Proton Consider Digital Twinning Technology in EV Production
Proton Consider Digital Twinning Technology in EV Production
As part of its strategy to navigate shifting global trends, Malaysia introduced the National Industrial Master Plan 2030 to accelerate manufacturing digitalisation. The country has consistently pushed for emerging technologies to advance this goal, with tangible effects across local industry. In 2022, Petronas entered discussions with Graffiquo Asia Sdn Bhd, a visual and geospatial intelligence centre, to expand digital twins of its oil and gas sites. Digital twinning replicates physical assets in a simulated environment, allowing real-world spaces to be uploaded and stress-tested so teams can identify which ideas deliver the best outcomes.
Adopting digital twins could significantly benefit the automotive sector. With a target of 3,000 smart factories by 2030, Malaysia can move beyond traditional methods—often disrupted by supply chain shocks and hard-to-predict bottlenecks—toward smarter, more efficient operations. A key domestic and regional player as well as a major GLC, Proton Holdings Berhad is pursuing new technologies to stay competitive. In May 2025, Proton announced a partnership establishing an R&D centre in Hangzhou Bay to accelerate technological advancement, particularly in artificial intelligence and machine learning—an important step amid wider policy shifts.
Under the Low Carbon Mobility Blueprint 2021–2030, the government aims for xEVs to reach 30% of annual car sales by 2030, 50% by 2040, and 80% by 2050. Aligning with these ambitions, Proton launched its first EV in 2024—the “eMAS 7”—and plans additional models such as the eMAS 5 in 2025. Sustaining progress will be challenging: maintaining accurate, efficient production lines is critical to meeting national targets, and as EV adoption grows, so is safeguarding connectivity and electrical systems. Glitches, interference, or poor integration can threaten passenger safety and dampen demand. Proton should diversify technologically to better manage EV production, protect quality, meet market needs, and support Malaysia’s twin goals of digitalisation and electrification.
Alternatives to digital twinning include methods rooted in traditional information modelling such as Discrete Event Simulation (DES). Like a twin, DES models system operations, but as a sequence of events over time (Jones, 2024). For example, when assessing production workflows, DES evaluates steps from stamping to final assembly to expose bottlenecks and cycle times, using predefined parameters like average cycles and breakdown probabilities (Siemens Digital Industries Software, n.d.). The key difference is that a digital twin synchronises continuously with real-world data, reflecting current machine status, workforce activity, and energy usage—giving Proton up-to-the-minute operational visibility.
The market is large and expanding: digital twin revenues are projected to grow from USD 24.48 billion to USD 259.32 billion by 2032, a 40.1% CAGR. This trajectory mirrors adoption by leading automakers. BMW, for instance, builds full digital replicas of factories using NVIDIA Omniverse, creating photorealistic virtual environments that fuse real-time sensor data to study robot paths and collision risks.
For Proton, implementing digital twinning aligns with Malaysia’s NIMP 2030 agenda. The company’s bold EV rollout should be matched by technologies that raise quality assurance. Given the relatively early stage of digital twins in Malaysia, Proton should pursue a phased deployment that prioritises readiness and capability before full rollout—aiming for low-risk, high-impact execution. This approach supports long-term scalability, enabling consistent adoption across sites. The technology is adaptable, too, spanning product design and supplier management. As global leaders show, digital twins are evolving into platforms that future-proof operations.
Source info here – Have a Story? Address it to the Editor and submit it here
Featured image Source: Porsche
Disclaimer
The information provided in this article is for general informational purposes only and from publicly available sources. While we strive for accuracy, we do not make any representations or warranties, express or implied, regarding the completeness, reliability, or validity of the content. This article does not make any direct claims about specific companies, individuals, or organizations. Any references to reports or external sources are for context and do not imply endorsement or verification of any specific allegations. Readers are encouraged to conduct their own research and seek professional advice before making business decisions. We disclaim any liability for any losses or damages incurred as a result of reliance on the information provided.