Production Control – Understanding and Optimizing Processes

In this article, we explain why this is so important and how it ensures the competitiveness of companies.

What is Production Control?

Production control is a term that refers to the operational coordination of production processes. In the context of detailed planning, this involves the practical implementation of all previous planning processes and specifications at the shop floor level. The objective is to optimize the manufacturing process in terms of time, resource utilization, and quality.

In contrast, production planning is more about the fundamental definition of meaningful production key figures – less about the actual implementation. Production management, on the other hand, is an even more comprehensive term that includes the planning, control, and monitoring of the entire production process. 

This effectively makes production control the link between strategic planning and operational execution.

Production Control – Overview of Tasks

One of the key tasks of production control is the fine-tuning of production processes in real time. This means that the current production progress is continuously monitored and, if necessary, corrective action is taken to bring the target and actual states into line with each other. This also includes the ongoing monitoring of production progress and subsequent quality control.

As already outlined, the implementation of production planning in concrete measures continues to be of central importance: This includes the allocation of orders to production machines in terms of time and content, ensuring an efficient flow of materials, and the targeted use or intelligent distribution of human resources – depending on where they are needed.

Finally, an important task of production control is to ensure the flexibility of processes within manufacturing so that even short-term disruptions or changes to orders do not interrupt the manufacturing process (for long). For example, in the event of machine failure or delivery bottlenecks, short-term adjustments must be made to avoid production downtime.

Production Control – Focusing on the Goals 

Based on the tasks mentioned above, the objectives of production control can be outlined in very concrete terms:

• Guarantee smooth processes
  From the provision of resources to the final quality control of goods, every step in the production process must be seamlessly integrated.
• Ensure deadlines are met
  Both the deadlines for machine throughput times and delivery deadlines for customers must be met. Ideally, each individual process step is precisely planned and scheduled.
• Efficient use of resources
  The efficient use of resources includes materials as well as machines, employees, and their working hours. Experience shows that optimized use of these resources offers companies particularly high added value.
• Ensure flexibility in the process
  A high degree of flexibility within production can encompass not only the manufacturing itself but also the underlying management concept. Both manufacturing and the management of the departments involved should demonstrate a high degree of agility.

In order to ensure that these goals are reliably and permanently met, appropriate controlling and monitoring measures are of course required. If delays occur or potential for optimization becomes apparent, companies should always strive to gradually improve their own processes.

Methods of Production Control

The tasks and objectives are now clear – but how is production control actually implemented in practice? In addition to a range of traditional and proven approaches, the trend is now increasingly moving toward digital methods, often even involving artificial intelligence.

• Kanban (standard)
  One of the classic methods of production control is the Kanban system, which can be classified as “agile process management.” In this system, material requirements are signaled by actual consumption on the production line—this means that only what is actually needed is produced.
• Control Station (standard)
  Control station control, which has been tried and tested over many years, provides a simple visual representation of current and planned production orders, machine utilization, and time slots. On this basis, the responsible production management can make timely decisions and avoid bottlenecks.

However, more and more companies are now taking the next technological step—moving away from simple overview boards and dashboards toward holistic, intelligent system solutions. Here, relevant data is constantly exchanged between ERP systems, MES software, and other modules—the result is precise process control that would not be possible using traditional methods.

Digital Methods of Production Control

A characteristic feature of digital methods is event-driven control, in which data from production - for example, from machine data collection - is evaluated in real time. This makes it possible to take immediate action in the event of any deviations.

Using algorithms, some of which are quite complex, the optimal production sequences and resource allocations can be continuously calculated and implemented based on current data. In this way, all delivery dates are met and the respective machine capacities are utilized to the best possible extent. 

The use of artificial intelligence (AI) in production is also becoming increasingly popular: AI systems can analyze large amounts of data, recognize patterns, and, on this basis, make recommendations for action or even make decisions independently. 

Now that the initial hype surrounding AI in production has died down, the actual strengths and ideal areas of application for LLMs and other AI systems are becoming increasingly clear – particularly through targeted training with suitable, company-specific data.

In conjunction with ERP (Enterprise Resource Planning) and MES (Manufacturing Execution Systems) as central interfaces, companies now have access to enormously powerful and intelligent tools for process control – for transparent real-time processes from order entry to completion.

Common Problems in Production Control

In order for production control to function as desired, the appropriate conditions must be in place within the manufacturing process. However, even in modern production environments, this is not always the case. The following challenges are therefore very typical during implementation:

• High complexity
  In production, there is an increasing demand for small batch sizes and different variants. Ever shorter product life cycles and individual customer requirements are making the entire manufacturing process more dynamic and reducing predictability—the complexity of processes is increasing.
• Slow response times
  Apart from acute incidents, most companies tend to be rather slow to act when decisions need to be made at shop floor level. In the worst case, this sluggishness is exacerbated by manual processes or insufficient information.
• Low transparency
  Especially in manual processes, it is often unclear how data is collected or maintained—not to mention the quality of the data itself. In many cases, however, these are processes that have developed over a long period of time and must be reviewed and resolved together with the parties involved.
• Lack of data exchange
  In many companies, real-time data exchange also poses a fundamental problem—for example, when incompatible systems lead to (inefficient) media breaks or data silos arise in certain areas that must be identified and integrated into the overall system.

The more completely a company can resolve these issues, the more successfully production control can be implemented and have a visible impact on manufacturing.

Production Control in the Context of the Smart Factory

The “conventional” image of a manufacturing facility is becoming increasingly blurred today – with advancing digitalization, the trend is moving ever further toward the smart factory. The main reason for this is the shift in requirements away from pure series production toward increasingly individualized and specific products. 

Under these conditions, only companies that adapt their processes and procedures accordingly can achieve economical production – this means seamlessly linking their own IT with OT (operational technology) and IIoT (industrial Internet of Things/sensor technology, etc.).

In such a smart factory, production control can then be structured as follows:

• Intensive networking
  Manual processes are being replaced by the complete networking of machines, plants, and systems via standardized interfaces. This results in a constant exchange of information, with data being transmitted and evaluated in real time. This enables completely data-based production control and also paves the way for predictive maintenance.
• AI integration
  The use of artificial intelligence can be particularly helpful in identifying relevant patterns from current and historical production data. These patterns assist in making forecasts and planning maintenance measures or distributing orders between plants. Today, AI-based systems already make numerous fundamental decisions on their own when necessary—alternatively, they serve as decision-making aids for those responsible for production.
• Digital twin
  Where simple simulations reach their limits, companies today are opting to create digital twins of their plants. These are virtual representations of real production systems based on real-time data. They can be used to simulate control scenarios in detail, test process changes in advance, and evaluate the effects on the production process. This not only increases planning reliability, but also significantly reduces the risk associated with the introduction of new processes or products.

If a company bases its production control on such a foundation, the starting point for optimizing processes is necessarily better than with “less intelligent” manufacturing systems. The initial effort required for setup and configuration is undoubtedly higher, but this investment usually pays for itself many times over through increased competitiveness.