The power of big data is changing manufacturing. An industry that once focused solely on productivity and efficiency is now using information to deal with issues such as adaptability, flexibility, and responsiveness. Big data and manufacturing are now inexorably intertwined.
In manufacturing, big data volumes have exploded in recent years, arising from sensors on machines and assets, in addition to real-time data from logistics, sales, and marketing systems. The advent of advanced communication networks has increased the volume of industrial data generated every second, reports Frost & Sullivan.
This knowledge can make a difference in business performance if the data that manufacturing firms acquire is stored, managed, and analyzed effectively. The good news, as consultant McKinsey recognizes, is that manufacturing companies that successfully build analytical capabilities can generate a competitive advantage.
If your firm is looking to make the most of information, it must approach big data in several stages. After assessing the range of sources, you’ll need to consolidate and manage that data before employing insight to hone performance—and perhaps even offer customization as a responsive manufacturing business.
Big data is changing how manufacturers operate in several ways. First, the decrease in the cost of sensors means manufacturers are adopting the Internet of Things (IoT). Estimates from Accenture suggest that the industrial IoT could add $14.2 trillion to the global economy by 2020. This proliferation of sensors in new equipment is producing huge amounts of information.
Second, CIOs now have access to technology like Hadoop that can store data from disparate sources in a single location. Businesses can collect time-series data from sensors in multiple locations, and can place this information alongside IT-related data, such as enterprise resource planning, maintenance, and transactional data from relational databases.
Other important sources of data for industrial processes can also be included. These sources include video and image files, such as inspections of manufacturing equipment. It’s now possible to bring this disparate information together because of advances in data lake technology.
Most crucially of all, once this data is in a single location, it becomes much easier for your business to take advantage of analytics. With a huge data store, a business can use machine learning to search for the familiar factors that helped create high-quality outcomes regarding uptime, quality, and yield.
However, identifying the factors that produce optimal performance should be just the starting point. Once you understand the factors for success, you can start monitoring these concerns in real time. Your organization can then use big data to create a more responsive manufacturing company.
It is even possible to start thinking about cost-effective forms of customization. When it comes to manufacturing, customization is the holy grail. It’s the ultimate form of production, where personalization can meet customers’ specific demands.
Adopting machine learning not only allows firms to support the flexible forms of production that customization requires, but also allows them to hone their manufacturing techniques. Businesses can use the data from their forays into customization to create a feedback loop of customer demands. As these results are monitored, customization becomes less expensive.
Manufacturing firms that adopt machine learning can undertake targeted market analysis with more precision than ever before. They can segment their markets and perfect production techniques via the feedback loop of information they receive. Such data-hungry firms can respond quickly and effectively to the reception that customers give their products in the marketplace.
Some businesses are still relatively unaware of the potential benefits of big data in manufacturing. These firms are making some attempt to monitor and improve their processes, but these approaches are centered on point monitoring, such as investigating how an individual piece of equipment or a plant is performing. It’s a limited strategy that doesn’t take advantage of the power of big data.
Smarter companies are going a step further. They recognize the need to create end-to-end optimization across the manufacturing process chain. More crucially, they want to push this optimization process across all equipment in all locations. This is where big data can help.
Your first step toward manufacturing optimization is the creation of a data lake. Store as much data as you can. Feed all your information from a variety of sources into your data lake. With all the data in one place, you can start to exploit new, higher levels of computing performance and undertake machine learning and in-depth analytics.
Your business will develop proper end-to-end optimization across all manufacturing processes through this holistic approach. You will start to develop models around key elements, such as predictive maintenance and yield. Your business can then use these models to monitor performance across your key areas in real time, improving and refining production on a constant basis.
A true data-led transformation of the manufacturing process will result from this approach. Your real-time access to data becomes a means to create a more responsive business. By harnessing the power of big data and manufacturing in harmony, your business can learn from the past and take game-changing action in the present.
Learn more about how big data is revolutionizing manufacturing.