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Optimizing the Product Development Process with Simulation

RubyNicholson

Guest blog written by Bill Mannel, VP & GM – HPC, Big Data & IoT Solutions, HPE Servers

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The rapid pace of technology advancements has caused product life cycles to shorten dramatically over time, permanently altering the competitive landscape across a range of industries.

Legacy products that initially defined long-term success are becoming a thing of the past, and rising percentages of company revenues are now derived from products launched within the last three years.

“Blink-and-you’ll-miss-it” product life cycles combined with increased competition and consumer demand for high quality at a low cost have forced manufacturers to seek ways to reduce development time and streamline production processes in order to bring new products to market faster and more efficiently.

The concept of operationalizing intelligence from Big Data is not new to manufacturing, and many companies have been leveraging predictive analytics, simulation and modeling techniques for some time to help improve performance. A best-in-class Big Data strategy is required to manage the data flow which enables these tools, and can help the manufacturing industry optimize the new product development (NPD) process.

The testing and production phases are considered to be the most expensive of the NPD process. Simulation powered by Big Data analytics enables companies to experiment with manufacturing processes in a virtual environment to determine a design’s manufacturability as well as forecast overall performance of the finished product.

This computer-based modeling of production systems, known as “process simulation” or “virtual manufacturing”, also helps companies predict product quality, identify potential issues in the production process, and reduce time and costs associated with physical testing. Insights from simulation can enable better data-driven decision-making, increase product ROI and boost the efficiency of engineering assets.  

Simulation and modeling techniques can be used in a variety of manufacturing applications to optimize the product development process:

  • Material data and information from CAD models can be incorporated to produce highly realistic simulations which help engineers achieve better Design for Manufacturability.
  • Simulation models can pinpoint unnecessary inventories and excessive machine idling times, as well as identify other inefficiencies down to the machine level to improve productivity and reduce waste.
  • Product defects can be reduced or eliminated by using simulations to predict machine performance and avoid potential bottlenecks in the production process.
  • Assembly line data can be collected during production to feed intelligence back into predictive models and enhance future simulations.

Surging volumes of data are disrupting traditional industry models for both business and IT in manufacturing. The key to survival is a shift away from legacy systems to a new style of IT which will help manufacturers successfully transform and align computing power with business goals.

Whereas traditional IT is very siloed, hybrid computing environments combining the best of on-premise and cloud environments offer the speed and agility needed to deliver real-time insights which allow manufacturers to move at the speed of the market.  

Simply creating new products is no longer enough to succeed, and today’s manufacturers must develop, test and bring new products to market faster than the competition in order to gain competitive advantage. Manufacturers who invest in IT transformation will become more agile and slash the time it takes to convert their new product ideas into business value.

 

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About the Author

RubyNicholson

I am a Senior Manager managing external content and social media for HPE Servers Awareness. Stay tuned for topics on Mission Critical Solutions, Core Enterprise and SMB Solutions, Next Gen Workload Solutions, Big Data and HPC, Cloudline and HPS Options! Follow me @RubyD_Nich