What is the role of technologyin the digital transformation process?

No one can reasonably aspire to be autonomous in terms of knowledge and control over the countless components of digital technologies. Strategically important is the ability to identify and pursue the most consistent partnerships in terms of technology and business with respect to one’s own business, size and globalization capabilities.

Cloud computing, embedded and edge computing, oblique and low-cost connection, are all recognizable technology trends that are revolutionizing our private and professional lives. However, a well-structured innovation project must start from an effective use of enabling technologies, and must be guided by a clear vision of the company management, which subdivides the advantages into specific company processes.

A first example of an actual digital transformation is represented by the use of data to achieve a drastic reduction of waste during processing (zero defects), starting from the knowledge derived from being able to navigate through massive data and to analyze them with the experience of those who know the processes from which they arise, then moving on to the use of data analytics algorithms and finally to artificial intelligence tools.

Holonix aims at developing a software to optimize production processes by introducing systems for real-time monitoring and predicting failures/defects, with consequent automatic machine setup in order to minimize waste; collected data will be historicized and the new knowledge acquired will become the basis for the implementation of necessary algorithms to start a Zero Defects production.

Going beyond the factory and reaching for Industry 4.0, the opportunities are even more remarkable. Exploiting the IoT potential to create new products-services, allowing for continuous interaction over time and for customer loyalty, as well as for an exponential data increase from the field available to the supplier; all of this is linked to a series of possibilities to improve design, thanks to the integration with corporate PLM systems.

The systematic collection of big data from machines, their sharing in the Internet of Things mode and their processing through advanced forms of Artificial Intelligence are the core of this epochal transformation process.

In this regard, it may be useful to explain how the use of new technologies and the achievement of its related advantages can be considered by manufacturers (and their customers) as the stages of a journey towards Maintenance 4.0.

The first stage is, of course, to make the machines “intelligent”, equipping them with a subsystem that continuously collects the most significant parameters of the machine status and sends them through a transmission network towards a secure data platform of unlimited capacity. Once this first step – which does not require a large investment – is completed, manufacturers can immediately reach the first goal of their journey.

A tool like Holonix i-Live Machines guarantees immediate real-time control of the status of the manufacturer’s installed equipment, as well as the collection of the complete data history of every single machine. It will be possible to use this new technological base to rapidly implement, for example, an innovative Condition Based Maintenance service, which calls for intervention on the basis of real-time detected parameters on the machine, thus allowing much higher levels of accuracy and safety than ever, even remaining within the logic of a preventive maintenance.

The real quantum jump will happen with the transition to preventive maintenance. The use of appropriate predictive analytics tools – starting from the machine model and its data history, correlation patterns will be identified between the evolution of physical parameters detectable in the machine and the detected failure modes – will allow for an implementation of just-in-time maintenance logics, minimizing unscheduled downtimes and radically maximizing the machine’s productivity and TOC.

Data may well be the new oil, but in this case the threat to our development model would seem to come from the unlimited potential for growth and the infinite availability of the raw material “information”, rather than from the finiteness of a resource which is limited and increasingly difficult to extract.

All joking aside, we should seriously ask ourselves how to ride this emerging and gigantic wave of data, in order to avoid being overwhelmed by it or losing our orientation and control.

One of the keys to success is the awareness of the need to consistently design the processes and tools used to extract and refine data keeping in mind the purpose of data acquisition and subsequently deciding what data to acquire, how to acquire them and how to analyze them (data-driving).

This is the pragmatic suggestion of our data scientists, the prerequisite for transforming new data into information that can be used in real data-driven processes.

Emblematic cases are those in which the effectiveness of the decision-making process depends on the availability of data which is unfiltered, unaltered and unalterable by third-party interpretations; or in which it depends on the possibility of analyzing data coming from the entire set of all possible operational situations and not only from a subset selected on the basis of already known criticalities; or again, in which it depends on the availability of data, adequately representative in terms of breadth and precision of the actual probabilistic distribution of critical parameters.

Truthfulness, coverage and breadth are in fact data quality requirements that are often decisive in bringing to light, through appropriate analytics, critical issues that can only be identified in a data-driven logic.

All these cases have been faced and solved by Holonix also within the EU Horizon2020 project called Lincoln (G.A. 727982, www.lincolnproject.eu), where a large amount of data is acquired from tracking and management systems to enable a process of fact-based design of boats.

The monitoring over time and the integral analysis of parameters measured in the actual use of the boats by a clustered user, allowed for the documentation of a very different use from what was declared, leading to the decision of the manufacturers to modify the mission profiles in some of the professional scenarios they addressed.

The high-frequency collection of salient design parameters during sea testing of prototypes has instead made it possible to link the complexity and variability of such parameters measured in real operating conditions with the modeling used during the design phase.

I.e., data-surfing is not enough for data-driven professional sailing: adequate data-driving is needed, too!

Thanks to the continuous evolution of digitization, companies today have the possibility to get in touch with a huge amount of their customers’ information and data from different sources: from purchases in stores, through on-line purchases, to tracking even the likes users leave on social networks to certain brands.

In order to be useful, the huge amount of various data collected by companies, the so-called Big Data, must be filtered and analyzed; it is in this context that Augmented Intelligence solutions come into play to automate this process and make it effective.

The problem will be managing this data, as companies have accumulated a large but not homogeneous amount, with different tools and methods, often not integrated with each other. The lack of integration of collected data prevents the exploitation of its richness and thus the real challenge will be to combine the data and obtain, through the implementation of algorithms, useful and applicable insights.

Data interpretation is fundamental in the new marketing perspectives, as it allows to know the potential customer in more detail, allowing companies to understand his/her needs and to offer a customized solution.

On these bases, within the EU Z-Fact0r project (G.A.723906 ), with the purpose to develop a specific software to optimize manufacturing processes, introducing real-time monitoring systems to predict failures/defects with consequent automatic machine setup in order to minimize waste, Holonix has been testing future developments for its augmented intelligence tool, i-Live Machines, in which collected data will be historicized and the new knowledge acquired will be the basis for the implementation of the algorithms necessary to start a Zero Defects production.

Manufacturers of industrial machinery, thanks to the use of this solution allowing for the collection of Big Data transmitted by each installed machine, will be able to provide an efficient and effective support service on the actual behavior of each machine in their fleet, in a continuous way and without intermediate filters, offering their customers customized solutions to meet their real needs.

i-Live Machines is also beneficial in that it allows manufacturers to propose themselves on the market with new components strengthening its current offer, and to undertake the path of digital transformation in order to be more competitive.

This transformation is not exclusively dedicated to Large companies, but rather to any reality that has the possibility to activate the services necessary to transform Big Data into value and to start its digital transformation path.

Holonix i-Live Machines, is addressed to manufacturers who want to receive real-time information about the operation of their machines. The data generated by the customer’s machine are processed and transmitted through a gateway to the i-Live Machines Cloud solution.

The need to generate value from the analysis of countless available information results in the creation of new procedures and rules, e.g. In order to define corrective, preventive and predictive maintenance actions.

For this reason, Holonix has been working on algorithms, developed with the application of big data and augmented intelligence technologies, specific for a machine model or for a single machine, that allow for the elaboration of rules in order to predict machine downtime, malfunctions, etc.

For the manufacturer, through i-Live Machines, it is possible to monitor multiple machines, thus having a real fleet management system. . As the number of connected machines increases, so does the amount of collected data, and it is therefore possible to talk about big data. Each machine is inserted into the system with a precise classification of its characteristics and geolocation. This allows for the space and time monitoring of the machine fleet after its sale.

The manufacturer is then able to understand how the machine is performing, recording any defects that will help to improve the product itself and support the user during maintenance.

Holonix’ experience in this area has been steadily growing, thanks also to its participation in several EU research projects. In the Z-Bre4k EU project (G.A. 768869| https://www.z-bre4k.eu/), the main objective is the creation of a predictive maintenance platform to eliminate failures, unexpected occurrences and to extend the life of manufacturing systems. The continuous feedback and the development of Augmented Intelligence algorithms, together with the best EU research centers, allow for a steady improvement of i-Live Machines, with functionalities aimed at responding to the needs of predictive maintenance.

It is necessary to follow particular methodologies, such as functional tests, negative testing, usability tests, maintainability and security evaluations, in order to define the fundamental company KPIs on which to base the intervention.

This is whyi-Live Machines becomes the ideal solution for all those companies that want to get value from data and use them to start a process of innovation within and outside the company itself.

Information technology, before the advent of blockchain, had managed to automate various procedures; however, some controls related to transactions have always been very much linked to human intervention. One of the areas that suffered from this lack of automation is the “Internet of Value”, i.e., the set of all those applications allowing for the exchange of (virtual) currencies or objects (concept of value).

After the advent of blockchain in 2008, about 10 years ago, not only did virtual currency solutions gain benefits, but also all those applications aiming at ascertaining the authenticity, security and consistency of data, as the control-related cost has since drastically decreased.

Thanks to the implementation of the blockchain technology, it is now possible to track data, controlling authenticity in real time with each new modification of the product. The blockchain network is also shared by all the actors along the supply chain, and each one of them contributes to powering it.

Holonix is experimenting with blockchain technology in one of the EU research projects in which it is a technological partner. Within the ManuSquare Project (G.A. 761145 | https://www.manusquare.eu/), the goal is the creation of a common platform for all the actors of the supply chain involved in the whole production process: from the creation of a new idea, to the manufacturing of the new product.

Our specific role within the project is to manage the innovation process and the integration of all our partners’ software components. Thanks to the use of the blockchain, we can guarantee the authenticity of the data and therefore strengthen the controls, carrying them out in an effective and efficient way.

Holonix wants to increase its know-how on blockchain for future implementations within its products. In the IoT field, in which we have been working for years, we aim to integrate blockchain technology into our solutions to allow for a greater control over sensitive data such as contracts, asset trading and controls over generated data. The success in the field of virtual currencies gave visibility to the blockchain technology.