Breaking Bottlenecks: AI-Driven Process Simulation and Digital Twins

Learn how advanced digital twins and AI are transforming outdated production simulations into efficient, high-fidelity solutions for modern manufacturing

Leaders in manufacturing face unrelenting pressure to optimize processes, minimize waste, and adapt quickly to changes in demand. Yet the process of commissioning new, more efficient production lines is extremely capital- and cost-intensive. Meanwhile, elongated time frames substantially impede producers’ ability to reach their customers. Whether filling bottles on production lines, assembling discrete components, or producing parts through additive manufacturing, these challenges are universal.

One of the key bottlenecks lies with outdated technologies for simulating production lines. While simulations have been commonplace for years, many companies have struggled to update their methods and overcome core inefficiencies. A major shift forward comes from powerful digital twins for high-fidelity simulations — built on scalable cloud platforms and enhanced with AI for rapid optimization. Read on to learn more about how this technology has matured to help manufacturing lines become more efficient and effective.

MAGNIFIED PRESSURES IN PROCESS MANUFACTURING: SPOTLIGHT PACKAGING AND BOTTLING

The pressure on producers has taken on a new degree of intensity. While the urgency for greener operations grows, manufacturing continues to account for nearly 20% of global carbon dioxide emissions. Compounding that issue, over 70% of manufacturers struggle with inefficiencies stemming from outdated processes — hurting their ability to attract talent. To top it off, as consumer trends shift toward personalization and rapid delivery, meeting these demands with traditional methods has proven unsustainable.

Food and beverage producers, for example, must contend with unique complexities tied to their reliance on precision and speed. Handling volatile ingredients, adhering to stringent hygiene standards, and minimizing resource waste are daily trials. Small inefficiencies can snowball into significant losses due to spoilage, shutdowns, or regulatory non-compliance. In this, as in other verticals, a new degree of efficiency is essential.

LIMITING FACTOR: TRADITIONAL SIMULATION METHODS

The striving for greater efficiency is nothing new in manufacturing. Simulations have long enabled recommendations for production improvements. Yet traditional simulations rely on static models and rough theoretical assumptions, lacking the ability to account for varied and dynamic physical conditions. These methods often fail to capture the complexities of real-world manufacturing environments.

Although transformation is underway, many actors still operate with discrete event simulations, isolated systems, and outdated tools. These limitations do not just affect simulation effectiveness — they compromise competitiveness. The question is no longer whether industries need new solutions. It is how they will succeed in implementing them to master their challenges.

TANGIBLE MANUFACTURING REVOLUTION: AI-ENHANCED DIGITAL TWINS

Digital twins, long heralded as revolutionary, have matured into a scalable and practical solution. Manufacturers have taken note: Just over 40% of organizations are in the pilot phase of digital twin implementation, though only 20% report full integration and 15% use it in selected areas.

At the heart of this technology lies its ability to transform a flood of data into insights that matter. Digital twins provide the answer, bridging the gap between raw data and decision-making. A digital twin can simulate thousands of scenarios in seconds, allowing manufacturers to determine the most efficient configurations without disrupting operations.

Furthermore, by integrating physical AI, digital twins can mimic real productive environments. These tools forecast wear and tear, optimize resource allocation, and even anticipate demand fluctuations. And the integration of powerful AI agents accelerates the optimization process even further with automatic evaluations and decision-making.

Far from theoretical, today’s digital twins combine real-time, synthetic, and historical data with sophisticated simulations and powerful AI to replicate and rapidly optimize physical processes — saving many hours of engineering and commissioning work in later phases and avoiding loops of changes and quality issues in the early phase. Taken together, that delivers reduced downtime, lower costs, and production processes aligned with business goals.

ECOSYSTEMS PAVING THE WAY FOR ROBUST DIGITAL TWIN SOLUTIONS

The advancement of AI-enhanced digital twins to feasible solutions enables manufacturers to test configurations digitally in minutes rather than wasting hours of production on physical adjustments. That is due to substantial advances from technology and implementation providers. Thanks to collaborations between technological leaders like Ansys (part of Synopsys), CADFEM, Microsoft, NVIDIA, and SoftServe, this approach now enables unprecedented precision and adaptability.

SEE DIGITAL TWIN INNOVATION IN ACTION

The maturity of digital twins isn’t just theoretical — it’s real and proven. The partner ecosystem of Ansys (part of Synopsys), CADFEM, Microsoft, NVIDIA, and SoftServe has already delivered it to Krones, the global process manufacturing line provider for bottling, canning, and packaging. In just two months, Krones gained the ability to simulate and optimize bottling lines in mere minutes with a digital twin enhanced with agentic and physical AI.

This success was made possible by close, interdisciplinary collaboration: regular engineering workshops, joint development sprints, and a clear focus on business results. The results? Faster commissioning of machines and systems, greater resource and process efficiency, and fewer downtimes. With AI and digital twins, Krones is poised to shape the future of the manufacturing industry.

Start a conversation with us