Viaduct for Manufacturers: Addressing the Quality Crisis

This document addresses four interconnected challenges — The Bottleneck, The Burden, The Blind Spot, and The Barrier — connected asset and discrete manufacturers face, and details Viaduct’s Generative AI and machine learning solutions to enable a new era of manufacturing and quality intelligence.

The Bottleneck: Data Sprawl and Manual Triage

The Challenge: Quality and manufacturing engineers spend hours triaging incidents due to data sprawl, having to pull and synthesize data from ten or more different applications. Large, high-volume datasets are time-consuming to query and consume.

The Solution: Viaduct Unifies Data and Accelerates Insight

• Quality Dashboard unifies quality signals into a single interface.
• Viaduct Quick View accelerates triage with AI-Powered Summaries (concise 3-4 sentence overviews) and a Unified Data Interface.
• GenAI for Related Events uses a proprietary TSI Engine and semantic correlation to automatically surface the most relevant data.
• LLM Memories allow organizations to train the underlying AI on specific terminology.
• The Query Engine handles hundreds of millions of daily "Signal Events," allowing simple queries and complex analyses to load in seconds.

Viaduct can accelerate overall incident review by 5x (80% reduction in time).

The Burden: When Quality Tools Hinder Value

The Challenge: Current quality systems (often FRACAS) devolve into time-consuming administrative systems for constant categorization and re-categorization of claims and defects. Automated attempts like "Auto-binning," "Defect Hierarchies," and "Fixed rules" fail to leverage rich unstructured data and can be ineffective or prone to bias. Sophisticated prioritization is also lacking, as issues are often only ranked by simple methods like total affected assets.

The Solution: Viaduct Prioritizes & Analyzes Top Quality Problems

• Smart Groups use Generative AI and semantic similarity to provide automated, unbiased groupings of quality incidents, leveraging rich unstructured data often ignored by legacy systems. This can automate 8-10 hour manual processes into quick 5-minute reviews.
• Smart Groups are tunable for granularity and leverage Memories to improve over time. They can be applied to "upstream," unstructured, and noisy signals to adopt a proactive posture.
• The Investigator provides powerful, easy-to-use analytics, allowing engineers to monitor trends and “pareto” issues. It features AI-computed Mentioned Phrases that automatically extract concepts like Root Causes, Locations, and Symptoms from unstructured data.

The Blind Spot: Reactive Detection & Missed Signals

The Challenge: Quality teams struggle to move from reactive problem-solving to proactive issue prevention. Typical approaches like simple pareto-based monitoring or statistical "predictive maintenance" models often have unacceptably high false-positive rates and fail to leverage underutilized software and sensor data across thousands of asset configurations.

The Solution: True AI-Driven Early Issue Detection

• Viaduct detects emerging quality problems early using the same feature that powers Intelligent Issue Monitoring: Smart Groups. Customers apply Smart Groups to any quality data source, including telematics. 
• A second, advanced capability called Suggested Issues detects potential quality problems with a 5x higher "hit rate" than typical unsupervised approaches. These issues are isolated to specific quality signals and precise asset sub-populations.
• These capabilities use proprietary unsupervised machine learning via the TSI Engine to surpass traditional methods.

With Viaduct, customers can detect manufacturing defect problems 2–3 months earlier and in-field asset issues 4–5 months earlier.

The Barrier: Predictive Maintenance, Predictably Hard

The Challenge: Industrializing predictive maintenance is difficult. Traditional modeling approaches rarely scale, and determining a clear course of action requires slow, manual analysis. Teams struggle to define robust failure modes and incorporate the operational and financial considerations needed to move from prediction to confident field action.

The Solution: Fast, Actionable Failure Mode Modeling

• Custom Signal Events allow customers to transform raw sensor and telematics data into meaningful indicators of asset behavior.
• The flexible no-SQL Failure-Mode Definition framework integrates diverse data sources (service notes, claims, telemetry) into clear representations of a failure and how it emerges over time
• Viaduct’s custom telematics-based machine learning models are trained on these precise events, yielding highly tailored predictions of asset risk to help prioritize proactive repair.
• The Business Case Evaluator (in development) enables manufacturers to design and test alternative field-action scenarios to evaluate customer impact and ROI

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