Mondo on track for improved digitalisation

Sports enthusiasts will undoubtedly recall the distinctive purple athletics track at the Stade de France, where athletes competed during the Paris 2024 Olympic and Paralympic Games. This track, composed of hundreds of rolls weighing an average of 250 kg each, showcases the expertise of the international Italian-origin group, Mondo.

Founded in 1948, the Mondo Group operates across three main divisions, each claiming a leading position: Sport & Flooring (flooring and sports equipment, including synthetic surfaces supplied to Olympic Organising Committees and World Athletics Championships since 1976); Contract/Rail (commercial coatings for trains and subways) and Toys (toys and balloons).

The group employs 800 people globally and operates nine manufacturing plants, one of which has been based in Foetz, Luxembourg since 1979. The Luxembourg site employs around 100 staff, including workers and administrative personnel, with production running continuously across three shifts, five days a week.

Optimising processes

In 2022, Mondo Luxembourg took its first steps towards improved digitalisation, supported by the Neistart Lëtzebuerg programme — a government initiative for economic recovery following the COVID-19 pandemic. Numerous sensors were installed on production equipment, marking the beginning of a broader digital transformation. This initial success motivated the company to engage with the Luxembourg Digital Innovation Hub (L-DIH).

"We wanted to assess other aspects, such as digital maturity and cybersecurity. We also wanted to explore potential solutions for our processes through the 'test before invest' service," explains Idriss Laanaite, R&D Expert at Mondo Luxembourg.

One key focus was addressing "batch-to-batch" variability — the differences in quality or properties observed between distinct production batches, even when they are intended to be identical. "The data collected by our sensors provides a significant opportunity to optimise process consistency. By leveraging this data, we aim to maximise batch compliance and enhance overall production efficiency."

The team members were attentive and eager to contribute to the success of our projects. Idriss Laanaite, Mondo Luxembourg

The collaboration progressed swiftly, with the L-DIH handling all initial administrative formalities. "The team was very flexible and cooperative," notes Mr Laanaite.

Identifying strengths and weaknesses

In partnership with the University of Luxembourg, NC3 and LIST, Mondo Luxembourg utilised the full range of services offered by the L-DIH, including a light and a full digital maturity assessment (DMA) and an IT cybersecurity assessment.

"The team members were attentive and eager to contribute to the success of our projects," says Mr Laanaite.

The light DMA revealed a moderately advanced level of digital maturity (54%) comparable to Luxembourg companies in the same sector, but higher than the European average for the manufacturing industry (44%). "The assessment highlighted our strengths, particularly in digital strategy, data management and fostering a digitally friendly culture and organisation."

However, it also identified areas for improvement, such as automation, artificial intelligence, green digital transformation and the adoption of cutting-edge technologies.

Test before invest

The full DMA classified Mondo Luxembourg as "Connected – Industry 4.0 fundamentals," confirming that the company’s infrastructure is robust and well-suited to advanced technologies, with key performance indicators effectively deployed.

"We identified areas requiring attention, such as increasing data digitisation to enable predictive maintenance, structuring the development of digital skills and improving IT/OT integration and automation."

In collaboration with the University of Luxembourg and LIST, Mondo Luxembourg will now follow a "test before invest" cycle to analyse the product within its production chain that holds the highest potential for quality optimisation.

"We will be able to use methods such as principal component analysis (PCA) to reduce the complexity of the data and identify groups or patterns. Then, we can give these groups a name (e.g., a defect type or a process condition) and see which variables are most related to the most frequent or important group."