Industry
Client Overview
The client is a global leader in packaging and manufacturing, with annual revenues exceeding $13 billion. They operate hundreds of facilities worldwide and manage one of the most complex supply chains in the industry. Their North American logistics network involves thousands of daily shipments, multiple carriers, and dynamic routing decisions, making it a prime candidate for AI-driven optimization. The pressure to improve efficiency, reduce carbon footprint, and cut operational costs from the C-suite was immense.
Client Testimonial
"The AI platform CIS built for us has fundamentally changed how we manage logistics. We've moved from making decisions based on historical data to predicting and shaping future outcomes. The 14% cost reduction is just the beginning; the improvement in delivery accuracy and the visibility it gives us are invaluable. CIS was a true partner in this, demonstrating deep expertise in both AI and the nuances of our complex supply chain." - Brandon P., Director of Supply Chain & Logistics.
Problem
The client's logistics planning was largely reactive. Shipment routes and carrier selections were based on static contracts and historical averages, leading to significant inefficiencies. They were unable to account for real-time variables like fuel price fluctuations, weather disruptions, carrier capacity changes, and fluctuating warehouse receiving times. This resulted in excessive shipping costs, frequent delays, and an inability to proactively manage disruptions.
Key Challenges
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01
Data Silos : Critical data was fragmented across multiple systems: their ERP, a legacy Transport Management System (TMS), carrier portals, and dozens of spreadsheets.
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02
Dynamic Volatility : The system could not adapt to real-time market conditions, leading to missed opportunities for cost savings and poor routing choices during disruptions.
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03
Lack of Predictive Insight : They could analyze what had happened but could not accurately forecast future costs, transit times, or potential bottlenecks.
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04
Scalability Concerns : Their existing infrastructure could not handle the massive volume of real-time data required for a true AI-powered optimization engine.
Our Solution
CIS architected and developed a custom, cloud-native AI platform to provide end-to-end predictive optimization for their logistics network. We assembled a dedicated POD featuring a solutions architect, two ML engineers, a data engineer, a full-stack developer, and a QA automation engineer.
Centralized Data Lake : We built a scalable data lake on AWS, creating automated ETL pipelines to ingest and unify data from all their disparate systems, including ERP, TMS, and real-time GPS feeds.
Predictive Cost & Transit Model : Our ML engineers developed a custom machine learning model that predicts shipping costs and transit times for any given route with over 95% accuracy, factoring in dozens of real-time variables.
Route Optimization Engine : We built an AI-powered engine that analyzes millions of potential route and carrier combinations in seconds to recommend the most cost-effective option that still meets delivery SLAs.
Intuitive Command Center Dashboard : Our UI/UX and development team created a web-based "Command Center" that visualizes the entire network, highlights risks, displays the AI-powered recommendations, and allows planners to run "what-if" scenarios.
Implementation & Execution
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Phase 1 (Weeks 1-4)
Deep-dive discovery workshops and architectural design. We deployed our "One-Week Test-Drive Sprint" to build a proof-of-concept data pipeline, proving our ability to connect to their core systems.
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Phase 2 (Weeks 5-12)
Focused on data engineering. We built and validated the ETL pipelines, ensuring a clean, reliable, and unified dataset for model training.
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Phase 3 (Weeks 13-20)
AI/ML model development. We trained, validated, and fine-tuned the predictive models, working closely with the client's logistics experts to ensure the model's logic reflected real-world constraints.
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Phase 4 (Weeks 21-28)
Application development. We built the backend microservices and the frontend dashboard for the Command Center.
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Phase 5 (Weeks 29-32)
Integration and User Acceptance Testing (UAT). We integrated the platform with their key systems and conducted rigorous testing with their logistics planners.
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Phase 6 (Week 33+)
Phased rollout across their North American operations, accompanied by comprehensive training and our ongoing MLOps support model to monitor and retrain the models.
Positive Outcome
1. 14% Reduction in Overall Shipping Costs
The platform saved the client over $4 million in its first year of operation by consistently selecting more optimal carriers and routes.
2. 21% Improvement in On-Time Delivery
By proactively routing around potential delays, customer satisfaction and reliability scores saw a significant boost.
3. 90% Reduction in Manual Planning Time
Logistics planners were freed from hours of manual data gathering and analysis, allowing them to focus on strategic exception handling.
4. End-to-End Network Visibility
For the first time, leadership had a single, real-time view of their entire logistics network, enabling smarter, faster strategic decisions.
Why Choose Us
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Domain Expertise
We understood the manufacturing and logistics business.
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ROI-Focused
The project was driven by the goal of cost reduction.
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CMMI Level 5 Process
Our mature process ensured the project stayed on track.
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Custom Solution
The platform was tailored to their unique network.
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Data Engineering Excellence
We successfully unified their messy, siloed data.
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Full-Stack Capability
We delivered the entire solution, from data to dashboard.
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Scalable Cloud Architecture
The AWS-based solution was built for growth.
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Transparent Collaboration
They had full visibility through weekly demos and Jira access.
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Long-Term Partnership
Our MLOps support ensures the value continues to grow.
Conclusion
This project demonstrates CIS's ability to move beyond simple AI development and deliver a transformative, enterprise-grade solution that tackles core operational challenges. By combining deep industry knowledge with world-class data engineering and machine learning expertise, we turned a complex, costly business function into a source of significant competitive advantage and financial return.