SIMIO® RISK BASED PLANNING & SCHEDULING (Simio RPS)

An iron foundry

Initial situation and challenges: Ineffective planning in Excel, infeasible production schedule, schedules are disconnected from the MES and ERP, poor production performance throughput.
Solutions: Integrated scheduling and MES solution, sequences that respect all process constraints, quick and efficient re-planning and scheduling, schedule presented on the MES operator screens. Effects: Greater production stability, Improved production throughput and efficiency, Data integration and integrity, Time feedback and re- planing.
Use and advantage of RPS over APS
Use and advantage of RPS over APS
Digital twin,     explaining RPS
Digital twin, explaining RPS
Case studies
Case studies
Tell us what requirements has your project and we help you find a good solution to realize it.
Contact us
Contact us

Scheduling software for complex systems with high variability

Logistic systems in industry, production and commerce are becoming increasingly complex and are often subject to interferences or disturbances which could be so far hardly assessed. Simio RPS offers simulation-based scheduling by using a flexible simulation model of the dynamic system, also called a Digital Twin. Read further about:
© 2018 Simulate First. All rights reserved.

Identifying risky orders

The order view shows which resources are planed for each order when and how long (including equipment, workers, vehicles). Likewise it shows what resources are delaying the processing of the order (see constraints). Finally the order view shows how likely it is for each order to be finished according to the plan, which of them are risky, which not.

Identifying problem areas and bottlenecks

The resource view shows which jobs will be processed on each resource as well as the jobs that will wait for that resource until it becomes available (see Constrained Entities). This makes it clear which resource causes a bottleneck and when. Simio then allows to interactively adjust the capacity of the resource directly in the diagram. Even sudden outages or planned maintenance can be entered interactively per resource.
Show evaluations
Show evaluations
Simio scheduling takes into account possible disruptions and actual system limitations Scheduling and simulation in one system - the digital twin Efficient evaluation options Case study Do you have further questions to investigate your projects?
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How reliable are the production plans from an APS?

Advanced planning and scheduling (APS) generates schedules by assuming there is no variation or uncertainty in the system. A deterministic APS schedule quickly becomes obsolete as machines break, processes vary in time, material arrives late, etc. APS schedules which appear initially feasible become infeasible over time as variation degrades performance. By ignoring variation APS schedules are optimistic by nature - they promise more than can be delivered.
Planned:	   Thu 27.Sep Expected:	     Fr 28.Sep Pessimistic:    Mo 1.Oct Optimistic:	   Thu 27.Sep Delivery date at risk!

Seeing the risk factors allows to take the right action

By providing up-front visibility into the inherent risk associated with a specific plan/schedule, RPS provides the necessary information to take early action in the operational plan to mitigate risks and reduce costs. Specific alternatives such as overtime or expediting external material/components from suppliers can be compared in terms of their impact. Emerging planning uncertainty, congestion, gridlock, delay in delivery, waste and risk must nevertheless remain manageable, so that the new target planning provides representative completion dates.

Adjustments for safe delivery

Possible process changes must be taken into account early in the planning, as purchase parts can arrive late at any time, employees fall ill or machines have technical faults. Only then the orders can be planned reliably, so that possible delivery delays are known in advance. Actual disturbances can be visualized on the basis of empirical values, so that order completion can be determined more and more precisely as time progresses.
Planned: 	   Thu 27.Sep Expected:	   Thu 27.Sep Pessimistic:  Thu 27.Sep Optimistic:	   Thu 27.Sep Delivery date safe !

Digital Twin

The virtual factory is a key component of the future-oriented smart factory. It includes both, a solid representation of the full production system in a simulation model, as well as a predictive planning tool to assess and compare alternatives.
ERP MES Digital Twin
3D simulation model order view     resource view    tables

Scheduling and simulation in one system

The Simio ® "Risk based Planning & Scheduling (RPS) " approach is to use one system to efficiently link, execute and visualize system information in one place. Simulation with scheduling generates a new schedule, lets view and understand its detailed functioning in an animated 3D model and then lets consider alternatives. Further it lets estimate the risks of completing production steps according to the schedule and find a more stable way if the risk is too high to accept (see the scheduling cycle in the picture). The simulation model can be defined down to any level of detail, with all the important conditions and random events that can occur in the real system.

Multiple heuristics optimization rules

Simio has many predefined optimization rules that let improve the sequence of the jobs in a particular section of the system. The typical rules focus either on maximizing throughput or minimizing cycle times. They would manage jobs around a bottleneck resource to maximize its utilization. Other predefined rules: reduce setups, waiting time, late orders, time in the process. The rules can finally be user defined exactly as needed for the given purpose.
critical ratio all operations ready shortest waiting time shortest order production shortest  set-up time earliest completion  some rules      of optimization
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© 2018 Simulate First. All rights reserved.
SIMIO® RISK BASED PLANNING & SCHEDULING (Simio RPS)
Advantage of RPS over APS
Advantage of RPS over APS
Digital twin,     explaining RPS
Digital twin, explaining RPS
Case studies
Case studies

Scheduling software for complex systems with

high variability

Logistic systems in industry, production and commerce are becoming increasingly complex and are often subject to interferences or disturbances which could be so far hardly assessed. Simio RPS offers simulation-based scheduling by using a flexible simulation model of the dynamic system, also called a Digital Twin. Read further about:
Show evaluations
Show evaluations
Simio scheduling consider disruptions

How reliable are the plans from APS?

A deterministic APS schedule quickly becomes obsolete as machines break, processes vary in time, material arrives late, etc. APS schedules which appear initially feasible become infeasible over time as variation degrades performance. The user of traditional APS has no way to assess or mitigate the underlying risk inherent in the schedule.

Adjustments for safe delivery

Possible process changes must be taken into account early in the planning, like purchase parts can arrive late at any time, employees fall ill or machines have technical faults so that possible delivery delays are known in advance. Actual disturbances can be visualized on the basis of empirical values, so that order completion can be determined more and more precisely as time progresses.

Review the risk factors to take an action

Simio RPS provides the necessary information to take early action in the operational plan to mitigate risks and reduce costs. Specific alternatives can be compared in terms of their impact. Emerging planning uncertainty, congestion, gridlock, delay in delivery, waste and risk must nevertheless remain manageable, so that the new target planning provides representative completion dates.

Planung und Simulation in einem System

The Simio ® "Risk based Planning & Scheduling (RPS) " approach is to use one system to efficiently link, execute and visualize system information in one place. Simulation with scheduling lets estimate the risks of completing production steps according to the schedule and find a more stable way if the risk is too high to accept (see the scheduling cycle in the picture). The simulation model can be defined down to any level of detail, with all the important conditions and random events that can occur in the real system.

Digital Twin

The virtual factory is a key component of the future- oriented smart factory. It includes both, a solid representation of the full production system in a simulation model, as well as a predictive planning tool to assess and compare alternatives.
ERP MES Digital Twin

Multiple heuristics optimization rules

Simio has many predefined optimization rules that let improve the sequence of the jobs in a particular section of the system. The typical rules focus either on maximizing throughput or minimizing cycle times. They would manage jobs around a bottleneck resource to maximize its utilization. Other predefined rules for an efficient system:
Scheduling and simulation - the digital twin

An iron foundry

Initial situation and challenges: Ineffective planning in Excel, infeasible production schedule, schedules are disconnected from the MES and ERP, poor production performance throughput.
Solutions: Integrated scheduling and MES solution, sequences that respect all process constraints, quick and efficient re-planning and scheduling, schedule presented on the MES operator screens. Effects: Greater production stability, Improved production throughput and efficiency, Data integration and integrity, Time feedback and re-planing.
Tell us what requirements has your project and we help you find a good solution to realize it.
Contact              us
Contact us

Identifying risky orders

The order view shows which resources are planed for each order when and how long (including equipment, workers, vehicles). Likewise it shows what resources are delaying the processing of the order (see constraints). Finally the order view shows how likely it is for each order to be finished according to the plan.

Identifying problem areas and bottlenecks

The resource view shows which jobs will be processed on each resource as well as the jobs that will wait for that resource until it becomes available (see Constrained Entities). This makes it clear which resource causes a bottleneck and when. Simio then allows to interactively adjust the capacity of the resource directly in the diagram. Even sudden outages or planned maintenance can be entered interactively per resource.
Efficient evaluation options Description of a case study Do you have further questions to your projects? 3D Simulationsmodell Auftragsplan Ressourcenplan Tabellen Planned:	   Thu 27.Sep Expected:	     Fr 28.Sep Pessimistic:    Mo 1.Oct Optimistic:	   Thu 27.Sep Delivery date at risk! Planned: 	   Thu 27.Sep Expected:	   Thu 27.Sep Pessimistic:  Thu 27.Sep Optimistic:	   Thu 27.Sep Delivery date safe ! critical ratio all operations ready shortest waiting time shortest order production shortest  set-up time earliest completion  some rules of optimization