Doctoral Dissertation: Development of a generic data model
Generic data model to reveal optimization potential under consideration of the throughput
The term "Industrial Internet of Things" was expressed for the first time in 2011 at the Hanover Fair. Today, the use of this term has already taken on inflationary features and many companies associate with this term currently more a marketing instrument. But the digital transformation of companies, which is to be approached in the sense of Industrial IoT, holds numerous optimization potentials. Flexibility in particular is becoming increasingly important in view of the inclined degree of product individualisation. Ensuring this flexibility sometimes requires additional production capacity. Companies are faced with the problem that a new machine would generate more output and more sales, but the machine would not be running to capacity. The payback period would be extended, which would have a negative impact on costs. Therefore it is important to start and to offer companies, by means of Industrial IoT solutions, possibilities to adapt their production with existing resources and to uncover unused capacity reserves.
Industrial IoT technologies offer completely new possibilities for the comprehensive collection and evaluation of machine-specific production data. The doctoral thesis will deal with the technical production formula for determining the capacity of machines Capacity = Availability *Throughput rate. The focus of the analyis is on the throughput rate. The aim is to create a generic model wich is data-based and will reveal unused potential and avoidable variances in throughput in order to indicate reserves in the capacity of machines. Therefore the user can learn about the reserves of his machine as well as data-based information on throughput when considering the type. On this basis, measures can be taken to optimise throughput in order to increase it. This allows, for example to rise the output volume in the company without having to raise funds for costly investment projects. This increases efficiency. Costs can also be reduced, which has a positive effect on pricing and gives a competitive edge. In addition, the company retains capital, e. g. for the purpose of investing in digital transformation.
August 2018 to July 2021
between the University of Applied Sciences Dresden and the TU Chemnitz