CPG | Manufacturing
Network and Route Scheduling
Objective
Optimize the scheduling and routing of shipments within the CPG supply chain to minimize transit time and costs while maximizing service level agreement (SLA) fulfillment.
Challenges
- High variability in shipment volumes and routes.
- Complexity in coordinating multiple hubs and vehicles.
- Need for real-time adjustments based on dynamic conditions.
Solution Proposed
- Using Microsoft SQL Server Integration Services, integrate sequence and route data, vehicle and shipment data, hub data, and cost data.
- Utilize ARIMA and LSTM to predict expected load levels for each origin-destination (O-D) pair.
- Implement optimization algorithms, such as Genetic Algorithms and Ant Colony Optimization, to determine the best sequences and routes for shipments and minimize transit time.
- Business Intelligence (BI): Develop an Excel-based tool for stakeholders to easily consume and plan.
Outcome
- Minimized transit time and maximized SLA fulfillment.
- Reduced costs lead to higher profit margins.
- Improved planning and operational efficiency.
Design & Thinking Wins
- Successful integration of real-time data for dynamic route optimization.
- Development of a user-friendly Excel-based tool for easier consumption and planning.
- Implementation of advanced AI/ML algorithms for accurate load forecasting and route optimization.
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.
