Digital Twins: Competitivity, Costs and Profitability for SMEs

February 19, 2024

Incorporating digital twins into small and medium-sized enterprises (SMEs) manufacturing processes marks a pivotal shift towards efficiency and sustainability. This transformation, driven by the virtual replication of physical systems, unfolds in a logical progression of cause and consequence across three key areas: energy savings, operational cost reductions, and improved decision-making and product quality.

Operational Cost Decrease via Optimized Flows

The optimization of manufacturing flows are a direct pathway to decreasing operational costs. Digital twins facilitate a deep dive into the production process, identifying bottlenecks and inefficiencies that, once addressed, streamline operations. This optimization of workflows ensures that resources are allocated more effectively, machinery is used at optimal capacity, and production timelines are minimized. The natural consequence of these improvements is a marked reduction in production operating costs in a range between 5-20%, positively impacting the P&L and cashflows.

Energy Savings through Efficiency

As a result of the above, using digital twins in SMEs primarily targets the elimination of unnecessary downtimes and maximizes asset utilization, leading directly to energy savings. By accurately simulating physical processes, digital twins identify and rectify inefficiencies, ensuring machinery and resources are deployed effectively. This not only prevents wasteful energy use but also enhances the overall productivity and sustainability of manufacturing operations.

Quality Improvement and Fact-based Decision Making

Finally, the integration of digital twins brings a shift towards improved quality and fact-based decision-making. By providing a detailed, real-time view of the entire manufacturing process, digital twins enable SMEs to monitor product quality at every stage of production. This visibility ensures that any deviations from quality standards are quickly identified and corrected.

Moreover, the supplementary layer of manufacturing intelligence brought by digital twins over daily generated manufacturing data supports data-driven decision-making, enabling predictive maintenance that prevents downtime and defects.

SMEs not only see an improvement in product quality but also base their decisions on solid, empirical evidence, further driving efficiency and profitability of the factory.


In conclusion, the logic of cause and consequence underpinning the adoption of digital twins in SMEs manufacturing processes highlights a clear pathway to reducing operational costs, achieving energy savings and improving product quality and decision-making.

Each step in the process is interconnected, with efficiencies gained in one area naturally leading to improvements across the board.

As SMEs face strong competition, uncertainties and workforce challenges, the strategic implementation of digital twins creates more sustainable, efficient, and competitive manufacturing businesses.

You might be interested into learning more about DispoX - the disruptive digital twin developed by Dillygence.

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