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Increased efficiency through data-based machine monitoring

IoT Use Case - in.hub
3 minutes Reading time
3 minutes Reading time

A traditional manufacturer of power tools monitors its heterogeneous machine park with a modern IIoT platform that enables universal and manufacturer-independent machine monitoring. The introduction of the platform led to a significant increase in production efficiency and machine availability.

The challenge: A heterogeneous and non-transparent machine park

The power tool manufacturer’s complex machine landscape was characterized by a wide variety of machines from different generations and manufacturers, which made it difficult to monitor the machines.

The condition of the machines was mainly manually and visually inspected by the staff. This method was time-consuming and it was difficult to assess the condition of the machine. Decisions were therefore often based on incomplete information. The company was looking for a better solution that could integrate and monitor all machines – regardless of manufacturer and age. It should enable comprehensive condition monitoring of the machines and take into account the specific requirements of the company as well as feedback from employees.

The solution: Acquisition and analysis of machine data

The manufacturer uses the IIoT platform in.hub. The open solution concept enables the operation of universal and manufacturer-independent machine monitoring. The solution was implemented in two main steps: implementation of condition monitoring and recording of machine downtimes using the MaDoW app.

Implementation of the in.hub IIoT platform

As a first step, in.hub connected all machines, regardless of manufacturer and age, to the IIoT platform. This was done either via the interfaces integrated in the machines or via additional sensors with which the machines were retrofitted.

This enabled the company to achieve standardized data acquisition for all machines. The aim was to obtain a complete and objective picture of the machine’s condition, which was previously not possible through manual inspections.

Implementation of the MaDoW app

The MaDoW app was implemented in the second phase. It allows real-time monitoring of machine conditions and ensures that relevant and meaningful information is immediately available. The application can also classify individual events. This improves planning and employees can react more quickly to interruptions in production.

The manufacturer was the pilot customer for the new version of the application. During this pilot phase, the developers were able to implement customer-specific requirements and wishes. This means that the application is precisely tailored to the company’s specific needs and can be optimally integrated into existing production processes.

The functions of the MaDoW app

The application records the current condition of the machines as well as the duration and triggers of downtimes. This provides the manufacturer with the basis for a comprehensive identification and analysis of the main reasons for downtime of machines. It also enables employees to provide feedback on downtimes and classify them.

An example from the pilot phase is the identification of clamping errors on milling machines. Although these did not occur very often, they led to significant production downtime. These errors are now accurately recorded and their impacts made transparent.

The result: Meaningful information about machine downtime

Through the use of the platform and the app, valuable data are generated for optimizing all processes. The company can therefore take targeted measures to increase efficiency and speed up production. Data transparency helped identify inefficient processes.

This resulted in improved utilization of the machines and an increase in overall productivity. The faster response to issues led to significant time savings in production. The faster response to issues led to significant time savings in production. Overall, the company has increased production efficiency, enhanced machine availability, and improved collaboration with employees.

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