Artificial Intelligence (AI) is one of the key technologies driving digitalization, making it a significant opportunity for companies in the manufacturing sector to gain a competitive edge. In the following sections, we explain why AI is so crucial in production, along with concrete use cases and potential applications of AI in this context.
1. Current Situation and Potentials of AI in Production
The COVID-19 pandemic exposed the fragility of global supply chains, prompting companies to reconsider and localize parts of their supply chains, often shifting them back to Europe. However, this shift has led to increased costs for companies in the manufacturing sector. The high-cost labor market has driven up production expenses, affecting their overall competitiveness.
Meanwhile, the digital transformation, coupled with technologies like Artificial Intelligence (AI), offers significant potentials. AI comes into play, particularly in tasks where human capabilities fall short. As a result, automation through AI in production becomes essential. On one hand, manufacturing companies can address rising costs and leverage untapped potential. On the other hand, AI in production promises efficiency enhancements, novel interaction models, and innovative business models.
2. Use Cases of AI in Production
The application areas of AI in production are diverse, results-oriented, and promising. Examples span from logistics and quality management to process optimization and product development. The diverse application areas of AI in production also involve numerous relevant technological methods. Accordingly, text, speech, image, and sound recognition can play an equally significant role in manufacturing and production as action planning or multidimensional pattern recognition.
2.1 Logistics and Resource Planning with AI
AI in production is essential for optimizing supply chains. Semantic networks and machine learning, for instance, integrate action planning and optimization algorithms. In short, these technologies optimize various aspects of the supply chain, including inbound logistics, storage, production, and distribution. For example, at Motius, we developed a digital twin of production for one of the world's largest medical technology companies. This digital twin integrates and analyzes extensive, complex data, proactively suggesting actions to optimize the company's supply chain despite complex just-in-time and make-to-order production.
2.2 Maintenance with AI
In production, AI is becoming increasingly important in maintenance, particularly in predictive maintenance. Action planning algorithms monitor predefined parameters or characteristics of equipment or processes to determine the optimal maintenance timing based on equipment wear and tear. This optimization of maintenance activities can help avoid unexpected downtime and reduce maintenance costs. In one of our projects, we developed a system that uses AI-based methods to identify critical status information for various machine components, automatically alerting users. This allowed our client to avoid unplanned downtime and optimize maintenance processes and costs.
2.3 Quality Management with AI
Quality management in manufacturing is essential for ensuring the highest product quality. Manual quality control is inconsistent and not scalable, so AI-based methods are increasingly being applied in quality management. Computer vision and machine learning, in particular, help with early detection of product defects, quality variations, or quality issues, saving time and costs in production while guaranteeing the highest product quality. For example, based on image processing algorithms and pattern recognition, we developed a system for one of our clients that automatically detects product defects so minimal that they are invisible to the human eye, allowing our client to guarantee the highest possible product quality and reduce warranty costs.
2.4 Product Development and Process Optimization with AI
AI in production can assist manufacturing companies in various phases of product development, simplifying, scaling, and accelerating processes. For example, pattern recognition can help evaluate test and simulation data more efficiently, while planning and optimization algorithms can speed up development processes. For one of our clients, we developed a machine learning-based data analysis system that can identify patterns in machine usage and predict machine failures, contributing to efficient product development.
2.5 Digital Assistants with AI
AI in production allows companies to meet the increasing flexibility and efficiency demands in manufacturing. AI-based methods like Natural Language Processing can be used to design the interface between humans and machines to support production processes. In one of our projects, we developed a chatbot that automatically communicates machine failures and alerts employees to issues, providing the right information at the right time and place. This application of AI in production minimizes downtime in production processes."
3. Economic Potentials and Impacts of AI in Production
In the above use cases, you can already see the economic potentials and impacts that AI can have in production. Concrete benefits include improved product quality and reduced downtime costs. AI in production can also lead to better machine utilization and shorter time-to-market. We have summarized the key points that help minimize costs, realize potentials, and maintain competitiveness:
- Minimizing downtime in the production process
- Optimizing quality management
- Supporting repetitive processes
- Increasing productivity and machine utilization
- Shortening time-to-market
- Facilitating the development of entirely new products and services
4. Research and Development Services for AI in Production
Artificial Intelligence has the potential to revolutionize the manufacturing industry.
As a specialized research and development service provider with expertise in emerging technologies, we at Motius are also engaged in projects involving AI in production. We collaborate with clients ranging from small and medium-sized enterprises to large corporations. In this capacity, we accompany companies from the initial ideation stage, through consultation and technology selection, to the development of initial prototypes, and finally, to the deployment of production systems.
