How an OEM Saves Millions with Andonix AI-Human enablement:

A Connected Worker & AI Success Story

Overview

A global automotive OEM recently launched two new electric vehicles (EVs) at one of its North American facilities. This plant, like many in the automotive sector, faced a worker shortage and needed an agile way to handle inbound quality challenges—particularly as they ramped up new technology with unfamiliar components. By adopting Andonix’s AI-driven connected worker platform, this OEM drastically simplified real-time containment of non-conforming parts, reduced downtime, and empowered a small team of engineers to operate with 3× the bandwidth of traditional methods.

The Challenge: Inbound Quality & Rapid Containment

As this OEM assembled EV parts and modules, some occasionally arrived defective. The plant’s existing process involved manual notifications, paper logs, and lengthy phone calls to multiple stakeholders—production scheduling, materials management, inbound quality teams, and suppliers. Each non-conforming part triggered a containment action where all in-house inventory of that commodity had to be inspected. The goal: ensure no defective part made it to the assembly line.

  • Immediate Containment Requirement: A 10–15 minute delay in detecting and isolating bad parts could halt a production line, leading to costly downtime at $50,000 per minute.
  • Supplier Coordination: Once a part was flagged, the supplier needed real-time alerts to ship replacements or accelerate shipments, which is especially critical for global supply chains.
  • Small Quality Team, Large Responsibility: Only five quality engineers handled potentially hundreds of containment projects daily—each requiring multiple work instructions, failure analyses, and data logs. The complexity threatened to overwhelm them without an automated system.

Why Traditional Methods Fell Short

This OEM historically relied on paper-based checklists and ad hoc communication (email, phone calls) to coordinate inbound quality issues. They also lacked a formal digital quality containment system in this specific EV launch line, meaning data was scattered across spreadsheets, supplier portals, and engineering notes. This manual approach caused:

  • Delayed Escalations: Hours could pass before the right engineer or materials manager learned of a problem.
  • Inefficient Work Instruction Creation: Repeatedly rewriting inspection or rework instructions at each containment station wasted valuable time.
  • High Risk of Error: Without real-time visibility or standardized workflows, the risk of shipping additional defective parts to the line grew, raising the possibility of scrap or line stoppages.
  • Inconsistent Audits: Some projects had high severity but weren’t always audited frequently enough due to resource constraints, while lower-risk items got the same or higher audit frequency.

The Solution: Andonix’s AI-Driven Connected Worker Platform

By implementing Andonix and its AndiX agentic AI, this OEM gained a unified digital workflow across inbound quality, supplier management, and production teams. Key functionalities:

  1. Real-Time Detection & Containment: As soon as a part failed inspection on the assembly line, Andonix automatically triggered alerts to quality engineers, suppliers, and materials managers. The system quarantined all in-house inventory digitally, ensuring no suspect parts were used in assembly.
  2. Automated Work Instructions: AndiX instantly generated digital SOPs for containment tasks—tailored to severity, commodity type, and known failure modes. Instead of manually rewriting instructions for each station, this OEM deployed the same digital work instruction at multiple inspection points instantly. This also extended to supplier locations.
  3. Dynamic Failure Mode Analysis: The system tapped historical inspection data (collected from prior incidents) to detect recurring patterns. If severity or frequency spiked, the AI recommended enhanced inspection steps or poka-yoke (error-proofing) methods—like additional functionality checks or deeper visual inspections.
  4. Workflow Orchestration & Audits: Andonix scheduled audit intervals based on risk priority. High-severity issues prompted daily or even per-shift audits, while lower-risk items triggered fewer checkups. This optimized the small team’s time.
  5. Instant Engineering Approval: Whenever AndiX recommended a more rigorous inspection or new steps, process engineers received a digital notification, reviewed the changes, and—on approval—updated the entire facility’s instructions in real time. This ensured consistent document control, versioning, and IATF compliance.

Results & Business Impact

  1. 3× Engineer Bandwidth: By automating administrative tasks (creating instructions, logging data, generating CAPA forms), this OEM freed its five quality engineers from constant firefighting. They focused on deeper root cause analysis and lesson-sharing, improving overall EV build quality. This single action created a salary avoidance of $1M dollars per year at $100,000 per engineer. Launches tend to demand a surge of resources and Andonix has been able to create a surge of capacity with digital quality engineers to support the launch
  2. Reduced Downtime & Scrap: Real-time containment and immediate alerting to suppliers cut reaction times from hours to minutes, minimizing line stoppages. This also slashed scrap by preventing defective parts from being installed. The downtime and scrap cost avoidance reached over $2M in six months and slashed it in 75% compared to similar launches.
  3. Scalable for Future Programs: As this OEM expands EV production lines, the small quality team can handle more projects without adding headcount. The AI-driven platform orchestrates multiple containment efforts simultaneously, adjusting audits and resource allocation as needed.
  4. Global Supply Chain Agility: With instant data sharing, suppliers worldwide saw live updates on defect rates, enabling them to expedite replacements or adjust processes. This OEM effectively overcame supply chain lags—crucial in launching new EV models where part availability is already tight.
  5. Continuous Improvement: Over time, the system’s data lake of failure modes enriches manufacturing engineering knowledge, guiding improvements in design and production. The iterative approach—detect, contain, analyze, revise—creates a robust feedback loop that fosters lean manufacturing principles.

Conclusion & Future Outlook

By partnering with Andonix, this OEM successfully navigated the complexities of inbound quality for new EV programs, despite limited manpower and the absence of a large-scale digital quality containment system. The connected worker platform and agentic AI delivered real-time visibility, automated tasks, and consistent process control across multiple stakeholders.

For companies with complex supply chains and several components that are being assembled within complex systems like an EV assembly line, this OEM example proves that:

  • Automated workflow orchestration ensures no non-conforming part slips through, even with skeleton crews.
  • AI-driven instructions and dynamic failure analyses free teams from endless document creation.
  • A lean manufacturing mentality is enhanced, not replaced, by Industry 4.0 technology.
  • The result is fast ROI, improved compliance, and the ability to confidently scale new product launches.

This OEM now plans to expand Andonix’s use in future EV lines and cross-functional programs, reinforcing the theme: small or large, any manufacturing plant can harness the power of an AI-driven connected workforce to overcome labor shortages and conquer complex quality challenges.

 

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