Digital transformation in enterprises: Industry 4.0

The term “Internet of Things” (IoT) has been used for the first time twenty years ago by a Professor of the Massachusetts Institute of Technologies named Kevin Ashton. IoT is the set of components and technological devices that can be incorporated into physical objects and machinery, which ensure the interface between the physical and digital world and allows to communicate through the Internet with other objects, to exchange information, to modify the behavior based on the inputs received, to memorize instructions and therefore to learn from the interactions. Focusing more on the factory side: IoT is the connection to the network of everything that is in a factory, such as the components of the products, the people who operate on the process, for instance through wearable devices, the productive resources and the equipment. IBM describes it as objects that connected to the network allow to unite real and virtual world. The components of the CPS system change their behavior following the inputs received from the exchange of information through the network.
The potential of IoT is clear, but companies are still struggling to adapt to the radical innovation and to be proactive in this area because there are difficulties and challenges to face: the costs for these technologies are high and they must be sure that their use brings benefits. The aim of IoT is basically to gather, monitor, control and transfer information and then carry out consequent actions. Lack of skills and difficulties in understanding the value offered by adaption of the IoT are barriers to adoption. To implement the IoT it is necessary to have appropriate knowledge in the whole value creation chain; thus recruitment of specialized staff and appropriate training programs addressed to employees already present in the company are necessary aspects to take into consideration.
The innovation of the IoT is a new form of interaction as well as between objects and objects (Machine to Machine), also between people and objects (Man to Machine), so a new form of interaction no longer not available to people.
The trend in recent years of spending worldwide in the IoT is increasing: according to Deloitte’s research, investment in projects that incorporate IoT technology are increased from 2015 to 2018 by over 80% and it estimates that the market reached over $ 750 billion in 2018. Italy follows the trend, in fact, according to the “Digital Innovation Observers” of the School of Management of the Politecnico di Milano, the Italian Internet of Things market continues to grow at a rapid pace. In 2018 it has reached the value of € 5 billion, with an increase of 35% compared to 2017 - the growth of the Italian market appears to be in line with the one of the other western countries, where it oscillates between + 25% and + 40%.
A mass of data passes over the Internet and it continues to grow exponentially. The amount of heterogeneous data generated by the web, mobile devices, apps, social media and connected objects opens up new opportunities for companies given the possibility of correlating and interpreting data. The large number of data transmitted by all networked objects, products, machinery and tools leads to a problem regarding the collection, identification and storage of such data.

“Big Data and Analytics” refers to the set of technologies that allow to storage high volume of data and its consequent processing, which makes it possible to transform it into useful information that is essential to implement an efficient corporate strategy.
Data analysis is usually conducted in accordance with three approaches complementary to each other:

• Descriptive approach: used to understand what is happening or has just happened by classifying and using clustering algorithms to represent the data available and to provide outputs such as historical reporting and comparisons of data.
• Predictive approach: the purpose is to predict future trends or behaviors by taking into account historical data to which apply statistical algorithms such as regression and machine learning. Such an approach could lead to the development of predictive algorithms which could help to better manage the product life-cycle, for instance.
• Prescriptive approach: it goes beyond the predictive approach and it provides information on possible scenarios, based on optimization algorithms, in order to provide strategical solutions to the decision maker.

The analyst Doug Laney, in 2001, defines the “3 Vs” - three dimensions of data which measures technological efficiency:
• Volume: it refers to the huge mass of information which cannot be collected with traditional technologies. Such volume of data is continuously growing: “the rapidly increasing volume and complexity of data are due to growing mobile data traffic, cloud-computing traffic and burgeoning development and adoption of technologies including IoT and AI, which is driving the growth of big data analytics market. Over 2,5 quintillion bytes of data generated every day. Data is created by every click, swipe, share, search, and stream, proliferating the demand for big data analytics market globally”. If technological devices were not able to analyze the data, the archiving would be considered useless.
• Velocity: it refers to the amount of time it takes for the data to be gathered, transferred to the processing centers and processed. Analyze data in real-time is a challenge, but it is essential to be able to take business decisions as fast as possible.
• Variety: Audio, text, video, images are different formats and different kinds of data; such as structured and unstructured data and internal and external data to the organization. The different types of data available today comes from an increasing number of heterogeneous sources and gather and analyze it requires technology and skills.

Today it has been added four “Vs” to the previous three:

• Variability: the same data can be interpreted differently in relation to the context in which it is placed and from which it has been gathered.
• Veracity: it is about making sure that the data is correct and accurate. Since bad data is worse than no data, data must tell the truth.
• Visualization: charts and graphs are essential to assure that an enormous amount of data can be read and interpreted effectively. Transforming big databases to data that is capable of being read fast, is critical.
• Value: big data analytics (from gathering it, to processing and delivering it) is a cost. The aim is to obtain value from it, by strategically influencing the decision making process in a way to be more efficient - which could lead to a competitive advantage.

According to the “Digital Innovation Observers” of the School of Management of the Politecnico di Milano, states that in 2018 Analytics market reached a value of € 1,39 billion with a growth rate of + 26% compared to 2017.
Supposedly, such positive dynamic is due to the greater awareness on the subject by large companies (>249 employees) who are responsible for 88% of total spending, while SMEs stop at 12% due to a technological and cultural delay, but also due to fewer resources.

IoT and Big Data Analytics, together define the concept of “Cyber Physical Convergence” (CPM): the diffusion and the use of mobile devices with network functionality is facilitating convergence between the physical world and the virtual world in which intelligent objects and human beings interact through sensor devices with computing and communication capabilities (IoT), generating huge volumes of data that are exchanged between the two worlds (Big Data Analytics).

The main opportunities and strengths of IoT and Big Data Analytics are linked to:
• gaining a competitive advantage attributable to a long-term strategic decision that can be taken due to the information available because of the interconnection of the IoT and Big Data;
• improvement of the time to market and the use of resources;
• being able to control and manage the process more efficiently.

The main threats of IoT and Big Data Analytics are linked to:
• high cost and technical difficulties on implementing a CPS;
• lack of prepared human resources able to cope such radical innovation.