PSIpenta Finite Capacity Scheduler is a tool for detailed planning, order management, implementation and monitoring of production and production deadlines. It is used to process the rough-cut planning data transferred from the ERP system. Powerful mathematical and heuristic algorithms and over 20 planning rules allow short-term, up-to-the-minute detailed planning for individual work places, taking available capacity into account.
The Finite Capacity Scheduler allows the user to implement a manual, semi-automatic or fully automatic sequence planning process, based on key deadlines from production planning and control. This allows the orders to be transferred from order management, planned in detail and forwarded to the shop-floor data collection module. From the shop-floor data collection module, progress messages are transferred from production and fed back to higher-level systems.
All planning data is visualised in real time in the graphical planning board (GANTT) and many other views. Furthermore, the production planner can schedule and reschedule directly using drag & drop. Current messages from production regarding the progress of individual orders are integrated directly. In parallel to the actual planning process, various alternative planning versions (what-if planning) can be simulated and compared. The planning scenario with the best achievement of objectives can then be adopted in actual operation.
Optimum resource allocation depends on a variety of parameters. The objectives can be often opposing, i.e. optimisation of one parameter influences one or several other parameters negatively. Qualicision® technology makes it possible to view multiple optimisation objectives simultaneously. The target functions are mapped using key performance indicators (KPI), e.g. utilisation, inventory, short mean lead times, minimal setup times, preference for job priorities or earliest delivery dates. The optimisation objectives are weighted independently of each other. This enables priorities to be taken into account during production detailed planning, depending on the current situation. The user can make manual adjustments to the work plan once automatic planning is complete. The feasibility and, in particular, the efficacy of prioritisations of optimisation objectives vary depending on the given production situation and so must be weighed against one another. Implementation of this task is supported by a learning algorithm. The results are determined using various different KPI priorities. The aim of the algorithm is to determine the maximum possible target achievement of each KPI and the interdependencies of the KPIs. When the desired priority of one of the key figures is selected, all other priorities are set accordingly. The results are displayed graphically in a diagram and convey the interrelationships of the key figures and their priorities.