Column Artificial Intelligence

HUMAN CREATIVITY AND ANALYTICAL AI — A SUSTAINABLE COMBINATION

Artificial intelligence (AI) is opening up new possibilities for us in both the private and industrial spheres. Applications with intelligent language assistants or  supercomputers for computing and analyzing otherwise unsolvable tasks have now  successfully demonstrated huge potential and have the ability to surpass the human capacity for processing enormous volumes of data. But are these technologies sufficiently advanced to actually replace humans and their professional expertise? Or can machines even go beyond this and develop their own innovative and creative mental capacity?

Increase in productivity versus lack of creativity

Nowadays, AI systems can process huge volumes of data and information and, by applying complex algorithms, are capable of choosing from previous and therefore familiar decisions. This allows them to provide recommendations for actions and  thereby support the decision-making process. The systems are therefore carrying out work, which was previously considered the preserve of humans alone. One undeniable advantage is that they often work more precisely and with unvarying reliability. This results in huge gains in productivity, but also brings radical changes regarding the  range of decisions in the professional world. Even though machines are acquiring  more and more intellectual capacities, the capacity for innovative and creative thinking beyond known approaches to a solution remains unique to humans. As many experts concede, AI is still in the early stages of its development. Compared to human life, AI  was in its infancy until recently when it was able to distinguish between basic  concepts (“mom”, “dad”) for the first time with a key word for an image or pattern recognition. Machines are now capable of solving more complex problems having been instructed accordingly and based on training data; the human guides the systems and “teaches” them.

Better performance in the warehouse

In logistics, self-learning systems are already performing tasks in customer service.  Bots, for instance, manage logistics processes by applying optimization algorithms and also enable the early recognition of risks within the supply chain based on the holistic evaluation of various factors. And there is justification for predicting that AI will take intralogistics processes to a new and more flexible level. Due to automation and extensive opportunities of digitalization, productivity in the warehouse is set to increase significantly. AI technologies also optimize picking performance thanks to  more reliable forecasts and stock level adjustment. Intelligent systems have a positive effect on picking performance as AI-controlled robots shorten picking times and increase reliability. Therefore, this raises the performance of the entire warehouse to a new level. At the same time, the integration of AI requires a lot of processing power as well as initial development and programming, which many companies are not (yet) willing or able to afford. This calls for considerable investment in staff and technology and a change in perspective in terms of project handling. Whether or not this will pay off in each individual case remains to be seen. Small- and medium-sized companies in particular are better off employing external service providers who can help them benefit from AI (similar to the use of cloud technologies) whilst taking their personal and financial capacities into account.

Holistic approach for success with AI

SSI SCHAEFER, just like many other enterprises, considers AI a central part of its company strategy and a decisive factor for the future core business and the emerging new wave of digitalization. A holistic approach is primarily being adopted here. Only by monitoring all interfaces and technologies, is it possible to ensure that self-learning systems have sufficient information for decision-making. A full-service provider such as SSI SCHAEFER offers the option of horizontal and vertical integrability of all components and system parts as the basis for implementing new AI  technologies. The interaction of hardware components and the corresponding software tools serves as the basis for allowing machines in the flow of goods to learn from each other – including across different levels – and ensures intelligent (local) control without compromising on quality or performance. Despite these technological advances, the level of success achieved by AI in ensuring (local) control is always determined by the individual benefit the customer gains from greater optimization, flexibility, and dynamism in warehouse operations.

Learn more about Artificial Intelligence in our Whitepaper.