Process optimization and data networking -
The central key topics for small and medium-sized enterprises
For companies, the use of artificial intelligence often means a change in their processes and presents them with challenges. Artificial intelligence is no longer only relevant for "global players", but is becoming increasingly important for medium-sized companies. Artificial intelligence makes it possible to adapt products and services to customer needs and create new business models. There is great potential in design, sales and predictive maintenance.
However, mid-sized companies often face challenges in introducing AI due to lack of know-how, skilled labor, high investment, complexity and lack of data. Technical skills, infrastructure, and company alignment influence preparation for AI deployment. Concerns about high barriers to entry, data sovereignty, assurance and certification of AI systems hinder adoption. There is great potential in midmarket data, but adoption can be challenging.
"AI-as-a-service" is recommended for companies with skills shortages or a small data base. NVIDIA and VMware offer AI for Enterprise Platform, a cloud-native software suite for AI and data analytics optimized, certified and supported by NVIDIA. The suite includes key NVIDIA technologies for rapidly deploying and scaling AI workloads in the hybrid cloud.
AI in production
Focus: Value chain optimization using AI-based applications
The use of AI (AI applications) in companies
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Potentials along the value chain
The opportunities for SMEs include efficiency gains, advantages over competitors, and increased profits and earnings.
Who benefits from AI-based applications? Experts see great potential for SMEs in areas such as logistics, sales, production, purchasing and customer management. Sensor technology and assistance systems are considered promising technologies. The benefits of AI can range from small marketing tools to the optimization of manufacturing processes and new business models. Medium-sized companies should include the use of AI in their investment plans and focus on key components to reap the full benefits.
AI applications according to different value creation areas
Intelligent assistance systems
Sensors
Robotics
Language/
Text processing
Text processing
Image/sound detection
Document analysis
Time series/
Cluster analyses
Cluster analyses
Time series/
Cluster analyses
Cluster analyses
Sales & Logistics
- Warehousing, sorting, delivery by autonomous vehicles/robots
- AI-based demand and routine planning
- Route planning & delivery
- Warehouse logistics and picking
Production
- Automation technology
- Anomaly detection
- Predictive maintenance
- Cognitive assistants
- Further development of smart products for new business models
- Robotics
- Maintenance
- Quality management and control
- Process optimization and control
Supply chain
- Supply chain optimization
- Intelligent sales forecasting
- Demand forecasting to predict sales
Procurement/purchasing and ordering
- Inventory & Inventory Management
- Automated warehousing through autonomous vehicles
- Supply chain operations/ AI-based processing: taking over from order process to delivery
- Procurement market research
- Resource planning
- Supplier management
Corporate infrastructure, administration and human resources
- Takeover of routine tasks
- Partially automated applicant management
- Document management
- Fraud detection
- Forecasting
- Automatic document recognition
- Intelligent dunning procedures
- Energy management
- Staff scheduling
- Personnel Recruiting
- Facility Management
Service and customer management
- Automated customer review analyses
- Support for customer interaction (e.g. chatbots)
Research and development
- AI-supported simulation of product behavior
- Analyses for product development
- Product and process development
Marketing and sales
- Automated data collection and analysis
- AI support for customer interaction
- Dynamic price optimization, product portfolio optimization
- Targeted advertising/promotion
- Chatbot
- Visual product search
- Consulting/sales robots
- Sentiment analysis in customer service
- Digital assistance systems
Quality control and assurance
- Visual inspection of components for defects
- Predictive quality; optical and acoustic quality assurance
Disruptive potential of AI in the value chain (source: Saarland University, based on expert interviews)
sysGen supports you in implementing AI solutions in your company simply and cost-efficiently. Take the chance not to miss the boat, because AI is no longer a question of "if", but of "when". The use of digital solutions is crucial for the competitiveness of medium-sized businesses.
How can companies benefit from the use of AI
in industrial value creation?
Focus: Production
One very frequently cited application example of AI in production is predictive maintenance of machinery and equipment. However, this is only a small part of the potential for production technology applications in the future.
- Predictive analytics (e.g. monitoring and maintenance of production facilities)
- Optimized resource management (e.g. optimization of production and manufacturing schedules)
- Quality control (e.g. checking the condition of components)
- Intelligent assistance systems (e.g. support in manufacturing processes), value creation potential of AI in production
- Knowledge management (e.g., data models for complex engineering processes)
- Robotics (e.g. learning, self-regulating gripper systems)
- Autonomous driving and flying (e.g. driverless transport systems)
- Intelligent automation (e.g., automation of routine processes in manufacturing and assembly) and
- Intelligent sensor technology (e.g. pre-processing of data when monitoring production facilities).
Maintenance as required - not according to planning interval
AI-based monitoring enables companies to monitor the condition of their plant and machinery in real time, using large amounts of digital information. However, to perform maintenance and servicing effectively, the data must be used wisely, for which AI can be a great help.
Predictive maintenance uses data analysis and algorithms to make predictions about impending shutdowns and calculate optimal maintenance times to prevent downtime. Combining past maintenance experience with current data analytics creates a computer- and AI-assisted prediction of maintenance. This allows technicians to be equipped with the right spare parts at the right time, resulting in reduced maintenance activities.
Asset Performance Management (APM) comprises three technology components: condition-based maintenance (Asset Health Insights) based on digital IoT data, predictive maintenance using data analysis, and Equipment Maintenance Assistant, an AI-based support for maintenance technicians.
Predictive maintenance uses data analysis and algorithms to make predictions about impending shutdowns and calculate optimal maintenance times to prevent downtime. Combining past maintenance experience with current data analytics creates a computer- and AI-assisted prediction of maintenance. This allows technicians to be equipped with the right spare parts at the right time, resulting in reduced maintenance activities.
Asset Performance Management (APM) comprises three technology components: condition-based maintenance (Asset Health Insights) based on digital IoT data, predictive maintenance using data analysis, and Equipment Maintenance Assistant, an AI-based support for maintenance technicians.
Optimized warehouse management and posture thanks to the use of AI
Companies are already using software to increase efficiency through algorithms in warehousing, including improved warehouse management, AI robotics to speed up picking, and autonomous transportation systems. The top three reasons for applying AI in logistics are: Predicting consumer trends, automating product movement in the warehouse, and optimizing transportation routes and movements within the warehouse.
Quality assurance - guarantor of success
Quality management is of great importance for manufacturing companies. AI-supported surface inspection, with camera systems and sensors, helps to detect deviations more quickly. Machine learning facilitates the search for causes of quality variations. Unclear products diagnosed as defects are subsequently inspected manually by experienced employees.
The use of AI in planning has two main goals: Reducing the workload of employees by automating simple planning tasks using planning algorithms and improving planning results in complex applications by using AI systems, including machine learning.
Making connections and patterns visible with the help of AI
AI detects correlations and patterns in big data that otherwise remain hidden from us. The analysis includes both current and historical data and, by expanding the scope of information, improves process results or compensates for process deviations. The focus is on automating control loops, with algorithms that offer greater availability and faster processing.
Another application example for AI technologies in process optimization using machine learning is the analysis of extensive data sets from complex systems, such as interlinked production lines. In complex systems in particular, there is the potential to uncover relationships that are not directly apparent to human observers because various preconditions occur that are beyond the observer's control. This can be used, for example, to uncover the causes of wandering bottlenecks in production. However, the successful use of AI depends on the quality of the data with which it has been trained in advance. It is only as good as the data on which it is based.
Another application example for AI technologies in process optimization using machine learning is the analysis of extensive data sets from complex systems, such as interlinked production lines. In complex systems in particular, there is the potential to uncover relationships that are not directly apparent to human observers because various preconditions occur that are beyond the observer's control. This can be used, for example, to uncover the causes of wandering bottlenecks in production. However, the successful use of AI depends on the quality of the data with which it has been trained in advance. It is only as good as the data on which it is based.
Digital assistance systems support people, for example, in gathering and processing information, making decisions, and executing and controlling people, machines, processes and products. They can also help people learn and practice tasks. They are becoming increasingly important in factories. Complex manufacturing tasks in particular can be performed quickly, with fewer errors and by less qualified employees.The areas of application for assistance systems range from intralogistics, assembly or quality inspection to production planning.
A distinction is made between perception-decision and execution assistance systems;
A distinction is made between perception-decision and execution assistance systems;
- Perception assistance systems improve information intake, from classic bills of materials to digital work instructions.
- Decision assistants support complex decisions through prioritization and data analysis.
- Execution assistants facilitate work through machine support, from simple hand pallet trucks to human-robot cooperation.
Production planning: faster from product design to delivery with AI.
AI technologies have a variety of uses in product development, depending on the product, processes, and organizational structure. Some applications include: Evaluation of test and simulation data through machine learning, optimization of processes through planning and optimization algorithms, automated generation of assembly instructions or bills of materials, support for design proposals, and plausibility and consistency checks. AI systems can also check the consistency of CAD designs with bills of materials. However, there are so far few standard software solutions that use AI technologies and offer added value for the user.
The introduction of industrial robots was challenging due to the high training effort and set-up costs. Low flexibility also made them economical only for frequently recurring processes. Thanks to AI technologies and image processing, it is now possible to set up and adapt processes more easily. By mimicking the movements of humans, transfer areas instead of fixed transfer points, and dynamic component recognition, flexibility and stability can be increased. With appropriate sensor technology and Natural Language Processing, direct human-robot collaboration and an intuitively designed human-machine interface can also be realized.
Medium-sized producers are often not aware of the data treasures they have - and how they can harness them with the help of AI applications. AI solutions can support the entire value chain process - from ideation to recycling - by helping with ideation, resource saving, fault diagnosis, energy saving, and more.
Generative design: developing ideas
Artificial intelligence (AI) enables rapid testing of many designs through generative design. The artificial intelligence combines predefined product parameters and delivers various design proposals within minutes. The design team decides which suggestions to pursue.
Digital assistants: Imparting knowledge
AI-supported digital assistants have already been present in our everyday lives for several years - be it in smartphones or in the form of smart speakers in many households. In industry, too, intelligent assistance systems can effectively support employees in their activities.
Intelligent logistics: optimising the flow of goods
AI processes can simplify the logistics of goods by capturing short-term customer orders and cancellations in real time, optimizing transport routes, and quickly calculating new routes. This results in on-time deliveries for customers and time and cost savings for transport companies.
Resources in view: environmentally friendly production
Companies consume a lot of energy and contribute to climate change. AI solutions can optimize energy consumption in industrial plants and help protect the environment and improve the quality of life.
Circular Economy: Closing production cycles
The "throwaway economy" consumes more and more raw materials for products that are later disposed of. The Circular Economy wants to replace this with return logistics: Products should be usable for longer and recycled at the end.
Data and AI-based value networks: potentials for SMEs
By linking and analyzing data with the help of artificial intelligence methods, new, customizable products and services can be developed. However, SMEs in particular rarely have the necessary data and technologies on their own to implement data-driven business models. In addition, companies often lack the necessary skills in the areas of data analysis and AI. Cooperation with providers of data, technologies and digital platforms can help to build up the necessary knowledge within so-called digital value networks and create added value from this.
Platform
Suppliers
SME or large company
Company
SME or large company
Customer
Cloud service provider
Value-added cycle:
- Data streams
- Service flows
- Payment flows
Continuous exchange of data among each other
Importance of value networks
What are value-added networks?
Rigid value chains are broken up by the emergence of value networks. Cooperation between different players leads to the creation of innovative service offerings. Secure and open data access/data exchange from various sources across company and industry boundaries is necessary for this.
What are data exchanged for?
Data exchange is indispensable for training AI systems. This requires the integration of electronics, sensors and actuators into objects such as devices, machines and vehicles, as well as the networking of production facilities via the Internet, giving rise to the Internet of Things and equipping objects with a digital twin.
How are new products and services created?
The use of AI leverages data from digitized objects, systems and environments to gain valuable information in real time. Data types such as operational, environmental or usage data are provided and combined, processed and analyzed on digital platforms. This enables scalable, individualized offers for customers through direct and indirect network effects.
Why are collaborations necessary?
AI-based data use requires multiple competencies. It is important to identify gaps in technology, economics, and data, to define one's own value proposition, and to identify suitable partners for data, technology, and competence to enable configuration and customization.
Implementation of a value network
Do's
Dont's
- Transparent network structure with clearly formulated value proposition of all parties involved
- Definition of a clear data strategy that determines the quality, relevance and availability of the data required for value creation
- Strategic and long-term (research) cooperation to generate own competencies in the area of data science
- Agile roll-out of the product to identify hurdles at an early stage and to be able to adapt the product
- Continuous review and adaptation of the business model and suitable financing models, e.g. revenue sharing
- Poorly communicated and coordinated projects
- Collecting data without clearly defined criteria and methods
- Complete outsourcing of AI competencies to external companies without continuous exchange and mutual coordination
- Rigid schedule setting and sticking to the original concept
- Adherence to classic business models and convincing network participants to bear the investment risk themselves
Did you already know? The path to digitization is supported by the state
"Digital Jetzt"- New funding for the digitization of SMEs
In today's world of work and business, digital technologies and know-how determine the competitiveness and future viability of companies. To enable SMEs to exploit the economic potential of digitization, the German Federal Ministry for Economic Affairs and Energy (BMWi) supports small and medium-sized enterprises (SMEs) with the "Digital Now - Investment Support for SMEs" program. The program offers financial grants and is designed to encourage companies to invest more in digital technologies as well as in the qualification of their employees.
For the support of the BMWI