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Simulacion luis garciaguzman-21012011

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Industrial and Operations Engineering College of Engineering Process Improvement with Discrete Event Simulation Luis Garcia Guzman, PhD Asst Research Scientist and Adjunct Professor Industrial and Operations Engineering The University of Michigan
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Page 1: Simulacion luis garciaguzman-21012011

Process Improvement with Discrete Event Simulation

Luis Garcia Guzman, PhD

Asst Research Scientist and Adjunct Professor

Industrial and Operations Engineering

The University of Michigan

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BiografíaEducación: IIS-90 (ITESM-Campus Estado de México) MSE and PhD (U of M) --- Industrial & Operations Engineering

Experiencia Laboral: Investigador y Profesor– Ingeniería Industrial, Universidad de

Michigan Ingeniero en Logística, Ingeniero de Producto y de Calidad~

Duroplast (Naucalpan), AMP Industries (Michigan), Daimler Chrysler (Michigan) y GM (Michigan).

Docencia: Probabilidad y Estadística Ingeniería Estadística Diseño de Experimentos Control de Calidad Simulación de Eventos Discretos Seis Sigma – Cursos de Green Belt y Black Belt

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Topics

I. Overview of Simulation Models

II. Steps in a Simulation Study

III. Process Simulation Examples

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What is Simulation Modeling?

A model is an imitation of a system (or process) in real-world over time.

A system is a collection of interrelated elements (or processes) which function cooperatively to achieve a stated objective. There is a measurement of performance

Model of a system (or process) should reflect and mimic the behavior of the system (or process) Understanding the model implies at least some

understanding of the real system

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System and Model

System Environment

System

Model

System Boundary

Model Scope

Endogenous Exogenous

Entity, Attribute

Activity, Event

State

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Components of a System(Example: Supermarket)

Entity Attribute Activity State of a system Event

Endogenous/exogenous (activity, event)

• Customer• Buying habits,

preference• Strolling through aisle • # customers in each

aisle• Started/finished aisle,

enter cashier queue, exit queue

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Types of Simulation Models Dynamic versus Static Stochastic versus Deterministic Discrete versus Continuous Since models mimic real-world systems, these

definition apply to systems as well.

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Why Simulate? Typical Decision Support Problems:

Evaluate alternative configurations of a system capacity, utilization, bottlenecks, scrap, etc.

Identify the desirable/feasible configuration(s) of the system for a specified objective (optimization)

Identify a robust strategy to achieve a specified objective for a system

Go – No Go decisions for project management Evaluate the value and the risk of an asset

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Ways to Study a System

System

ExperimentWith the

Actual system

Experimentwith a model of the system

Physicalmodel

Mathematicalmodel

Analyticalsolution

Simulation

Why model? - describe - explain - predict - demonstrate

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Experiment with Actual System Advantages

Don’t have to spend time/resource to model the system

No loss of accuracy Disadvantages

May interfere with current operation, or is cost inhibitive

May be difficult to repeat, e.g. war game Not possible if there is no real system yet

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Analytical Methods Advantages

Low requirement on modeling efforts Provide great insights on relationships among

variables Answer is exact (not necessarily accurate)

Disadvantages May need lots of variables or distributions Closed form solution may not exist or is difficult to

derive

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Advantages of Simulation Models Most complex systems cannot be accurately described by

the alternatives (e.g., analytical math models) Allows estimating the performance of an existing system

under some projected set of operating conditions without disrupting ongoing operations without committing resources for acquisition of new hardware

Promotes the understanding of how the system works Test hypotheses about how or why phenomena occur Obtain insight about the interaction of variables Obtain insight about the importance of variables to performance Bottleneck analysis

Control over experimental conditions Allows great flexibility for ‘what-if’ analysis

Enables comparison of alternative system designs

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Disadvantages of Simulation

Simulation models can be expensive and time consuming to develop Lots of upfront work, e.g. input modeling, computer coding Requires special training, open to interpretations

Simulation results may be difficult to interpret Each run produces only estimates of a model’s true

characteristics for a particular set of input parameters Computer model may be wrong, e.g. programming bugs

The large volume of numbers or the persuasive impact of realistic animation often creates a tendency to place greater confidence in the results than is justified Possibility of misinterpretation of random results

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Simulation is not appropriate when… The problem can be solved using common sense The problem can be solved analytically It is easier to perform direct experiments The costs exceed possible savings Resources are not available Time is not available No data, not even estimates, are available Not enough time to verify and validate Managers have unreasonable expectations The system behavior is too complex or cannot be

defined

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II. Steps in a Simulation Study Problem formulation Setting of objectives and overall project plan Model conceptualization Data collection Model translation Verified? Validated? Experimental design Production runs and analysis More runs? Documentation and reporting implementation

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Is simulation appropriate?Define alternative systems

Project planning

Steps in a Simulation Study

Formulation

Define Project Goal & Plan

Data CollectionModel Conceptualization

Model Translation

Verified?

Validated?

No

Yes

No No

Yes

What is the problem?

How?

An ArtStart simpleThen expand

Is code OK?

Represents the system well?

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Steps in A Simulation Study

Experimental Design

Production Runs& Analysis

More Runs?Yes Yes

Documentation& Reporting

No

Implementation

scope of this class

What runs to make to answer question

efficiently?

Estimate theperformance measures

Program and ProgressCustomer acceptance

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Process Simulation – Queuing ModelsDescribed by Customer Population Queue Channels and Phases Customer Arrival Process Service Process Queue Discipline

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1. Customer Service Populations Infinite

Cars Passing Toll Booth Supermarket, Bank, Restaurant Customers Telephone Calls at Service Center

Finite Geriatric Patients under nursing care TV Networks Students in course

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2. Queue Channels and Phases Servers Single Server (Single Channel) Multiple Server (Multiple Channel) Phases Single Phase (Single Service) Multiple Phase (Multiple Sequential Services)

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3. Customer Arrival Processes Constant

Example: Scheduled Outpatient Care

Variable Arrivals (random variable) Independence (between customers) Single Customer

Example: Emergency Room Care Batches of Customers

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4. Service Process Constant Service Rate

Automated Assembly Line Automated Car Wash Streaming Video Distance Learning

Variable Service Rate (Random) Gasoline Station Shopping Center

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5. Queuing Discipline First Come, First Served Priority Customers Shortest Processing Time Reservations First Limited Needs Other

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Simulation and Six Sigma

Six sigma is a data-driven methodology for improving quality in many aspects of a company’s products and services

Phases of six sigma methodology typically are: Define, Measure, Analyze, Improve and Control (DMAIC) for existing processes or Define, Measure, Analyze, Design, Verify (DMADV) for new processes or major changes or re-designs (Design for Six Sigma)

Simulation is one of the available tools in a Six-Sigma initiative. Particularly within the Analyze and Improve of the DMAIC project or Analyze and Design of a DMADV project or Design and Optimize of a IDDOV project

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Simulation and Six Sigma

Benefits of simulation in the context of six sigma: Considers process variances, uncertainties and

interdependencies Easy to include and study alternative solutions Models can be developed without disruptions to

existing processes Takes subjectivity and emotion out of decision

making (data-driven=six sigma) Animation tool helps illustrate and convince others

on the best solutions Reusable models can encourage continuous

improvement

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III. Process Simulation ExamplesProcess Simulation Examples

1. OEM Paint Shop Operations

2. OEM Work In Process Inventory (WIP) reduction

3. Supply Chain Optimization

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1. North American OEM Paint Shop Problem Description: The paint shop assembly

line at an OEM plant is complex and can be improved. Currently, 80% of the painted vehicle bodies are

declared a success.

Project goal: To increase the number of successfully painted vehicle bodies by: Decreasing system down time, Optimizing color sorting, and/or improving paint

robot success rates.

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Plant Layout

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NA OEM Paint Shop Process improvement opportunities:

System down time - paint machine color cartridge replacement process

Machine operating speed, machine age, and total machine operating time.

wait time between locations. Approach:

First, a model of the actual system was constructed. Then the model was verified and validated. Alternative configurations developed and tested to find best solution

Results Recommend layout solution, increased the yield from 80% to

90% Reduced downtime costs by $2,700 per day  

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2. OEM WIP Reduction Problem Description: Excessive WIP in the Assembly

Area

Project Goal: to decrease excess WIP in the workshop.

Process Improvement Opportunities: large lot sizes long set-up times long lead times Ineffective production scheduling Breakdowns of machines Non-value-adding activities of Operators

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OEM WIP Reduction Approach:

First, a model of the actual system was constructed.

The reasons for excess WIP in the workshop were analyzed and identified.

Then the model was verified and validated. After that, the problem solving approach was

developed. By testing the results of changes on variables, the minimum stock level was reached.

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OEM WIP ReductionRecommendations: The proposal for decreasing WIP were divided into

two groups: Scheduling:

creating lot sizing methods material pulling to the system (the number of pieces going into

the systems should be equal to the required number of output) lead time monitoring and lead time reduction through waste

elimination machine-operator assignments done according to priority of jobs increasing the number of multi-process material handling

operators Technological:

reduction of set up times methodical improvements automation of machines where possible layout optimization the preventative and productive maintenance

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OEM WIP Reduction Results:

There was a 48% reduction on the average WIP in the assembly floor

As a result of the improvements in WIP the cost of material was reduced by the same amount. There was a 14% improvement by implementing

only the scheduling rules.

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3. Supply Chain Optimization Problem Description: Excessive lead time for the

distribution of confectionary products in India

Project Goal: to cut the lead time from factories to retail depots. Determine the optimal amount of trucks to be utilized to minimize lead time at a reasonable cost.

Approach: First, a model of the existing supply chain. Then the model was verified and validated. After that, alternative supply chain model was built and

simulated to compare with initial model.

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Supply Chain Optimization This model is based around a central

warehouse used for storage and as a distribution point for some routes.

Existing Supply Chain

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Existing Supply Chain

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Proposed Supply Chain Products are shipped directly from the

factories to the individual depots much of the burden is shifted to the factories increase in the number of trucks required to meet

demand. higher cost, however, cost savings occur due to the lack of maintenance of a larger distribution center and reduction in lead time.

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Supply Chain Optimization

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Supply Chain Optimization Results:

The proposed supply chain cuts costs by 50%

Lead time would be reduced by almost six times.

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Logistics – Energy Services Company Problem: High level of maintenance costs at

local maintenance centers (26 locations around the world)

Long delays in completing maintenance jobs Goal of simulation: Study the effects of

maintaining a single global maintenance center where experts can perform the job more quickly and cost effectively.

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Initial Results Results:

The proposed model could cut maintenance costs by 20%

Increase the service level (e.g. probability of having available tools at the oil rigs from 70% to 85%)

Lead time could be reduced by almost 30%.

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Call Center Evaluation Comparison of 2 different layouts:

Current layout Planned improvement to a cell fashion layout

Results: Reduction of number of lost calls Reduction of average holding time Reduction of maximum hold time

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Summary

A simulation model is an imitation of a system (or process) in real-world over time.

Simulation can be a useful tool in decision making Allows great flexibility for ‘what-if’ analysis Enables comparison of alternative system designs

Simulation models are “run” rather than solved Assumptions of model should be validated based on

model characteristics and behavior Simulation applications are vast particularly in

manufacturing and transactional processes

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