About Client
A global logistics company specializing in freight and supply chain management faced significant challenges in revenue forecasting. The company, with operations spanning over 50+ countries, had to manage complex transportation, warehousing, and delivery systems. Despite a robust infrastructure, the company struggled with inaccurate demand forecasting, leading to inefficiencies such as stockouts, overstocking, fluctuating transport capacities, and missed revenue opportunities.
Industry
Logistics
Geography
50+
What we did for the client:
Integration of Generative Artificial Intelligence
In Logistics and Supply Chain Management
Business Challenges
The current forecasting approach heavily relied on traditional statistical models and human judgment, which struggled to keep pace with rapidly changing demand patterns, especially during peak seasons. These limitations highlighted the critical role of artificial intelligence in logistics and supply chain management, emphasizing the need for predictive analytics in supply chain operations to improve revenue forecasting and streamline resource allocation.
Key Challenges
- Limited Technology Capabilities: Legacy systems based on outdated monolithic platforms hindered scalability and innovation in the rapidly evolving AI in logistics industry.
- Demand Variability: The supply chain faced high demand volatility across regions due to seasonal trends, market shifts, and geopolitical events, making it challenging to implement effective AI in logistics and transportation.
- Complex Supply Chain: Managing a global network comprising warehousing, transportation, customs, and last-mile delivery required seamless integration and the adoption of artificial intelligence in logistics and supply chain management for accurate and real-time forecasting.
- Data Silos: Disconnected data sources and lack of centralized access obstructed a unified view of operations, preventing the effective use of AI in warehousing and predictive tools for optimization.
- Lack of Real-Time Forecasting: Dependence on historical data alone failed to capture emerging trends or sudden disruptions. Leveraging generative AI for logistics and AI in supply chain and logistics offered opportunities to address this gap and enhance agility.
Business Objectives
The implementation of generative AI in logistics and supply chain started with the following objectives in mind:
- Enhance Forecasting Accuracy: Leverage predictive analytics in supply chain operations and AI in logistics and transportation to achieve more precise demand forecasts, reducing reliance on traditional methods and improving responsiveness to market dynamics.
- Modernize Technology Infrastructure: Transition from legacy systems to an advanced technology stack that integrates artificial intelligence in logistics and supply chain management, enabling scalability, agility, and innovation in operations.
- Mitigate Demand Variability Impacts: Utilize AI in logistics and supply chain to adapt to demand fluctuations influenced by seasonal trends, market shifts, and geopolitical factors, ensuring consistent service delivery.
- Streamline Supply Chain Complexity: Implement AI in warehousing and generative AI for supply chain solutions to optimize workflows across warehousing, transportation, customs, and last-mile delivery, ensuring a seamless global operation.
- Foster Data Integration and Real-Time Insights: Break down data silos by centralizing access and employing AI in logistics industry tools, facilitating a unified view of operations and enabling real-time forecasting of emerging trends and disruptions.
- Optimize Resource Allocation: Apply generative AI for logistics to improve resource planning and utilization, minimizing waste, reducing costs, and maximizing operational efficiency.
- Drive Innovation in Logistics: Harness the latest applications of artificial intelligence in logistics to stay competitive, create value, and support long-term strategic goals in an increasingly dynamic market.
How we used Product Engineering
The Technology Transformation journey of our client was a significant mix of new system development and refactoring of existing systems.
Design
Approach
Approach
Objective:
Approach:
- Evaluate Current Capabilities: Assesing the limitations of the monolithic system, identifying data silos and operational inefficiencies.
- Definition KPIs: Identifying key objectives, such as improving demand forecasting, enhancing fleet efficiency, optimizing inventory through AI in warehousing, and reducing the overall costs across logistics and supply chain management.
- Modular Microservices Approach: Break down the monolithic system into modular services that enable the integration of artificial intelligence in logistics for real-time insights.
- Data Inventory & Integration: Identify and consolidate data from key sources, including IoT sensors, fleet management systems, and customer data, into a centralized data warehouse.
- Data Quality & Preparation: Enhance data accuracy through cleaning, deduplication, and normalization to support predictive analytics in supply chain operations.
- Budgeting and Team Definition: Conduct resource planning, including team structuring, tools, and infrastructure needs.
Platform Development
& Transformation
& Transformation
Objective:
Approach:
- Centralized Data Integration Unified Data Sources: Aggregated data from warehouse systems, transportation logs, customer orders, and external sources like weather and trade activity, ensuring a single source of truth.
- AI-Driven Data Cleansing: Applied artificial intelligence in logistics and supply chain management to clean, normalize, and enrich raw data for accurate and consistent forecasting inputs.
- Third-Party Integration: Enabled seamless interoperability with over 100+ external systems, leveraging APIs for real-time data sharing across AI in logistics industry platforms.
- Comprehensive Backoffice Admin Panel
Implementation Infrastructure
& Cloud Migration
& Cloud Migration
Objective:
Approach:
- Continuous Re-architecting for By-Parts migration of system modules from On-Premises physical servers to AWS-based Cloud Environment
- Data Migration
- Built CI / CD Pipeline for Automation
- Used New Age Serverless Cloud Services to get best of both worlds
RoadMap for Implementation
| Time Period | Phase | Activities | Outcomes/Deliverables |
|---|---|---|---|
| Months 0-0.5 | Design Phase |
|
|
| Months 1-2.5 | Platform Development |
|
|
| Months 2-3 | Implementation Infra |
|
|
| Months 3-5 | Expansion & Scaling |
|
|
95%
Forecast Accuracy
18%
Cost Reduction
10%
Customer Satisfaction
The Results
- 95% Revenue Forecast Accuracy: Increased from 60%, enabling precise resource allocation and planning.
- 18% Cost Reduction: Optimized transportation routes and warehouse operations to cut excess expenses.
- 10% Customer Satisfaction Boost: Improved delivery accuracy and stock availability through better demand planning.
- Enhanced Flexibility: Enabled proactive adjustments with real-time forecast adaptability.
- Real-time Forecast Adjustments: Responded effectively to market and operational changes.
- Helped establish a strong Technology Team.
What Client says about us
Abhishek and the team helped us reach a major technological transformation from using a legacy platform to a new-age agile and highly scalable platform. The team enhanced our operational efficiencies by automating & centralising management of many business processes which resulted in 20x growth of business in five years.
