Part 4.1 Intro to Process Simulation using processmodelTM

Part 4.1 Intro to Process Simulation using processmodelTM -...

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Click to edit Master subtitle style 7/7/11 Process Simulation using 11 MGSC 485 – Business Process Management Professors Sanjay Ahire and Jack Jensen Moore School of Business University of South Carolina
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7/7/11 Simulation Methodology for conducting experiments using a mathematical model of a real system. System Experiment with actual system Experiment with a model of the system Physical model Mathematical model Analytical solution Simulation
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7/7/11 Deterministic Stochastic Simple Complex Spread sheets Queuing Systems Simulation
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7/7/11 Obj102
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7/7/11 Discrete Event Digital Simulation Discrete: Changes in a system under study may be represented by a collection of individual events. Thus, the state of the system changes in discrete increments over time. Event: Any action that changes the state of the system. This is most often distinguished as a change in the status of an entity. Entity: Unit transformed by the system over time. Activity: Any collection of functions that transform the state of an entity. Input Process Output Feedback Environment
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7/7/11 Simulation Example Assume that you know nothing about We know that this problem may be Unmanaged job arrivals average 4 per hour. Your process can complete each job in approximately 12 minutes but each Solving this problem in POM for Windows: L = 80% ±q = 3.2 customers ± = 4.0
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7/7/11 Step 1: Define the entity that represents customers Select ‘box’ entity to represent a job. Generate the “periodic” interarrival of jobs by using an exponential distribution with mean of 15 minutes. One job arrives at a time and the first job arrives as soon as the simulation begins
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7/7/11 Select “process box” to represent our process The time to complete each job is an exponentially distributed random variable with a mean of 12 minutes. Only one job can be processed at a time, up to 999 jobs can wait in queue, and the simulation begins with the input queue to this Step 2: Define the process
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7/7/11 Set simulation options to run model for 4000 hours once. Animation and Scoreboard turned off. Step 3: Define the simulation parameters and run the simulation
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7/7/11 Step 4: Review simulation output Estimate of (80% in analytical model) Estimate of Lq (3.2 in analytical model) Sum equals an estimate of L (4.0 in analytical model) Estimate of Wq (.8*60=48 in analytical model) Sum equals an estimate of W (60 in analytical model) What additional information can simulation provide about this
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7/7/11 Example 2 – a few model Begin with the previous simulation of an M/M/ 1 and make the following changes: Two processing steps instead of 1 Step 1: One workstation, processing time E(12) Step 2: Two interchangeable workstations, processing time E(25) It takes 1 minute to travel from process 1 to process 2
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7/7/11
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7/7/11
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7/7/11
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7/7/11 Example 2a Assume that the workstations at process 2 are not interchangeable They work independently Each workstation can process 50% of the jobs Will this change the shop’s performance?
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Part 4.1 Intro to Process Simulation using processmodelTM -...

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