model building procedure for new production system

7/28/2019 MODEL BUILDING PROCEDURE FOR NEW PRODUCTION SYSTEM

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Impact Journal of Technology & Science India Volume 25, Issue 07 (2010) 78- 82

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MODEL BUILDING PROCEDURE FOR NEW PRODUCTION SYSTEM

Pathak R. Somani S(*, A, B)

, Pathak Ravindra(A), Somani S.K.

(B)

(A) Research Scholar, S Gyan Vihar University, Jaipur,india

(B) Professor, Medicaps Institute of Technology & Management, Indore, India

Abstract

Production system is very important aspect in industry and before making the investment to i nstall the new system or

modif ication in existing system, we can analyze the system performance only by simulation and modeling. Thi s paper

provides a method for development of mathematical model f or new production system. A mathematical model uses

symboli c notation and mathematical equati on to present a system. The art of modeling is enhanced by an abil it y to

abstract the essential featur es of problem, to select and modi fy basic assumpti ons that char acteri ze the system and then

to enrich and elaborate the model unti l a useful approximation anal ysis.

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1. Introduction

In manufacturing sector modeling has gained importance

in the past few years and allows engineers to imagine new

systems and enabling them to both quantify and observe

behavior. If the system is a production line, then

importance of Modeling is increased because it can be used

to study and compare alternative designs or to troubleshoot

existing systems. With simulation models, how an existing

system might perform if altered could explore, or how a

new system might behave before the prototype is even

completed, thus saving on costs and lead times. Modeling

and simulation are emerging as key technologies to support

manufacturing in the 21st century. However, there are

differing views on how best to develop, validate and use

simulation models in practice . Production and business

systems are key building blocks in the structure of modern

industrial societies. Companies and industrial firms,

through which production and business operations are

usually performed, represent the major sector of todays

global economy. Therefore, in the last decade, companies

have made continuous improvement in their production

and business systems a milestone in their strategic planning

for the new millennium.

It is usually asserted that production and business

operations have the potential to strengthen or weaken a

companys competitive ability. To remain competitive,

companies have to maintain a high level of performance by

maintaining high quality, low cost, short manufacturing

lead times, and a high level of customer satisfaction. As a

result of fierce competition and decreasing business safety

IMPACT JOURNAL OF TECHNOLOGY &SCIENCE

ISSN 0927405426


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margins, efficient and robust production and business

operations have become a necessity for survival in the

marketplace.

Many industrial engineering subjects, such as operations

research, quality control, and simulation, offer robust and

efficient design and problem-solving tools with the ultimate

aim of performance enhancement. Examples of this

performance include the throughput of a factory, the quality

of a product, or the profit of an organization. Simulation

modeling as an industrial engineering tool for system

design and improvement has undergone tremendous

development in the past decade. This can be pictured

through the growing capabilities of simulation software

tools and the application of simulation solutions to a variety

of real-world problems in different business arenas.

With the aid of simulation, companies have been able to

design efficient production and business systems, validate

and trade off proposed design solution alternatives,

troubleshoot potential problems, improve systems

performance metrics, and consequently, cut cost, meet

targets, and boost sales and profits. In the last decade,

simulation modeling has been playing a major role in

designing, analyzing, and optimizing engineering systems

in a wide range of industrial and business applications.

Recently, six-sigma practitioners started to recognize the

essential simulation role in system design, problem solving,

and continuous improvement. System-level simulation in

particular has become a key six-sigma tool for representing

and measuring the time-based performance of real-world

stochastic production and business systems. Examples

include six-sigma studies that are focused on enhancing the

productivity, quality, inventory, flow, efficiency, and lead

time in production and business systems. In these studies,

simulation is particularly essential for system

representation, performance evaluation, experimental

design, what-if analysis, and optimization. With such

capability, simulation modeling can be utilized as an

important tool in six-sigma.

What Is Modeling: A model is defined as a representation

of a system for the purpose of studying the system.

Modeling is the process of producing a model. A model is

similar to but simpler than the system it represents. One

purpose of a model is to enable the analyst to predict the

effect of changes to the system. On the one hand, a model

should be a close approximation to the real system and

incorporate most of its salient features. On the other hand,

it should not be so complex that it is impossible to

understand and experiment with it. A good model is a

judicious tradeoff between realism and simplicity.

Simulation practitioners recommend increasing the

complexity of a model iteratively. An important issue in

modeling is model validity. Model validation techniques

include simulating the model under known input

conditions and comparing model output with system

output. Generally, a model intended for a simulation study

is a mathematical model developed with the help of

simulation software.

Models can be classified in mathematical or physical. A

simulation model is a particular type of mathematical

model of system. Simulation model may be classified in

static or Dynamic, deterministic or stochastic and discrete

or Continuous. Static simulation model is representation of

system at a particular point in time where as Dynamic

simulation represent as they change over time.

Deterministic models have a known set of inputs and in

stochastic has one or more random variables as inputs. A

discrete system is one which the state variables change only

at a discrete set of points in time but a continuous system is

one which the state variables change continuously over

time.Today, simulation is being used for a wide range of

applications in both manufacturing and business

operations. As a powerful tool, simulation models of

manufacturing systems are used:


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To determine the throughput capability of a manufacturing

cell or assembly line

To determine the number of operators in a labor-intensive

assembly process

To determine the number of automated guided vehicles in a

complex material-handling system

To determine the number of carriers in an electrified

monorail system

To determine the number of storage and retrieval machines

in a complex automated storage and retrieval system

To determine the best ordering policies for an inventory

control system

To validate the production plan in material requirement

planning

To determine the optimal buffer sizes for work-in-progress

products

To plan the capacity of subassemblies feeding a production

mainline

For business operations, simulation models are also being

used for a wide range of applications:

To determine the number of bank tellers, which results in

reducing customer waiting time by a certain percentage

To design distribution and transportation networks to

improve the performance of logistic and vending systems

To analyze a companys financial system

To design the operating policies in a fast-food restaurant to

reduce customer time-in-system and increase customer

satisfaction

To evaluate hardware and software requirements for a

computer network

To design the operating policies in an emergency room to

reduce patient waiting time and schedule the working

pattern of the medical staff

To assess the impact of government regulations on different

public services at both the municipal and national levels

To test the feasibility of different product development

processes and to evaluate their impact on companys budget

and competitive strategy

To design communication systems and data transfer

protocols

2. Model Building Procedure

The following procedure was developed based primarily on

the steps proposed by Shannon [2] and Banks [1]:

Objectives of Production System: Every study should

begin with a statement of problem. The analyst must ensure

that the problem being described is clearly understood.

Setting of Objectives and overall project plan: The

objective indicate that it should include the plans for the

study in terms of the number of people involved, the cost of

the study and the number of days required to accomplish

each phase of the work along with the result expected at the

end of each stage. Being sure that we have sufficient and

appropriate personnel, management support computer

hardware and software resources to do the job.

Conceptual Model Formulation: Developing a

preliminary model either graphically (e.g. block diagram or

process flow chart) or in pseudo-code to define the

components, descriptive variables, and interactions (logic)

that constitute the system. Selecting the factors to be

varied, and the levels ofthose factors to be investigated, i.e.what data need to be gathered from the model, in what

form, and to what extent. Agreeing the required outputs of

the experiment

Data Collection: Identifying and collecting the input data

needed by the model. Defining the data sources and

standardizing the formatting.

Model Translation: Models require a lot of information

storage and computation so formulating the model in an

appropriate simulation language and coding the data.

Verification: Concerns the simulation model as a

reflection of the conceptual model.Does the simulation

correctly represent the data inputs and outputs.

Validation: Provides assurance that the conceptual model

is an accurate representation ofthe real system. Can the


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model be substituted for the real system for the purposes of

experimentation?

Final Experimental Design: Designing an experiment

that will yield the desired information and determining

how each of the test runs specified in the experimental

design is to be executed.

Experimentation: Executing the simulation to generate

the desired data and to perform sensitivity analysis.

Analysis and Interpretation:Drawing inferences from the

data generated by the simulation runs.

Implementation and Documentation: Reporting the

results, putting the results to use,recording the findings,

and documenting the model and its use

3. Areas of Modeling

In general, whenever there is a need to model and analyze

randomness in a system, Modeling is the tool of choice.

More specifically, situations in which simulation modeling

and analysis is used include the following:

(a) It is impossible or extremely expensive to observe

certain processes in the real world, e.g., next year' cancer

statistics, performance of the next space shuttle, and the

effect of Internet advertising on a company's sales.

(b) Problems in which mathematical model can be

formulated but analytic solutions are either impossible

(e.g., job shop scheduling problem, high order difference

equations) or too complicated (e.g., complex systems like

the stock market, and large scale queuing models).

(c) It is impossible or extremely expensive to validate the

mathematical model describing the system, e.g., due to

insufficient data.

4. Conclusion

The model proved to be an effective design and planning

tool. The model was an integral part of the facility design

process. It was used as a decision support system to help

designers quickly assess the performance of various

alternative production configurations and resource

allocations without investment. It is useful for testing,

analysis or training where real-world systems or concepts

can be represented by a model.

REFRENCE

[1] Chance, F., Robinson, J., and J. Fowler, Supporting

manufacturing with simulation: model design,

development, and deployment, Proceedings of the 1996

Winter Simulation Conference, San Diego, CA, 1996, pp.

1-8.

[2] Ycesan, E. And Fowler, J. ,Simulation Analysis of

Manufacturing and Logistics Systems, Encylclopedia of

Production and Manufacturing Management, KluwerAcademic Publishers, Boston, P. Swamidass ed. , pp. 687-

697., 2000.

[3] Schruben, L., and T. Roeder, Fast simulations of large-

scale highly congested systems, Transactions of the


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Society for Modeling and Simulation International, Vol.

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[4] Jankauskas, L. and S. McLafferty, BESTFIT,

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model building procedure for new production system

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