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Further Products for optimizing manufacturing

• InFrame Synapse MES Production planning and control for high-tech fabrication sites

• InFrame Synapse PCS Process & equipment control to improve quality and yield

• InFrame Synapse PLC-Connector SEMI PV02 compliant equipment interface for photovoltaic industries

FAB Optimization

Optimizing a factory is a goal to be achieved in acp-IT projects. From a digital FAB point of view this means increasing the productivity by lowering tact time, and for our clients this can be translated in higher profit with fewer costs. Different areas of the simulation process are taken in consideration in order to obtain the optimal model, main focuses being on:

• Preliminary investigations
  • Find out the type and number of required equipments beginning from the early phases of planning
  • Evaluate different layout alternatives, from throughput and utilization perspectives
  • Identify the FAB behaviour in case of breakdowns
  • Evaluate operator behaviour impact on the FAB output
• Finding possible bottlenecks and explain possible deviation from static calculations
  • Deadlock situations
  • Lack of advanced controlling / planning logics
  • Downtime propagation
• Providing solution for achieving the desired throughput
  • Transport prioritization;
  • Additional buffers/bins/storage facilities
• Optimizing the proposed solutions in order to reduce costs
  • Find the number and position of necessary buffers/bins/storing facilities
  • Evaluate performance due to setup downtimes caused by product change

In order to reach the desired goals, we use AnyLogic tools together with the specialized optimization tool OptQuest. The main techniques used to obtain essential results are:

• Parameter variation
  • Identify the relation between number of buffers/bins/storing facilities and factory throughput
  • Feasible to try each configuration
  • Run several simulation experiments with different buffers/bins/storage facilities configuration covering a given area of possibilities
• Parameter optimization
  • Identify the optimal buffer configuration minimizing the costs and maximizing the throughput
  • Advanced heuristic algorithm is needed to select a reduced number of possible configuration
  • Fitness function must be defined in order to rank different configurations allowing evolutionary algorithms to synthesize the next configuration to try

When having faced a result that shows very promising outcome it must go through one more step consisting in the validation. Result validation happens after:

• Optimization results are analyzed and the approved solution must be selected considering costs and benefits
• The selected solutions can be manually tested and analyzed for further improvements