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June 30, 2022

DATA DRIVEN QUALITY

THE IMPORTANCE OF KNOWING YOUR "WHY" IN QUALITY MANAGEMENT

Introduction

Quality management systems ensure that products are manufactured to the highest standards, standards expected by the market or customer. By collecting data from the manufacturing process, quality teams can identify potential issues and take action before products are made incorrectly. However, it's important to remember that data should be collected for a purpose. As a quality management team, it's crucial to always be aware of your "why." Why are you collecting data? Why are you running tests? Why are you performing inspections? By keeping your "why" at the forefront of your mind, you can ensure that you're only collecting the most critical data and running the most impactful tests. Value comes from collecting and analyzing the relevant data points needed for efficient decision-making.

The Three Key Elements of Data-Driven Quality Management Systems

There are three key elements of data-driven quality management systems: data collection, data analysis, and data-driven decision-making.

Data Collection 

Think about all the data points that go into manufacturing a product. Everything from the raw materials to the production line and the finished product must be measured. In the past, most of this data was collected manually. Operators would physically write down or record every single piece of data. But this was time-consuming and often led to errors. It also meant a delay between capturing the data and taking corrective action. In this modern digital age there are better ways to collect data, thanks to mobile devices, IIoT and the cloud. The industrial internet of things, or IIoT, refers to using connected devices to collect data from manufacturing processes. By combining IIoT with a quality management system like QIS, manufacturers can instantly collect and analyze large amounts of data.

Data Analysis 

In any manufacturing company, quality control is of the utmost importance. After all, no one wants to end up with a faulty product. To ensure that products are of the highest quality, manufacturers must analyze quality control data as quickly as possible in the process. By doing so, they can identify any problems that might be causing products to fail and take steps to fix them before they generate a batch of defective parts. In addition, analyzing quality control data can help manufacturers to improve their processes and prevent future issues from arising. As a result, taking the time to analyze quality control data is essential for any manufacturing company that wants to produce high-quality products and keep customers happy.

Data-Driven Decision-Making 

Data-driven decision-making allows manufacturing companies to make better use of resources and optimize processes. It also helps to ensure that products are of the highest quality and meet customer needs on a continuous basis. In addition, data-driven decision-making can help identify opportunities for cost savings and new revenue streams. Using the data capturing techniques available, companies can obtain all the data they need for effective decision-making. However, this can be a double-edged sword when too much or irrelevant data is collected. Quality control software like QIS can collate and display vast quantities of data in real-time. Knowing which materials work best, when process adjustments are optimal, having clear evidence of non-conformance and the most common causes of production inefficiencies - all contribute to reduction and even elimination of the preventable costs associated with process operations. Less waste, less rework, less downtime, happier customers and, subsequently, lower costs and higher margins. But it's crucial to know which data the quality team actually needs. Otherwise, they could spend time focusing on non-essential areas at the cost of dealing with critical ones. As such, it's vital to map out the data needed before implementing your system.

Conclusion

Data-driven quality management systems are essential for ensuring that products are manufactured to the highest standards. By collecting and analyzing data from the manufacturing process, quality teams can identify defects and make adjustments before products are made incorrectly. But, data should be collected for a purpose, and quality teams should focus on collecting the most critical data points necessary for efficient decision-making. The key to success is to identify and focus on the ‘right things’ that provide differentiated value to customers and deliver continuous improvement over the long term. Ultimately, by using relevant data, manufacturing companies can make more informed decisions that will help them succeed in the marketplace. The goal – to reduce quality losses and costs and improve production flexibility and product consistency – all things that lead to greater customer satisfaction and higher profits.

DATA DRIVEN QUALITY

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THE IMPORTANCE OF KNOWING YOUR “WHY” IN QUALITY MANAGEMENT

Introduction

Quality management systems ensure that products are manufactured to the highest standards, standards expected by the market or customer. By collecting data from the manufacturing process, quality teams can identify potential issues and take action before products are made incorrectly.

However, it’s important to remember that data should be collected for a purpose. As a quality management team, it’s crucial to always be aware of your “why.” Why are you collecting data? Why are you running tests? Why are you performing inspections?

By keeping your “why” at the forefront of your mind, you can ensure that you’re only collecting the most critical data and running the most impactful tests. Value comes from collecting and analyzing the relevant data points needed for efficient decision-making.

The Three Key Elements of Data-Driven Quality Management Systems

There are three key elements of data-driven quality management systems: data collection, data analysis, and data-driven decision-making.

Data Collection 

Think about all the data points that go into manufacturing a product. Everything from the raw materials to the production line and the finished product must be measured. In the past, most of this data was collected manually. Operators would physically write down or record every single piece of data. But this was time-consuming and often led to errors. It also meant a delay between capturing the data and taking corrective action.

In this modern digital age there are better ways to collect data, thanks to mobile devices, IIoT and the cloud. The industrial internet of things, or IIoT, refers to using connected devices to collect data from manufacturing processes. By combining IIoT with a quality management system like QIS, manufacturers can instantly collect and analyze large amounts of data.

Data Analysis 

In any manufacturing company, quality control is of the utmost importance. After all, no one wants to end up with a faulty product. To ensure that products are of the highest quality, manufacturers must analyze quality control data as quickly as possible in the process. By doing so, they can identify any problems that might be causing products to fail and take steps to fix them before they generate a batch of defective parts.

In addition, analyzing quality control data can help manufacturers to improve their processes and prevent future issues from arising. As a result, taking the time to analyze quality control data is essential for any manufacturing company that wants to produce high-quality products and keep customers happy.

Data-Driven Decision-Making 

Data-driven decision-making allows manufacturing companies to make better use of resources and optimize processes. It also helps to ensure that products are of the highest quality and meet customer needs on a continuous basis. In addition, data-driven decision-making can help identify opportunities for cost savings and new revenue streams.

Using the data capturing techniques available, companies can obtain all the data they need for effective decision-making. However, this can be a double-edged sword when too much or irrelevant data is collected.

Quality control software like QIS can collate and display vast quantities of data in real-time. Knowing which materials work best, when process adjustments are optimal, having clear evidence of non-conformance and the most common causes of production inefficiencies – all contribute to reduction and even elimination of the preventable costs associated with process operations. Less waste, less rework, less downtime, happier customers and, subsequently, lower costs and higher margins.

But it’s crucial to know which data the quality team actually needs. Otherwise, they could spend time focusing on non-essential areas at the cost of dealing with critical ones. As such, it’s vital to map out the data needed before implementing your system.

Conclusion

Data-driven quality management systems are essential for ensuring that products are manufactured to the highest standards. By collecting and analyzing data from the manufacturing process, quality teams can identify defects and make adjustments before products are made incorrectly.

But, data should be collected for a purpose, and quality teams should focus on collecting the most critical data points necessary for efficient decision-making. The key to success is to identify and focus on the ‘right things’ that provide differentiated value to customers and deliver continuous improvement over the long term.

Ultimately, by using relevant data, manufacturing companies can make more informed decisions that will help them succeed in the marketplace. The goal – to reduce quality losses and costs and improve production flexibility and product consistency – all things that lead to greater customer satisfaction and higher profits.