Automation – How to Use It to Increase Performance and Reduce Costs in Manufacturing?

Why do some companies still operate inefficiently?

The most common mistake we observe in manufacturing companies is that they try to optimize production without having accurate and relevant real-time data available. They increase the number of employees, push for higher performance, invest in more and more technologies, change processes… But without understanding where the real problem lies.

The core of the problem is that most companies still work with a lack of information, which is also delayed or highly inaccurate. They collect data manually, rewrite it into spreadsheets, and evaluate it retrospectively. In practice, this means only one thing: they make decisions based on the past, not the current reality.

Take a quick test:

• Do you react too late, only after problems have already occurred?
• Do you fail to see their real causes?
• Do you make decisions more on assumptions than on data?

If you answered “yes” to at least one of these questions, your production probably does not operate as efficiently as it could, because it lacks automation. And if you ignore automation in the long term or implement it incorrectly, you risk unused potential of existing resources, rising costs without real performance, and therefore a gradual loss of competitiveness.

What is automation?

❕ Automation represents the replacement of manual activities with systems that can independently work with information and manage processes based on data.

While in manual management decisions are made retrospectively, based on reports created hours or days after an event, automation enables immediate reaction, directly at the given moment. Simply put, it is a transition from the model of “a person managing a process based on intuition” to the model of “a system managing a process based on data”.

This means that the system can:

• Collect accurate information directly from sensors, equipment, and machines
• Continuously evaluate it in real time
• And then automatically respond without human intervention

Mechanization, automation, and robotization

The terms mechanization, automation, and robotization are often confused. However, the difference between them is fundamental, especially when deciding where to invest and what real benefits to expect.

➡️ Mechanization represents the first step toward improving efficiency. It is a situation where a machine helps a person perform physical work, but the actual process control still remains in human hands. A typical example is a conveyor belt that moves material instead of a worker, while the operator still decides when it starts, stops, or what will be produced.

➡️ Automation goes one level further. It is no longer just assistance with work, but the actual control of the process. The system operates based on predefined rules and data, which it continuously evaluates. This means, for example, that a production line can automatically adapt to current conditions, react to deviations, or optimize its performance without constant human intervention.

➡️ Robotization then represents another step in evolution. It is an advanced form of automation, where systems (often in the form of robots or software agents) can perform more complex tasks and make decisions independently to a certain extent. They use advanced algorithms, data from multiple sources, and in some cases even elements of artificial intelligence.

Automation and robotization therefore build on mechanization and move it to a higher level. That is why every company should think of these stages as a gradual evolution: first improve work efficiency (mechanization), then start managing it based on data (automation), and only then scale and optimize it using advanced technologies (robotization).

• Mechanization – the machine helps the person, but control remains in human hands
• Automation – the system works independently according to rules
• Robotization – an advanced form of automation with autonomous decision-making

Types of automation

In practice, there is not just one type of automation. Companies use different approaches depending on the processes they manage, the level of flexibility they need, and the stage of digitalization they are in.

➡️ Fixed automation is the simplest and at the same time the most stable form of automation. It is designed for repetitive processes with minimal variability. A typical example is production lines where the same product is continuously manufactured in the same volume. The system is configured for a specific purpose and operates very efficiently, but at the cost of low flexibility. If the product or production process changes, modifying the system is often difficult and also expensive.

➡️ Programmable automation represents a more flexible approach. The system can be modified according to current production needs, most commonly through PLC (Programmable Logic Controller) systems. One production line can therefore manufacture multiple product types, while its behavior changes based on the configured program. Today, this type of automation is the standard in most modern manufacturing companies.

➡️ The highest level is represented by intelligent automation. This no longer operates only on predefined rules, but uses data, analytics, and often elements of artificial intelligence. Systems can evaluate large volumes of data in real time, identify deviations and patterns, and optimize processes automatically without manual intervention. This enables, for example, the ability to predict machine failures, optimize energy consumption, or dynamically adjust the production plan according to the current situation.

• Fixed automation – repetitive processes
• Programmable automation – flexible setup (PLC systems)
• Intelligent automation – integration with data and analytics

Automation of work, production, and buildings in practice

The difference between the various types of automation becomes fully visible only in practice – in how you manage daily processes within the company, whether in administration, production, or building management.

➡️ Work automation is not limited to manufacturing. In modern companies, it also affects daily administrative and management processes, such as reporting, planning, and decision-making itself. This means that tasks which were once manual, time-consuming, and prone to errors are now handled by systems. A typical example is automated data collection and visualization.

➡️ Naturally, the greatest impact of automation is in manufacturing, where it directly affects performance, costs, and product quality. Properly implemented automation can optimize the use of machines, materials, and workforce, reduce downtime, and increase production stability. A key role here is played by systems that integrate information across the entire production process and create a unified view of its operation.

➡️ Automation does not end in manufacturing. It also plays an important role in building management, where it has a direct impact mainly on energy efficiency. Building automation systems (BMS/BAS) ensure energy consumption management, automatic control of lighting, heating, and air conditioning, as well as monitoring of security and the technical condition of the building.

What advantages and disadvantages does automation bring?

When automation is implemented correctly, it represents a fundamental change in how a company operates, makes decisions, and manages its processes. The greatest advantage is that it replaces assumptions with accurate data and moves problem response from the past into the present. This is then reflected in concrete results, which can become visible within just a few weeks, even without the need for massive investments in new technologies.

✅ The company gains better control over costs, because it can clearly see where losses occur. Whether it is downtime, inefficient use of machines, or excessive energy consumption, automation makes it possible to identify and address these problems systematically, not randomly.

✅ At the same time, productivity increases. The very introduction of measurement and transparency often leads to an immediate improvement in performance, because all management levels work with the same data and see the actual state of production.

✅ And finally, automation significantly reduces errors. By eliminating manual data collection and repetitive tasks, the impact of the human factor, one of the most common sources of inaccuracies, is minimized.

• 🟢 Lower costs
• 🟢 Higher productivity
• 🟢 Elimination of errors
• 🟢 Faster and higher-quality decision-making
• 🟢 Greater competitiveness

At the same time, however, it must be said that automation is not a universal solution. Most problems companies have with automation do not arise because of the technology itself, but because of the wrong approach to its implementation.

⚠️ The most common limitation is data quality or the design of the solution itself. Automation works precisely with the information you provide it. If the data is incomplete, delayed, or incorrect, the system may function, but its outputs lead to wrong decisions. And if an inefficient process is automated without prior analysis, the system only accelerates existing problems instead of eliminating them.

⚠️ Another problem is the absence of a clear goal. Automation is often implemented because the company “needs to digitalize,” not because it solves a specific problem. The result is a solution that exists, but does not deliver a measurable impact on performance or costs.

⚠️ Finally, the human factor is often underestimated. Employees who use the system are a key part of the entire solution. If they do not understand its benefits or do not know how to work with it, automation will not be fully utilized in practice.

• 🔴 Requires high-quality data
• 🔴 Will not deliver results without proper design
• 🔴 Needs a clear goal
• 🔴 Does not work without employee cooperation

So how do you start with automation the right way?

The most important advice we can give manufacturing companies is this: if you want to use automation to its full potential, do not start with technology. Start by understanding the problem. We very often encounter situations where companies invest in solutions before they know exactly what they expect from them. The result is a system that technically works, but does not deliver real value.

The foundation of success is therefore to ask several key questions before implementation itself:

• What specific problem do you want to solve?
• What result do you expect? Cost savings, higher performance, better planning…?
• Where in your processes do the biggest losses or inefficiencies occur?

Once your goals are clear, analysis follows. It is necessary to understand how your processes actually work, identify bottlenecks and inefficient steps, and above all identify the data you are currently missing for high-quality decision-making. Only then does it make sense to define what data you need to collect, in what quality, and at what frequency.

One of the most effective ways to minimize investment risk is to start with a small pilot project, a so-called Proof of Concept (PoC). Instead of immediately implementing the solution across the entire production or company, you test it on a smaller part of the processes. You verify whether the data makes sense, whether the system works as expected, and what real impact it has on performance and costs.

And it is equally important to realize that automation does not end with deployment. On the contrary, that is where its true value begins. Successful companies approach automation gradually. First, they understand their processes, then they ensure high-quality data collection, and only afterward do they start using the data for management and optimization. This cycle continuously repeats: collect data → evaluate it → take action → monitor results → optimize further.

It is precisely this approach that transforms automation from a one-time investment into a long-term improvement tool.