Artificial intelligence has become a pivotal force in footwear manufacturing within the region, with one of the world’s leading centers for innovation pushing the boundaries. The HE Shoe Technology Center at Inescop developed an intelligent system that acts like a guardian of quality inside shoe factories. Led by the robotics division, the project aims to give companies granular control over every pair produced, addressing the limitations of human visual inspection.
The primary goal is to offer precise and efficient quality control by introducing X-ray machines into the final stage of production. The system relies on deep learning to process images from the scanner, analyzing each shoe’s internal structure and composition to detect defects invisible to the naked eye. This breakthrough not only promises to transform quality control but also hints at AI integration across other stages of the footwear workflow, from design to manufacturing.
With the neural network built at the technology center, images are labeled and tagged to flag unusual elements in the footwear. Operators in factories can continue their routine tasks while the AI software identifies problems and alerts workers for immediate intervention. In this way, workers are supported rather than replaced as the system conducts ongoing shoe analysis, explained Carlos Vélez, the project lead in Inescop’s robotics team.
Currently at the prototype stage, the project has collaborators who share the goal of turning this technology into a market-ready product that enhances production quality. The Alicante footwear industry notes that when X-ray checks first emerged, they revealed internal details but also created efficiency bottlenecks. Years of experience show that manual checks were tedious and could slow the entire line. Vélez emphasizes that the next phase focuses on streamlining the process so workers can maintain high throughput while the system handles complex analysis.
To illustrate the system, an image shows the X-ray based technology developed by Inescop in action. This visualization helps engineers and factory staff understand how the setup maps internal features and flags anomalies for quick action.
To complete the software, Inescop collaborates with various regional shoe factories. This partnership ensures the estimation of real industry needs and the timely delivery of targeted solutions, with continuous data feeding strengthening the AI once deployed.
Vicente Pastor, the manager of Creaciones shoe factory in SW, is among the collaborators testing the solution at the Peter area. He recalls a recent visit to Inescop and the discussion about improving quality control, which sparked a mutual interest in moving forward together. Pastor notes that the project is moving into broader feasibility checks and refining the machine learning workflow to better reflect real production conditions. The aim is to blend technical advances with practical factory requirements, expanding data capture and traceability so future records cover a wide range of events and build trust with customers.
Industry challenges
The innovation holds promise for the footwear sector, though it faces hurdles in digital transformation. The industry must gather enough data to train robust algorithms while maintaining efficient operations. A broader push to digitalize processes is essential if AI-driven improvements are to be realized at scale. Researchers at Inescop stress the importance of raising data literacy and ensuring that factories have access to the digital infrastructure needed to leverage these tools. If the sector hopes to reap the benefits of AI, it must commit to digitizing workflows and addressing gaps in image availability compared with other industries.
Strategic modernization and the adoption of new technologies are crucial to keep the footwear sector competitive on the global stage. Most partner companies express a desire to become more digital, yet the daily realities of a shoe factory can make rapid digitization challenging. Short-term economic returns may lag, but the path forward is clear: progress now or risk falling behind. The objective is to raise a shared level of advancement across the industry, said Vélez.
According to Vélez, this progression will further enrich the region’s shoe industry. By differentiating itself from competitors, the local sector can maintain competitiveness with nations where labor and manufacturing costs are lower. Digitalization and AI-driven efficiency are seen as necessary to stay competitive, enabling production at current workforce levels while delivering greater value and potential wealth creation in the process.
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AI’s applicability is not limited to quality control; Inescop envisions use across multiple facets of shoe manufacture. For example, AI support in design could assist model-building and creative exploration, akin to how large language models inspire designers to generate fresh collections more efficiently and creatively.
Yet in the production phase, Vélez notes that substantial work remains before automation can replace human labor entirely. The current reality is that the manual dexterity required for shoe making still outpaces what AI can directly replicate. Hardware limitations, including the availability of robotic hands capable of mimicking complex human movements, pose obstacles to full automation. A future where multiple disciplines converge to achieve this goal remains a long-term vision. The data show that AI in shoe production is evolving, not complete.
Vélez also stresses safety as a priority. Automating tasks that pose health risks to workers, such as removing injected soles from molds, demonstrates how computer vision enables machines to handle hazardous operations while humans focus on higher-value tasks. The guiding idea is clear: less risk, more value-added functions, he explains.