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DMDII Future Showcase


NOVEMBER 15, 2018

Predictronics had a fruitful experience at the DMDII Future Factory Technology Showcase at UI Labs in Chicago, IL on November 13th.

The purpose of this event was to bring together academics, manufacturers, and solutions providers to solve problems with and cultivate a greater understanding in digital twin technologies.

CTO Dr. David Siegel presented a DMDII research project that is a collaboration amongst Predictronics, Lockheed Martin, Northeastern University, and FORCAM. This project, titled Achieving Smart Factory through Predictive Dynamic Scheduling, integrates machine health and overall equipment effectiveness (OEE) measurements into one interface and focuses on predictive factory environments, reducing unexpected downtime and saving manufacturers time, money, and resources.

Senior Data Scientist Matthew Buzza also exhibited our PDX software platform and Factory Sentinel program, which offer businesses data-driven predictive analytics and industrial AI solutions.

Industry leaders from Coca-Cola, John Deere, Duracell, Microsoft, and Northrop Grumman made noteworthy appearances. Many discussed the issues they have faced in digitizing their factories. These challenges include having numerous tools and/or an abundance of data but not enough knowledge to use them properly, finding experts who understand both manufacturing processes and data science, knowing if their data is quality or useful, requiring more valuable sensor data, and needing a standard OEE metric.

DMDII and UI Labs hope the collective efforts of the industry researchers and solutions developers in attendance will help to further solve these problems.

Members of Team Predictronics share their seven takeaways from the event:

  1. The digital twin concept in manufacturing can mean many things, but the current focus is on models. Utilizing CAD models to simulate production and processes is essential to ensuring the efficiency of fast-paced and dynamic assembly lines.
  2. The value of technology needs to be emphasized over exciting technological buzzwords. Manufacturers sometimes go after trending innovative technologies without first knowing why or if these solutions are necessary for their business. Solutions providers must help guide them towards setting realistic goals and objectives and choosing the right technology for their needs in order to achieve true progress and growth.
  3. Many manufacturers have not yet reached the point where they require data analytics and the development of digital twins. Big companies want predictive solutions, but several are still perfecting their data collection process, which is absolutely essential to acquiring good data. Good data will ensure successful visualization and machine learning and deliver effective and lucrative analytics.
  4. Solutions providers must focus on working with one another to join their technologies and/or create end-to-end solutions. Often manufacturers invest in numerous different kinds of software, but they don’t know how to use them together or the various pieces of software don’t integrate well with one another. Software developers must give more attention to the overall process vision and fulfill the needs of the customer by offering more whole package solutions and forming more partnerships with other providers.
  5. Manufacturers find more value in utilizing the consulting and engineering services offered by solutions providers, rather than investing in software and doing the legwork of data acquisition in-house. Traditional machine makers, such as those who create CNC machines like mills, routers, and grinders, only often see a 6% profit margin. By switching their business model and utilizing outside solutions services, that margin would increase to 15% or more.
  6. Forecasting is an important aspect to production planning. Through analysis and actionable intelligence, a company can predict, on a daily or weekly basis, the results of their production cycle and process. This helps improve maintenance scheduling for machinery, equating to less downtime, and optimize decision-making for operational logistics, reducing cost and waste from unused materials and components and saving a business time and efficiency. This planning will better dictate a company’s investment in new technologies and innovation.
  7. Predictive quality is a priority amongst manufacturers. Companies want to invest in data-driven solutions for production to ensure their consumers receive properly functioning products that are free from defects and deficiencies.

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