Overcoming Limited Visibility in Product Traceability
Jabil is a major American multinational manufacturing services company, delivering innovative engineering, supply chain management, and manufacturing solutions at global scale. Founded in 1966, the company is ranked among the world’s largest manufacturing solutions providers with over 140,000 employees worldwide and annual revenue of $29.8 billion (FY25).
The client faced challenges in tracking product lineage across multiple production stages and systems:
- Lack of End-to-End Visibility in Manufacturing: Jabil struggled to trace components and materials throughout the entire production lifecycle, from raw material intake to final product shipment. The limited traceability made it difficult to detect defects early or isolate faulty batches, leading to potential quality and compliance risks.
- Inability to Quickly Respond to Quality Issues or Recalls: Without intelligent traceability, identifying the source of a fault and initiating a product recall was time-consuming and prone to errors. This delayed corrective action, risking reputational damage and increasing operational costs.
As production complexity increased, Jabil required a scalable solution capable of capturing, processing, and analyzing traceability data in real time to improve visibility, quality control, and operational efficiency.
Engineering a Scalable Product Traceability System
Jabil was looking for a partner able to design and implement the product traceability system to increase security and safety throughout the whole production chain and to set an agreeable model for raw material supply.
Our team engaged as a strategic engineering partner to design and implement a scalable traceability platform, ensuring data quality, and enabling fast analysis across the entire manufacturing lifecycle.
1. Implementing an AWS-Based Data Lake
A cloud-based data lake was designed to ingest and store massive volumes of production and traceability data efficiently. Data was stored in flat file formats to optimize for cost-effectiveness while maintaining scalability across growing datasets.
A flexible storage layer enables the organization to capture data from multiple systems without imposing rigid structure upfront, ensuring long-term adaptability as data sources and requirements evolve.
2. Deploying a Data Processing Engine
A dedicated data processing engine was implemented to handle data transformation and preparation. Incoming data is processed, validated, and structured to ensure consistency and reliability across the platform.
Built-in validation mechanisms ensure that only accurate and high-quality data is used for reporting, while transformation logic prepares datasets for fast and efficient access in downstream analytical workflows.
3. Developing a High-Performance Reporting Layer
A reporting layer was developed to support fast and standardized product traceability reporting across the entire manufacturing lifecycle. Structured datasets enable users to quickly access relevant information without complex data preparation.
High-performance reporting capabilities ensure that operational teams can rely on timely and accurate insights for daily decision-making and quality control.
4. Enabling Ad-Hoc Data Exploration and Analysis
An ad-hoc reporting tool was introduced to provide self-service data exploration capabilities for end users. Users can analyze relationships between products, components, suppliers, and customers without relying on technical teams.
Flexible querying of large datasets enables faster insight generation and supports more informed decision-making across production and supply chain operations.
“On this project, about 30% of the work was deep engineering and 70% was navigating enterprise complexity. We ran the engagement as a long-term roadmap, with each initiative building on the last while working within highly manual processes. The technical scope included optimizing Snowflake, building LLM-powered semantic layers, and deploying anomaly detection pipelines. The real win was making advanced engineering succeed inside rigid enterprise delivery models, bridging cutting-edge technology with large-scale collaboration.”
Maciej Trzaskalski
Project Manager – Addepto
Turning Data Platform into Real-Time Intelligence Engine
The partnership between our team and the client transformed a fragmented and inefficient data environment into a high-performance, AI-enabled platform for connected vehicle intelligence.
By modernizing the data foundation and integrating generative AI and machine learning, the organization achieved a fundamental shift from delayed, manual reporting to real-time, proactive decision-making.
Before
- Updated once daily; took ~1 hour to process
- Performed in Tableau UI layer pulling raw datasets
- Tableau extracting millions of rows for client-side aggregation
- Required SQL proficiency and direct database access
- Reactive; manual incident response after customer calls
After
- Sub-second query responses with near real-time data visibility
- Pre-computed in Snowflake materialized views with incremental refresh
- Optimized Snowflake queries returning only aggregated results
- Natural language queries via LLM-powered chatbots with semantic understanding
- Proactive; automated anomaly detection with pre-emptive alerting
Today, teams across the business can access accurate insights instantly, operations teams can anticipate and address issues before they escalate, and the organization is equipped with a scalable platform that supports ongoing innovation.
As connected vehicle ecosystems continue to grow in complexity, this ability to process, interpret, and act on data in real time has become a defining competitive advantage.
Ready to build your AI-powered data ecosystem? Contact us today!