simulacion luis garciaguzman-21012011
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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
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
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
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.
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
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
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?
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
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
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
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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