by editor Comment Closed

6 key losses -The start up Defects

Understanding Start-Up Rejects in OEE: 

A Hidden Quality Drain

Within the framework of Overall Equipment Effectiveness (OEE), Quality stands as one of the three core pillars, alongside Availability and Performance. While many manufacturers focus on production-time defects, a less-discussed yet equally critical source of quality loss is the Start-up Reject—a type of defect that occurs immediately after a machine or line begins operation.
 

What Are Start-Up Rejects?

Start-up rejects are defective units produced during the initial phase of machine operation—either at the beginning of a shift, after a changeover, or following maintenance or equipment downtime. These are not incidental flaws; they are often symptomatic of deeper inefficiencies in the warm-up or ramp-up phase of production.
These defects usually happen because machines, tools, or processes haven’t reached optimal operational conditions. Factors like temperature stabilization, incorrect calibration, residual materials, or operator oversight during start-up can all contribute to this issue.
oee 6 losses the start up defects

Why Start-Up Rejects Matter

Although they might seem insignificant in isolation, start-up rejects can accumulate to represent a considerable portion of total quality loss over time. More importantly, they provide a predictable opportunity for improvement, unlike random in-process defects.
Start-up rejects are often categorized under the “Six Big Losses” in the OEE framework as part of Quality Losses, alongside production rejects. However, their recurring and predictable nature makes them especially actionable.
If your Quality metric—calculated as:
Quality (%) = (Good Units / Total Units Produced) × 100%
—shows fluctuations particularly at the start of production runs, it’s likely that start-up rejects are skewing your yield downward. And when quality dips, the entire OEE score follows suit, undermining process efficiency and customer satisfaction.

Why Start-Up Rejects Matter

Although they might seem insignificant in isolation, start-up rejects can accumulate to represent a considerable portion of total quality loss over time. More importantly, they provide a predictable opportunity for improvement, unlike random in-process defects.
 
Start-up rejects are often categorized under the “Six Big Losses” in the OEE framework as part of Quality Losses, alongside production rejects. However, their recurring and predictable nature makes them especially actionable.
If your Quality metric—calculated as:
Quality (%) = (Good Units / Total Units Produced) × 100%
—shows fluctuations particularly at the start of production runs, it’s likely that start-up rejects are skewing your yield downward. And when quality dips, the entire OEE score follows suit, undermining process efficiency and customer satisfaction.
 

Detecting and Tracking Start-Up Rejects

 Identifying start-up rejects typically requires clear data segregation between start-up phase and steady-state production. This can be done in multiple ways:
  • Time-based tagging: Mark the first X minutes or Y units after start-up as “start-up phase.”
  • Sensor-triggered events: Use PLC signals or IoT device triggers to identify exact machine start points.
  • Manual entry: Operators log the first-good part or timestamp, though this is more error-prone.
With smart IoT integration, using tablet-based inputs, HMI touch panels, or edge devices, start-up phase data can be cleanly segmented. From there, systems like AdisRa SmartView on a centralized on-prem server can collect, visualize, and store this data for trend analysis.
 

Analyzing Root Causes of Start-Up Rejects

Once start-up rejects are isolated, tools such as the Pareto Chart, Fishbone Diagram, and 5 Whys become useful in identifying and eliminating root causes. Typical contributors include:
  • Incorrect parameter presets
  • Residual material contamination
  • Mechanical drift or wear
  • Improper tool alignment
  • Inadequate pre-startup checks
In many cases, standardizing a Start-up Checklist or integrating automated warm-up procedures into machine logic can drastically reduce these losses.
 

Enhancing Start-Up Quality with AI and Analytics

 The application of predictive analytics and AI-based models can significantly improve start-up quality by identifying patterns in machine behavior before and during ramp-up. By feeding historical start-up data into machine learning models (like Random Forest, Support Vector Machines, or Time-Series Models), manufacturers can:
  • Predict when a machine is ready for optimal output
  • Trigger alerts if parameter drift is detected
  • Adjust warm-up profiles dynamically
  • Correlate reject types with specific operational anomalies
These systems rely on robust data pools, ideally collected and structured over time through edge computing devices, IIoT gateways (such as Axiomtek), and smart HMI panels.
Local SCADA dashboard on AIOT OEE INSIGHT connect

AIoT OEE Insight Connect is a groundbreaking, next-generation solution designed to deliver deep, real-time visibility into manufacturing performance. More than just an OEE (Overall Equipment Effectiveness) tracker, it offers a comprehensive and intelligent approach to uncovering the true drivers behind production efficiency — empowering smarter, faster decisions.

At its core, AIoT OEE Insight Connect excels in high-fidelity data acquisition, capturing critical information from:

  • Sensors and machines,

  • Controllers and PLCs,

  • Operator and human inputs.

This multi-layered data integration ensures a true 360° view of what impacts OEE, drilling down to the root causes that traditional systems often miss.

Hybrid-Cloud Flexibility: Modern, Secure, and Scalable

Harnessing the latest advances in cloud technology, AIoT OEE Insight Connect seamlessly streams all operational data to a secure, intuitive cloud dashboard.
At the same time, it gives manufacturers the freedom and flexibility to deploy the system fully within their on-premises infrastructure if preferred — maintaining control, security, and customization according to operational needs.

Actionable Intelligence with Generative AI

Where this solution truly stands apart is in its analytical intelligence:

  • Advanced reporting and analytics uncover patterns, bottlenecks, and optimization opportunities.

  • Generative AI integration goes a step further, automatically generating actionable recommendations and task lists designed to continuously enhance process performance.

By bridging real-time shop floor insights with AI-driven action plans, AIoT OEE Insight Connect ensures that continuous improvement becomes an embedded, automated process — not just an aspiration.

Key Benefits at a Glance:

  • Comprehensive OEE measurement and deep root-cause insights

  • Real-time, flexible access via cloud or on-premise deployment

  • AI-powered action plans for continuous process optimization

  • Seamless integration with existing industrial systems and infrastructures

AIoT OEE Insight Connect is not just a tool — it’s your partner in building a more efficient, agile, and intelligent manufacturing future.