11. June 2021
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Optimize availability and utilization of machines – with digital technologies

More and more industrial companies are analyzing the performance, availability and utilization of their machines in great detail in order to optimize their production and reduce costs. Mechanical engineers who can offer their customers supporting digital solutions for this task therefore have a clear competitive advantage.

But which technological approaches are particularly suitable and how can they be implemented without high effort?

Optimize availability and utilization of machines

Optimization calls for facts

Besides the organizational options, production companies now have a number of technologies at their disposal that enable them to optimize machine utilization. However, operators should first determine where such measures make sense. What's more, it must be possible to verify their success at a later date. There are a number of key performance indicators for both purposes.

The most important key performance indicators

The most important key performance indicators for initiating and monitoring improvement measures are the availability level (machine availability), performance level, machine utilization level (machine utilization) and machine hourly rate.


The maximum possible production time must first be determined for the calculation of organizational and technical availability by calculating the company's own varying cycle time and taking into account shutdowns, shifts and non-working days. 

Machine availability percentage = (actual production time / maximum capacity) * 100

If values are considerably below 100 percent, the reasons must be analyzed and remedied – including through the use of modern digital technologies.


The performance level refers to the production quantity and tells us how fast a machine produces. 

Performance = actual output quantity / possible output quantity

If the possible output quantity is not achieved, the underlying reasons must be investigated.


In order to be able to calculate the degree of machine utilization, the utilization (actual production quantity) and the maximum available capacity must be put into relation. Moreover, a calculation based on time units is possible.

Utilization rate = actual production time / target production time

Inadequate machine utilization usually indicates excessive machine downtime. This problem can be addressed with digital technologies, among other things.


Production controllers like to use the machine hour rate for analysis and monitoring of plant efficiency. When compared over time, the machine hour rate formula can be used to determine whether the plant is improving or deteriorating.

Machine hourly rate = sum of machine cost / running time in hours

Deterioration is caused by either decreasing runtimes (e.g. due to breakdowns) or increasing operating costs. Both causes can be quickly analyzed and remedied in a modern factory with a high degree of digitalization.

Digitalization for optimizing key performance indicators 

As mentioned at the outset, the outlined key performance indicators can be improved by means of various organizational and technical measures. One key lever is the digital transformation of manufacturing. The Internet of Things (IoT) plays a particularly important role here. In connection with adjacent technology fields, it provides, for example, the following potential applications:

(1) Use of machine data to improve production and resource planning

(2) Minimizing downtime with predictive maintenance

(3) Automation and optimization of material supply

(1) Use of machine data to improve production and resource planning

Every modern production plant generates data. Those who read and analyze this data gain valuable insights. This means that not only can the current machine utilization and technical availability be calculated, but the reasons for suboptimal machine utilization can also be investigated in more detail. In practice, these are some relevant questions:

  • Do downtimes occur regularly at certain times?
  • Can the downtimes be attributed to technical or organizational deficiencies?

One possible finding could be that the previous production schedule was insufficiently synchronized. Likewise, there may have been too few machine operators to fully utilize the available machines. In cases such as these, an optimization of the resource and schedule planning can then be considered. It may also be necessary to recalculate the cycle time, as the original calculation contained errors. To put it briefly: The targeted use of data, for example on machine utilization, can ultimately be used to derive organizational improvement measures.

However, not only a retrospective view of the data is possible, but also real-time monitoring of the current machine performance. Thanks to the real-time availability of data, production planners can continuously calculate machine utilization and react immediately to spontaneous failures, which also has a positive effect on machine utilization.

In an advanced stage of development, machine data helps companies achieve a greater and greater degree of utilization (100 percent utilization rate). This is made possible by automated data exchange via the industrial Internet of Things. In such future scenarios, the machinery organizes itself as far as possible and uses intelligent algorithms to ensure that downtimes are reduced to a minimum.


(2) Minimizing downtime with predictive maintenance

A prime starting point for optimizing production is machine availability. By offering innovative maintenance approaches such as predictive maintenance , mechanical engineers can make a significant contribution to reducing downtime.

IoT technologies make it possible to derive conclusions from machine data. For instance, deviations from a certain standard range can point to a need for maintenance or an imminent failure. Based on this, maintenance measures can be initiated specifically to prevent the failure. Likewise, maintenance work is reduced to what is actually necessary, which additionally increases machine availability.


(3) Automation and optimization of material supply 

Inadequate material supply can be the reason for insufficient performance and machine utilization. Digital solutions can also contribute to improvements in this area. If production systems, machines and intralogistics can be connected with each other, material requirements can be planned autonomously and replenishment deliveries to the belt can be automated. The objects involved communicate via the Internet of Things.

Transparency of machine data

As a whole, there are numerous aspects that affect the performance, availability and utilization of machines. When companies read their machine data and put it in context with their processes, it becomes transparent.

In practice, however, such projects occasionally fail due to the complexity and heterogeneity of machine parks. There are often systems from different manufacturers in use, which in turn provide different data formats. Medium-sized industrial companies in particular lack the budget and expertise to develop systems for harmonizing and merging this data on their own.

Cooperation & collaboration for common standards

Mechanical engineering companies must therefore cooperate in the interests of their customers to enable the holistic use of machine data. This is exactly the goal that is pursued by ADAMOS – an association of more than 30 partners from the mechanical engineering industry. Based on their combined knowledge, the network participants have created the ADAMOS HUB, an integration platform that enables the exchange of data in production across different technologies and manufacturers. This is realized via predefined, neutral interfaces and integration points. As soon as the connection to the machines has been established, machine operators can access the production and machine data using apps. This enables them to achieve simple, centralized data management – completely independent of the machine manufacturer. Our goal is to establish ADAMOS as a global standard in the industry. ADAMOS participants thus put their customers in a position not only to optimize their machine utilization selectively, but also to improve entire production lines. This significantly benefits competitiveness.

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