Last Updated on May 2, 2022 by rabiamuzaffar
In this guide, we will discuss the various challenges companies face in implementing and integrating the latest big data technologies. The focus is on leveraging analytics proficiencies and the big data technology stack for increased company value. The comprehensive guide to What is Manufacturing Analytics? | Knime.com is available for download. Are you interested in learning more about this topic? Download the free PDF version to start your journey towards achieving measurable business outcomes. Alternatively, you can order the complete guide today.
IoT in manufacturing
The IoT or the Internet of Things can improve business processes as well as manufacturing. Its widespread application can provide equal transformational opportunities to small and midsize enterprises (SMEs). The IoT can help SMEs achieve digital transformation thanks to cloud computing and universal software. IoT solutions help in predictive maintenance. With the help of artificial intelligence algorithms, they can predict when machinery will break down and need replacement. The connected sensors also send data to the Cloud, which can help detect anomalies and predict useful life. By using IoT, manufacturing facilities can improve their maintenance operations and manage their equipment more efficiently. With its diverse applications, it’s easy to see why companies embrace IoT in manufacturing.
Querying petabytes of data
The rapid growth of mobile navigation services and smartphones has opened up new opportunities for improving urban mobility and reducing friction in global transportation. With the growing data generated by these devices, companies can also develop better models to improve the performance of their operations. One such solution is querying certain service provider. With this service, data engineers can perform queries on petabyte-scale datasets without needing a distributed SQL engine.
A recent study published in Science and Technology in December 2015 showed that Facebook’s data warehouse could process up to one petabyte of manufacturing data in just three months. The company began building Presto in the fall of 2012 and, by early 2013, had it operational. Currently, there are more than 1,000 Facebook employees who use the service daily. I
Using a data orchestration platform
Manufacturing companies should use a data orchestration platform to unify their disparate data sources. It is crucial for generating business value, as data can inform decisions in real-time. Without a data orchestration platform, companies are left to sift through multiple data silos and may not be able to extract the most valuable information. However, the technology behind data orchestration can make the data available and accessible.
Managing and analyzing vast volumes of data is time-consuming. Data technology evolves every three to eight years. Therefore, a 21-year-old company might have had 7 data management systems in its history. That means that data from these systems is scattered across many plans. Companies that want to leverage their data can use data orchestration to remove bottlenecks, improve data quality, and comply with data privacy laws.
Importance of a digital twin in manufacturing
A digital twin is a computer simulation of a real-world asset, such as a building or a vehicle. It simplifies the complex manufacturing process by creating a digital creation footprint. It can be interconnected and generate real-time data, which can predict problems and optimize manufacturing processes. Using a digital twin also allows businesses to test prototypes and simulate the effects of changes on real-world assets.
A digital twin can also predict future outcomes. It is essential since it enables manufacturers to track their products’ health in real-time and identify problems before they happen. Digital twins are an integral part of manufacturing data analytics, and they can improve operations by improving the designs of products. Manufacturers can ensure the design of their products is for maximum efficiency, improve processes, and minimize downtime by using these technologies.
Tools available for manufacturing data analytics
Several different tools are available for manufacturing data analytics, including DA solutions, which can help a manufacturer identify and measure performance and quality metrics. The available tools in the market can track metrics overwork cells, machines, and operators. Some of these solutions also offer tools for reporting quality and OEE. These solutions also allow manufacturers to collect data from various sources, including gauges and sensors. For example, they can gather data from machine operators and external sensors.
A primary metals manufacturing company recently implemented an advanced manufacturing data analytics solution. As an outcome, the company increased production rates by 50% using real-time performance visualization. Other tools, including predictive maintenance and automated material tracking, are helping engineers analyze the performance and failure characteristics of significant equipment. These tools help manufacturers improve their manufacturing processes and increase productivity. The tools are available for both on-premise and cloud-based environments. To learn more about these analytics solutions, contact us today.