In the first part of this article, we saw the dynamics of a “Smart Factory” (factory of the Industry 4.0 era). All the machines were connected and coordinated in an automated way to carry out the production of canned soft drinks up to the logistics to deliver the product to the vending machines where it was needed.

In addition, the smart factory also connected with people through the “cloud” to meet the demand for special events.

The “Smart Factory” is a global trend due to the benefits it promises to bring to society, minimizing costs, maximizing the use of resources and serving the market in a personalized or specialized way. But how much time is needed to make these benefits tangible, are radical changes required or is it possible to do it progressively, what challenges should be faced first to enhance the evolution towards “Industry 4.0”?

Below, we discuss the main challenges that are foreseen to evolve towards a “Smart Factory”:
The Organization Culture. Perhaps the primary challenge of a company is to underpin the entire organization towards a vision and this requires a “positive culture” (1) whose dynamics make it possible to successfully meet the particular challenges of the company.
This is of course not new, but such is the degree of complexity of the challenge that the various advances in neuroscience, psychology, sociology and economics have given rise to new methodologies such as Market-Based Management (MBM庐) of Koch Industries, Inc. (2).

Connectivity. Connecting all sensors, drives and all cybernetic systems not only represents a great investment, but also a great technical challenge (3) that has evolved into a mega trend, the “Internet of Things” (or simply IoT). Connecting millions and millions of devices represents in itself a major security risk (3). Just imagining that the sensors in a factory start to fail due to a slow network or due to an attack by hackers, makes you think about the vulnerability of the current Internet network.

Interoperability. Perhaps greater than IoT is the technical challenge is getting all the systems to communicate at different levels. This means integrating OT with IT, i.e. all operating technology such as Cyber-Physical Systems (Cyber-Physical Systems), e.g. robots controlled by computers that are in turn connected to the “cloud”. New standards and communication protocols will be necessary, a task that requires collaboration between international companies from different industries.

Business Intelligence. Once everything is interconnected and interoperating, the next level is to abstract the entire operation of the company in intelligent systems or cognitive computing systems (“Cognitive Computing”) (5). This level of technology can be composed of different platforms: The most important is the Artificial Intelligence Platform that makes decisions and is the brain of the “Smart Factory”. This platform is supported by another platform that today is also another mega trend in technology: Big Data, which is composed of all the technology for data analysis and data mining.

The great challenges of Industry 4.0 involve other mega technology trends such as “Big Data” and “AI” (Artificial Intelligence) that make the goal of Industry 4.0 still seem very far away. But the technology is advancing by leaps and bounds because thousands of engineers in hundreds of companies around the world are working simultaneously to meet the challenges and gain an edge that will put them at the forefront of the market.

Notas bibliogr谩ficas:

1) MBM Guiding Principles de Market-Based Management (MBM庐).

2) Market-Based Management (MBM庐,

3) Three Major Challenges Facing IoT (

4) “OT” from “Operational Technology” all the technology that is in charge of controlling the physical processes of the production plant, therefore, it has a direct relationship with the “hardware”. IT, on the other hand, refers to Information Technology, which are computer systems and networks that work only with data and its management, such as storage and processing.

5) “Cognitive Computing” (CC) are technology platforms that encompass machine learning, reasoning, natural language processing, speech and vision recognition (object recognition), human-computer interaction, dialogue and narrative generation, among other technologies.