The rise of the ‘Brilliant Factory’ is quietly molding the future of the manufacturing world. A concept conceived by progressive manufacturing giant, GE, it is leading the way towards a dynamic industrial vision. Fueled by industrial internet and innovative data software, the Brilliant Factory routinely predicts production process and product problems that translate into streamlined solutions.
“This ‘Predictivity’, is the digital thread that ties together engineering, design, manufacturing and supply on the factory floor.’’ Says Dr. Stephan Biller, chief manufacturing scientist at GE Global Research. The intrinsic factors driving GE’s new infrastructure, ‘data highways’, at the company’s new battery manufacturing plant in New York, is data collection, feedback analysis and solutions implementation in real time. Data Highways collects 10,000 variables of data, every 250 milliseconds, from about 10,000 sensors that are linked to integrated digital control and information systems of GE’s 400 global factories.
Data is increasingly becoming the cornerstone of manufacturing evolution today. Real-time data analytics is helping manufacturers improve product quality, increase shop floor reliability, boost throughput, predict maintenance requirements, enhance safety and eliminate downtime. By deploying predictive data analytics, Intel prioritized silicon chip inspections, and significantly reduced the number of quality assurance tests. Processing 5 terabytes of machine data per hour, the company saved $3 million in manufacturing costs for a single line of Intel Core processors.
Companies like Ford and GM are also investing in huge quantities of data collection through sensors and processors and internal and external sources, to mitigate energy costs and amplify profits. Implementing MES (manufacturing execution systems), a software solution that collects and analyzes factory-floor data, Raytheon determined precise machine and component use outcomes. The company profited from sensor embedded, automated machines constantly conveying high-quality data.
Along with considerable benefits, data also presents the need for strong information-management capabilities in manufacturing firms. As effective data use improves efficiency it necessitates greater agility and deeper integration across functions and facilities. Growing data presents a number of challenges too. First, is sheer volume – a single machine dispenses about 5 gigabytes of data per week. The magnitude of data extracted from human sources and sensor networks inside and outside the factory is also overwhelming. Second, is variety—a wide range of systems from an array of suppliers, are used by manufacturers, that produce structured and unstructured data. Third is velocity-with rapid change in structure and flow of manufacturing supply chains, data becomes more dynamic, and difficult to analyze.
It is pertinent for manufacturers then, to, with a focused approach, delineate what they want to learn, improve and change before diving into big data. They also need to consider underutilized data already at their disposal and capitalize on it. Often vast troves of data, are used by manufacturers only for tracking purposes and not for improving operations. Manufacturers can optimize existing process data through a steady investment in skill sets and systems. Firstly, by hiring capable data analysts trained in spotting data patterns and drawing actionable insights. Secondly, by analyzing centralized or indexed data from multiple sources. Successfully building such capabilities, will assist firms in setting themselves apart from their competitors.
Now, data opportunities emerging from intelligently integrating automated systems is not just restricted to improving manufacturing at the factory level alone. It extends to the supply chain as well, with Internet-connected, smart factories communicating with logistics providers. And, it also extends to the customer, directly, via the Internet, or indirectly, via data that is collected from RFID readers, bar codes, tags and sensors embedded in manufactured products.
US Company Local Motors is at the forefront of networked manufacturing. In its 18 micro factories, situated near cities, LM produced the world’s first commercial 3D-printed car. Using an automatic online interface, it allowed its customers to personalize their cars. LM then, dramatically reduced distribution costs by using 3D printers located near its target markets. Another case in point is a $2 billion company that generated most of its revenue from custom product design and manufactured products to order. This company used big data to analyze repeat customer behaviors and determine the viability of its products thereby shifting to lean manufacturing.
A new industrial revolution is underway. Smart manufacturing environments, charged with integrated data technologies, self-configuring automated systems and connected devices are radically altering the nature of productivity. “By building a smart manufacturing ecosystem that allows people to innovate, we want to make our factories more agile.” Says Dr. Biller. While greater productivity and operational excellence will be achieved with advanced and integrated data capabilities in industrial manufacturing, what remains to be seen is, whether manufacturers can transform this future vision, into a present day reality.
The question for manufacturing is not whether advanced, integrated, systemic data capabilities are the pathway to greater productivity and operations excellence— certainly, they are. The real question is how manufacturers can make this vision of the future a reality today. One thing is clear though, data-centric businesses are deciding the future. So, the sector needs a robust infrastructure background with measured approaches to manage critical, real time, and mobile data. Backed up with investment, coherent strategies, and talent, the future success of manufacturing firms will be determined by their ability to wrangle data as well as the quality of their products.
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