Despite many methodological advances, automated production planning optimization is still not widely used in practice. Instead of relying on Advanced Planning and Scheduling (APS) systems, many companies delegate operational planning decisions to their employees in individual production departments – a task that becomes increasingly difficult due to market volatility and complexity of the production processes. Thus, companies are unable to achieve potential savings which amount to millions of Euros each year. Unlocking this potential would lead to opportunities such as the reduction of the cost pressure arising out of the production location in high-wage countries within the EU, as well as investing in increased capacity and modernization (e.g. environmental) of the existing plants.
In SimGenOpt2, we pursue a new and complementary approach to exploit the potential savings in production planning. With the developed simulation generator SimGen at FH OÖ Steyr Campus, production processes which include planning and control of various companies can be modeled and analyzed holistically. The resulting digital model of the production company will be controlled, similarly to the real company, by the use of production planning methods such as Material Requirements Planning (MRP) and matched regarding the real and virtual key performance indicators.
However, the aforementioned planning methods require a multitude of parameters, for instance MRP requires 3 parameters per material and low-level code, which are critical for production efficiency. Due to the wide-spread use of these strategies, negative effects of bad parameterizations are noticeable, on the one hand in the higher operational planning effort of the individual departments to handle the increased burden of work, and on the other hand in worse inventory and service levels.
With the developed optimization environment HeuristicLab at FH OÖ Hagenberg Campus, these planning parameters will be improved regarding cost efficiency and service level through an integrated simulation-based optimization approach. A tradeoff between cost efficient and stable respectively robust parameters should be achieved. Insights into the evaluation of stability and robustness during the optimization run are obtained and new methods for optimization are developed. Additional insights are gained in terms of the potential savings’ extent arising out of the use of improved parameters. With the partners oxaion GmbH and Banner GmbH an exploitation partner and an end user support this endeavor.