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ARTIFICIAL INTELLIGENCE: NEW BUSINESS OPPORTUNITIES FOR INDUSTRIAL LAUNDRIES?

by
MARZIO NAVA

Artificial Intelligence represents one of the key drivers of innovation for the industrial laundry sector, not only as a technological tool, but also as a factor in business competitiveness and growth. AI can optimise the entire washing process through the continuous analysis of data generated by machines. A multitude of sensors and algorithms can automatically adjust programmes, dosing, and water and energy consumption according to the load size, fabric type, and level of soiling, reducing waste and operating costs. Everything revolves around data management. For machinery manufacturers, Artificial Intelligence should not be seen merely as an additional feature, but as a fundamental design element. New machines should be designed from the outset to collect and process data, be easily integrated with management software, and offer intuitive platforms that make information accessible even to less skilled operators. How far has AI advanced in the design and development of machinery?

"Artificial Intelligence is opening new opportunities in the professional and industrial finishing sector", explains Andrea Astolfi, Sales Director at PONY SpA, "providing tools capable of improving not only machine performance, but also the organisation of the entire production process".

As a manufacturer of professional ironing equipment, what added value does AI provide?

"For a manufacturer like PONY, AI represents an opportunity to develop solutions that are increasingly user-friendly, efficient, and reliable. In the nearest future, machines could support operators by automatically adjusting parameters such as temperature, steam, pressure, and ironing cycle times according to the specific garment that need to processed. This would help ensure consistently high-quality results while further reducing energy consumption and human errors. It should be noted, however, that on some models equipped with the Pony PLC, continuous monitoring of the main operating parameters - allowing performance tracking of operators, detection of anomalies and scheduling of maintenance interventions - is already a reality. The PONY TOUCH TECHNOLOGY guarantees total machine control. Touch Logic lets users easily customise and save work cycles, monitor productivity, access diagnostic functions and maintenance alerts, run self tests and perform software updates. When connected to a network via an Ethernet port, all these activities can also be carried out remotely".

In which other areas could AI play a significant role?

"AI could also bring improvements to service and maintenance. In these specific areas, we could see the development of intelligent systems capable of helping operators and maintenance technicians quickly and effectively find information, identify spare parts, and troubleshoot problems. Finally, it is important to emphasise that, for PONY, Artificial Intelligence does not replace people's expertise. It is rather a tool for enhancing it, transforming data into faster decisions, more efficient processes, and solutions that are increasingly aligned with customer needs".

Artificial Intelligence will give automated logistics a new boost and become its main engine of evolution. This is the direction outlined by METALPROGETTI SpA, a company specialising in handling and storage systems for traditional and professional laundries. The company sees AI not as an alternative to existing automation, but as the technology capable of enhancing it, making workflows smarter, more adaptive and predictive, and transforming installations from simple automated systems into real data-management and high-value service platforms. While the use of Artificial Intelligence is still limited in traditional laundry shops, applications are already a reality in large laundries handling workwear, healthcare facilities and nursery homes, explains Massimiliano Calisti, Sales Director at METALPROGETTI SpA. AI supports the optimisation of sorting processes even when the items are identified through UHF RFID technology, improving algorithms that must manage thousands of pieces with increasingly complex routes, up to the personalised hand over to each guest or user. Among the most promising fields is automated quality control. Thanks to vision systems, a camera can compare each garment with reference models, detect anomalies and automatically decide whether to send it to the next phase or re insert it into the process. This solution reduces processing times and costs as well as the need for personnel dedicated to quality checks. Artificial Intelligence also plays an important role in predictive maintenance. Through sensors and data analysis it is possible to detect early signs of malfunction, schedule targeted interventions and assist technicians in fault diagnosis, minimising plant downtime. At the same time, Metalprogetti SpA continues to invest in the development of automated laundry shops, already widespread in several European countries and the United States. Today, the integration of automation, dedicated apps and software enables the management of delivery, collection and payments completely autonomously, even in the absence of staff. The next AI enabled evolutions will allow a qualitative leap: the systems will be able to document every phase of the process and provide complete, real time traceability. This will translate into fewer errors, faster handling times and a more transparent, personalised service for the end customer. For Metalprogetti SpA, the challenge of the coming years will be exactly this: to integrate automation, software and artificial intelligence in order to transform laundry logistics into an ever more efficient, reliable and service oriented system, concludes Calisti.

Artificial Intelligence is rapidly entering businesses, but its adoption risks becoming fragmented if it is not supported by a clear strategy. This is the view of Andrea Casati, the CEO of KONA, who believes that AI represents a great opportunity, provided it is applied to the real needs of industrial laundries and supported by reliable data. Artificial Intelligence is not a solution to everything. It is essential to identify the areas where it can truly make a difference, without neglecting critical aspects such as data protection and the reliability of the information it provides.

In your opinion, where can AI make the greatest impact?

"The most promising application field is logistics. The growing demand for digital services, from order management and linen traceability, to delivery route planning and collection scheduling - requires tools capable of supporting fast, well-informed decisions. One example is the acquisition of a new customer. Determining the initial linen0020allocation, organising delivery routes, or planning production are all activities that can be significantly improved through the analysis of historical company data. Using machine learning, software can compare similar situations, identify recurring patterns, and provide operators with well-founded recommendations. The real value of AI does not lie in replacing mathematical algorithms, but in integrating and making complex information more accessible, turning years of collected data into a decision support tool. RFID traceability is also part of this evolution. Beyond tracking the path of garments, it generates a valuable database, useful to analyse processes, build predictive models, and improve operational efficiency. For KONA, the direction is clear: to develop software in which Artificial Intelligence and the company's informational resources work together to make industrial laundries increasingly efficient", concludes Casati.

Artificial Intelligence is emerging as one of the most significant innovations for the industrial laundry sector. Alfonso Caselli of ALGITECH believes that the greatest value of AI lies in its ability to improve processes and productivity while requiring relatively modest investments. Until recently, achieving certain results meant investing substantial amounts in new machinery. Today AI algorithms can deliver significant benefits at a much lower cost. Artificial Intelligence also supports sustainability by optimising processing cycles, reducing waste, lowering energy and resource consumption, and cutting operating costs. Thanks to data processing, we can improve process management and use resources more efficiently. ALGITECH is already developing practical AI applications that will be showcased at the exhibition. Another strategic area is predictive maintenance. By continuously monitoring machine operating data, it is possible to anticipate component wear, schedule maintenance interventions, and reduce the risk of breakdowns, thereby increasing plant reliability. Industrial laundries are especially suitable for AI because they handle large volumes of items and numerous processes. Integrated data analysis enables optimisation of costs, times and organisation. Equally crucial is the integration of machinery, traceability systems and management software - a pathway begun with 4.0 Industry and now indispensable. It is no longer enough to build an efficient machine; each equipment must communicate with the entire production system. This is the market direction, and we are already embedding artificial intelligence into our machines.

Artificial Intelligence applied to industrial laundries is not limited to optimising washing processes, consumption, and predictive maintenance. There is a less visible but equally strategic area, explains Gabriele Parravicini, Project & Service Manager at ZCS AUTOMATION. It concerns the day-to-day relationship with people who use uniforms and linen. Size changes, damaged items, requests to refill dispensers, delivery checks - simple yet numerous activities that takes valuable time for operators and wardrobe staff. This need led to the development of wAssistant, the solution from Zucchetti Centro Sistemi: a virtual assistant available on WhatsApp, a channel already familiar to all users. Its innovation lies not only in being available 24/7, but also in its ability to communicate in natural language, without having to learn codes or procedures. Requests such as I need a larger size or The dispenser is empty are understood by the assistant, which interprets the user's intent, asks clarifying questions when necessary, and initiates the appropriate process. It can collect information, request photos, access company systems, and update the status of requests in real time, all while maintaining a natural conversation. This model eliminates the traditional intermediate steps: department, wardrobe staff, or reference person - enabling direct communication between the laundry and the end user without losing control of the underlying processes. The result is a more immediate and transparent relationship: users receive quick responses and feel better supported, while laundries gain a clearer understanding of their customers' actual needs, reduce processing times, and improve their service. Wardrobe staff and technicians are not replaced; instead, they are freed from repetitive tasks so they can focus on areas where their expertise really makes a difference. AI thus becomes a new channel for building relationships: more human because it understands people s language, closer because it removes operational barriers, and more efficient because it turns every conversation into a traceable digital process integrated with management systems.

L’intelligenza artificiale è ormai una realtà consolidata anche inArtificial Intelligence is already well-integrated into the company s internal processes explains Claudio Montanari, the CEO of MONTANARI ENGINEERING CONSTRUCTION Srl from software development and engineering design to sales activities and customer support. "In the office it is a daily to use tool: we employ it to draft complex quotations, perform translations and develop software. Certain parts of our programs are now created with the support of dedicated AI systems. The contribution also extends to research and development, especially when analysing new technical solutions, although it does not replace traditional design work. On the machinery side, the most significant development concerns the management software developed by Cartesia, a company within the Montega Group. The platform incorporates AI-powered features that allow operators to query the system, troubleshoot faults, consult technical documentation, and automatically generate assistance tickets complete with the machine serial number, technical specifications, and all the information required by service technicians. This provides practical support for preventive maintenance and significantly speeds up service management.

You mentioned that AI also plays an important role in monitoring energy consumption... Artificial Intelligence is also becoming increasingly important for monitoring consumption. Through sensors installed on our machines, the software collects real-time data on energy usage and machine performance, providing analytical tools that help identify inefficiencies and opportunities to optimise production.

What is the real added value? The real potential of AI lies in analysing the vast amount of data generated by interconnected production systems. Today, thanks to Industry 4.0 and 5.0 technologies, we have access to a huge amount of information. The next step is to use that data intelligently, transforming raw data into operational recommendations without requiring staff to perform continuous manual analysis. The conversation concludes with a reflection on AI's impact on employment. AI brings enormous benefits in terms of precision and efficiency, but it is inevitable that some jobs will be gradually replaced. This transformation must be carefully managed, because it will fundamentally change the way we work.

"Our machines are already 4.0 Industry and can collect a large amount of information. This is the premise from which Giorgio Castino looks to the future of Artificial Intelligence applied to industrial laundry systems and the solutions developed by IMPIANTI NOVOPAC. The technology is actually already present in the form of monitoring and data collection systems. On higher-value machines, customers can request monitoring of components subject to wear, scheduling maintenance interventions before a failure occurs. If we know the useful life service of a chain, for example, the machine can count operating hours and issue a warning when replacement moment is approaching. This method opens the way to predictive maintenance. The level of control can be very high. Through PLCs and dedicated systems, Novopac can generate reports on production, downtime, and even the time between an anomaly and the operator s intervention. The customer tells us which information they want to know, and we study how to collect and deliver it. This level of customisation increases with the value and complexity of the system. There is, however, a particular characteristic of the industrial laundry sector: a Novopac machine is not perceived as directly involved in production. Yet if it stops, it can block the entire process, causing delivery delays. Our goal is to build a reliable machine that the customers barely notice they have. This is precisely where Artificial Intelligence can become strategic: using the data already available to anticipate anomalies, optimise maintenance, and further increase reliability and operational continuity. Technology should make the machine more reliable", not more complicated, concludes Castino.

We interview Elena Russ, Product Lead at HUBLO, to learn more about her role and her insights on a few key topics.

"Which AI features are already available in your software and which are still under development?"

For our dry-cleaners, we built a SaaS that uses AI to help them out. Our SaaS is composed of a 4K camera and a tablet with a connexion to the POS (point-of-sale). This software can help the manufacturers, by identifying in less than 5 seconds the type of clothing, the colour, the brand, etc. But mostly it can help on two issues. Firstly, the identification of holes, specific fabric (e.g., pearls) and spots. Secondly, the AI can trace the piece of clothing, if you are looking for what was in this order, when it arrived at the store, etc. All these questions that a dry cleaner manager asks himself in most of the litigations daily. What is still under development is the type of server we are using, that needs to be of a bigger performance. Also, we are constantly improving and training the models, for example for fabrics, to make it perfect.

How is operational data collected, processed and made available to support operational decision-making? "Operational data is mainly gathered in dry cleaners and stored on our servers (hosted in France). It consists of high-res garments pictures that are carefully reviewed and injected in our datasets. These datasets are used to train the foundation models (garment type, garment material, brand ) that will input information to infer risks and recommended treatments on garments".

What business opportunities and competitive advantages do you believe artificial intelligence will offer industrial laundries in the coming years? "In the coming years, many experts from the textile care industry will retire, and there will be some part of the demand which will be left unanswered. This SaaS offers a competitive advantage to the dry cleaners that will be offering the highest level of care and service to customers. This will defy competition completely".

We conclude this feature with Christopher Murphy , Managing Director of SPINDLE EUROPE/APAC, whom we asked: which AI functionalities are already available in your software and which are still under development? Spindle provides the capabilities needed for AI-supported operations, including production monitoring, labour productivity analysis, equipment utilisation, downtime and throughput reporting, staffing calculations, production-flow visibility, preventive maintenance, and comparisons across sites, shifts, machines, teams, and operators. AI-assisted features under development include anomaly detection, automated performance summaries, natural-language access to operational information, predictive insights, bottleneck identification, forecasting, and recommendations highlighting operational or financial impact. The objective is not to replace managers, but to help them understand what is happening and where action is required.

How is operational data collected, processed and made available to support operational decision-making? "At Spindle, AI development begins with a reliable digital foundation. Our platform collects real-time information from machine and PLC signals, equipment interfaces, employee activity, washfloor systems, IoT devices, maintenance records, ERP systems, databases, and data shares. The data is standardised and available through live displays, dashboards, reports, alerts, and management tools".

What business opportunities and competitive advantages do you believe artificial intelligence will offer industrial laundries in the coming years? "AI can create business opportunities for industrial laundries by turning operational data into faster, clearer, and more profitable decisions. AI will help laundries improve labour utilisation, release unused equipment capacity, reduce downtime, strengthen production planning, and connect output with energy, water, and other resources. The competitive advantage will come from combining accurate real-time data with AI-supported decision-making. Laundries will be able to produce more with existing resources, reduce costs, improve reliability, make better investment decisions, demonstrate sustainability gains, and protect margins".

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