How v4c.ai Helped a Manufacturer Streamline Custom Routings with AI and Automation

‍Client Overview

The client is a global manufacturer known for producing highly customized, engineered components. Each item on the shop floor requires a unique routing sequence, making standardization difficult and routing decisions heavily reliant on expert input. This manual dependency led to inconsistent routing logic, long lead times, and a growing backlog of pending items.

To address these inefficiencies, the client engaged v4c.ai to streamline routing generation through automation and data-driven intelligence. The goal was to build a scalable solution that could recommend accurate routing drafts based on historical data, reduce reliance on manual inputs, and provide planners with a centralized tool for validation and decision-making.

Challenge

The client’s routing process was highly bespoke and lacked standardization. Each routing sequence had to be created from scratch, often relying on the domain expertise of a few key individuals. This manual dependency resulted in several critical issues:

• High Variability in Routing Times: Routing generation could take anywhere from a couple of hours to up to 120 hours, depending on the complexity and availability of experts.
• Backlogs and Bottlenecks: The manual effort required led to a growing queue of pending routings, slowing down overall production planning.
• Lack of Visibility and Consistency: With limited tooling in place, routing decisions varied widely, and teams lacked a centralized view to assess or validate past routing logic.

The client needed a scalable solution to accelerate routing generation without compromising on accuracy or flexibility, one that could learn from past decisions and provide a guided starting point for new routing tasks.

Solution

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To address the client’s challenges, v4c.ai delivered a scalable, AI-assisted system that automated key parts of the routing generation process while keeping expert oversight intact. The solution included three major components:

Master Data Pipeline:

v4c.ai built a robust data pipeline that consolidated multiple raw data sources into a single master table. This included:

• Applying business rules jointly defined with the client team
• Running data quality checks to resolve inconsistencies
• Standardizing terminology across varied inputs through a preprocessing framework
• Documenting the data flow for transparency and future maintenance

Similarity Matching Model:

A sophisticated matching engine was developed to recommend routing drafts by identifying historically similar items. Key elements included:

• Custom preprocessing techniques to handle domain-specific vocabulary
• Edit-distance-based matching algorithms to compute similarity scores
• A multi-level filtering system using planner codes, designators, and timestamps to narrow down the most relevant historical matches
• A standardized replacement dictionary to unify variant spellings and abbreviations

Interactive Dashboard:

To make the system user-friendly and review-ready, v4c.ai deployed a web-based dashboard that allowed planners to:

• Search items and instantly view top match recommendations
• Visualize text differences with color-coded highlights for quick evaluation
• Inspect full operation sequences and resource usage data before finalizing routes
• Validate and confirm or adjust suggested routings, creating a feedback loop for continuous improvement

By combining automated intelligence with human validation, the solution reduced manual effort while maintaining control and transparency.

Impact‍

The solution delivered by v4c.ai significantly improved the efficiency, consistency, and transparency of the client's routing process:

• Reduced Routing Time and Backlogs: Automated draft generation enabled planners to process routing requests faster, clearing backlogs and reducing delays in production planning.
• Improved Accuracy in Capacity Planning: With more consistent and reliable routings feeding into the RCCP (Rough-Cut Capacity Planning) module, the client gained better insights into resource demand and production capacity.
• Centralized and Scalable System: The interactive dashboard provided a single interface for routing decisions, reducing reliance on individual experts and creating a repeatable, scalable process.
• Increased Planner Productivity: By automating routine and repetitive matching tasks, planners were able to focus their time on reviewing edge cases and high-impact decisions.
• Better Business Decisions through Data Transparency: The clear visualization of match quality, operation sequences, and historical data empowered teams to make informed routing decisions with confidence.

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Conclusion

This engagement demonstrates how strategic platform modernization, anchored on Databricks Lakehouse and infrastructure as code, can unlock real-time insights and governance at enterprise scale. By replacing outdated workflows with automated pipelines, unified access controls, and modern analytics tooling, the client has built a foundation for long-term agility and data-driven growth.

v4c.ai served as an embedded partner throughout the engagement, designing architecture, operationalizing tools, and enabling teams to confidently own and scale their data platform.

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