1 om2, supplementary ch. d simulation ©2010 cengage learning. all rights reserved. may not be...

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OM2, Supplementary Ch. D Simulation

©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

SIMULATION

SUPPLEMENTARY CHAPTER D

DAVID A. COLLIERAND

JAMES R. EVANS

OM2

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LO1 Explain how to simulate a simple queuing application and how simulation works for fixed-time and next-event simulation models.

LO2 Describe how to apply simulation to a fixed period inventory management system.

LO3 Explain the importance of verification and validation of simulation models.

LO4 Describe how commercial software and simulators are used for advanced simulation applications.

Supplementary Chapter D. Learning Outcomes

l e a r n i n g o u t c o m e s

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As the saying goes, “A picture is worth a thousand words.” General Mills found that an animated simulation-based analysis of its supply chain was worth millions of words and also saved millions of dollars. As Mike Geddis, director of engineering at General Mills noted, “Your ability to actually see the activity in action and plot out the results allows you to understand impacts much better while the supply chain is running. We can run six months of supply chain data in a matter of minutes.” Geddis went on to say, “What we really found to be very powerful was the actual building of the model. It forced a certain diligence in terms of saying: If we change the system here, this happens over here. We had not gone to that level of understanding the interconnectionsof our supply chain before…

Supplementary Chapter D. Simulation

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Can you think of similar situations in which a manager might want to make changes to a system to understand what effects it might have?

What do you think?


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Computer simulation is the process of developing and analyzing a logical model of a system, process, or management decision and conducting computer-based experiments with the model to describe, explain, and predict the behavior of the system or outcomes associated with the decision. Dynamic, or system simulation models, trace the detailed logic or actions that occur in the system in a step-by-step fashion over time.


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Advantages of Simulation• A simulation model provides a convenient

experimental laboratory. An analyst can perform “what-if” studies, that is, change the design characteristics or operating rules to determine their impact without changing the actual system.

• The modeler does not have to disrupt the current process to examine alternative equipment, material and information flows, decision rules, and configurations.


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Disadvantages of Simulation

• Can be difficult to collect the appropriate input data

• Can be difficult to select the correct probability distributions to model uncertainty

• Accuracy of outputs is not always certain.• Can be hard to design good experiments to

test for statistically significant differences in various scenarios and changes in decision variables

• Can be hard to clearly determine cause and effect


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Fixed Time-Increment Queuing Model

A simulation model that increments time in fixed intervals like this is referred to as a fixed time-increment simulation model.

Supplementary Chapter D. SimulationExhibit D.1

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Customer Arrival Data and Random Number Assignments

Supplementary Chapter D. SimulationExhibits D.2 and D.3

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Simulated Arrivals


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Service Times


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Simulation Logic – First Minute


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Simulation Logic – Second Minute


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Simulation Logic – Third Minute


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Exhibit D.9 Supplementary Chapter D. Simulation

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Simulation Results


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Spreadsheet Implementation

Excel function RAND( ) generates random numbers between 0 and 1.

Exhibit D.11

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Random Number Intervals for Service Times


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Spreadsheet Model


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Simulated Results – Number Waiting

Exhibit D.14

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Next Event Simulation ModelsSimulation models that increment time

by the occurrence of the next event (time of next arrival, time of next service, and so on) are referred to as next-event simulation models.

An event is any action that changes the system.


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Arrival and Service Times


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Exhibit D.16 Supplementary Chapter D. Simulation

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Inventory Simulation Model

• Demand uncertainty• Lead time uncertainty• Order cost = $40• Carrying cost = $0.20/unit/day• Shortage cost = $100/unit

Find the order quantity and reorder point that minimizes total cost

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Inventory Simulation Logic (1 of 2)

Exhibit D.18

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Inventory Simulation Logic (2 of 2)

Exhibit D.19

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Demand and Lead Time Distributions

Exhibit D.20

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Spreadsheet Model

Exhibit D.21

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Simulated Results

Exhibit D.22

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Verification and Validation

Verification refers to the process of determining if a simulation program performs as intended.

Validation is concerned with determining whether the conceptual model is an accurate representation of the real system under investigation.

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Verifying Models• Develop a logical flowchart carefully• Conceptualize the problem in smaller

pieces, or modules, and write the computer code in modules that can be linked together

• Check code by someone other than the programmer

• Study outputs from the model for reasonableness.

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Validating Models

1. Develop a model with high face validity – the model should be reasonable to someone who understands the real system

2. Validate model assumptions – compare input probability distributions with real data

3. Validate model output – compare model output with data from the real system using same or similar inputs

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Simulators

A simulator is a simulation model that has been programmed for a specific problem or repetitive situation, such as a job-shop manufacturing process or a call center operation.

• Often developed for management training

• User-friendly, with graphical models and animation

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Daniel’s Auto Parts Case Study

The company would like to determine the best reorder point and reorder level for this situation using simulationanalysis. (Hint: Modify the Sound Systems spreadsheet model.)


1 om2, supplementary ch. d simulation ©2010 cengage learning. all rights reserved. may not be...

Documents

accessible website

computer simulation

system simulation models

validation of simulation

event simulation models

learning outcomesl e

months of supply chain

management decision