Course 2 – MK. Simulasi Komputer
System Dynamics
Benazir Imam Arif Muttaqin, S.T., M.T.
Teknik Industri, Institut Teknologi Telkom Surabaya
benazir.imam.a.m@ittelkom-sby.ac.id
OUTLINE
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Introduction
System Definition
System Elements
System Complexity
System Performance Metrics
System Variables
System Optimization
The Systems Approach
System Analysis Techniques
1. Introduction
WHY SYSTEM UNDERSTANDING IS NEEDED?
• Knowing how to do simulation doesn’t make someone a good systems
designer any more than knowing how to use a CAD system makes one a
good product designer. Simulation is a tool that is useful only if one
understands the nature of the problem to be solved.
• Simulation exercises fail to produce useful results more often because of a
lack of understanding of system dynamics than a lack of knowing how to
use the simulation software.
• The challenge is in understanding how the system operates, knowing
what you want to achieve with the system, and being able to identify key
leverage points for best achieving desired objectives.
ILLUSTRATIONS
ILLUSTRATIONS
WHY SYSTEM UNDERSTANDING IS NEEDED?
• This example illustrates the nature and difficulty of the decisions that an
operations manager faces.
• Managers need to make decisions that are the “best” in some sense.
• To do so, however, requires that they have clearly defined goals and
understand the system well enough to identify cause-and-effect
relationships.
• While every system is different, just as every product design is different,
the basic elements and types of relationships are the same.
• Knowing how the elements of a system interact and how overall
performance can be improved are essential to the effective use of
simulation.
2. System Definition
SYSTEM DEFINITION
• A system, as used here, is defined as a collection of elements that
function together to achieve a desired goal (Blanchard 1991).
• Key points in this definition include the fact that (1) a system consists
of multiple elements, (2) these elements are interrelated and work
in cooperation, and (3) a system exists for the purpose of achieving
specific objectives.
• Examples of systems are traffic systems, political systems, economic
systems, manufacturing systems, and service systems.
• Our main focus will be on manufacturing and service systems that
process materials, information, and people.
MANUFACTURING SYSTEMS
• Small job shops and machining
cells or large production
facilities and assembly lines.
• Warehousing and distribution
as well as entire supply chain
systems will be included in our
discussions of manufacturing
systems.
SERVICE SYSTEMS
• Service systems cover
a wide variety of
systems including
health care facilities,
call centers,
amusement parks,
public transportation
systems, restaurants,
banks, and so forth.
3. System Elements
SYSTEM ELEMENTS
ENTITIES
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Entities are the items processed through the system such as products, customers,
and documents.
Different entities may have unique characteristics such as cost, shape, priority,
quality, or condition.
Entities may be further subdivided into the following types:
1. Human or animate (customers, patients, etc.).
2. Inanimate (parts, documents, bins, etc.).
3. Intangible (calls, electronic mail, etc.).
For most manufacturing and service systems, the entities are discrete items.
For some production systems, called continuous systems, a nondiscrete substance
is processed rather than discrete entities. Examples of continuous systems are oil
refineries and paper mills
ACTIVITIES
• Activities are the tasks performed in the system that are either
directly or indirectly involved in the processing of entities.
• Examples:
1. Entity processing (check-in, treatment, inspection,
fabrication, etc.).
2. Entity and resource movement (forklift travel, riding in an
elevator, etc.).
3. Resource adjustments, maintenance, and repairs (machine
setups, copy machine repair, etc.).
RESOURCES
• Resources are the means by which activities are performed.
• They provide the supporting facilities, equipment, and
personnel for carrying out activities.
• Examples:
1. Human or animate (operators, doctors, maintenance
personnel, etc.).
2. Inanimate (equipment, tooling, floor space, etc.).
3. Intangible (information, electrical power, etc.)
CONTROLS
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Controls dictate how, when, and where activities are performed. Controls
impose order on the system.
At the highest level, controls consist of schedules, plans, and policies. At the
lowest level, controls take the form of written procedures and machine control
logic. At all levels, controls provide the information and decision logic for how
the system should operate.
Examples:
1. Routing sequences.
2. Production plans.
3. Work schedules.
4. Task prioritization.
5. Control software.
6. Instruction sheets.
4. System Complexity
WHY SYSTEM IS COMPLEX?
• Unfortunately, unaided human intuition is not very
good at analyzing and understanding complex
systems.
• Economist Herbert Simon called this inability of the
human mind to grasp real-world complexity “the
principle of bounded rationality.” This principle states
that “the capacity of the human mind for
formulating and solving complex problems is very
small compared with the size of the problem whose
solution is required for objectively rational behavior
in the real world, or even for a reasonable
approximation to such objective rationality” (Simon
1957).
SYSTEM COMPLEXITY FACTORS
• Interdependencies between elements so that each element affects other
elements.
• Variability in element behavior that produces uncertainty
INTERDEPENDENCIES
• Interdependencies cause
the behavior of one
element to affect other
elements in the system.
• For example, if a machine
breaks down, repair
personnel are put into
action while downstream
operations become idle for
lack of parts.
VARIABILITY
• Variability is a characteristic inherent in any system involving
humans and machinery.
• Uncertainty in supplier deliveries, random equipment failures,
unpredictable absenteeism, and fluctuating demand all combine to
create havoc in planning system operations.
• Variability compounds the already unpredictable effect of
interdependencies, making systems even more complex and
unpredictable.
VARIABILITY
5. System Perforance
Metrics
SYSTEM PERFORMANCE METRICS
• Metrics are measures used to assess the performance of a system.
• At the highest level of an organization or business, metrics measure
overall performance in terms of profits, revenues, costs relative to budget,
return on assets, and so on.
• These metrics are typically financial in nature and show bottom-line
performance.
• Unfortunately, such metrics are inherently lagging, disguise low-level
operational performance, and are reported only periodically.
• From an operational standpoint, it is more beneficial to track such factors
as time, quality, quantity, efficiency, and utilization.
FLOW TIME
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The average time it takes for an item or customer to be processed through the
system.
Synonyms include cycle time, throughput time, and manufacturing lead time.
For order fulfillment systems, flow time may also be viewed as customer
response time or turnaround time.
A closely related term in manufacturing is makespan, which is the time to
process a given set of jobs.
Flow time can be shortened by reducing activity times that contribute to flow
time such as setup, move, operation, and inspection time.
It can also be reduced by decreasing work-in-process or average number of
entities in the system.
UTILIZATION
• The percentage of scheduled time that personnel, equipment, and
other resources are in productive use.
• If a resource is not being utilized, it may be because it is idle, blocked,
or down.
• To increase productive utilization, you can increase the demand on the
resource or reduce resource count or capacity. It also helps to balance
work loads.
• In a system with high variability in activity times, it is difficult to achieve
high utilization of resources.
VALUE-ADDED TIME
• The amount of time material, customers, and so forth spend
actually receiving value, where value is defined as anything for
which the customer is willing to pay.
• From an operational standpoint, value-added time is considered
the same as processing time or time spent actually undergoing
some physical transformation or servicing.
• Inspection time and waiting time are considered non-value-added
time.
WAITING TIME
• The amount of time that material, customers, and so on spend
waiting to be processed.
• Waiting time is by far the greatest component of non-value-added
time.
• Waiting time can be decreased by reducing the number of items
(such as customers or inventory levels) in the system.
• Reducing variation and interdependencies in the system can also
reduce waiting times.
FLOW RATE
• The number of items produced or customers serviced per
unit of time (such as parts or customers per hour).
• Synonyms include production rate, processing rate, or
throughput rate.
• Flow rate can be increased by better management and
utilization of resources, especially the limiting or
bottleneck resource.
INVENTORY / QUEUE LEVELS
• The number of items or customers in storage or waiting
areas.
• It is desirable to keep queue levels to a minimum while still
achieving target throughput and response time
requirements.
• Where queue levels fluctuate, it is sometimes desirable to
control the minimum or maximum queue level.
• Queuing occurs when resources are unavailable when
needed.
YIELD
• From a production standpoint, the percentage of products
completed that conform to product specifications as a
percentage of the total number of products that entered
the system as raw materials.
• If 95 out of 100 items are nondefective, the yield is 95
percent.
• Yield can also be measured by its complement—reject or
scrap rate.
CUSTOMER RESPONSIVENESS
• The ability of the system to deliver products in a timely
fashion to minimize customer waiting time.
• It might be measured as fill rate, which is the number of
customer orders that can be filled immediately from
inventory.
VARIANCE
• The degree of fluctuation that can and often does occur in
any of the preceding metrics.
• Variance introduces uncertainty, and therefore risk, in
achieving desired performance goals.
• Manufacturers and service providers are often interested in
reducing variance in delivery and service times.
• For example, cycle times and throughput rates are going to
have some variance associated with them.
6. System Variables
DECISION VARIABLES
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Decision variables (also called input factors) are sometimes referred to as the
independent variables in an experiment.
Changing the values of a system’s independent variables affects the behavior of
the system.
Independent variables may be either controllable or uncontrollable depending
on whether the experimenter is able to manipulate them.
An example of a controllable variable is the number of operators to assign to a
production line or whether to work one or two shifts. Controllable variables are
called decision variables because the decision maker (experimenter) controls
the values of the variables.
An uncontrollable variable might be the time to service a customer or the reject
rate of an operation.
RESPONSE VARIABLES
• Response variables (sometimes called performance or output variables)
measure the performance of the system in response to particular decision
variable settings.
• A response variable might be the number of entities processed for a given
period, the average utilization of a resource, or any of the other system
performance metrics.
• In an experiment, the response variable is the dependent variable, which
depends on the particular value settings of the independent variables.
STATE VARIABLES
• State variables indicate the status of the system at any specific point in time.
• Examples of state variables are the current number of entities waiting to be
processed or the current status (busy, idle, down) of a particular resource.
• Response variables are often summaries of state variable changes over time.
• For example, the individual times that a machine is in a busy state can be
summed over a particular period and divided by the total available time to
report the machine utilization for that period.
• State variables are dependent variables like response variables in that they
depend on the setting of the independent variables.
7. System Optimization
SYSTEM OPTIMIZATION (1)
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Finding the right setting for decision variables that best meets performance
objectives is called optimization.
Specifically, optimization seeks the best combination of decision variable values
that either minimizes or maximizes some objective function such as costs or
profits.
An objective function is simply a response variable of the system. A typical
objective in an optimization problem for a manufacturing or service system might
be minimizing costs or maximizing flow rate.
For example, we might be interested in finding the optimum number of personnel
for staffing a customer support activity that minimizes costs yet handles the call
volume.
In a manufacturing concern, we might be interested in maximizing the throughput
that can be achieved for a given system configuration.
SYSTEM OPTIMIZATION (2)
• Optimization problems often include constraints, limits to the values
that the decision variables can take on.
• For example, in finding the optimum speed of a conveyor such that
production cost is minimized, there would undoubtedly be physical limits
to how slow or fast the conveyor can operate.
• Constraints can also apply to response variables.
• An example of this might be an objective to maximize throughput but
subject to the constraint that average waiting time cannot exceed 15
minutes.
SYSTEM OPTIMIZATION (3)
8. System Approach
FOUR STEP ITERATIVE APPROACH
IDENTIFYING PROBLEMS AND
OPPORTUNITIES
• The importance of identifying the most significant problem areas and
recognizing opportunities for improvement cannot be overstated.
• Performance standards should be set high in order to look for the greatest
improvement opportunities.
• Companies making the greatest strides are setting goals of 100 to 500
percent improvement in many areas such as inventory reduction or
customer lead time reduction.
• Setting high standards pushes people to think creatively and often results
in breakthrough improvements that would otherwise never be
considered.
DEVELOPING ALTERNATIVE SOLUTIONS
• It is necessary to begin developing a solution to a problem by
understanding the problem, identifying key variables, and describing
important relationships.
• This helps identify possible areas of focus and leverage points for applying
a solution.
• Techniques such as cause-and-effect analysis and pareto analysis are
useful here.
• Once a problem or opportunity has been identified and key decision
variables isolated, alternative solutions can be explored.
EVALUATING THE SOLUTIONS
• Alternative solutions should be evaluated based on their ability to meet
the criteria established for the evaluation.
• These criteria often include performance goals, cost of implementation,
impact on the sociotechnical infrastructure, and consistency with
organizational strategies.
• Many of these criteria are difficult to measure in absolute terms, although
most design options can be easily assessed in terms of relative merit.
SELECTING AND IMPLEMENTING
THE BEST SOLUTION
• Often the final selection of what solution to implement is not left to the
analyst, but rather is a management decision.
• The analyst’s role is to present his or her evaluation in the clearest way
possible so that an informed decision can be made.
• Even after a solution is selected, additional modeling and analysis are
often needed for fine-tuning the solution.
• Implementers should then be careful to make sure that the system is
implemented as designed, documenting reasons for any modifications.
9. System Analysis
Techniques
SYSTEM ANALYSIS TOOLS
• Systems analysis tools, in addition to simulation, include simple
calculations, spreadsheets, operations research techniques (such as
linear programming and queuing theory), and special computerized tools
for scheduling, layout, and so forth.
• While these tools can provide quick and approximate solutions, they tend
to make oversimplifying assumptions, perform only static calculations,
and are limited to narrow classes of problems.
• Additionally, they fail to fully account for interdependencies and variability
of complex systems and therefore are not as accurate as simulation in
predicting complex system performance.
HAND CALCULATIONS
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Quick-and-dirty, pencil-and-paper sketches and calculations can be remarkably
helpful in understanding basic requirements for a system.
Many important decisions have been made as the result of sketches drawn and
calculations performed on a napkin or the back of an envelope.
Some decisions may be so basic that a quick mental calculation yields the needed
results.
Most of these calculations involve simple algebra, such as finding the number of
resource units (such as machines or service agents) to process a particular
workload knowing the capacity per resource unit.
For example, if a requirement exists to process 200 items per hour and the
processing capacity of a single resource unit is 75 work items per hour, three units
of the resource, most likely, are going to be needed.
SPREADSHEETS
• Spreadsheet software comes in handy when calculations, sometimes
involving hundreds of values, need to be made.
• Manipulating rows and columns of numbers on a computer is much
easier than doing it on paper, even with a calculator handy.
• Spreadsheets can be used to perform rough-cut analysis such as
calculating average throughput or estimating machine requirements.
• The drawback to spreadsheet software is the inability (or, at least, limited
ability) to include variability in activity times, arrival rates, and so on, and
to account for the effects of inter- dependencies.
OPERATIONS RESEARCH TECHNIQUES (1)
• Traditional operations research (OR) techniques utilize mathematical
models to solve problems involving simple to moderately complex
relationships.
• These mathematical models include both deterministic models such as
mathematical programming, routing, or network flows and probabilistic
models such as queuing and decision trees.
• These OR techniques provide quick, quantitative answers without going
through the guesswork process of trial and error.
• OR techniques can be divided into two general classes: prescriptive and
descriptive.
OPERATIONS RESEARCH TECHNIQUES (2)
• Prescriptive OR techniques provide an optimum solution to a problem,
such as the optimum amount of resource capacity to minimize costs, or
the optimum product mix that will maximize profits.
• Examples of prescriptive OR optimization techniques include linear
programming and dynamic programming.
• Descriptive techniques such as queuing theory are static analysis
techniques that provide good estimates for basic problems such as
determining the expected average number of entities in a queue or the
average waiting times for entities in a queuing system.
SPECIAL COMPUTERIZED TOOLS
• Many special computerized tools have been developed for forecasting,
scheduling, layout, staffing, and so on.
• These tools are designed to be used for narrowly focused problems and
are extremely effective for the kinds of problems they are intended to
solve.
• They are usually based on constant input values and are computed using
static calculations.
• The main benefit of special-purpose decision tools is that they are usually
easy to use because they are designed to solve a specific type of problem.
EXERCISE
• Buat kelompok berpasangan (2 orang)
• Amati sebuah sistem
• Identifikasikan sistem tersebut:
- Jenis (manufaktur vs jasa)
- Elemen (entitasnya apa, aktifitasnya apa, resource yg digunakan apa
saja, bagaimana kontrolnya)
- Bagaimana interdependencies dan variabilities dalam sistem tersebut
- System performance metric dalam sistem tsb apa aja
- Variabel sistem (variabel keputusannya apa, variabel respon apa, state
variable apa)
- Bagaimana teknik analisis sistem dilakukan? (menggunakan
spreadsheet, menggunakan or, atau bantuan komputer)
THANK
YOU!