Artificial intelligence continues to seep onto manufacturing floors in the form of robots, sensors, and machinery that make production systems faster and more efficient. Many of these technological advances in smart factories result in continuous data collection of the production systems, and big data and artificial intelligence further advance the smart manufacturing process by integrating the system and helping businesses better leverage the data collected. Big data analytics are utilized to process the data captured and perform advanced capabilities such as analysis of non-conformance reports, enhanced security, predictive and preventative maintenance, plant load optimization, improved supply-chain management, financial risk analysis, and operational monitoring to improve overall equipment effectiveness. Big Data also helps to integrate the previously siloed systems to give companies a clearer picture of their manufacturing processes while automating data collection and analysis throughout. The difficulties associated with data in manufacturing systems have shifted from that of being able to collect a sufficient quantity of data, to how best utilize the now vast amounts of data available in order to make better informed business decisions.
The Internet of Things integrates systems and has many analytic applications. One application is data analysis in manufacturing, which can be utilized to improve efficiency by watching for non-conforming events in production systems. Non-Conformance report analytics are used to reduce errors and identify faulty products on the production line, before they can be released to business partners or customers. Studying these events leads to less non-conformance instances in future production.
Another strong case for big data analytics in smart manufacturing is their application in predictive maintenance processes. Systems put in place can send out repair alerts and preventative maintenance reminders to stop equipment breakdowns before they occur. Integration of sensors for condition-based monitoring can also be integrated to monitor equipment performance and health in real time, improving overall equipment effectiveness on the factory floor. By minimizing failures and enabling better preventative maintenance practices, manufacturers see less failures, better equipment productivity and more reliability – all of which improve the overall equipment effectiveness metrics.
Big data applicability also lies in the systems involved in manufacturing such as enterprise resource planning systems, supplier relationship management systems, and product lifecycle management systems. Sales and operations planning processes can begin to be automated by using big data analytics to review historical loads, customer data, and changes to major projects which will help companies optimize their plant loading. This strategy helps define load forecast and establish more informed manufacturing processes. IoT can also be integrated to monitor inventory, track parts as they move through supply chain, and collect data for the ERP systems. This data driven method of supply chain management helps to reduce inventory and capital requirements.
In addition to improving the manufacturing process itself, big data analytics can also be used to improve the safety of the work environment. Key Performance Indicators collected for health and safety monitor absences, injuries, and incidents that occur on the job. This data can be stored and utilized in audits and management discussions to identify gaps in the safety of a production environment, and drive some common health, safety, and environment concerns.
Big Data is being utilized by manufacturing companies of all sizes and sophistications. General Electric’s New York factory is equipped with over 10,000 sensors linked to global factories that report on manufacturing processes and building condition. Employees can view the data, receive alerts of production deviations, improving quality and the overall process as it happens in real time. McKinsey and Company assisted a pharmaceutical manufacturing company in increasing vaccine production by 50 percent by segmenting the manufacturing processes and assessing different parameters and their impact on yield. Intel used big data in the predictive analytics context and lowered the amount of quality testing, saving an expected $30 million in chip and processor manufacturing. BMW improved quality by utilizing data from sensors on prototypes to identify design weaknesses and recognize vulnerabilities before car designs hit the market, boosting their brand reputation and ensuring higher quality products.
Big Data and IoT span a variety of industries. In engineering orientated segments (such as aerospace, heavy machinery, etc.) big data analytics are used to improve factory efficiency and examine history to optimize supply chain management. In pharmaceuticals, sensors speed up R&D trials by speeding up screening processes. In the oil and gas industries, data used for preventative maintenance on the pumps that pull oil from wells can save over $50 million per day, saving money and reducing downtime. The automotive industry can improve vehicle design by using sensors to report data on speed, fuel consumption, and oil temperatures while in use. Across industries, big data analytics and the Internet of Things are disrupting the manufacturing sector and enhancing production processes and company’s ability to compete.
Forbes predicts that digital content will have multiplied by 50 times by the year 2020, further necessitating strides in big data analysis to manage the uptake in content. Simply collecting the data doesn’t allow manufacturers to compete, it is the transformation of data into tangible ways that will improve efficiency that will gain manufacturers an industry advantage. Sophisticated data analytics will continue to help manufacturing companies make more informed business decisions, minimize production risks, and improve their manufacturing processes by increasing efficiency, reducing waste, and improving product quality. Big data analytics is crucial in allowing businesses to compete in the ever-evolving manufacturing industry.
Written by Irving Wittenbur, a writer with DO Supply, Inc. who writes about Automation, Robotics, and Manufacturing. When not writing, Irving can be found in a coffee shop or working on a project in his garage.