Servitization: turning data into customer value
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Goglio is a leading company in packaging equipment and flexible packaging: it designs, develops and manufactures complete packaging systems.
Since its inception in 1850, it owes its success to its ability to anticipate market needs through innovative solutions.
Adopting a business model like Servitization enables the data-driven user economy. The analysis of production, operational, and process data collected allows for the provision of new innovative services aimed at improving maintenance levels and efficiency.
In this new ecosystem, clients can transform data derived from machinery into valuable business insights, creating value through process optimization, increased efficiency, and reduced unforeseen events.
IoT became crucial for Goglio in 2014 when the first customer requested responsibility for the efficiency of its packaging lines for the next five years. The initial connection was established via VPN, but later, Goglio developed its proprietary IoT platform, Goglio MIND, to offer the same services to all customers.
In 2017, our collaboration began with the goal of optimizing system performance.
Thanks to extensive process expertise, the system was expanded, improving performance and connecting more than 100 machines worldwide.
Servitization benefits both technology users and machine manufacturers.
Manufacturers gain insights into their machines even after delivery, allowing them to improve designs and provide more efficient service offerings. Customers, in turn, optimize their maintenance strategies for maximum efficiency.
As a result, machine performance improves over time. Instead of replacing equipment, customers can follow data-driven recommendations to maintain high operational efficiency.
The Servitization model provides precise cost visibility by linking service contract costs directly to actual production volumes, moving beyond traditional preventive maintenance.
Connected machines enable Condition Monitoring, allowing interventions only when necessary, replacing only the components that have reached the end of their lifecycle.
This selective and precise maintenance approach reduces operating costs by 10-20% compared to traditional models.
The data collected from machines is raw and requires Machine Learning or AI processing to become actionable insights. Human expertise remains essential in interpreting data and making strategic decisions.
Servitization places humans at the center of the business, empowering them to refine their expertise over time and drive continuous improvement.