Big Data use in manufacturing to drive value in 2020 and beyond


Manufacturing industries contribute almost 20% of the GDP in India. In February 2020, manufacturing sector witnessed a positive growth of 4.5% output. But soon after the coronavirus induced nationwide lockdown began, the output slumped by 20% in March and further down to 60% in April. All categories of manufacturing were affected and the automobile sector is the most hit due to the halt of the non-essential businesses.

To our relief, to revive back and boost the efficiency, promisingly, big data and data engineering can help the manufacturing sector both now and in the future.

Speaking of which, manufacturers are starting to implement IoT technologies and are operating on the resulting streaming data to improve industrial processes. With the use of more IoT systems comes the huge amount of real-time streaming data. For this big data to get ingested and utilized efficiently, they are to be cleaned before applying analytics.  The artificial intelligence (AI) and Machine Learning (ML) systems help in discovering patterns and build models that can help in automation and scale.  

With these big data insights, the sky is the limit for the manufacturers to deliver value. Let’s see some of the big data opportunities:

  • Increase in operation efficiency

In addition to the standard practice of automated production lines, manufacturing big data can exponentially improve line speed and quality.

For instance, ML-driven automated tests can quality check products by analyzing photographs, X-rays and temperature measurements and thereby reducing human error in identifying anomalies in the product quality. It can help increase the screening speed of products too.

Sensors and RFID data helps in tracking the tools and other inventory in real-time thus reducing any possible interruptions and delays. These kinds of data can also be used in managing the supply chain to optimize the demand forecasting, price and inventory management.

Read here about the upcoming trends in warehousing and logistics.

  • Predictive maintenance schedule

The data and information about an equipment’s wear & tear and the past failures are analyzed to identify the lifecycle of the equipment. This helps the manufacturers, in setting a predictive maintenance schedule that can be time-based or usage-based.

This directly helps the organization from downtime and waste of unexpected failure. A report from PWC and Mainnovation the adoption of predictive maintenance have reduced costs by 12%, improved uptime by 9%, Cut safety, health, environment, and quality risks by 14%, Extend equipment lifetime by 20%.

  • Optimize Pricing

AI-driven analysis of manufacturing big data helps organizations to comparatively analyze their own pricing with that of the competitor’s to produce optimized price variants. Furthermore, ML can help manufactures of build-to-order products with accuracy and customized configurations and to configure-price-quote (CPQ) workflow.

Overall, the balance period of the financial year, of 2020 – 2021, is likely to be difficult, but manufacturing companies can salvage their positions with big data and analytics to build for the future.


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