From Data to Action at Scale

How a Global Electronics Manufacturer Achieved Near-100% Automated Downtime Labeling and Instantly Scaled Across SMT Lines

A global electronics manufacturer partnered with Arch and Fuji to improve how downtime is identified and addressed across SMT operations. By leveraging high-frequency data from Fuji pick and-place machines, Arch implemented Automated Downtime Labeling (ADL) to structure machine signals, automate root cause identification, and enable faster operational response.

At the lighthouse site, automated downtime labeling increased from ~30% to near 100% in just a few weeks. This significantly reduced manual effort while giving teams immediate clarity into the causes of downtime, enabling faster, more consistent action on the shop floor.

Once optimized, the model was deployed to additional Fuji sites, delivering immediate performance gains. With no retraining required, sites quickly reached high levels of automation and insight, allowing the organization to standardize downtime analysis and accelerate time to value across its global operations.

Automated Downtime Labeling on SMT Lines _ Arch + Fuji Case Study

HIGHLIGHTS:

Near-100% automated downtime labeling

95%+ automated root cause identification

$150K+

$150K+ annual savings in recovered production time

10–13 hours of production time recovered per week,
per line by enabling faster and more consistent response to downtime
0 retraining required to scale across Fuji sites,
enabling rapid deployment and consistent performance

The Challenge

The manufacturer lacked a consistent, scalable way to interpret and act on downtime across Fuji SMT lines.

Manual, Inconsistent labeling: Operators classified most downtime events, limiting reliability
Delayed insight: Issues were often identified downstream rather than in real time
Untapped machine data: Large volumes of Fuji machine data were available but not actionable
Operational inefficiency: Time was spent interpreting data instead of resolving issues
Limited scalability: Improvements could not be easily replicated across sites

As a result, teams were slow to identify root causes and lacked a consistent basis for action across operations.

The Arch Approach

Arch transformed raw Fuji machine data into structured, real-time insight using Automated Downtime Labeling (ADL).

Structuring Fuji Machine Data

Standardized and interpreted high-frequency machine signals
Created a reusable model for cross-site deployment

Learning from Production

Improved rapidly using live data and expert-defined logic
Expanded to capture nearly all downtime events
Increased accuracy by codifying expert decision-making

Enabling Faster Action

Delivered clear, real-time insights to operators
Enabled faster response and reduced reliance on dashboards

Key Outcomes

Near-100% Automated Labeling in Weeks

Increased automated labeling to near 100% (from ~30%) within weeks, closing visibility gaps across downtime
Achieved 95%+ automated root cause identification, accelerating issue resolution, and reducing manual effort
Gained near-complete visibility into downtime drivers, enabling more confident and timely decision-making

Rapid Path to Near-100% Automated Downtime Labeling

Increased from ~30% to near 100% within weeks, revealing true downtime causes

Downtime Classification Refines as Accuracy Improves

Unique downtime reasons expand, then stabilize as labeling approaches near 100%

Faster Time to Action

Shifted from delayed detection to real-time identification
Enabled clear, immediate next steps for operators and engineers
Converted faster insight into measurable downtime reduction, recovering 10+ hours of production time per week per line

Operator Efficiency Gains

Eliminated manual labeling for 95%+ of downtime events
Allowed operators to focus on production and problem-solving
Freed up operator time while improving the consistency and reliability of downtime data
Reallocated human effort from classification to resolution, amplifying the impact of each intervention on uptime

Recovered Production Time and Measurable ROI

Reduced downtime by ~10–13 hours per week per line by shifting root cause identification from manual to automated
Translated recovered time into ~$3K per week per line, or $150K+ annually per line
Improved SMT line utilization by reducing downtime and increasing available production capacity

Instant Scale Across Fuji SMT Lines and Sites

A second Fuji site started at ~20% automated coverage
Coverage increased rapidly after deployment with no retraining
The lighthouse model was reused without redefining downtime logic
Enabled rapid rollout of proven downtime intelligence, reducing deployment time and ensuring consistent performance across the network

Site 2
Instant Performance Gains Without Retraining

Coverage rapidly increased from low to high immediately after deployment, demonstrating scalable impact across sites.

Strategic Lessons

Learn once, scale efficiently: Structured downtime intelligence enables rapid cross-site deployment
Visibility drives performance: Better insight into root causes leads to faster, more effective action
Machine data needs structure: Value comes from interpretation, not volume alone
AI supports operational decisions: Turning signals into actions is more impactful than dashboards
Consistency enables scale: Standardized logic creates alignment across sites
AI reallocates human effort: Automation frees teams to focus on resolution