Client Objective
Client Introduction
Challenge
The key challenges faced by the client included:
- Inefficient Route Planning: The existing route planning process was not optimized, resulting in suboptimal routes, excess fuel consumption, and increased operational costs.
- Client Prioritization: The client needed to prioritize clients for service based on their location, capacity, and service level agreements (SLAs) to ensure efficient and timely pick-up.
- Order Volume Forecasting: Accurately forecasting tire order volumes was crucial to plan for resource allocation and route optimization, particularly for new clients.
What We Did
To address these challenges, a comprehensive route optimization solution was implemented. The services provided included:
- Client Segmentation: The client base was segmented based on various factors, including geographical location, client capacity, and SLAs. This segmentation was critical for customizing service and route planning.
- Order Volume Forecasting with RNN: Recurrent Neural Networks (RNN) were employed to forecast tire order volumes at the individual segment level for existing clients. RNNs can capture sequential patterns and were used to make accurate predictions for clients with historical data.
- Regression Analysis for New Clients: For clients without sufficient historical data, regression analysis was employed to estimate their future tire order volumes based on relevant parameters such as their location, industry, and service agreements.
- Route Optimization: With forecasted order volumes and client segmentation in place, the most efficient pick-up routes were planned to minimize fuel costs and maximize resource utilization.
Outcome
The implementation of the route optimization project led to several significant outcomes:
- 8% Reduction in Fuel Costs: The optimized route planning resulted in an 8% reduction in fuel costs, contributing to significant cost savings for the client.
- Efficient Client Servicing: Prioritizing clients based on location and SLAs allowed the client to provide more timely and efficient service, improving customer satisfaction.
- Resource Optimization: The accurate order volume forecasting and efficient route planning enabled the optimal allocation of resources, reducing waste and improving operational efficiency.
- Improved Sustainability: By reducing fuel consumption and optimizing routes, the project contributed to a more sustainable waste tire management process.
Conclusion
