AI-Based Product Traceability in Manufacturing

Traceability is typically considered crucial only for sectors such as food, automobiles, or aircraft, where products might be recalled very easily. With time, it became clear that every manufacturing industry should have implemented Product Traceability processes in order to increase the quality of end products.

With intelligent product traceability modules, every failure of the production chain can be easily detected and so quickly withdraw faulted batches of products. That is exactly the whole point of a traceability system: connecting the physical flow of goods with the flow of information and ensuring complete documentation of all stages of the supply chain and production. 

Jabil faced a problem with Product Traceability and legacy system performance. Company needed to build a solution that will track every part of the product throughout the manufacturing process, from the moment when raw materials enter the factory to the moment when final products are shipped.



Meet Our Client


Jabil’s services include design engineering, manufacturing, supply chain services for the EMS and consumer industries; and materials technology services (plastics, metals, automation, and tooling).

The portfolio of its clients consists of reps from numerous industries, including healthcare, life sciences, clean technology, instrumentation, defense, aerospace, automotive, computing, storage, consumer products, networking, and telecommunications.

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Case Study Shortcut


Challenge


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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. This made it difficult to detect defects early or isolate faulty batches, leading to potential quality and compliance risks.

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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.

Goal


Company 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.


  • Increase quality and safety

  • Improve product recalls

  • Improve inventory tracking

  • Improve customer service

Outcome


The developed application helps improve quality and efficiency by giving manufacturers real-time visibility into their operations and facilitating root-cause analysis.

The manufacturing traceability  solution supports finding key points where quality check-ups should be added. The company can quickly answer all production-related problem questions, increasing transparency and accountability throughout the supply chain.

Traceability with direct part marking provides a documented trail of each product, its history, components, quality, and safety, ensuring that products meet given standards or comply with industry regulations.



Before


  • Challenging Operation Transparency
  • Slow and demanding Data Analysis Processes throughout Supply Chain
  • Data Closed in Silos


After


  • Improving Operation Transparency
  • Increasing Customer Confidence
  • Ensuring Product Quality
  • Facilitating Root-cause Analysis

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Case Study Details


Approach


Implementation of a Data Lake (AWS-Based)


  • Designed to ingest and store massive volumes of data efficiently.
  • Stored as flat files to optimize for cost-effectiveness and scalability.

Deployment of a Data Processing Engine


  • Processes data ingested into the Data Lake.
  • Validates data quality to ensure accurate reporting.
  • Prepares data for fast and structured access in the reporting phase.

Development of a Reporting Layer


  • Built to enable fast, standard product traceability reports across the manufacturing lifecycle.
  • Supports high-performance access to structured information for daily operations.

Implementation of an Ad-Hoc Reporting Tool


  • Allows end-users to perform self-service data exploration.
  • Enables discovery of relationships between products, components, suppliers, and customers.
  • Supports quick querying of large datasets for faster insights and decision-making.


Our Team Expert Opinion




We have started the project with a mutual solution design with the customer. Our goal was to clearly define system features, components, and infrastructure so we ensure that the development process is going smoothly. Later we delivered solutions in desired timelines and using modern approaches like serverless, IaaC, and other modern Data Engineering technologies.


Edwin Lisowski CSO and Co-founder – Addepto

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About Addepto


Addepto, a fast-paced, growing company focused on innovations in AI-related and data-oriented areas, supports digital transformation at companies working on electronics manufacturing services.


Here you can learn more about the technologies used in this project:



We help them find ways to use their data effectively with data lakes, data platforms, data engineering and so on.


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