency on external vendors. This solution incorporated better inventory management and loading volume based on trailer capacity and come up with the right set of paths and routes with optimized distance to cover ratio as well as volume delivery to distance coverage metrics.


We can break the problem into two buckets

Variant Diversity
  • Automated certain manual mechanisms of indexing and maintaining parts through Part Numbers (PN)
  • Understanding major diversity and variety among parts and KPIs to measure the variety and significance of variations over the product lifecycle.
  • Develop strategies to modularize parts by understanding various needs and challenges during production till integration
  • Implementing proper risk management frameworks, diagnosing mechanisms and proper reporting methods to track significant performance KPIs over the entire product lifecycle.
Inventory volume and Routing
  • Captured all the necessary data including parts volume, trailer volume, destination (location, demand, volume, delivery frequency etc.), real-time inventory tracking, etc.
  • Developed various KPIs and reporting mechanisms to realize real-time impact of strategic changes.
  • Developed Genetic Algorithm based heuristic methods to find optimal routes subjected to various constraints


The latest strategies have shown significant improvements in various KPIs while incurring a reasonable overhead of real-time monitoring and reporting mechanisms. However, once the fine-tuning of the strategy converges, a real-time monitoring mechanism may no longer be needed, and sporadic surveillance should be sufficed in the long run.