Publication date:
Publication from:
IT-Enterprise joins European WASABI project to develop an AI assistant for GIPANIS, helping monitor EVA component production and reduce manufacturing waste
IT-Enterprise, footwear manufacturer GIPANIS, the ASTAR Sustainable Development Agency, and EDIH CLOTEX HUB have joined forces within the European WASABI project to bring artificial intelligence directly into footwear manufacturing.
As part of the CIRC-SHOE experiment, GIPANIS is deploying AI-WVMA (AI Waste Valorization & Monitoring Assistant), a digital AI assistant designed for the production of EVA footwear components. The solution will work with process data directly on the production line, helping monitor key manufacturing parameters and identify deviations in real time.
The project is being implemented under the EU’s Horizon Europe programme. For the partners, the objective is not only to develop an AI solution but also to validate its practical value in a live manufacturing environment.
GIPANIS is the industrial partner and production site for testing the AI solution. IT-Enterprise is responsible for ERP integration and the analytical component of the project. The ASTAR Sustainable Development Agency and EDIH CLOTEX HUB support the company’s digital transformation and the dissemination of the project’s results.
WASABI is particularly interesting because it brings together the interests of all the project partners. For GIPANIS, it is an AI tool that addresses day-to-day production challenges here and now. For the ASTAR Sustainable Development Agency, it is a practical example of digital transformation that can be shared with businesses across the region. And for IT-Enterprise, it is an opportunity to advance AI technologies using real industrial data. That is why we see this project as a model worth scaling across the entire industry,
says Oleksandr Ochkasov, Head of AI Projects at IT-Enterprise.
Manufacturing EVA components requires precise control of production parameters. At GIPANIS, a KCLKA EK3–6E2 injection molding machine is used to manufacture soles, insoles, clogs, boots, and other footwear components.
Temperature, pressure, injection volume, and other process parameters directly affect the outcome of each production cycle. Even relatively small deviations can affect product quality or result in higher material and energy consumption.
Waste is another important aspect of the production process. EVA manufacturing generates sprues, offcuts, and non-conforming products. Without systematic tracking, it is difficult to determine the causes and scale of these losses or identify opportunities to reuse the material.
This is one of the areas where AI can provide an additional layer of production control.
AI-WVMA will collect data from the manufacturing process and monitor its key parameters. When the system detects a deviation, it can alert the relevant employees, allowing them to assess the situation and respond.
The assistant will also analyse accumulated production data. By comparing indicators across different production cycles, it will be able to identify recurring patterns and early signs of potential problems that may not be immediately visible during routine monitoring.
The approach shifts the focus from responding to consequences to identifying potential issues earlier. When a production specialist receives information about unusual process behaviour during a production cycle, there is more time to inspect the equipment or adjust the process before the issue affects product quality.
For the manufacturer, this can help reduce defects and downtime, improve the use of raw materials and energy, and make the production process more predictable and manageable.
At the same time, AI-WVMA is not designed to make technological decisions on behalf of employees or operate equipment autonomously. Its role is to collect and analyse production data and provide specialists with timely information to support decision-making.
Deploying AI directly on a production line makes it possible to test the technology under real operating conditions rather than in a laboratory or test environment. This is important for further scaling: real production data shows how effectively the algorithms perform and what adjustments may be required when adapting the solution to other manufacturing processes.
For the footwear and broader light manufacturing sector, the experience can be applied in several areas. Monitoring process parameters can help stabilise production and reduce the risk of defects. Analysing EVA waste provides a basis for more accurate tracking of material losses and identifying opportunities for reuse. Energy-related data can support more informed decisions about resource consumption and optimisation.
The goal is to connect these different data points with specific situations on the production floor rather than simply accumulate another set of production indicators.
The experiment also has potential beyond a single company. In Ukraine’s Khmelnytskyi region, the possibility of deploying the solution at another 8–12 companies over the next two years is being considered. If the results of CIRC-SHOE confirm the expected value of the approach, the model could be adapted for other light manufacturing businesses.
For IT-Enterprise, participation in WASABI is also an opportunity to further develop AI solutions using real industrial data and validate their performance in specific manufacturing processes. This kind of hands-on experience helps move from individual AI capabilities to integrated tools that can support production management.
WASABI is a European project focused on the practical adoption of digital and AI solutions by SMEs to improve efficiency, sustainability, and competitiveness. As part of WASABI, the CIRC-SHOE experiment focuses on developing an AI assistant for footwear manufacturing. The project is implemented under the EU Horizon Europe programme and aims to deploy artificial intelligence in manufacturing environments across different countries.
IT-Enterprise is a Ukrainian product IT company with more than 35 years of experience supporting the digital transformation of major companies in the country’s real economy. Its customers include Interpipe, Ferrexpo, Nibulon, DTEK, Ukroboronprom, Ukrposhta, Ukrnafta, Ukrzaliznytsia, and other major organisations. Since 2014, IT-Enterprise has been developing AI infrastructure and has delivered more than 40 Industrial AI implementation projects. Its portfolio includes more than 1,450 completed projects across MilTech, agriculture, energy, retail, manufacturing, financial services, and banking.
