A critical problem with traditional APS is that it requires that all data be deterministic. For example all processing times must be fixed (no variation) and there can be no unexpected events (e.g. machine breakdowns) or unexpected delays (e.g. purchased materials arriving late). Hence the resulting schedule with APS is by nature optimistic (i.e. the "happy" path that assumes everything goes as expected), and is typically very different from what occurs in the real facility.
No matter how powerful the scheduling engine ignoring variation will typically produce large discrepancies between predicted schedules and actual performance. It is common that what starts off as a feasible schedule turns infeasible over time as variation and unplanned events degrade performance. With traditional APS we are forced to work with an overly optimistic schedule that promises more than we can deliver in terms of meeting critical customer requirements. As a result the scheduler is forced to buffer the expected degradation with some combination of extra time, inventory, or capacity; all adding inefficiency and cost to the operation. The challenge is to know what combination of these buffers is necessary to produce a robust schedule at minimal cost.
Simio Detailed Production Scheduling extends traditional APS to fully account for the variation that is present in nearly any production system, and provides the necessary information to the scheduler to allow the upfront mitigation of risk and uncertainty. Simio Enterprise Edition is a simulation-based approach featuring Simio Detailed Production Scheduling that makes dual use of the underlying simulation model. The simulation model can be built at any level of detail and can incorporate all of the random variation that is present in the real system.
Simio Detailed Production Scheduling begins by generating a deterministic schedule by executing the simulation model with all randomness turned off. Note that this is equivalent to the APS solution. However Simio Detailed Production Scheduling then uses the same simulation model with randomness turned on to replicate the schedule generation multiple times (employing multiple processers when available), and record statistics on the schedule performance across replications. The recorded performance measures include the likelihood of meeting a target (e.g. due date), the expected milestone completion date (typically later than the planned date based on the underlying variation in the system), as well as optimistic and pessimistic completion times (percentile estimates, again based on variation).
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How Risk-based Planning and Scheduling to help you drive more revenue while reducing risk and costs.
Expose hidden and unnecessary cost and time by accurately modeling critical constraints.
Quickly react to changes and update schedules in response to unpanned events.
View schedules using interactive Gantt charts with companion tools to expose the root cause for non-value-added time.
Mitigate schedule risk early while avoiding cost with unique insights provided by 3D animation.
Use Simio's rapid modeling environment and flexible data interfaces to quickly implement a cost-effective solution.