The Challenge

In high-throughput manufacturing, undetected quality issues frequently lead to costly waste and downtime, significantly impacting profitability. For many years, a leading manufacturer had collected extensive operational data but had not leveraged this wealth of information effectively for predictive analytics. Consequently, production teams were often unaware of emerging quality issues until post-production lab tests revealed them—typically too late to avoid significant waste.

Recognizing the untapped potential in their historical data, the manufacturer partnered with mAiksense, experts in applying Operational Data Science (ODS), to predict quality failures ahead of time and dramatically reduce quality-related waste.

Implementing Predictive Analytics: Step-by-Step

1. Data Collection

Data collection was extensive and diverse, pulling information from multiple systems and sources:

• SCADA System: Provided detailed machine operational parameters, including extruder settings, feeder speeds, and temperature controls.

• MES System: Supplied material consumption and batch production data, enabling insights into operational efficiency.

• Excel Spreadsheets: Captured laboratory test results, critical for understanding historical quality issues.

Data used for the model training covered a comprehensive period of 5 months, while an additional 2 months of data was reserved strictly for testing and validating model predictions.

2. Data Cleaning and Preparation

Given the multiple data sources, significant efforts were needed to align, synchronize, and clean the data. Critical challenges included handling missing information, notably the absence of downtime reasons and setup times. These gaps were documented, and it was noted that incorporating such information could potentially improve future results even further.

3. Feature Engineering

Effective feature engineering transformed raw operational data into meaningful predictive signals. This involved carefully selecting, extracting, and combining data from different parameters:

• Analyzing variability and stability of feed screw speeds.

• Tracking consistency and deviations in temperature profiles.

• Monitoring feeder performance metrics closely tied to product quality.

This step was crucial to achieving accurate predictions and ensuring operational relevance.

4. Model Development & Training

The predictive model was built using AWS Lookout for Equipment, a sophisticated yet user-friendly platform enabling rapid deployment and iterative refinement of machine learning models. Training was conducted using historical data, with the model learning complex patterns indicative of emerging quality issues.

5. Actionable Insights

The model delivered impressive predictive capabilities, accurately forecasting 60% of genuine quality issues in average 2.5 hours before lab identification. This critical lead-time provided production teams ample opportunity to respond proactively, addressing issues before full batches became unsalvageable.

An additional insight surfaced during validation: several anomalies initially flagged by the model as potential issues—initially dismissed as false positives—were later confirmed as genuine, undocumented quality problems. This demonstrated an extra layer of value provided by the advanced predictive analytics system.

Impact and Results

• Dramatic reduction in quality-based waste.

• Enhanced operational efficiency and stronger confidence in predictive insights.

• A remarkable annual financial return exceeding $500,000 was calculated, highlighting the compelling value of predictive analytics.

A Transformational Success Story

By leveraging years of unused data through advanced predictive analytics, this manufacturer significantly enhanced operational efficiency and reduced quality waste. Partnering with mAiksense and embracing predictive capabilities has transformed their operational approach—from reactive to proactive—resulting in sustained profitability and a culture increasingly driven by informed decision-making.