4 ways to transform the manufacturing industry using Big data analytics
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4 Ways Big Data Analytics Is Transforming the Manufacturing Industry

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For a long time, the manufacturing industry was associated with a slew of problems – health risks, worker unions, poor optimization methods, and what not. However, technology and Big Data Analytics (BDA) to be precise, emerged as a game changer and is now taking the factories and production units to the next level.

  1. Making Factories “Smarter”

    Industrie 4.0 is a quintessential example of how modern factories will look like. It’s a German government initiative – a high-tech strategy to promote computerization of manufacturing that has laid the foundation, the roadmap of smart factories covering every process, from product idea to development and from recycling to maintenance.

    Industrie 4.0 comprises of:

  • Interoperability:

    Machines and sensors are connected to a network and work in sync.

  • Automation: 

    Physical devices are capable of making decisions on their own and thus, are automated.
    While experts believe that India is one of the ideal countries to benefit from the Industrie 4.0 model, Cincinnati, Ohio has already declared itself an “Industry 4.0 demonstration city” and is investing a significant amount of money for innovation and development in this area.

Read Also: How To Solve The Biggest Industry Problems With Big Data Analytics

  1. Optimizing Quality Checks

    Intel has been one of the biggest companies to actively incorporate BDA into its manufacturing processes. Since quality assurance is an important part of its chip-manufacturing process, as is with most manufacturers, it has to run about 19,000 tests on each individual chip. However, harnessing the power of BDA it was able to drastically reduce these steps. For instance, Intel’s analytics system can now go through historical data collected during the manufacturing process at the wafer level and identify only those chips that actually need testing. The chipmaker saved about $3 million in manufacturing costs way back in 2012 using the predictive analytics process implemented on its line of Intel Core processors.

  1. Improving Accuracy and Quantity of Production

    McKinsey gave the perfect example of how BDA can improve manufacturing practices to a great extent. A bio pharmaceuticals manufacturer that produces a certain category of pharma products involving blood components, hormones, and vaccines has to monitor more than 200 variables to ensure purity. However, surprisingly, the yields of two separate batches of the same product produced using the exact same process can vary by as much as 50% to 100%. Given how expensive health care products can be, even a 10% yield difference can cost a lot. Fortunately, there is an easy solution. By dividing the entire production process into smaller segments and applying data analytics on each, the project team can process the inter dependencies and identify the parameters directly responsible for the yield difference. So, modifying these parameters accordingly the team can improve the production quantum by as much as 50% easily, thus saving annual costs by as much as $10 million.

    Read Also: How To Solve The Biggest Industry Problems With Big Data Analytics

  1. Bettering Collaboration to Promote 3D Printer Factories and MaaS

    3D printers are trending as much BDA. A 3D printer factory can work naturally and most efficiently on a foundation set by BDA. Moreover, we can have a new type of service – Manufacturing-as-a-Service (MaaS) just like Software-as-a-Service we have today.
    3D printer manufacturers such as Materialise and Shapeways are already working on MaaS. With a production of about 200,000 items a month, the latter is doing an astounding level of business with the help of automated software and 3D printers that run 24/7. With BDA, these factories are able to work in a highly collaborative environment where the flow of data and information through engineering, machine operators, quality control, etc. is seamless. The result is remarkable efficiency and quick feedback implementation.

To conclude, Big data analytics (BDA) is the future of manufacturing. It’s providing us the tools and the technology to help create the world where there are automated factories that produce at their highest efficiency and cause minimum wastage of time and resources. Also, the top players are already aware of it and so have taken the lead.

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