Data Screening
& Analysis

FAQ

What is Data Analysis in Production?

With the advent of automation, Big Data and predictive analytics, manufacturing companies are gaining valuable insights that can help them improve efficiency, reduce costs and improve quality. By analyzing their process and quality data, manufacturers can identify trends, spot anomalies and better understand how their processes work. This approach can help manufacturers make smarter decisions, develop better products and ultimately increase their sales and profits.
Data Analysis in Production refers to the systematic examination of data collected from a variety of sources within a manufacturing company, from receiving, to engineering and manufacturing, to quality assurance, as well as shipping, service and customer feedback. It is designed to help companies make informed decisions to improve their operations and achieve their business goals.

How is Data Analysis in Production being utilized? 

In the manufacturing industry, data can come from a variety of sources, including machines, sensors, production systems, and enterprise resource planning (ERP) systems. The data collected can be used to measure key performance indicators (KPIs) such as production efficiency, product quality and equipment effectiveness.
Data Analysis in Production can take many forms, including descriptive statistics, predictive modeling, machine learning, and statistical analysis. By using these techniques, manufacturers can gain insight into their operations, identify areas for improvement and drive continuous improvement.
To ensure that results are accurate and meaningful, it is important to focus on data quality. Poor data quality can negatively impact data analysis results and lead to incorrect conclusions and decisions that can actually worsen the baseline.
To achieve high data quality, organizations must implement robust data management practices, including data validation, cleansing and normalization. Data validation ensures that data meets required standards for accuracy and consistency, while data cleansing removes all errors, duplicates and irrelevant data. Data normalization ensures consistent and standardized data, which is essential for accurate and meaningful analysis.

What are typical use cases of Data Analysis in Production?

• Supply Chain Optimization: Data Analysis can uncover inefficiencies in the supply chain and suggest improvements to reduce costs and increase on-time delivery.
• Predictive Quality: Data Analysis can be used to identify correlations between production data and quality data to derive predictive and optimization models.
• Predictive Maintenance: Data Analysis can predict when and where maintenance is likely to be required, enabling proactive rather than reactive maintenance planning.
• Optical Inspection: Data Analysis for image data can be used to create machine vision models, which can be used to optically inspect components in real-time and in-process.

What are the benefits of Data Analysis in Production?

• Improved Efficiency: Data Analysis can help production teams identify areas of inadequacy and gain insight into how to improve efficiency through process or system changes.
• Increased Productivity: Data Analysis can be used to identify production areas that can be optimized to increase productivity. Using this method, production teams can identify areas that need improvement and develop strategies to achieve higher levels of productivity.
• Lower costs: Data Analysis can help identify areas of overspending or underperformance that can then be addressed to reduce costs.
• Improved Quality: Data Analysis can uncover potential quality issues and provide insight into how to improve quality and scrap rates.