Manufacturing | Industry
Network/Route Optimization
Objective
The client faced inefficiencies in their supply chain network, leading to increased transportation costs and delayed deliveries. The goal was to optimize routes and network logistics to enhance delivery speed and reduce costs using AI/ML, BI, and engineering solutions.
Challenges
- Notable reduction in transportation costs
- Improved delivery times, enhancing customer satisfaction.
- Increased operational efficiency, reducing idle time in logistics operations.
- Enhanced customer satisfaction scores through better delivery services.
Solution Proposed
- Implemented AI/ML algorithms like Genetic Algorithms to find the most efficient paths for route planning.
- Used PBI to analyze historical data and predict demand patterns.
- Integrated data from IoT sensors and GPS for real-time tracking and dynamic route adjustments.
- Leveraged engineering solutions to design a robust logistics network with optimal warehouse locations.
Outcome
- Reduction in transportation costs by 20%.
- Improved delivery times, enhancing customer satisfaction.
- Increased operational efficiency, reducing idle time in logistics operations.
- Enhanced customer satisfaction scores through better delivery services.
Design & Thinking Wins
- Successful integration of AI/ML and BI tools for real-time decision-making.
- Scalable solution adaptable to future growth and market changes.
- Improved data accuracy and reliability through IoT integration.
- Enhanced visibility and control over the entire supply chain network.
Disclaimer: The outline showcases the typical challenges, solutions, designs, and outcomes for industries and functions, in general, based on Affine’s prowess in the Industry. The outcomes would be much higher for specific clients as they would be based on their data and specific problems to be solved.
