Paraphrasing Gartner’s experts, artificial intelligence technologies represent an unprecedented opportunity to solve insoluble problems with only human skills. The phenomenon is now pervasive and in fact, according to the analyst’s estimates, by 2021 40% of the Enterprise applications marketed by the service providers will use Ai functionality. There are different areas of application of Artificial Intelligence technologies and the possible results varies from consumption optimization to the identification of market’s needs.
Among the most effervescent application areas, the financial sector will increasingly use predictive algorithms and features to identify fraud or analyze customer behavior. Healthcare will take advantage of the evidence generated by machine learning technologies to advance research, refine diagnoses and improve the delivery of patient care.
Industry 4.0 at full intelligence
However, another high potential sector is emerging as a playground for the new challenges of Artificial Intelligence, or the manufacturing sector according to the new models of Industry 4.0.
In modern intelligent factories, the Internet of Things has now made its triumphant entry. Sensors and smart devices distributed widely within production plants and logistics centers are drastically increasing efficiency and creating new business opportunities.
Also according to Gartner statistics, in 2019 there will be 14.2 billion connected objects in circulation and will reach the mysterious share of 25 billion in 2021.
Among these, many will be located within manufacturing plants and will pave the way for interesting applications of industrial automation, predictive maintenance, asset management and energy consumption control.
The information collected by smart devices will feed the analytical engines. Here they will be crossed with data from management systems (Customer Relationship Management or Enterprise Resource Planning solutions), from online sources (such as social networks or ecommerce platforms ), from mobile apps and smart products sold to customers.
Ai and IoT: practical examples
The information assets released by Smart Manufacturing technologies represent gasoline for Artificial Intelligence applications.
Consider, for example, predictive maintenance applications: sensors allow condition monitoring of machinery and components, detecting status and operating parameters (pressure, temperature, vibrations, etc.) and transmitting them to analytical systems through the network. The comparison of these data allows to identify any anomalies or malfunctions. It allows to promptly intervene (with the support of the maintenance team or by triggering self-regulation mechanisms) before failures or production stoppages occur.
The management of assets remotely and through artificial intelligence offers advantages not only in terms of maintenance, but also for theworkflow optimization. Machines can be regulated in power according to the task and the type of production, all thanks to the information collected by the sensors and the calculation models.
The same intelligent mechanisms can be used to maximize and keep the consumptionof the individual machinery and the entire system under control. Artificial intelligence helps in the definition of energy performance indicators, as well as in identifying any deviations, with the possibility of correcting the route .
Artificial intelligence can also make a valuable contribution in intercepting market needs thanks to the analysis of the moments of buyer-seller relationship. They are today increasingly consumed through online channels and digital services enabled by smart products. The indications provided by the analytics allow targeted marketing campaigns (also by means of automatisms) and product innovation based on consumers’ preferences and habits.
AI is a cultural leap
If the path of artificial intelligence seems to be paved with gold by virtue of the advantages offered, however, it must not be imagined without obstacles or taken for granted: first of all a solid knowledge of business dynamics is needed, the rationalization of business and data management processes, the choice of solutions that guarantee the construction, training, distribution and maintenance of algorithms, the introduction of specific skills, with a leading role for data scientists.
This is because artificial intelligence first of all presupposes aleap in thought, a cultural rather than a technological and infrastructural revolution: therefore, foresight at the management level is needed, but also preparation at all levels of the organization chart.
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