The Rise of Intelligence: How AI is Redefining the Manufacturing Execution System

Artificial intelligence is now viewed as a general-purpose technology like electricity or the internal combustion engine. It has come to stay – and it will also permanently change production. A Forbes article describes why manufacturing execution systems are not the right basis for this and what characterizes the alternatives. In this article, the argument is supplemented by AI-supported solutions.

Manufacturing execution systems are a product of the 80s and 90s that have become surprisingly well established in the thinking of the current management generation. Understandably, you focus on what you know from the past.

Even Forbes magazine has now taken up the topic of why implementing a manufacturing execution system is no longer a good idea in the 2020s. Even if the article is not easy to read, it is very valuable for all managers who are in the process of selecting shop floor software and are making a strategic, long-term decision.

In essence, the article says that a modern digital strategy cannot be built on outdated technology and architecture. The future of software support for production consists of composable, app-based solutions.

We passionately share this position at and want to add a perspective to the Forbes article: Artificial Intelligence.

If you ask an AI which top 10 use cases its technology can support the shop floor of the future with, you will get the following list – for the quick reader, the first words of each bullet line are sufficient:

  • Predictive maintenance: AI algorithms can analyze sensor data from machines to predict when devices are likely to fail. By identifying problems before they occur, maintenance can be planned proactively, minimizing downtime and preventing costly failures.
  • Production planning optimization: AI can analyze historical production data, market demand and resource availability to optimize production schedules. This ensures that resources are used efficiently, reducing idle time and maximizing performance.
  • Improving quality control: AI-powered computer vision systems can inspect products in real-time as they move along the production line. This allows defects to be detected more accurately and quickly than manual inspection, reducing waste and improving product quality.
  • Energy management: AI algorithms can analyze energy consumption patterns within the factory and identify optimization opportunities. This may include adjusting equipment settings, scheduling production during off-peak times, or implementing energy-efficient technologies to reduce costs and environmental impact.
  • Inventory optimization: AI can analyze demand forecasts, production schedules, and supplier lead times to optimize inventory levels. By ensuring the right amount of inventory is available at the right time, companies can reduce inventory costs while avoiding stock-outs and backorders.
  • Process automation: AI-powered robotic process automation (RPA) can automate repetitive and mundane tasks on the factory floor, such as: B. Data entry, assembly and packaging. This frees up human workers to focus on more complex and value-added activities, improving overall productivity.
  • Workforce Management: AI algorithms can analyze employee planning data, skills and performance metrics to optimize staff deployment. By ensuring the right people are assigned the right tasks at the right time, companies can improve efficiency and morale.
  • Demand Forecasting: AI algorithms can analyze historical sales data, market trends, and external factors (such as weather or economic indicators) to more accurately predict future demand. This helps manufacturers optimize production schedules and inventory levels to meet customer demand while minimizing overstock or stock-outs.
  • Simulation and modeling: AI-driven simulation and modeling tools can create virtual replicas of the factory environment to test different scenarios and optimize processes. Manufacturers can use these simulations to identify bottlenecks, experiment with process improvements, and make data-driven decisions to increase productivity.
  • Manufacturing Analytics: AI can analyze large amounts of production data to identify opportunities for process optimization and innovation. By continually monitoring and analyzing performance metrics, companies can identify trends, patterns and anomalies that can lead to productivity improvements over time.

The development in the AI ​​fields of Large Language Models has just begun in November 2023 with the publication of ChatGPT and the development – including and especially among the open source models – is rapid. In spring 2024, Open Source Time Series Foundational Models were released, which are specially trained to analyze time series data and thus open up even more possibilities for the above use cases – what a time to be alive.

And now, as a decision-maker, you inevitably have to ask yourself whether you believe that ONE software provider can offer the best – or even just a good and future-oriented – solution in all of these fields based on its old technology and architecture (see above, Forbes).

The alternative architecture, as formulated in the Forbes article, consists of the selection of specifically the best manufacturing execution support apps, which make up what has been called the manufacturing execution system in the past.

This means that alternative providers are those who dig deep into one of the solution spaces and drive progress there – including with the help of the current and future possibilities of AI. This is what does in the field of the last topic on the list – “Manufacturing Analytics”.

In the target architecture, you then have an app for manufacturing analytics, an app for predictive maintenance, an app for inventory optimization, etc. in your software landscape – each from a provider who really focuses on the solution space and its current and future possibilities.

The transformer technology, on which a large part of the AI ​​solutions currently under discussion is based, is still very young. The progress of providers such as OpenAI, Google, Meta or Mistral is very great. If you would like to discuss how this technology can be incorporated into the field of “Manufacturing Analytics”, please contact us at

Author: Linus Steinbeck