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KPI-controlled optimization of production processes

Control of production processes

The Qualicision technology is based on complementary extended fuzzy logic and helps to integrate decision-making know-how in the form of software into business processes. Uncertainty and fuzziness in business processes do not only arise from the forecasting nature of the process plan data used. It arises equally from the diversity and the complexity of the interactions between the possibilities to control the business processes and the resulting process goals the so-called key performance indicators (KPIs).


In Qualicision-based optimized business processes the interactions are recorded in the form of matrices (impact matrices) based on the process data. From the impact matrices, a mathematical conflict and compatibility analysis (KV analysis) is used to calculate which decision alternatives are to be selected in order to achieve the process goals as precisely as possible. From a technical point of view, the KV analysis makes the so-called combinatorial variety of control options manageable with regard to the optimization of the KPIs.


Copyright: PSI FLS

Copyright: PSI FLS


The Qualicision Functional Decision Design Scheduling Engine (QFDDS) is a Qualicision based support for shop floor planning and is integrated within an ERP system. Work orders for the production process are managed in the ERP system and are made available to the QFDDS Engine for detailed planning. QFDDS generates an occupation plan according to the desired optimization priorities or key performance indicators (KPIs) such as maximum usage, minimum stock, short lead time, minimum setup times, preference for job priorities and approaching delivery dates and makes these available to the surrounding systems for further processing at a BDE terminal, for example. To help planners find suitable priority settings for KPIs, a learning algorithm is integrated into the QFDDS, which permutes different priority settings and thus analyses optimized occupation plans according to different KPIs in order to maximise the key data generated by the system.

The results of KPI optimization can be visualised as the KPI Viewer in an additional explanation facility (see orange area in figure 2). The maximum characteristics (utopia points) that can be achieved for each KPI during the learning phase are shown in the orange area. To help select a particular priority setting the planner can enter a preference pattern (yellow area on the diagram) and is then automatically shown the best priority setting (green area on the diagram).