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September 18, 2016

Leverage Big Data Analytics to Achieve Faster Time-to-Market

Suresh Aswani

In today’s highly competitive marketplace, manufacturers are striving to conceive, test, and deliver quality products at increasingly faster rates. Companies that keep pace with rapid technological advancements will be able to shorten design times, develop better products well ahead of the competition, and dramatically improve profitability.

The use of 3D simulation has revolutionized the daily operations of manufacturing and engineering (M&E), allowing manufacturers to better visualize new offerings, improve design productivity, and streamline the product development process. Companies are now leveraging complex datasets from these simulations in order to identify patterns, reduce defects, and optimize the factors that produce the greatest possible yield.

Information technology is driving a massive paradigm shift to M&E operations as companies employ Big Data analytics to optimize supply chains, accelerate time-to-market, and achieve higher return on investment (ROI). By tapping into a variety of data types such as historical processes, simulations, and unstructured data sources, manufacturers are able to simulate tailor-made products, which conserves valuable time and resources and significantly reduces production costs.

Big Data is responsible for increasing the quality and speed of production by providing better visibility into supplier performance, recognizing deficiencies, and driving greater productivity overtime. In a 2014 survey conducted by SCM World, 47% of respondents expected Big Data analytics to have the biggest impact on company performance.

Source: SCM World, The Digital Factory: Game-Changing Technologies, 2014

Source: SCM World, The Digital Factory: Game-Changing Technologies, 2014

Thanks to advancements in Big Data technology, manufacturers can effectively leverage simulation and modeling techniques to provide a 360-degree view of the development lifecycle, enabling them to realize operational efficiencies and accelerate time-to-market. This computer-based modeling of production systems known as “virtual manufacturing” benefits M&E companies in multiple ways:

  • Advanced simulation capabilities
  • Recognition of simulation patterns to predict future deficiencies and efficiencies
  • Real-time, data-driven decisions and alerts based on manufacturing data
  • Integrated manufacturing and business performance information for smarter decision-making
  • Improved forecasting of products and production
  • Analysis of trends to predict equipment lifespan

As data becomes an increasingly valuable asset to production, IT departments are dedicating larger portions of their budgets to adopt the most cutting-edge Big Data solutions. The International Data Corporation (IDC) reports that global Big Data and services spending will reach $23.8 billion this year. By 2019, Big Data and analytics revenues will exceed $187 billion.

The massive increase in data velocity, variety, and value poses a substantial challenge to manufacturers, particularly those operating with legacy systems that cannot efficiently manage their rising data volumes. Unreliable and lethargic technology means losing out on the flexibility and performance necessary to support valuable simulation capabilities. Now more than ever, investing in high-performance computing solutions to support immense data workloads is non-negotiable for manufacturers looking to compete and thrive in this ever-evolving marketplace. Best-of-breed engineering software is key to analyzing troves of complex data to empower a data-driven organization.

Surviving in today’s competitive climate requires much more than just designing and bringing new products to market quickly. Successful manufacturers can now harness the full power of Big Data to enhance simulations and streamline the product development process. Companies that invest in these cutting-edge solutions will achieve higher quality in terms of both products and performance, speed time-to-market, and grow revenue.