Predictive Prevention in HR Management

Predictive Prevention in HR Management

Harnessing the Internet of Things and Social Media to optimize Staff Scheduling

Predictive Prevention - what's it all about?

In the manufacturing sector, it takes a great deal of time and effort to reduce or completely avoid the risks associated with machine breakdowns and the potential resulting costs. This approach, known as predictive maintenance, is one of the key innovations of Industry 4.0. Its main objective is to deduce certain information and thereby draw conclusions based on various (realtime) data to facilitate early or just-in-time maintenance.

What applies to, and is good for, plant and machinery maintenance should also be implemented as a precaution for company staff, and scheduling of such staff. Ultimately, employees are a company's greatest asset. The procedures underlying predictive maintenance can also be used equally well in HR management where key duties undertaken include workforce planning in relation to employees with suitable qualifications as well as staff scheduling in terms of their working hours and locations. The same also applies here, namely to predict or identify a "breakdown" of resources early on, and to take appropriate preventive action - this is predictive prevention!

The Internet of Things as a Basis

The Internet of Things (IoT) serves as the foundation stone for predictive maintenance and predictive prevention.

The umbrella term "IoT" describes a network of individually connected devices that can communicate with one another via the Internet. Data and information is exchanged and made available for further processing by the recipient. The Internet of Things can be used to automate applications and to complete tasks without any external intervention.

In general, with IoT, a distinction is made between private and industrial use cases. In the private domain, the primary objective is to make the user's daily life easier and to add a greater degree of comfort (for example, through the use of smart home applications, wearable technology, and so on), whereas the main objective in business is to digitalize work and production processes, and to make them more efficient.

In practice, IoT is combined with various Industry 4.0 technologies. Notably, these include links to social media, mobile applications, data analyses and cloud computing.

Intelligently combining and integrating available technologies opens up a whole host of new options. These can also be used in HR management and range from automating administrative processes (processing sick leave, requests for vacation leave, salary payments, expenses, and so on) through to connecting company cars to the Internet to simplify business trip accounting in the HR department.

Using Social Media for Predictive Workforce and Project Planning

Another particularly interesting option involves extracting data from social medial (for example, Twitter, Facebook, Instagram, and so on) in order to analyze certain health-related trends such as flu epidemics.

Prospective sick leave can be predicted on the basis of defined keywords (for example, flu, cough, fever, Corona, and so on) and the use of text mining procedures such as geodata. Information obtained in this way is then used to optimize workforce planning, which, in turn, is essential for realistic project planning.

Just like in plant and machinery, predictive methods are also used here to anticipate a "breakdown" of staff resources and to take appropriate action in good time. Personal data is explicitly not collected here. Rather, it is about identifying trends and potential disruptions (for example, resulting from flu epidemic) before they arise.


You can benefit greatly from the Internet of Things, not only in all areas of your company but also in your daily life. Automating operations saves time, thus enabling your staff to concentrate on value-adding tasks.

Through the smart use of predictive maintenance models, you can also reduce downtimes and the resulting costs as well as optimize your project planning.

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