{"id":24650,"date":"2020-06-26T06:00:00","date_gmt":"2020-06-26T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=24650"},"modified":"2020-06-24T15:24:27","modified_gmt":"2020-06-24T22:24:27","slug":"challenges-to-consider-while-implementing-big-data-strategy-in-manufacturing","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2020\/06\/26\/challenges-to-consider-while-implementing-big-data-strategy-in-manufacturing\/","title":{"rendered":"Challenges to Consider While Implementing Big Data Strategy in Manufacturing"},"content":{"rendered":"\n
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In this special guest feature, Piyush Jain, Founder and CEO of Simpalm<\/a>, discusses the many ways in which Big Data has positively influenced the manufacturing industry. Simpalm is a mobile app development company in the USA. Piyush founded Simpalm in 2009 and has grown it to be a leading mobile and web development company in the DMV area. With a Ph.D. from Johns Hopkins and a strong background in technology and entrepreneurship, he understands how to solve problems using technology. Under his leadership, Simpalm has delivered 300+ mobile apps and web solutions to clients in startups, enterprises and the federal sector.<\/em><\/p>\n\n\n\n

We have seen multiple waves of the industrial revolution in the last 200 years. With the current fourth industrial revolution, all things are getting connected to the internet including machinery and equipment. These machines (i.e. plants, hardware sensors, CCTV cameras, robotic machinery etc.) produce a large amount of industrial data, which is valuable. This data is different from the internet big data generated by social media, blogs and other sources. Industrial big data is used by managers, decision-makers, policymakers to improve the process, machines and predict future needs. Predictive maintenance and real-time monitoring can be done with industrial big data, probability of failures can be detected, and maintenance cost can be reduced. For example, in chemical plants, getting insights about the fluid\/gas flows in pipes can be helpful to predict the maintenance timing. Big data strategy is being rapidly adopted by the manufacturing industry to increase efficiency and productivity. Advanced analytics helps decode complex manufacturing processes, it replaces human-made decisions with automated algorithms and makes production more efficient and fast.<\/p>\n\n\n\n

What is Big Data?<\/strong><\/p>\n\n\n\n

Big Data refers to the huge volume of data that can not be stored and processed using the traditional approach within the given time frame. Being able to utilize this huge amount of data can be beneficial for industries, there are big data technologies available to leverage the processing of such a large amount of data. For example, Hadoop<\/a> is a framework designed to store and process data in a distributed data processing environment with commodity hardware with a simple programming model. It can store and analyse data present in different machines with high speed and low cost. There are other technologies available such as MongoDB<\/a>, Rainstor<\/a>, Hunk<\/a> and more.<\/p>\n\n\n\n

The BMW Group relies on the intelligent use of production data for efficient processes and premium quality, it is the best example of generating value from big data. Building a car generates a massive amount of data throughout the value chain. The BMW Group uses its smart data analytics digitalization cluster to analyze this data selectively and enhance its production system. Results from intelligent data analysis make an effective contribution towards improving quality in all areas of production and logistics.<\/p>\n\n\n\n

Big Data has positively influenced the manufacturing industry in many ways<\/strong><\/p>\n\n\n\n