Book Excerpt: The Printed Circuit Assembler’s Guide to Smart Data

Estimated reading time: 3 minutes

Introduction
Whenever we discuss data, keep in mind that people have been collecting data, verifying it, and translating it into reports for a long time. And if data is collected and processes are changed automatically, people still will be interpreting and verifying the accuracy of the data, creating reports, making recommendations, solving problems, tweaking, improving, and innovating. Whatever data collection system is used, any effort to digitalize needs to engage and empower the production team at the factory. Their role is to attend to the manufacturing process but also to act as the front line of communications and control. When operations are not performing as expected, they need to be able to:

1. Act as the first line of issue containment so that they can minimize the effect of the problem on the final product or process through problem-solving and corrective actions in real-time.
2. Act as intelligence gatherers during escalation events when engineering is called to the work cell so that they can spend less of their time on data gathering and more on re-engineering for root-cause elimination to remove the possibility for any undesired condition to reappear.

To help your factory take the next steps in the journey to digitalization, in this book, we’re going to look at some of the major hurdles that your teams face in collecting manufacturing data that then will be useful—not only for improving processes but also for improving materials and supply chain management, tracing the sources of problems and defective or counterfeit parts, and providing trends analysis for business forecasting and reporting. In our previous book The Printed Circuit Assembler’s Guide to… Advanced Manufacturing in the Digital Age, we looked at what needs to be done to create a smart factory that is able to collect data—including how to remove barriers in an organization, protect the data, and create a system for managing it.

In Chapter 1, we will look at the challenges of collecting good data and where collecting it makes sense for the factory and improving business. In Chapter 2, we will examine what makes data smart, meaning the difference between data on its own and analytics. In Chapter 3, we will cover how data can be distributed from the underlying infrastructure for external use. We also detail some of the tools available today to help you put these principles into practice and look at a real-world example of how companies are reaping the benefits of putting their data to good use with analytics.

The requirements for product quality and reliability contribute to the growing need for meaningful analytics in manufacturing. With growing demands from quality-sensitive industries—such as aerospace, automotive, smartphones, and medical—manufacturers need to ensure their factory operations work properly. Analyzing data simply is not enough. Company managers need to use analytics to create knowledge that can positively affect manufacturing.

Today, with internet of things (IoT) technology entering the manufacturing world, factory managers can take their efficiency and waste-reduction efforts to the next phase using big-data analytics. Advanced big-data analytics can help electronics manufacturers cope with the sheer number and complexity of production activities that influence yield, providing a granular approach to diagnosing and correcting process flaws.

Advanced analytics refers to the application of statistics and other mathematical tools to business data to assess and improve practices. In manufacturing, operations managers can use advanced analytics to take a deep dive into historical process data, identify patterns and relationships among discrete process steps and inputs, and then optimize the factors that prove to have the greatest effect on yield.

With IoT applications gathering huge amounts of real-time, shop-floor data constantly, what the electronics manufacturing industry now needs are analytics solutions that can aggregate these isolated data sets and analyze them to reveal important insights. These insights can be leveraged to enable better decision-making and ultimately reduce cost and waste.

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