Efficiency increase with AI productivity tools

Operating Industry 4.0 without artificial intelligence is like driving a car without an engine: The bodywork is there - elegant and promising - but the thing that really drives it forward is missing.

A man is sitting at his desk and works on his laptop.

In this article

  1. Leveraging AI to reduce Downtime and Plant Shutdowns
  2. AI Resource Management
  3. Increase labor productivity through AI
  4. How can AI further enhance Throughput and Efficiency
  5. AI for quality control and AI-based predictive maintenance
  6. Summary: Streamlining Operations with AI Tools for Productivity

In order to optimize production processes, the use of artificial intelligence will play a more central role in the future to increase both efficiency and effectiveness. This is because AI can help to improve efficiency by speeding up processes and optimizing the use of materials and energy. This leads to lower costs and faster production. On the other hand, AI increases effectiveness through more precise and intelligent decision-making processes, for example in quality control and predictive maintenance. This ensures that production targets are not only achieved faster, but also with higher quality. In this article, we will show you relevant use cases of how the use of artificial intelligence can increase productivity in production.

Leveraging AI to reduce Downtime and Plant Shutdowns

In order to optimize the utilization of machines, artificial intelligence must reduce technical and organizational downtimes in your production. We explain how this works in detail in the context of MES and ERP systems as well as IoT platforms, which become significantly more effective with the help of artificial intelligence:

AI in MES systems

MES (Manufacturing Execution System) systems use AI to analyze real-time production data and use it for optimization. They plan the deployment of personnel and resources as well as capacity planning and order scheduling. Artificial intelligence learns from the data and enables the MES system to continuously optimize planning for reduced downtimes and optimized capacity utilization.

AI in ERP systems

ERP systems keep an eye on the entire supply chain. With a sales forecast, they also calculate the demand forecast. In a similar way to MES systems, they use artificial intelligence to create better forecasts. This increases delivery reliability, but also optimizes warehousing so that safety stocks can be reduced and production downtimes can be avoided by having sufficient material in stock.

AI in IoT platforms

A typical use case for IoT platforms, some of which are offered by machine manufacturers themselves, is preventive wear or defect detection. By continuously recording and evaluating machine parameters, future failures can be predicted more preciselyprecise. This is optimized through the use of artificial intelligence, as AI can uncover correlations in data that a human would not notice.

Note: Use cases, such as production planning, can often not be assigned to just one system and are often mapped on a very company-specific IT basis, which has been simplified in this article for better understanding.

AI Resource Management

Efficient resource management is the key to optimizing production processes and increasing productivity. AI is fundamentally changing how resources are allocated and utilized across the manufacturing spectrum. AI-powered resource management systems can dynamically allocate both material and labor based on real-time demand and production status. This not only ensures optimal utilization of materials, but also balances the workload across employees, preventing burnout and underutilization. AI-supported resource management can be seamlessly integrated with modern ERP and MES systems.

Increase labor productivity through AI

Labor productivity will be increased by training employees in the use of artificial intelligence. AI training aimed at improving specific skills in dealing with AI not only promotes employee motivation, but also makes a positive contribution to the necessary change management. The same applies here as it does to digitalization in general: if you want to successfully establish AI in the company, you have to get all employees on board. As every employee has different requirements and knowledge, an individual AI training program is important. We show you, how our Learner Paths close your AI skills gap to increase the productivity of your production:

Learn more about our Learner Paths

How can AI further enhance Throughput and Efficiency

On the one hand, throughput times are reduced by reducing downtimes. There are also other ways to achieve lead time reduction and higher productivity through the use of artificial intelligence.

Improved planning accuracy

AI in the company can also reduce overproduction and underproduction through optimized sales and demand forecasts. In addition to this advantage, production cycles can also be planned more tightly and thus directly optimize throughput times.

Optimization of the production flow

Another use case for AI in production is the optimization of the production flow. Through monitoring and data analysis, AI algorithms can actively optimize material flow and process steps in order to shorten throughput times.

Dynamic adjustments

Changes in the supply chain are a daily challenge for most companies: Suppliers break away, shipping routes are blocked, quality problems in the delivered goods... The expenses for necessary planning changes are high and increase with the demands on planning quality. Artificial intelligence can be used here to bring about optimal decisions much faster and thus keep the production process running without throughput times being extended by suboptimal planning.

AI for quality control and AI-based predictive maintenance

The quality control and predictive maintenance use cases increase production reliability. We want to address this aspect separately. 

AI-supported quality control systems use advanced image processing and machine learning to detect errors and deviations in real time. These systems improve the consistency of product quality and reduce waste in production by intervening at an early stage before faulty products leave the production line.

Predictive maintenance uses AI to predict machine failures with a higher statistical probability before they actually occur. By analyzing operating data and detecting patterns that indicate impending problems, maintenance work can be planned without interrupting the production flow. This proactive maintenance minimizes unplanned downtime and keeps production efficiency at an optimal level, reducing the total cost of ownership.

Read more about quality control here

Summary: Streamlining Operations with AI Tools for Productivity

  • AI is a great productivity tool for operational efficiency that companies do not want to miss in the future. Industrial automation in combination with artificial intelligence (AI) is rapidly changing the production landscape by optimizing processes and significantly increasing productivity in manufacturing
  • By using intelligent automation systems, repetitive and manual tasks can be made more efficient and human error can be reduced, which directly contributes to the optimization of production processes. 
  • AI algorithms offer the ability to analyze complex amounts of data and gain insights that can be used to fine-tune production processes. This enables more precise production planning and continuous adjustment of processes in real time, based on current operating conditions.
  • The integration of AI into industrial automation makes it possible not only to optimize production processes, but also to refine overall production planning. This leads to a more effective use of resources and a reduction in waste, which in turn lowers costs and increases overall efficiency.

The development of AI use cases with a positive cost-benefit ratio regularly presents companies with a major challenge. For this reason, the AppliedAI initiative offers use case development to create solutions for you that offer real added value.

More information on Use Case Development