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Improving manufacturing processes with AI

According to Marc Benioff, CEO of Salesforce, “the only constant in the technology industry is change”. It is also said, that the same influential entrepreneur, when asked to describe the changes that the 4th Industrial revolution brings to the global business, commented with “speed is the new dollar in the market”. And indeed, if one adds the word quality to that statement of the experienced CEO, one has a complete description of the purpose of the Industry 4.0 era.

The 4th Industrial Revolution negotiates the complete digital transformation of all the resources and processes of a company, with the primary goal of industrial production. Clearly, other areas of different economic activity are not ruled out, as the creation of digital ecosystems is the crowning achievement of a successful value chain.

Automated and robotic systems allow manufacturers achieve maximum quality and speed levels during the production process, whereas the use of artificial intelligence and big data analytics significantly improve the quality of the produced products. Last but not least, intelligent interfaces enable remote, end to end supply chain management; from the production and storage, to the distribution phase. All of the above sum up to the so-called “smart factory”, a holistic system that uses algorithms and robotic systems and stands at both the present and the future of industries

In fact, according to McKinsey research,

Last but not least, quite recently, a pioneering interdisciplinary research presented at the closing conference of FORCE, a Jean Monnet Project [ERASMUS +] , claimed that artificial intelligence tools can make a positive contribution to 79% of the 169 Sustainable Development Goals.

Coming back to manufacturing industry, we will see numerous examples of companies who used robotic systems and optimized production, with specific measurable results. Robotic systems improve the internal operation of the industry and the quality of the products produced, providing unimaginable precision. Of course, human presence is not a substitute for the production process; on the contrary, should be trained on the use and management of robotic systems. In other words, there is a change of supervisory roles.

In fact, as part of the multibillion-dollar diversified CK Birla Group, Birlasoft has identified some remarkable use cases of AI in the manufacturing industry, including the one of French food manufacturer Danone Group, who uses machine learning to improve its demand forecast accuracy and the one of the BMW Group, which uses automated image recognition for quality checks, inspections, and to eliminate pseudo-defects.

But what is the significance and value of the 4th Industrial Revolution (4IR)? Reviewing experts’ articles, such as the How Are AI and Robotics Increasing Manufacturing Quality and Efficiency? – IoT Times ( and the Here’s how AI is improving manufacturing in 2021 – Tech Wire Asia , we realize that the 4IR enables companies to operate more efficiently, easily and quickly. Although

digital technologies are at the heart of this, the driving force behind evolution is not technology itself. The real disruption comes from utilizing business data and creating value from it. Data is the fuel of the new era, and utilizing it in an organization presupposes organizational flexibility, continuous flow of information, skills, cooperation between all parties and a willingness to change. Data-driven companies use analysis tools, enjoying up to 35% reduction in production time, up to 25% reduction in inventories, up to 3% increase in revenue.

The final goal should be the formation of a dynamic operating model, where the automated production line is part of a data-driven organization, and where humans are in constant interaction with technological systems.