Big Data is a buzzword in many industries. While it has proven useful in most digital applications, its use case in other industrial applications has been limited for the most part. One of the biggest challenges is the collection of useful data for analytics. While data analytics has expanded into many more aspects, big data depends on the volume of data to provide useful insights. This is where the manufacturing industry can expect the highest potential from big data solutions.
Manufacturing has the highest potential to generate data if all processes can be monitored using data-generating technologies like sensors. It is also an industry that can use wide-ranging analytics for growth and optimization. However, most processes in manufacturing can not take advantage of analytics unless it is real-time. The major source of data and the scope of its use in manufacturing is within the physical production processes, which involve machinery, equipment, labor, plants, etc.
Real-time big data solutions in manufacturing need deep integration with production processes, responsive nature, and use advanced decision-making to provide maximum benefit. Enabling these characteristics requires the use of smart technologies to create this ecosystem of monitoring, analytics, and automation of response. Here’s how manufacturers can realize such big data solutions.
Emerging Technologies at Scale
The biggest challenge for manufacturers in implementing big data solutions is the data gathering technologies. Emerging and mature technologies like IoT, image recognition/processing, various kinds of sensors, etc. can be used to fill this gap wherever they can be applied at scale. These technologies should bridge the gap between physical processes and components with the digital ecosystem in order to extract the most relevant information.
For instance, European manufacturers like BMW use image processing and other measurement technologies for quality assurance. Similarly, IoT devices can connect with sensors and provide relevant data to monitor and analyze a process. Depending on the production process and the product, the application of data-generating technologies can be scaled and applied to enable maximum benefit from big data solutions.
Responsive Systems with Automation
In the example of European manufacturers, quality checks are performed through an integrated system of imaging and processing to analyze products. However, the activity can not be completed without a response based on the analysis. Many data and analytics solutions provide actionable insights that can be handled manually, but in a real-time analytics solution, automation is the key. The systems must be designed with decision engines to drive the right actions at the right time autonomously with exceptions for manual intervention.
Autonomous responsive systems can greatly increase the efficiency, safety, and performance of a manufacturing process using big data solutions. Preventive management and cost optimization are the major advantages of such a system. This is especially advantageous for manufacturers that use digitally controlled processes and robotics heavily for production.
Unleash AI Analytics
Responsive systems are reactive in nature. However, they are a precursor and the foundation of a proactive approach through machine learning and AI. Big data solutions largely depend on automation for real-time advantages and AI/ML technologies are the best decision engines to support them. AI analytics in big data solutions can provide incremental advantages and returns over time, plus they are adaptive systems, making them future-proof.
With proactive AIs, predictive analytics can yield more output from the solution. It can make production processes more efficient and safe. One of the notable additions of AI enabled big data solutions is predictive management, which can help manufacturers extract more out of changes in supply chain, market demand, and machinery.
One of our client, USA’s 3rd largest convenience store chain, saved $2Mn+ in spillage losses from fuel pumps using AI-enabled predictive maintenance. You can read the case study here.
Big data solutions are highly flexible systems which makes them future-ready. They can easily expand their scope to other aspects of the business and can be leveraged for a variety of strategic needs.