Artificial intelligence and the associated algorithms and technologies such as machine learning and deep learning are gaining acceptance in more and more areas of corporate processes. If AI-based predictions in marketing and CRM are already standard in many tools, there are currently exciting projects revolving around the use of artificial intelligence in human resources.
Imagine the following situation: A medium-sized company from the mechanical and plant engineering sector, active both nationally and internationally, has around 3,000 employees, including many specialists and experts. In the context of the shortage of skilled workers, the company naturally wants to keep the highly specialized employees and is trying to make working conditions and working environment as good as possible. Terminations due to dissatisfaction should be avoided.
However, the reality is different: around 15% of employees leave the company every year and must be replaced by the talent pool available on the job market. It takes time. And money. And it is often unsuccessful. Management believes that this loss of employees (employees leaving, either alone or due to layoffs) is unacceptable to the company for the following reasons:
The company has set itself the goal of ideally not letting it get this far. A study was commissioned with the aim of identifying the relevant internal and external factors that lead to employees leaving the company. Personnel management and management would like to concentrate on these factors, as far as they can be influenced, in order to reduce the loss of labor.
The central question is: Which changes have to be made to make most of the employees stay. These can be factors in the area of the workplace or also monetary and time incentives, possibly also more flexibility in the design of the job or support with childcare.
Management also wants to know which of these factors is most important and needs to be addressed immediately.
The case study is to be carried out on the basis of data analysis instruments and by using machine learning and regression analysis. The results of the data analysis can then be used by management and HR management to understand what changes they should make to employees' workplaces in order to persuade them to stay.
To achieve the goal, an intelligent tool of 40 ° is used. This builds on a large number of anonymous empirical values and answers e.g. the question of why the best and most experienced employees leave the company prematurely. At the same time, the tool is able to predict on an existing data set whether there is a risk that certain employees could leave the organization. And it shows what factors are still underdeveloped, so there is a risk of losing workers.
The core values that are analyzed include:
There are also parameters relating to the current situation in the company, i.e. Changes and transformations, possibly takeovers and new / old products (portfolio).
Additional factors can also be determined very well via employee surveys. It is important that this tool works completely anonymously and is therefore GDPR-compliant. The tool cannot be used to assign the results and forecasts to individual employees.
The tool is currently in the beta stage of development at 40 °. It is to be rolled out as a product at the end of 2020.
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