46%
Reduction in test time
83%
Increase in test throughput
$6M
Estimated savings over 3 years
Stop Trading Off Product Quality for throughput. Do both.
Speed up your understanding of process and quality issues on the shop floor, and improve scrap, rework, and FTT.
Trusted by Leading Manufacturers Worldwide
The next generation
of manufacturing analytics
Identify and resolve defects early to prevent costly delays and protect customer confidence.
Old Way
Production line defects become more damaging the longer they go undiscovered. Root causes get more complicated to identify, costs escalate dramatically, and customer confidence takes a big hit.
New Way
Acerta LinePulse is a purpose-built tool for discrete manufacturing processes. It uses AI and machine learning to catch and solve issues sooner, reducing the amplified effect catching defects downstream can have on your bottom line.
How Acerta Works
See measurable improvements
from day one.
Deploy in 30 days. Save 80%.
Our versatile ingestion API and discrete manufacturing focused data format streamline deployment compared to other solutions. Our automotive deployment team can have the platform ready to use in 30 days.
Enable predictive monitoring,
in real-time.
With just a few clicks, create personalized dashboards with the data you care about, monitor key process parameters, and get alerted in real-time before issues occur on your lines. Save hours per day evaluating data and running capability reports.
Investigate complex
problems quickly.
Solve complex problems (and simple ones too) with AI-driven root cause analysis. Deploy machine learning algorithms that detect escalating failure patterns and pinpoint contributing factors instantly.
Make intervention
decisions confidently.
No limits. No compromises. Unlimited data ingestion meets advanced machine learning to deliver confident decisions backed by your complete production data. Every signal, every insight works together to power manufacturing excellence without constraints.
Teams
Production intelligence that helps every team reach their goals.
Process Teams
Chart, graph, and analyze anomalies within minutes. Meet production volume and capability goals.
Quality Teams
Quickly diagnose root causes and implement rapid fixes. Minimize quality incidents, and renew customer confidence.
Management
Reduce employee frustration and turnover by giving them the right tools to manage quality and throughput goals.
Case Studies
Proven Impact
Reducing Hydrogen Fuel Cell Test Times by 46%
46%
Reduction in test time
83%
Increase in test throughput
$6M
Estimated savings over 3 years
On-road engine failure predictions 100km in advance
90%
Injector model accuracy
100km
Warning distance before failure
90%
Sensor model accuracy
Resources
Helpful Resources
How to Monitor Manufacturing KPIs Automatically with Machine Learning
In the high-stakes world of manufacturing, every second counts. When quality issues arise, delays in identifying and addressing the root cause can lead to wasted resources, missed deadlines, and dissatisfied customers. Enter Quality Monitors, a powerful feature in LinePulse that leverages machine learning to monitor KPIs like scrap rate, yield, and throughput in real time. In this blog, we’ll explore how Quality Monitors work, demonstrate their value through a real-world example, and explain how manufacturing engineers and managers can use them to achieve tangible results. What are Quality Monitors? Quality Monitors are a predictive tool designed to track and analyze key performance indicators (KPIs) on the production line. Using historical data and machine learning algorithms, they provide real-time alerts when performance trends in the wrong direction. Unlike traditional monitoring tools, Quality Monitors pinpoint the specific signals that are statistically correlated, empowering teams to find the root cause quickly and give them a head start on implementing a corrective action. Here’s a lightning-fast overview of the feature in the LinePulse platform: How do Quality Monitors work? The process to set up a Quality Monitor can be broken into three key steps: Configuration: Users select a KPI to monitor (e.g., failure rate) […]
Real-Time SPC: LinePulse vs. Q-DAS, Minitab, and Others
Limitations of legacy real-time SPC Compared to the tools of today, Q-DAS, Minitab, QC-CALC, Win SPC and others can be challenging to use in modern, complex manufacturing environments due to: Manual data handling: They may require manual data inputs or have disconnected spreadsheets, making real-time monitoring less efficient Reactive focus: They’re built primarily for identifying issues after they occur, rather than enabling proactive process improvements Scalability challenges: They are only designed to monitor isolated processes or machines, not across multiple lines, processes, or even plants Required expertise: They are often not user-friendly, and instead designed for use by someone with a background in statistics Not designed for the shop floor: They may lack robust dashboarding, monitoring, and alerting features that are useful for the manufacturing environment High price tag: Legacy real time SPC tools are typically priced on a per-seat basis, making them expensive compared to per-plant models Lack of features: They are limited to real-time SPC features only – meaning other types of analysis or reporting must be done using different software These limitations exist because these real-time SPC tools, although cutting-edge at the time, were created long ago on outdated technological foundations. Imagine an older software system is […]
How Acerta Works