brisklogic Driving Force is Artificial Intelligence

Industry 4.0’s Driving Force is Artificial Intelligence (AI)

The majority of the buzz about Artificial Intelligence in Manufacturing focuses on industrial automation, however this is just one part of the intelligent factory revolution which is a natural step forward in the quest for efficiency. What artificial intelligence can bring to the table of manufacturing is the ability to create completely new opportunities for businesses.

Below is a description that covers the two aspects of artificial Intelligence within the Industry 4.0 model, and the way in which this technology is already being utilized by the manufacturers to increase efficiency, increase quality and manage supply chains better.

Industrial AI’s Impact on manufacturing

The impact of artificial intelligence on manufacturing is categorized into five major areas:

  1. Qualitative Predictive and Yield
  2. Predictive Maintenance
  3. Human-robot collaboration
  4. Generative design
  5. Market adaptation/supply-chain


Let’s talk about each one individually:

1. Qualitative Predictive and Yield

Eliminating production losses and preventing manufacturing process inefficiencies has been an ongoing challenge for all manufacturers of all sizes. This is more so than ever before, especially as increasing demands are met with an increase in competition.

On the other hand, consumer expectations are higher than they have ever been. The global market is becoming “westernizing” while the population growth continues. According to many studies over the last few years, the population of the world will increase by 25 percent by 2050. This equates around 200,000 more people to feed each day.

However, the consumers are never faced with as many options available and in nearly every product that is imaginable. Recent studies reveal that this wide array of options makes consumers more likely to abandon even their most loved brands when, for example, there’s no product in the store.

With this backdrop the manufacturers cannot afford to tolerate the inefficiencies of their processes, and the related losses, and their associated losses, with ease. Every loss in the form of yield, waste, quality, or throughput eats away from their bottom line and gives another inch to their competitors -as long as their manufacturing methods are efficient.


The problem for many manufacturers, especially those that have complicated processes is that they will eventually reach the ceiling of the process’s optimization. There are instances of inefficiencies that don’t have an apparent cause, and so process experts are in a bind to explain these inefficiencies.

predictive quality and yield employs Industrial Artificial Intelligence to reveal the causes that lie beneath the recurring production losses that companies face every day. It is achieved through continuous multivariate analysis by using Machine Learning algorithms that are specially trained to be able to comprehend every single production process. The particular AI/Machine Learning method that is used in this case is known as “supervised learning” in which the machine learning algorithm has been trained to recognize patterns and trends in the data.

Learn how to identify the various kinds of Machine Learning, and how they can be applied to various manufacturing issues, in our no-cost Brisk Logic

Automated suggestions and alerts are then generated to notify production teams and process engineers of the imminent issue, and also to easily share information about how to avoid loss before it happens.


2. Predictive Maintenance

Predictive maintenance is one the most fundamental and widely-known applications that make use of Industrial AI. Instead of performing maintenance according to a predetermined schedule, predictive maintenance uses algorithms to predict the next failure of a component/machine/system and then alerts personnel to perform focused maintenance procedures to prevent the failure, but not too early so as to waste downtime unnecessarily.

Predictive maintenance systems are based heavily on Machine Learning techniques to formulate their own predictions (albeit they fall into a different category, unsupervised, instead of controlled). There are many benefits and they can dramatically reduce expenses while eliminating the need to plan maintenance in many instances.

By anticipating failures with an algorithm that learns from a machine learning system, the system can keep running without interruptions. If maintenance is required it is very targeted – technicians are aware of parts that require inspection, repair or replacement and the best tools to employ and what procedures to use.

Predictive maintenance can also lead to longer Remaining Usable life (RUL) of machines and equipment as secondary damage is avoided, while fewer workers are required to carry out maintenance processes.


3. Human-robot collaboration

In accordance with the International Federation of Robotics (IFR) at the year 2020, an estimate of 1.64 million robotics in the industrial sector were operating all over the world.

The standard approach is that when jobs are replaced by robots workers will be provided instruction for positions that require higher levels of design, programming and maintenance. Meanwhile the effectiveness of the human-robot collaboration process is increasing since manufacturing robots are being approved to work in tandem with humans.

As the usage of robotics in manufacturing grows, AI will play an important role in ensuring security of humans and also making robots more accountable to make choices that will further improve processes using real-time data from the floor of production.

4. Generative design

Manufacturers may also employ artificial intelligence during the designing phase. With a clear design brief engineers and designers could utilize the AI algorithm, commonly known as generative design software, in order to look at all possibilities of the possible solutions.

The brief may include restrictions and definitions of material types, manufacturing methods, budgetary constraints, and time limits. The solution set generated by the algorithm could then be tested with machine learning. The test phase offers additional details about the design decisions that were successful, and which ones failed. So, more enhancements can be made to the design until a solution that is optimal is discovered.

5. Market adaptation/ Supply Chain

Artificial Intelligence is everywhere in the industry 4.0 ecosystem and isn’t just limited to the manufacturing floor. An example is the application in the use of Driving Force is Artificial Intelligence (AI)algorithms to improve the supply chain for manufacturing processes, and also to aid them in responding better to, and anticipate changes in the marketplace.

To estimate demand for goods and services the algorithm takes into account patterns of demand categorized by date, place and socioeconomic characteristics, macroeconomic behaviour as well as the status of the political, weather patterns and many more.

This is a breakthrough for the manufacturers who can make use of this data to improve staffing, inventory control energy consumption, the use of raw material, as well as make more informed business decisions in terms of financial strategy.


Industry 4.0 requires collaboration

The difficulty of applying artificial intelligence to manufacturing automation requires that producers work with experts to create specific solutions. The process of building the necessary technology can be costly, and the majority of manufacturers lack the capabilities and expertise on their own.

A Industry 4.0 system consists of various components or phases that must be configured according to the requirements of the manufacturer.

  • Historical data collection
  • Live data capturing via sensors
  • Data aggregation
  • Connectivity through communication protocols, gateway and routing devices
  • Integration with PLCs
  • Dashboards to monitor and analyze
  • AI applications Machine learning and other methods

Appropriate AI Technology:

After several decades that saw Industrial AI being a distant goal for many manufacturers, these technologies are actual and easily accessible. Naturally, the primary factor to reap the maximum benefit of the Industrial AI solution is knowing what kind of Driving Force is Artificial Intelligence (AI) is appropriate to the specific business issue.

Find the Manufacturer’s Guide to Choosing the right industrial Driving Force is Artificial Intelligence (AI)Solution Learn an easy and effective formula to match your specific manufacturing needs to the appropriate AI technology:




Your email address will not be published. Required fields are marked *

type your search

We are a “YOU” organisation.

This isn’t about what we’re capable of. It’s all about what you can accomplish with us by your side.

Reach out to us anytime and lets create a better future for all technology users together, forever.