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Manufacturing

Unplanned downtime costs $50K per hour. Fix maintenance, quality, and scheduling before the line stops again.

We build predictive maintenance systems, computer vision inspection tools, production scheduling optimizers, and supply chain visibility platforms for manufacturers. Our software catches failures before they happen and defects before they ship.

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50%

Downtime reduction

80%

Faster defect detection

Overview

Unplanned downtime is the most expensive problem you're not predicting

Manufacturing software development at 1Raft targets the four cost centers that eat margin fastest: unplanned downtime, quality escapes, production scheduling gaps, and supply chain blind spots. We bring patterns from 100+ products across adjacent industries to build predictive maintenance models, visual inspection systems, scheduling optimizers, and supplier risk dashboards - each engineered to reduce waste and protect throughput.

Manufacturers lose an average of $50K per hour of unplanned downtime, and most plants still run maintenance on fixed calendars instead of actual equipment condition. The result: either premature part replacement that wastes money or unexpected failures that stop the line.

Quality inspection is the other margin killer. Manual visual inspection catches 70-80% of defects at best, and the ones that slip through create warranty claims, rework, and customer trust damage that compounds over time.

We build software that turns sensor data, production logs, and visual feeds into real-time decisions. Maintenance models that predict failures days before they happen. Vision systems that catch defects humans miss. Scheduling engines that optimize across constraints your ERP can't handle. Every product integrates with your existing MES, ERP, and SCADA systems.

Experience Signal

1Raft builds predictive maintenance systems, computer vision inspection tools, production schedulers, and supply chain visibility platforms for manufacturers. Our engineering draws on IoT and anomaly detection patterns validated across 100+ products - including sensor-driven monitoring first built for proptech and logistics.

50%

Downtime reduction

80%

Faster defect detection

Industry Pain Points

What's broken in manufacturing

01

Unplanned downtime costs manufacturing plants $50K-$250K per hour, with the average facility experiencing 800+ hours of downtime annually

02

Manual visual inspection catches only 70-80% of defects, letting 2-5% of defective products reach customers as warranty claims

03

Production scheduling relies on static ERP rules that can't adapt to real-time machine availability, material delays, or rush orders

04

Supply chain visibility ends at the first tier - 68% of disruptions originate from sub-tier suppliers that manufacturers can't monitor

05

Maintenance teams replace parts on fixed schedules, wasting 30-40% of remaining useful life on components that don't need service yet

Solutions

Problems we solve in manufacturing

Each solution is built from patterns we've validated across 100+ products. No experiments on your budget.

01

Predictive Maintenance

ML models trained on vibration, temperature, pressure, and current data from equipment sensors. Predicts failures 5-14 days in advance with component-level specificity. Generates work orders ranked by criticality and production impact so maintenance teams fix the right things first.

02

Computer Vision Quality Inspection

Camera-based inspection systems that detect surface defects, dimensional deviations, assembly errors, and color inconsistencies at line speed. Catches defects manual inspection misses while reducing inspection labor and eliminating sampling-based quality control.

03

Production Scheduling Optimization

Constraint-based scheduling engine that optimizes across machine availability, changeover times, material readiness, labor capacity, and order priority. Re-optimizes in real time as conditions change - rush orders, machine breakdowns, material delays.

04

Supply Chain Visibility and Risk

Multi-tier supplier monitoring that tracks lead times, quality trends, financial health signals, and geopolitical risk factors. Surfaces supply disruption risk weeks before it hits your production floor.

05

Overall Equipment Effectiveness (OEE) Analytics

Real-time OEE dashboards that decompose availability, performance, and quality losses by shift, line, and product. Identifies the specific bottlenecks dragging down throughput - not just the top-level number, but the root causes behind it.

Use Cases

Real-world use cases

Predictive Maintenance for an Automotive Parts Manufacturer

Problem

A mid-size automotive parts plant with 120 CNC machines experienced 14 unplanned stoppages per month, averaging 3.5 hours each. Maintenance ran on a fixed 90-day cycle regardless of machine condition.

What we built

We deployed vibration and temperature sensors on critical machines and built ML models trained on 2 years of failure and maintenance history. The system predicted failures 7-12 days ahead with component-level recommendations. Maintenance shifted from calendar-based to condition-based scheduling.

Result

Unplanned stoppages dropped from 14 to 3 per month. Annual downtime hours fell 72%. Parts replacement costs decreased 28% by servicing components at optimal intervals instead of fixed schedules.

Vision Inspection for a Consumer Electronics Assembly Line

Problem

A consumer electronics manufacturer relied on 8 manual inspectors per shift to check solder joints, connector placement, and housing alignment. Defect escape rate was 3.2%, generating $1.4M in annual warranty claims.

What we built

We installed camera arrays at 4 inspection stations and trained defect detection models on 50K labeled images. The system classified each unit as pass, fail, or needs-review at 0.8 seconds per unit - faster than line speed.

Result

Defect escape rate fell from 3.2% to 0.4%. Warranty claims dropped 78%. Inspection headcount was redeployed to higher-value quality engineering roles.

Supply Chain Risk Dashboard for a Food Manufacturer

Problem

A packaged food manufacturer sourced 340 ingredients from 180 suppliers across 12 countries. Two supply disruptions in the previous year caused $3.8M in production losses and missed retail commitments.

What we built

We built a supplier risk scoring system that monitored lead time trends, quality variance, financial indicators, and logistics disruption signals. The dashboard scored every supplier weekly and flagged emerging risks before they impacted production schedules.

Result

Supply disruption response time improved from 3 weeks to 3 days. The team identified and qualified alternate suppliers for 22 high-risk ingredients before the next disruption hit. Production losses from supply issues dropped 61%.

Our Approach

How we approach manufacturing projects

1
Phase 1· Weeks 1-2

Plant Assessment and Data Audit

We assess your production environment, sensor infrastructure, data systems (MES, ERP, SCADA, historian), and maintenance records. We quantify the cost of downtime, quality escapes, and scheduling inefficiency with your actual numbers.

Deliverables

  • Downtime and quality cost analysis with root cause breakdown
  • Sensor and data infrastructure readiness assessment
  • Prioritized opportunity list ranked by cost impact and feasibility
2
Phase 2· Weeks 3-4

Architecture and Sensor Planning

We design the product architecture, specify sensor requirements for any gaps, and map integration points - MES, ERP, SCADA, historian, and edge compute infrastructure. Data pipelines are designed for real-time and batch processing.

Deliverables

  • Technical architecture with MES/ERP/SCADA integration plan
  • Sensor deployment specification for any coverage gaps
  • Phased delivery roadmap with pilot line selection
3
Phase 3· Weeks 5-10

Build and Line Pilot

We build in sprints and deploy to a pilot production line first. Real production data validates model accuracy and operational fit before plant-wide rollout.

Deliverables

  • Working system deployed on pilot line
  • Model accuracy metrics validated against actual outcomes
  • Operator feedback and UX iteration backlog
4
Phase 4· Weeks 11-14

Plant-Wide Rollout and Optimization

We roll out across production lines and facilities with line-specific tuning. Models retrain on accumulating data. Ongoing monitoring keeps accuracy improving as the system sees more failure modes.

Deliverables

  • Full deployment across targeted lines and facilities
  • Operational dashboards for maintenance, quality, and OEE
  • Model retraining schedule and continuous improvement plan

Outcomes

Measurable outcomes

35-50% reduction in unplanned downtime through predictive maintenance
60-80% improvement in defect detection rates via computer vision inspection
10-20% increase in production throughput from scheduling optimization
25-40% faster supply chain disruption response through multi-tier visibility

Pattern Transfer

1Raft first built IoT-driven monitoring and anomaly detection for proptech clients - tracking building system health from sensor data and predicting HVAC failures before tenants complained. That same pattern - real-time telemetry ingestion, condition-based scoring, automated alerting - is exactly what powers our manufacturing predictive maintenance. Cross-industry pattern transfer turns a 12-week build into something that outperforms 12-month enterprise rollouts.

Services

Services for manufacturing

Frequently asked questions

At minimum: vibration, temperature, and current draw from critical equipment. If your machines already have sensors feeding a historian or SCADA system, we can often start with existing data. We assess sensor coverage in Phase 1 and specify any additional instrumentation needed.

Next Step

Every month without predictive maintenance costs you 60+ hours of unplanned downtime.

One call with a founder. No sales team, no follow-up sequence. If we can't help, we'll say so.

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