The uses for AI applications such as machine learning are also becoming more and more diverse in the industry. Read here why companies will be increasingly interested in machine learning in 2023. Machine learning has been hyped in the industry in the past. However, the reality has fallen so far beyond expectations. The technology is undoubtedly used in production, but its spread has been significantly delayed. That will now change. Industrial companies will increasingly rely on machine learning in 2023, mainly for the following four reasons.
Volatile Environmental Factors
The worldwide crises lead to delays in the delivery of incoming materials and make sales forecasts more difficult. If industrial companies want to consider all influencing factors, their corporate planning becomes highly complex. This complexity can be mastered with the help of machine learning systems. They can help companies significantly forecast developments and take a wide variety of scenarios into account – thus ensuring reliable delivery to the end customer.
Individualized Production
Industrial companies’ batches are getting smaller because their customers increasingly expect individual product solutions. To keep up with this development, they have to increase the efficiency of their manufacturing processes. For this reason, industrial companies will increasingly implement predictive maintenance and predictive quality applications. They make it possible to avoid unplanned downtimes and the occurrence of rejects by intervening in good time, thereby optimizing overall equipment effectiveness.
Energy Scarcity And ESG
The current energy shortage could remain the norm for the foreseeable future. Industrial companies are therefore forced to make their production as energy-efficient as possible. Machine learning systems can measure and analyze energy consumption online and take it into account in production planning. Energy data collection also enables them to meet the increasing ESG requirements. For example, they can equip their products with environmental and energy labels or provide traceable evidence of ESG compliance at any time by logging the data.
Demographic Change
The workforce is aging, and many employees will soon be retiring and need to be adequately replaced due to the shortage of skilled workers. Industrial companies lose valuable know-how for machine management. In many manufacturing processes, influencing factors such as materials are subject to strong fluctuations that cannot be compensated for by a recipe.
Therefore, machine operators level out these fluctuations through process interventions based on many years of experience. So that this know-how is recovered, companies will try to bring it directly to the machines. Unique machine learning approaches based on ontologies, such as Bayesian networks, are best suited for this.
Use The Foundations You’ve Already Laid
Machine learning can provide the best answer to many of the challenges industrial companies face. The conditions for this are favorable because, in recent years, many companies have already worked on equipping their machines with sensors for data acquisition through digital retrofitting, networking the devices, and bringing the data to the cloud. They can take the next step and profitably analyze their data with machine learning algorithms.
The Challenges Awaiting Machine Builders
Most OEMs focus on customer needs and need to think more about how to pave the way for their success. To produce cost-optimized machines, it is necessary to establish where to start and where to go. Exploring digital possibilities for optimizing machines without facing huge investments can be challenging. Lack of knowledge, overload of digital technology, and potential safety risks during the machine’s lifecycle further complicate matters. This brings us back to the main question: How can it achieve a 20% profit from digital services within five years with a business model that can benefit the OEM and its customers? And what kind of customer is willing to pay for it?
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