How Big Data Can Help Improve Manufacturing Quality

We've all heard countless examples of how big data has helped companies enhance their marketing effectiveness and comprehensively evaluate their customers. In addition to these, there are many other industries that have been impacted by big data, especially manufacturing. In recent decades, the manufacturing industry has been collecting and aggregating large amounts of information. As machine-to-machine and human-to-machine data becomes more prevalent, the amount of data collected is continuing to grow significantly. The advent of the Internet of Things makes manufacturing an ideal candidate for big data solutions.

History of Manufacturing Data

Data analytics is nothing new to the manufacturing industry. Over the past two decades, manufacturers have relied on software such as enterprise resource planning (ERP) and product life cycle management (PLM) to improve productivity and ensure product quality. Much of the data that is captured and stored is not used because it is slow to be used, and because it is stored independently of the organization so that users cannot access the information.

For many organizations, data analytics is a means of solving problems after they occur, not a proactive means of preventing them before they occur.

How Big Data Solutions Work

With an enterprise Hadoop solution, manufacturers are not only able to manage massive amounts of data from sensors and automated programs, but they are also better able to analyze and share that data. As a result, problems can be solved quickly, while manufacturers gain valuable proactive insights.

In the area of service management, manufacturers are able to install and monitor sensors, track how products are used, and visualize the service needs required for their products. Using this data can effectively impact other areas of the business. For example, providing more targeted solutions to customers based on how they use the product.

On the operations and maintenance side, Hadoop can also help optimize after-sales repair service processes. Sensors are responsible for collecting data about the operational status of equipment, allowing manufacturers to perform maintenance when needed and identify problems early. This not only reduces costs, but also improves service quality.

Industry Example

Previously, Duke Energy's approach to monitoring its manufacturing plants was to send monitoring specialists to each plant and have them collect data on portable devices. In this case, 80 percent of the specialists' time was spent on data collection, and only 20 percent of their time was spent analyzing the data.

After deploying the big data solution, Duke Energy's experts were able to remotely monitor anomalous data from all of their equipment and quickly resolve issues as they arose.

Conclusion

Big data solutions present a huge opportunity for manufacturers. They can use big data solutions to reduce costs, increase productivity, and ultimately improve product quality by quickly resolving issues and improving products based on how users actually use them. As the IoT continues to grow, so will the benefits manufacturers derive from this data.

The above is what I have shared with you about how big data can help improve manufacturing quality, for more information you can follow the Global Green Ivy to share more dry goods