Nicole Stricker

Concept of an intelligent production control for global manufacturing in dynamic environments based on rescheduling

This paper presents an approach towards an innovative and intelligent production control for a highly flexible and efficient production in an increasingly dynamic and complex environment. For this purpose, an approach is developed on the basis of intelligent rescheduling for global manufacturing networks using information of cyber physical systems.

Selecting key performance indicators for production with a linear programming approach

Modern production systems are prone to disruptions due to shorter product life cycles, growing variant diversity and progressively distributed production. At the same time, reduced time and capacity buffers diminish mitigation opportunities, requiring better tools for production control. Performance measurement with key performance indicators (KPIs) is a widely used instrument to detect changes in production system performance in order to coordinate appropriate countermeasures. The main challenge in planning KPI systems consists in determining relevant KPIs. On the one hand, enough KPIs must be selected for a sufficiently high information content. On the other hand, the cognitive abilities of users are not to be overstrained by selecting too many KPIs. This tradeoff is addressed in a proposed selection process using an integer linear programme for objective KPI selection. In order to achieve this goal, crucial facets of the information content requirement are formalised mathematically. The developed method is validated using a practical application example, showing the influence of model parameter selection on optimisation results. The formalisation of the information content is shown to be a novel and promising approach.

Solving multi-criteria problems under risk: an approach explained using the example of rescheduling in dynamic environments

AbstractThere is a decent number of possible heuristic methods to solve an actual problem in production planning and control. Usually, each solving method leads to a different alternative. In dynamic production environments, decision makers often have to decide between uncertainty and risk. Making multi-criteria decisions under risk is a well-known problem. In this paper, we will consider rescheduling as an example for decision-making in a dynamic production environment. It is used to present an intelligent manufacturing approach for multi-criteria decisions under risk that combines a method for decision-making under risk and a multi-attribute decision-making method. Moreover, for frequently appearing problems, such as rescheduling, a procedure to evaluate the used solving methods is presented. We use this information to achieve a sustainable improvement for the solving procedure of future manufacturing problems.

Calculation of Maintenance Costs on Poor Data Basis and Implementation in Industrial Practice

Life-cycle cost statements become increasingly important for OEMs to stay competitive in today’s global environment. However, OEMs have to create this information from a poor data basis. Generating reliable statistical LCC statements despite the poor data is the methodical challenge. Consequently, it’s crucial to set up an instrument that prepares the data for analysis and simplifies future data collection. Therefore, a database was set up for Licon mt GmbH & Co. KG and the Weibull solution method was adapted by using a weighted pseudo inverse matrix. Both, the database and the adapted method are presented.