How Big Data is Modernizing Manufacturing Processes

Big data permeates every aspect of modern life, from banking and sports to eCommerce and healthcare. Official statistics support this notion as well: The big data market is predicted to reach a value of more than $57 billion by 2020.

The industrial sector is prepared to participate in the big data explosion, given its competitive challenges and search for high productivity. Can this technology rule in manufacturing plants, though? Let’s investigate the main advantages of big data in manufacturing to get to the correct conclusion.

Big data as a bet to reduce downtime

Given the detrimental effects that such stoppages have, downtime is an utter nightmare for every industrial sector. An average of 800 hours of downtime per year for manufacturers results in productivity losses of five to twenty percent.

But there is no time for pessimistic attitudes. For instance, General Electric (GE) presented its best practices at the London conference “Minds and Machines Europe.” Former GE CEO Jeff Immelt described how the corporation supports transformation in various industries, including healthcare, energy, and aviation, by combining technology.

Big data analytics, in conjunction with materials science, as well as “intelligent machines” with sensor technologies, may, in Immelt’s opinion, harness the power of industrial data in real-time and provide significant advantages.

As a result, the business could predict when a machine or specific component would break, automate its manufacturing processes, improve performance, and do away with downtime. Their annual revenue of $45 billion is concrete evidence of their accomplishment.

Here’s an illustration of how remote monitoring and problem detection work in the aviation industry. Every 30 seconds, data is collected by a collection of sensors connected to gas engines.

The Hadoop program then enters the picture. To lay the groundwork for incredibly quick MapReduce-based concurrent calculations, it divides the gathered data into manageable chunks and distributes it across thousands of nodes using its fault-tolerant, redundant HDFS file system.

The three Vs. of big data — volume, velocity, and variety — are successfully handled by such extensive data processing, which also enables GE to address any potential production faults. According to research, customers may save $2 billion by increasing gas engine performance by at least 1% annually with Hadoop-enabled analytics.

In the era of big data, reducing supply chain risks

Uncertainties abound in the supply chain. Again, data analytics will be necessary if you want to lower the potential dangers and foster positive relationships with retailers and customers. Traceability, purchasing, and warehousing are the three key silos around which big data applications in the supply chain revolve.

For instance, firms can track products and avoid adverse scenarios by using IoT-facilitated data that is productively employed to provide insightful insights. This can involve deciding immediately on the best track routes in the event of a food crisis or quickly spotting instances of food contamination.

Natural catastrophes and extreme weather are the leading causes of supply chain disruption, according to the Chartered Institute of Procurement and Supply. Companies can examine weather statistics for tornadoes, earthquakes, hurricanes, etc., and utilize predictive analytics to assess the likelihood of delays to ensure that these gloomy scenarios won’t result in business interruption.

Additionally, manufacturers can identify future trends and gain crucial time for disaster preparedness by mining historical and current data from external and online sources, such as financial analyst recommendations and media reviews. Big data is also used for enhancing purchase decisions and keeping inventory at the ideal level.

Frequently asked questions:

How might manufacturing be improved by big data?

Operations managers in manufacturing can utilize advanced analytics to analyze historical process data, spot trends and connections between discrete process stages and inputs, and then focus on optimizing the elements that have the biggest impact on yield.

How can data analytics enhance the manufacturing industry?

Manufacturing analytics can be used to improve an organization’s ultimate product. This is achieved through a variety of techniques, including trend analysis of consumer feedback and purchase trends, defect density level control, and data-driven product optimization.

How effective is big data in various sectors?

By analyzing and forecasting consumer behavior using data from social media, GPS-enabled gadgets, and CCTV footage, big data has been employed in the business to deliver customer insights for transparent and simpler goods. Big Data also enables insurance businesses to better retain their customers.