Jane Edwards
Gregory Bowman, chief innovation officer of Siemens Government Technologies, said adopting digital thread and digital twin technologies could help government and corporate organizations address challenges associated with reshoring manufacturing supply chains.
A digital twin uses machine learning, simulation and reasoning to improve decision-making, while a digital thread is a data-based architecture that connects supply chain data, designs, performance data, simulations and other capabilities of digital twins to improve communications and provide an integrated picture, SGT said Friday.
“While localization/reshoring has its share of challenges, the good news is that a comprehensive digital twin of the product, production method, manufacturing line, and structure can dramatically reduce costs, risks, and time,” Bowman, who also serves as vice president of corporate development at SGT, wrote in a blog post published on May 4.
“Moreover, the data generated will allow key leaders to make critical decisions faster and more accurately before ever bending metal or pouring concrete. Simply put, it allows targeted investments, increased predictability, and maximized flexibility,” he added.
Bowman discussed how digital models could help engineering executives look at several “what-if” scenarios when planning to build a new manufacturing plant or reuse existing facilities and how such tools could enable organizations to improve manufacturing operations through anomaly detection and predictive maintenance.
He also talked about Siemens’ partnership with Deloitte and how the digital tools from this industry team could help companies and agencies build smart factories in support of their reshoring efforts.
