Challenges faced by the client:
Operational Optimization:
The client struggled with optimizing its operations to ensure timely and cost-effective deliveries for their customers.
Delayed Decisions:
With many manual dependencies in place, the operations, and stakeholder communications got delayed which led to untimely decision-making processes within the company.
Cost Management:
Managing quarterly operational costs efficiently was a major lag for the client.
Focus areas and objectives raised by the client:
Enhancing demand forecasting processes.
Optimizing delivery routes for all-around delivery efficiency.
Improving resource allocation strategies.
Implementing strategies to reduce quarterly operational costs.
Dynamic Pricing Optimization
A dynamic pricing engine was deployed in place that adjusts prices in real time based on changing demands, competitor pricing, and market conditions.
End-to-end Predictive Maintenance
Utilized predictive maintenance models to proactively schedule maintenance activities based on fleet sensor data and historical maintenance records.
Supplier Performance Forecasting
Demand Sensing for Seasonal High
Leveraged predictive analytics to sense and respond to seasonal demand peaks.
Optimize resource allocation during high-demand periods.
Fleet Performance Monitoring
Implemented predictive analytics to monitor and analyze fleet performance, identifying areas for improvement in fuel efficiency and overall operational effectiveness.
Routing and Scheduling
Customer Satisfaction
Implemented proactive issue resolution strategies in place to enhance customer repetition rate.
Weather Impact Analysis
End-to-end inventory management led to reduced stock holding costs by 20% and improved inventory turnover by 25%.
The client achieved a 15% increase in overall revenue and improved competitiveness through real-time pricing adjustments.
Achieved a 30% reduction in maintenance-related downtime and improved overall fleet reliability by 15%.
With supplier performance forecasting, a 19% reduction in supply chain disruptions was achieved, leading to improved supplier relationships and reliability.
18% increase in operational efficiency during peak seasons and improved B2B relations through timely deliveries.
With real-time fleet performance monitoring, the company managed to reduce fuel consumption by 12%.
Our collective efforts led to streamlined routing and scheduling, helping them achieve a 15% reduction in delivery delays plus improved fuel efficiency by 10%.
Weather Impact Analysis helped them achieve a 20% reduction in delivery delays due to climate disruptions.
All-in-all, their stakeholders and our deployed team collectively addressed initial operational challenges with the introduction of multiple use cases including dynamic pricing, weather impact analysis, fleet and supplier performance monitoring, and sustainability initiatives. This approach cemented our client’s position as an industry leader in using data in the logistics and transportation industry posting them ahead for business expansion and profitability.
Data Engineering
AI & Machine Learning
By Use Cases
Business Intelligence & Tableau
