Pemodelan sistem

SYSTEM

Definition:

A system is understood to be a whole composed of elements that are related to each other.

SISTEM MODELING

The cohesion will emerge from the fact that

the elements are linked together by their Materi 1 ₂ relations.

System

Briefly defined, one might say that a system is

a collection of elements in their entirety and the relations between them.

SYSTEM System (Inner-Outer)

Theory: General system theory (L.V Bertalanffy,1956)
Symbolization of the concept sy
–Inner-System Boundary-Outer (Environment)
Content of the sys
The complete collection of all elements without the interrelations of these elements being taken into

Elements and the Relations between Sub-System

Elements

Elements of a system are characterised by certain features –>physical,geometrical,aesthetic,social-psychological or econ
If a relationship exists between one or more elements

then if the characteristics of those elements change then the other elements will similarly change ⁵ ⁶

Relations between the elements are indicated by means of a simple line.
Relations can be in terms of technical, economic, socio- psychological aspects.

Relationship between System:

System thinking Elements

Thinking in terms of systems -as opposed to seeing

snapshots or discrete events with little or no interrelationship.

Peter Senge, in his bestselling book The Fifth Discipline:

describes how our mental models, or personal paradigms, are often developed from our tendency to

break down large problems into smaller manageable parts. Why we need Systems Thinking System thinking

Senge views the problem as systemic in nature System thinking: Thinking in terms of and not easily or quickly overcome in organizations. systems

The core of the problem is that our world is one of
dynamic complexity, the mastery of which cannot

System thinking: A problem methodology

be achieved by focusing on details alone without systems fluency.

approach

Senge describes a simulation, called The Beer Game.

₉

¹⁰

can fall into a trap of thinking

of their actions as isolated and helps them to better understand their roles within systems.

System:Mental Model Aspects System

Represents a partial collection of the system relation • There are various aspects of system –e.g.

technical, economic, socio-psychological aspects

The various aspects have been connected together by means of what are termed

Integrated System

13 ¹⁴ Example-1: Multi Disciplinary Approach Multi-Disciplinary Approach

For A Multi-Aspect Problem

A mono-disciplinist is seen as someone with specialist knowledge in the area of a single – mono-discipline (He will only be able to construe a mono-aspect v
With increasing complexity of technical system

and increase in the number of relevant aspects –

Example-3: An Interdisciplinary Example-2: Multi Disciplinary Approach Approach For A Multi-Aspect Problem

For A Multi-Aspect Problem

17 ¹⁸ Industrial Engineering (IE) What Does IE Do? Typical IE Questions

Is the system providing the best possible economic

return to its owners?

Are some products not economically viable? Which ones?
Are resources being utilized appropriately? Where is capacity excessive or inadequate?
Is the mix of resources appropriate? Are new technologies needed?

²¹

^{22<Are the resources organized and managed properly?
Are suppliers' prices, terms, delivery, and quality}

appropriate?

Are the products/services meeting customers'

needs? How could they be improved? Needs A Comprehensive Approach: Systems Modelling

Increased Complexity of Today’s Decision Making
The 20 ^th

Information Technology has revolutionized commercial activities
The creation of huge multi-national corporations
The problems of overpopulation and others

Materi 2 System Thinking ²⁵ System Thinking

century has been marked by unprecedented technological progress

SISTEM MODELING

Today’s world has increased in complexity
The traditional methods of problem solving based on the cause-and effect model cannot cope any longer.

Construction of the Aswan Dam

Intended to increased agriculture produc
It also caused an unprecedented increased in
⇒accelerated the shift from public to private

Increased car owner after WW II

schistosomiasis (affected 60% fellahin)

It trap fertile silt, increase use of fertili
Together with poor drainage causes salinization

⇒ land unsuitable for agriculture

Deterioration of Urban Transport

Costs

Unit production cost is all material, energy, & labor
The firm may be very efficient in the use of its

costs then dividing the total by number of parts produce.

resources, but this efficiency is not put to

The efficiency assessed on level of unit production effective use in term of the firm’s overall cost.

objectives of goals.

That rule works fine for simple one stage production
Technical efficiency, using a given set input to

process, when no difficulties in selling all its output

produce the maximum level of output, or

How if we faced with complex multi-product – multi-

₂₈

₂₉

producing a given level of output with the

stage process? The machine center’s looks good, minimum amount of input. but ends up with excessive intermediate parts stocks that are costly to finance & maintain & run

Economic efficiency, in term of maximizing the

the risk of obsolete difference between revenues & total cost.

Unplanned & Counterintuitive

Effectiveness, looks at how well the goals of

Outcomes activity are achieved.

Trade-off between variables will affect overall Action A will cause the desired outcome B to be effectiveness of the system. realized.

Efficiency is minimizing waste and maximizing But in addition to B, A also causes C, D, and E to utilization of resource.

happen Some of these outcomes are unintended, Efficiency is not the enemy of effectiveness.

unpredicted, and may negate the outcome B.
Efficiency takes overall goals of organization into

Cause-and-Effect Thinking
All phenomena are explainable by using cause-and-
The traditional scientific model of thought are effect relationship.

based on two major ideas: Reductionist and

A is taken to be the cause of B

Cause-and-Effect Thinking

Reductionism: Everything can be reduced,

Viewing the world in this way, everything can be

explained by decomposing it into parts and looking decomposed, or disassembled to ultimately for cause-and-effect relationship between the parts. simple parts.

Breaking a problem into a set of simpler subproblems,

But it may be inadequate to examine the causal solving each of these individually and then
³² ³³ relationship one by one. assembling their solutions into overall solution fo
New properties may emerge through the interaction whole problem.

But sum of individual solutions does not necessary between the parts (emergent properties).

produce a best solution for the whole system

Systems Concept Systems Thinking

Defining Systems:

Out-There View of Systems

With the systems thinking, something • e.g., our solar system (sun & its 9 planets).

to be explained is viewed as parts of a

Seen as absolute; Exists out-there; Viewed as independent of observer.

larger whole, a system, and is explained

Inside-us View of Systems in term of its role in that system.

Depend on what the person viewing something as a system • Different people may define the same system in different way.

Systems as a Human Conceptualization
World view of observer/Weltanschauung

Only human observer that may view something as a system.

(personal factors)

For examples:

An estuary is viewed as a beautiful place (not a system)

A few feet away from the path (not a system)

As an ecological system
>Effect of Previous Knowledge • System’s definitions are subjec
Cannot be labeled “right” or “valid” & “wrong” or

“invalid”

36 Defining Systems

The point that system are human conceptualization is clearly driven home by the fact that majority of system conceive are not our personal view of some real assembly of thing out there in the real world.

³⁷ Subjectivity of Systems Descri
Valid for the person making it

Dependent on the aim and purpose for building it
>Compo
Relationship • Behavior (or the activities or the transformation
Environment

System as Black Boxes Hierarchy of Systems _Environment
Formal Definition of The concept System
1. A system is an organized assembly of components 2.
The system does something.
3. Each component contributes towards the behavior
of the system and is affected by being in the system.
4. Groups of components within the system may by
The Crucial elements of a system
process)
The complexity of real live
⇒ have no or only incomplete knowledge of the inner workings of _{Wider system of interest} system, even where the physical components are able to identify.
This lack of knowledge is affected by random aspects.

In other situation the transformation process is
₄₀ _{Narrow system} ₄₁
known exactly. However, rather than represent _{of interest} it in full detail, it may be perfectly adequate to view as black box by single functional relationship.
Hierarchy system is the nesting systems within
System Behavior systems.
The containing system becomes the environment of the contained system.
System state, the set of value assumed by all state
variables as of given point in time.
Containing system exercise some control over
State variables are attributes of system component, the contained system.
at any point in time each state variables has a given
The controlling may set the objective of the numerical or categorical value.
contained system, monitoring it achieve the
Variety of system behavior, system behavior can be objectives, & have control over some crucial almost infinitely varied, even for very simple system.
Control System Types of Systems
Discrete system, the states of system jumps through a
sequence of discrete states.
Control is exercised by imposing something on the system in form of inputs
Continuous system, the state of the system change

A set of decisions or decision rules, or simply an initial continuously, because there are continuous variables.
state for the system.
Deterministic system, the behavior is predictable in every • detail.
Three conditions are needed for exercising control over system behavior
Stochastic system, the behavior is not completely
₄₄ _{45<A target, objective, or goal for the system predictable, affected by random or stochastic inputs.
A system capable of reaching the target or goal
Closed system, is one that not receive anything from its
Some means for influencing the system behavior - the environment.}
control inputs
Open system, interact with the environment, by receiving inputs from it & providing output to it.
Types of controls
Open loop controls • Often in the form of a recipe or a set of rules to follow (e.g.
starting the engine)
Closed loop controls (Feedback contr
Information about the system behaviour is fed back to the controller for evaluation.

This may lead the controller to adjust the control signals (e.g.
Control the temperature of the shower water)
A System is described by:
System in a decision making
Observer: Who is interested in the system?
Decision maker context
Purpose: Why define the system?
Improve, output of interest
A system is an organized se
Environment: outside the system. Define the system
boundary components and relationships that do • Inputs: Affect the system but are not affected by it. something that none of the components
Can be controllable or uncontrollable. Decision
variables/parameters can do alone.
Outputs: Are affected by the system. These are of
interest to the observer. Include measure of success
We use system models as a convenient ₄₈ ₄₉
Components: Both affect and are affected by the
way to view something in order to aid system. decision making (to solve a problem).
Relationship/transformation process: Between system inputs, outputs and components.
Definition of A Problem
There must be an individual (or a group of
individuals), referred as the decision
PEMODELAN SISTEM
maker who:
Is dissatisfied with the current state of affairs
Materi 3
Knows when goals have been achieved
_{51<Has control over aspects of problem situation}
Problem Formulation that affect the extent to which goals can be achieved
Four Elements of A Problem Problem Elements:[LOD]
The decision maker: the LOD manager

The decision m
The objective: keeping the cost of the LOD’s
The decision maker’s objective
operation as low as possible, subject to
maintaining the same level of customer service.
The performance measure for assessing
The performance measure: the total
how well the objectives have been operating cost of the LOD
System Relevant [LOD]
The widest system: the company as a
whole
The refinery: one of its subsystem _{54 system}

The LOD: a subsystem of the refinery
_{55<Within LOD system:
the production/inventory control operation form one of its major subsystem (i.e. the narrow system of interest)}
Components of the Stock Influence Diagram [LOD] Replenishment System
Influence diagram shows: See Table 5-1, page 110
>How the control inputs and other inputs affect the system variables for various system components, and
How these in turn affect the system outputs, in particular the performance outp
Note: the easiest approach to draw an influence

Performance measure

Uncontrollable Inputs •
Controllable Inp>
• The collection of all possible courses of action you might
>Interaction among These Components
58 ⁵⁹ Components of a Model
Parameters
• These are logical, and mathematical function representing
the cause-and -effect relationships among inputs, parameters, and the outcome.

There are also set of constraints which apply to each of t
Actions
PEMODELAN SISTEM
Materi 4 System Characterization ⁶¹ System Characterization:
[Murthy et.al,1990]
The SYSTEM APPROACH Offers a universal framework for treating such distinctly different problems

In the systems approach the real world associated with
A total description -unmanageable

Not all features of the real world are
the problem is viewed as a system
The solution to the problem is viewed as a study of the
A partial description is often adequate
system with a defined goals
System Characterization: [Murthy et.al,1990]
relevant to the problem or its solution
System/Variable/Param>A Black Box descript>Variables are attribute needed to describe interaction between objects (components)

Parameters are attribute intrinsic to an ob>Described only by the variables through which it interacts with its environment, and the inner structure of the system is ignored (A Black Box descript>System/Environ>The interaction between the system and its environment is through variables common to >A White Box descript
If one can describe all objects in the system and their attributes (through variables and relationships) -describing the system a greater detail (A White Box description)
64 Basic Concepts
Relations
The interaction between objects are described through relationships linking the variables of the interacting objects
⁶⁵ Degree of Detail
Degree of Detail
System Characterization
Static vs Dyn
The degree of detail needed to describe a
>static systems -time does not play any part
dynamic systems-time plays a very important role
system appropriately depends on many factors
If all the details are included - the description is
unmanageable
Continuous Time vs Discrete
In dynamic systems either some or all of the variable are changing with time -the change take
However, if significant details are omitted the
Keputusan Pembelian Terigu System Characterization • Pabrik roti membeli terigu dengan harga $1000 per ton.
Time Scale in Dynamic Systems
Kebutuhan terigu relatif konstan selama setahun dengan
the variables change with time
total permintaan per bulan sebesar 20 ton. Terigu dikirim
The term ‘time scale’ is used to indicate the dari pabrik terigu dengan menggunakan truk dan ongkos duration for significant changes to occur in a per sekali kirim $132, tidak tergantung dari jumlah terigu variable yang diangkut.
Uang yang digunakan untuk membeli terigu berasal dari Deterministic vs Stochastic suatu investasi dengan interest sebesar 8% per tahun.
If the values assumed by the variables are
₆₈ ₆₉ Juga, terigu yang disimpan diasuransikan dengan premi predictable with certainty (Deterministic systems) 16% yang dihitung berdasarkan nilai rata
If not, then uncertainty is a significant feature of persediaan per tahun. Manajer pembelian ingin the system -the changes in the variable are mendapatkan kebijakan pembelian terigu yg lebih baik random and unpredictable (Stochastic systems) dari yang terjadi sekarang.
System Description Mathematical Model
72 Mathematical Model
73 Mathematical Model EOQ Model
Solution
76 Validation:[Internal]
77 Validation:[Internal] System/Model Overview
Introduction • What is a mathematical model?

Why do we build a mathematical model?

How to build a mathematical model?

An illustrative case (Case of LOD)

Formal Approaches for finding the optimal
SISTEM MODELING
Materi 5 Mathematical Modeling ⁸¹ OUTLINE
solution
INTRODUCTION
A mathematical model: Express, in
We use the OR/MS Methodology •
To capture the relationships between various elements of the relevant system in a mathematical model and explore its solution.
What is a mathematical model?
quantitative term, the relationships
between various components, as defined in the relevant system for the problem (e.g. using Influence Diagram).
Terminol
Objective function (the performance measure is expressed as a function of decision variables)

Uncontrollable inputs: parameters, coefficients, or constants

Constraints –limit the range of the decision variables
⁸⁵ Relationship Between Input-
84 What is a mathematical model?
System-Output Why build mathematical models?
Real-life tests are not poss>Disruptive

Risky

Expensive
Properties of Good mathematical Properties of Good mathematical models models
Simple –simple models are more easily Easy to communicate with –easy to
understood by the problem owner prepare, update, and change the inputs and get answer quickly
Complete –should include all significant
aspect of the problem situation affecting Appropriate for the situation studied – the measure of effectiveness
₈₈ produces the relevant outputs at the ₈₉ lowest possible cost and in the time
Easy to manipulate –possible to obtain
required answer from the model
Produce information that is relevant and

Adaptive –changes in the structure of the
appropriate for decision making –has to
problem situation be useful for decision making
The Art of Modeling The Art of Modeling
An iterative process of enhancements

A scientific process
–begin with a very simple model and
More akin to art than science
move in an evolutionary fashion toward
A few guidelines
more elaborate models
Ockham’s Razor:
Working out a numerical example – Math. Model For The LOD Problem
Simplifica>Constraints (Warehouse space & mixing and filling capacities)

Two decision variables (cutoff point, L and order siz>First Approxima
Ignore the constraints

Involve only one decision variable, Q
₉₂ _{93<>Performance meaTotal annual relevant cost (TAC) (per year)
TAC=Annual stock holding cost+Annual set up cost+Annual handling cost+Annual product values}
Math. Model For The Problem LOD
Math. Model For The LOD Problem
Annual stock holding
(Average stock level x Unit product value) x
= + + + T ( Q ) [ .
5 Qvr ] [ sD / Q ] [ h D ] [ vD ]
1
1
1
1 Holding cost/$/year
Annual set up
Setup cost per batch x Annual number of stock T ( Q , L ) [ sN ] [ h D ] [ .
5 Qvr sD / Q ] [ h D ] = + + + +
2
2
1
1
1 replenishments Math. Model – LOD
[Second Approximation] • Two decision variables, L and Q.
Two additional costs

The annual set up cost for special production
Annual volume by special prod.runs x Product handling
cost per unit
The annual handling cost for big order

Production setup per batch x Annual number of special
⁹⁶ ⁹⁷
prod.runs
Total cost =
The annual set up cost for special production run + The annual handling cost for big order + Associated annual EOQ cost given L + The annual handling cost for small order.
Deriving A Solution To The Model Deriving A Solution To The Model
Enumeration

Enumeration • Number of alternatives of action is relatively small.
Computational effort is relatively minor
Search Methods
Optimal solution is obtained by evaluating the
Algorithmic Solution Methods performance measure for each alternatives.
Classical Methods of Calculus
>Search Met>Heuristic Solution Met
e.g. Golden section search
Deriving A Solution To The Model
Classical Methods of Calculus •
Heuristic Solution Methods
Impossible to find the optimal solution with the computational means currently available (intractable)

If the optimal solution is possible to obtain, but the potential benefit do not justify the computational effort needed.

Heuristic methods: to find a good solutions or to improve an ₁₀₀
₁₀₁ existing solutions (out put based techni>Simulation
For complex dynamic systems • To identify good policies rather than the optimal one.
Model Testing & Sensitivity Analysis
Internal Validity (Verification)

Is the model mathematically correct and logically consistent?

This also involves verifying that each expression is
SISTEM MODELING dimensionally consistent.
External Validity (Validat
Is the model a sufficiently valid representation of
Materi 6 ₁₀₃
reality?
Model Testing & Sensitivity
Testing the solution perform
To determine the expected benefits, such as net
Analysis profits or net savings
Analysis of Sensitivity of Solution Rules for Testing Validity
The evaluation of the proposed policy has to be
Analysis of Sensitivity of Solution based on observations of actual (or
Sensitivity analysis (evaluate the response simulated) performance of the best solution to changes in various inp>The data used for the test shoul
Error analysis
independent of the data used to derive the
The input parameters are estimated on the basis ₁₀₄
₁₀₅
If the optimal solution is relatively
to changes in the input parameters D, s, v, and r?
3.Compute the actual value of the objective function if the pseudo-optimal policy determined in (1) were implemented.
Sensitivity Analysis For The LOD Problem
/ ) 5 . (
2 / 2 ) 5 . (
/
Ds vr sD Ds vr vr Q T Q sD Qvr Q T
Q sD Qvr Q T / ) 5 . ( + =
Ds vr EOQ / 2 =
108 Math. Model For The LOD Problem
4.Find the difference between the optimal objective function values obtained from (2) –using the correct value and (3) –using the estimate value.
2.Assume that the value of the input parameter p differs from the correct value, P (p=kP). Find the optimal policy, using the (assumed) correct value P.
p , is in error).
1.Determine the optimal policy based on the best estimate values for all input parameters (assume that one of these, say
Sensitivity analysis is used for exploring how the optimal solution changes as a function of
such uncertain data ¹⁰⁷ Procedure of Error Analysis
resource)
scarce resource (shadow price of the
the value of additional amounts of each
can place more confidence in the validity and usefulness of the model
insensitive, then the decision maker and user
Sensitivity analysis provides information about
106 Purpose of Sensitivity Analysis
How do EOQ expression and T(Q) respond
=

=
₁₀₉
x ^{D implied (4140/k) 20700 8280 5175 4140 3450 2760 2070 1035} ¹¹¹ ^{error in demand -80% -50% -20% 0% 20% 50% 100% 300%} ^{k (known) 20% 50% 80% 100% 120% 150% 200% 400%} ^{T(EOQ) based on D 6552 4144 3276 2930 2675 2392 2072 1465} ^{T(50.87) based on D 8790 4395 3296 2930 2686 2442 2198 1831} ^{Cost increase 34.2% 6.1% 0.6% 0.0% 0.4% 2.1% 6.1% 25.0%} ^{cost increase} ^0.0% ^5.0% ^10.0% ^15.0% ^20.0% ^25.0% ^30.0% ^35.0% ^40.0% _{-100% 0% 100% 200% 300% 400%} _{cost increase}
2 = = = vr Ds EOQ 2482 $
−
2442 2442 2482 =
% 64 . 100 1 %
T
)( 18 . 320 )( 87 . ) 5 . 50 ( 87 . 50 ( = + =
87 . ) 50 / 2875 ( 18 )
( 2875 18 ) 2 /
110 An Example _{• A year later its is discovered that actual demand in not 4140, but only 2875 (over} _{estimate 44%)}
42 ) ( 18 . 320 /
( 4140 18 ) 2 / = 2 = = vr Ds EOQ 4 .
50 ) ( 18 . 320 /
^{If we had implemented this solution, the minimum relevan cost, would have been} _{• Instead, we implemented the pseudo-optimal solution}

The difference beetween actual cost & the minimum cost (lost in potential benefit)
T 87 .
4 . 42 ( = =
( 18 . 320 / 2442 $ ( 2875 18 ) 2 )
Implementation
Putting the tested solution to work

Means translating the mathematical
SISTEM MODELING
solution into set of easily understood operating procedures or decision rules Materi 7 ¹¹³
Training all people involved for the proper
Implementation of Solution application of the rules, executing the transition, and preparing complete documentation for future reference
Problem of Implementation Implementation 1. • Not easily handled by a systematic Relating to the physical task of
approach, good organization and implementation coordination
2. Relating to the problem user and other • Fraught with difficulties that are largely of individuals affected by the solution –e.g.
a human nature –e.g. consider the LOD their personalities, their motivation and ₁₁₄ ₁₁₅
OR/MS analyst pays attention of the first
The tendency is to neglect and overlook the human factors of (2) and (3).

Note: neglecting the human constraints in
More involvement of problem user(s)

Individual who could become obstacles to
116 Problem of Implementation
factor
a system can easily lead to a solution that is one on paper only and is not
The human constraints may be relaxed in a number of way:
proper implementation could be transferred (?)
Technical solution can be adjusted by
workable in practice. ¹¹⁷ Problem of Implementation
simplifying the policy or solution rules
Starts at the outset of any project
The stock clerk was viewed as the most important stakeholder for the user’ group (pulled in as an active project team member)
_{119<It is not sufficient to start planning for the implementation once the solution has been tested and the report submitted}
118 Problem of Implementation For LOD case:
Planning for Implementation
Establishing effective lines of
Ordering of special equipment and
Developing all software

Planning and executing the actual process
Exploring and managing the prior
120 Planning for Implementation
communication
expectation for the project
Keeping the problem owners and problem
Regularly following-up sessions with problem users
Checking out availability and sources of all
The environment is constantly undergoing
A change in the form or nature of an input
users regularly informed
input data needed ¹²¹
Planning for Implementation
commercial computer software
of implementation
Inputs into the system are also changing

Such change may be quantitative or
_{123<For example –LOD problem (?)}
Controlling and Maintaining the Controlling and Maintaining the Solution
122 Controlling and Maintaining the Solution
change
Controlling and Maintaining the Solution
is called structural –affects the influence relationship between the input and one or more intermediate variables in the model.
Solution
Establishing controls over the How each input has to be measured to
solution consists of:
assess if a change is significant
Listing for each input (parameters, Assigning responsibility for the control of
constraints) –the quantitative change each item ¹²⁴ ¹²⁵
Listing of structural form of all influence How the solution has to be adjusted in relationships between inputs and response to changes (quantitative and intermediate variables; intermediate and structural changes) outcome variables.
Following Up Implementation Following Up Implementation and Model Performance and Model Performance
Monitoring Implementation:
If any misapplication or misinterpretation
of the solution show up, corrective action
The job of the OR/MS analyst is not finished once the solution has been must be initiated. implemented.
Performance audit:
After some time. Enforcement rules for ₁₂₆
₁₂₇
A Systems View of Manufacturing
Manufacturing can be viewed either as a transformation process or as a system.

As a Transformation Process: A narrow
SISTEM MODELING
definition of manufacturing is that it a process of transformation where raw material are converted into products.
Materi 8 ₁₂₉
As a System: Manufacturing is not limited to
Manufacturing System materials transformation performed in a factory.
Rather, it is a complex system comprising of several elements.
Flow of Materials, Information and A Systems View of
Cost _PLANNING Manufacturing ⁿ
_o _rm _a _ti
This involves an internal environment and _In _fo an external environment.
_MATERIALS
^{RAW Flow of Materials} ^{PROCESS PRODUCTS} _of The inputs are from the external _F _low ₁₃₀ environment and involve several variables. ₁₃₁
_{- FINAN CIAL}
_{INP UTS} _{EXT ERN AL} _{EN VIR ON M EN T} A Systems View of _{- H UMAN} _{- MATER IAL} _{- INFO RMATIO N} Manufacturing ^{- TEC HN O LO G Y<
_{PRO C ESS} _{TR ANSFO R MAT IO N}}
Can be viewed from three different Aspects: ^{LAB OR ’S EF FO RTS} ^{INT ERN AL} ^{ENV IRO N M ENT}
Technical: Dealing with engineering, science _{TECH NO LO G Y} and technology issues. ^{O PER ATIO N S}
_{MANAG E MENT}
_{PR O DU CTS/SER VIC ES marketing, legal issues}
_{FIN ANC IAL} _{O UTPU TS} ₁₃₂
Commercial: Dealing with financial, ₁₃₃ ^{C ON SEQ UEN C ES} ^{H UMAN} • _{C ON SEQ UEN C ES} Management: Dealing with planning,
operations and other related issues such as _{Figure 2.2: Systems View of M anufacturing (Murthy, 1995a)} information. And also organisational issues dealing with human related issues.
MANUFACTURING: DIFFERENT
Firm Level PERSPECTIVES
It can also be viewed from three different Manufacturing at the firm level involves
perspectives: several variables which can be broadly
Firm level,
grouped into the following three categories:
Industry level and

Technical • Regional or global level. ₁₃₄
₁₃₅
Technical Commercial
The commercial side deals with issues

The technical side has been discussed such as costs, sales, revenue and profits.
before and involves various issues Each of these involves many variables. related to the science, engineering and
For example, the costs can be investment technology aspects of manufacturing. ₁₃₆
costs, unit manufacturing costs, cost of ₁₃₇ rework and so on. The basic bottom line for a firm is that it must make reasonable profit for its investment.
Management National Level
Deal with managing the various activities In the national level, a manufacturing firm at strategic and operational levels taking is influenced by factors such as market into account the many different legal and competition and government policies. socio-political aspects and various
Obviously, the market competition is not constraints.
₁₃₈ only determined by manufactured goods ₁₃₉
Regional and Global Levels National Level
Some of the government policies that Manufacturing at the regional and global affect the manufacturing enterprise are levels deals with the following factors : indicated below.
Fierce competition
>Industry or Technology develop
Open market - free trade
policies,
Trade organisation - regional partnerships
¹⁴⁰ ^{141<>Taxation policies, • Environmental policTrade policies – export and import}
subsidies, protection
Financial investment
A THREE LEVEL MODEL OF _{Level 3} _{Strategic Leve} _l MANUFACTURING ^{Research and} _{Developm ent} _{Finance Partnerships} ^{Level 2} ^{Operations Level}
Manufacturing is a complex system _{Accounting Legal Planning} involving several elements. It can be _{Level 1} _{Process Level} viewed as a three level system (Level 1 - _{S UP PLIERS} _{Inputs Processes Outputs CUSTOMERS}
3) ^{Hum an Envir onmental M arketing}
Note that Level 1 is nested within Level 2, ₁₄₂ _Resource ₁₄₃
Level 1 Level 2
This level corresponds to the shop floor This level includes Level 1 as well as six
level and has three key elements - Inputs, new elements. The focus of this level is Process and Outputs. management of business at the operations level and the six new elements with a ₁₄₄ range of operational issues. ₁₄₅
Level 3
This level includes the Levels 1 and 2 and
six new elements. Here, the management focus is long term and strategic.
This is important, as survival of businesses
in the fiercely competitive global market ₁₄₆

Pemodelan sistem

SISTEM MODELING

SISTEM MODELING

36 Defining Systems