Product Layout Improvement by integrated use of Design of Experiments and Discrete Event

Simulation

Manan Saraiyaa, S Narayanan

a, S Mukherjee

b

manan.saraiya2009@vit.ac.in

aDepartment of Mechanical Engineering, VIT University, Vellore – 632014, India

bProduction and Quantitative Methods Department, IIMA, Ahmedabad – 380015, India

Abstract

This research is focused on developing and validating a novel integrated approach to solving re-layout

problem in a product type layout found at a medium sized Indian submersible pumpset manufacturing

company. The approach combines use of design of experiments (DoE) and discrete event simulation to

arrive at an optimized facility layout. Critical factors that impact the performance metrics such as output,

work in progress, capacity utilization and cost savings were identified by using DoE principles and given

factor weights to arrive at a common index. Seven factors which can influence the performance metrics of

the system were identified by DoE, such as grouping of sequential work activities into work stations to

achieve higher utilization of labor, batch size of bottle neck operation, level of automation of internal

transportation and layout alternatives and were studied in depth for suggesting optimal re-layout. Taguchi

design was employed to reduce the number of experiments from 128 to 8 by using L8 design. Using

Arena, a discrete event simulation software, the As-Is model was simulated in order to verify

and validate the accuracy of the model, following which, the 8 models were simulated for a period of

1000 hours or 5 months, each with 25 working days. The data obtained from these simulation runs was

used to generate the main effect plots using Minitab statistical software in order to study the interaction of

the various factors and arrive at the optimum combination of factors. The best re-layout alternative was

identified from the 128 alternatives which achieves a 129% improvement over the as-is model with

minimal disturbance to heavy equipment and with bare minimum structural change.

1._Introduction

Several heuristic algorithms for designing plant facilities have been topic of research in past few decades.

Software packages also exist for solving layout problems, but most of these techniques try to locate the

machines in the facility with the sole objective to reduce the distance travelled by components. However,

the performance of a facility can also depend on factors such as grouping of sequential activities to reduce

idleness and increase labor utilization, selecting appropriate batch size of bottleneck operation, level of

automation of internal transportation etc. Thus, there is a need to develop an approach that can study the

effect of such factors on the performance of a product/line layout.

The key objective of this research work is to effectively and efficiently generate the optimum layout for

the particular manufacturing facility in consideration by conducting a series of experiments between the

factors that could affect its performance. Pareto analysis was carried out to identify products which

contribute to 80% of the annual revenue. Furthermore, seven factors that could affect the performance

metrics are chosen and the experiment is conducted with two levels for each factor using Arena

simulation software (Kelton, Sadowski, and Sadowski 1998). The responses from the experiment are

analyzed to measure the main effect and interactions between the factors. Through this analysis, the

significant factors affecting the performance metrics can be identified. On the basis of the responses from

these experiments, optimum layout design can be identified.

This research work is carried out for a submersible pumpset manufacturer. To maintain the anonymity of

the company, its products and process details and the name of the company is not mentioned. However,

the data used for conducting this research work is real and not hypothetical.

Section 2 contains a literature review of the existing techniques related to facility design and comparison

of this research work to other relevant research publications. Section 3 describes the problem statement,

data collection, analysis of data and an overview of the potential factors considered that can affect the

manufacturing system in consideration. Section 4 describes the nature of experimental design and the

methodology followed. Section 5 lists the results of the designed experiment and describes the

performance metrics of best layout out the 128 alternatives. Lastly, conclusions and the scope of

application of this research are discussed.

2. Literature Review

2.1 Facility Layout

Facility Layout design study has always been an area of active research in the industrial engineering field.

The main objective of these studies is to determine a layout which optimizes one or more parameter of

production efficiency. The layout problem is applicable to many environments such as warehouses,

banks, airports, manufacturing systems, etc (K. Balakrishnan, S. Anand and David Kelton, 2000). Each of

the above has its distinct characteristics. In any facility design problem, there are some common

objectives such as, efficient space utilization, minimize work in progress, reduce material handling cost

(S. Nahmias 2005)

2.1.1 Choosing Layout Type

Plant layout can be broadly classified into four basic categories-

1. Process layout

2. Product layout

3. Fixed-position layout

4. Combination layout

Apart from the above four types, various hybrids design also exist which combine the advantages to best

suit the specific need of system. This leads to the question, what are the factors that influence layout? A

few of them are nature of the product, production process, space requirements of machinery, uniformity

of demand, safety.

Owing to the similarity in processing requirements and the relative uniformity of demand, product layout

would be best suited for the company under consideration.

2.1.2 Product Layout Planning

It is imperative to understand the product and its processing requirements in order to develop an effective

product layout. The following 4 step approach is generally employed to achieve an understanding of the

requirements.

1. Define Processes

2. Develop Precedence Diagram/Flow Diagram

3. Develop Assembly Chart

4. Balance the Assembly Line

2.1.3 Heuristic Algorithms for Assembly Line Balancing

There are several heuristics available for Assembly Line Balancing; the most common ones are listed

below.

1. Longest Operating Time

2. Most Following Tasks

3. Ranked Positional Weight

4. Shortest Operation Time

5. Least Number of Followers

The two major assumptions all the above heuristics make are

1. Constant Operating Time (Deterministic and not Probabilistic)

2. Multi-skilling throughout the line

2.1.4 Limitation of Heuristic Algorithms

Real time manufacturing systems rarely exhibit these two assumptions in combination. Hence, there

arrives a need for a tool that allows for probabilistic time assumptions owing to the varying nature of

product sub categories and segment wise multi-skilling. Simulation is a tool which can handle the

complexity of real time manufacturing systems.

2.2 Modeling and Simulation

2.2.1 Modeling

Modeling is the process of producing a model; a model is a representation of the construction and

working of some system of interest. A model is similar to but simpler than the system it represents. A

good model is a judicious tradeoff between realism and simplicity. 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 (Anu Maria 1997)

2.2.2 Simulation

A simulation of a system is the operation of a model of the system. The model can be reconfigured and

experimented with; usually, this is impossible, too expensive or impractical to do in the system it

represents. The operation of the model can be studied, and hence, properties concerning the behavior of

the actual system or its subsystem can be inferred. Simulation is used before an existing system is altered

or a new system built, to reduce the chances of failure to meet specifications, to eliminate unforeseen

bottlenecks, to prevent under or over-utilization of resources, and to optimize system performance. (Anu

Maria 1997. There are several Discrete Event Simulation software available, for the purpose of this

research work Rockwell Automation‟s Arena Version 14.0 was employed.

2.3 Design of Experiments (DoE)

Design of Experiments (DOE) techniques enables designers to determine simultaneously the individual

and interactive effects of many factors that could affect the output results in any design. DOE also

provides a full insight of interaction between design elements; therefore, it helps turn any standard design

into a robust one.

Some terms are commonly used in design of experiments. Factors are defined as a controllable

experimental variable, which variation influences the response variable. (Mason 2003) Each factor must

assume some values, defined as levels. The changes occurred on the mean of the values of the response

variable correspond to the effects. Besides the effects caused by the factors, the effects created by the

interaction of the factors can be determined. These interactions correspond to combined effects, where the

effect of each factor depends on the levels of the other factors.

Taguchi technique is step by step approach to identify causal relationship between design factors and

performance, which results in enhanced quality performance into processes and products at development

as well as production level. Taguchi‟s technique used by a numerous industries to optimize their process

design, through identifying independent and dependent variables with the help of identified factors and

factor levels.

2.4 Comparison of Existing Research to Current Research

There are currently three research publications which combine the use of DoE and Simulation software

for studying/improving layout. None of three has focused on Product/Line layout. Furthermore, the Multi

Criteria Decision Analysis technique has also been employed to suggest the optimum layout best on four

output parameters. Thus, this research work aims at adding to the literature of conjoint use of DoE and

Simulation software to facility design study. Table 1 shows comparison between existing research work

and the current research work.

Table 1 : Comparison of research work

Author DoE Simulation

Software

Type of Layout

studied

Multi Criteria

Decision Analysis

R Jayachitra - 2010 ✓ ✓ Process ✗

S Ramakrishnan - 2008 ✓ ✓ Process ✗

K. Balakrishnan - 2000 ✓ ✓ Process ✗

CURRENT RESEARCH ✓ ✓ PRODUCT ✓

3.1 Problem Description

Our objective is to design an efficient layout which reduces work in progress, cost of labored transport,

improves utilization and output. As the building which houses the facility is more than 30 years old, it is

undesirable to break the internal walls which would make the structure weak and lead to additional costs

for restructuring. Furthermore, it is also undesirable to move old heavy equipments such as center lathes

as it may lead to expensive breakdowns in the process. Working within these two major constraints was a

challenge.

3.2 Data Collection and Analysis

Data collection is one of the first steps involved in solving a manufacturing layout problem. The accuracy

and the extent of the data collected reflect the precision of the results. It is important that all the necessary

data required for modeling the layout be collected for the parts that will be manufactured and the

machines that will be used for production during the time horizon for which the layout is planned. So,

proper analysis of the collected data is required before modeling the layout1.

3.2.1 Data Collection

Product models contributing to more than 80% (A type) were selected. Following data was collected for

the study

1. Sales Volumes

2. Part Routing

3. Process Timings and Setup times

4. List of Equipments and respective operating costs

5. Labor requirements at each workstation and cost of labor

3.4 Performance metrics and respective factor weights

Through brainstorming activity with the various stakeholders, the following performance metrics were

selected and given factor weight relative to their criticality as shown in Table 2

Table 2 : Performance metrics and respective factor weights

Sr. No. Output Parameter Factor Weight

1. Output 21 %

2. Work in Progress 25 %

3. Cost Savings 34 %

4. Utilization 20 %

3.5 Selection of Factors

Factors which could affect the performance metrics were identified by brainstorming activity conducted

with the various stakeholders.

Table 3 one shows the factors and their corresponding levels

Table 3 : Factors and their corresponding levels

Factors 1 to 4 - Grouping of Activities signify the activities which collectively have a process time which

is lesser than the bottleneck process time and thus can be grouped together in order to increase labor

utilization and reduce idleness. Eight such activities were identified where the operators were skilled

enough to operate both processes.

Factor 5 - Batch size of Oven. Heat treatment process has a process time requirement of 8 hours.

Currently there is one oven which has a capacity of 60 pieces. It is proposed to install two ovens, each

having a capacity of 30 pieces which would reduce the waiting time.

Factor 6 - Level of Internal Transport Automation. Currently the casual labors are employed to transport

parts in the facility. It is desired to explore the effect of automation such as forklifts and gravity

conveyors can have on the output parameters.

Factor 7 – Layout. The suggested layout will relocate certain sequential operation closer together in order

to reduce wasteful moves.

Sr. No. Factor Level

1 Grouping of activities

a. Rod Cutting, and Crimping

Twisting Pressing

b. Shaft Inserting

G1 - Grouped

UG1 - Ungrouped

2 Grouping of activities

a. Circlip operation and Lapping

b. Rotor Balancing

G2 - Grouped

UG2 - Ungrouped

3 Grouping of activities

a. Stator Stack Making

b. Stator Welding

G3 - Grouped

UG3 - Ungrouped

4 Grouping of activities

a. Stator Tube Insertion and

Broaching

b. Flange Fitting and Welding

G4 - Grouped

UG4 - Ungrouped

5 Batch size of Oven (bottleneck

operation)

30

60

6 Level of Internal Transport

Automation

Manual

Semi-automatic

7 Layout Current

Suggested

4. Experimental Design and Methodology

4.1 Experimental Design

The involvement of the above mentioned factors and their respective levels makes the system to be

studied robust but complex at the same time. In order to study the effect of interactions of each of the

factors, DoE technique is employed. As each factor has 2 level s and there are 7 factors in all, this leads to

a total of 128 combinations/alternatives. Simulating 128 models is a tedious task and thus using Taguchi

design, the responses from the 128 alternatives can studied by simulating 8 models. This significantly

reduces the Simulation effort while maintaining the effectiveness of the experiment.

4.2 Methodology

The following 10 step methodology was followed.

STEP 1. Define Objectives and assigning factor weights based on AHP

STEP 2. Collection real time data

STEP 3. Identification of factors and respective levels

STEP 4. Construction of Simulation Model

STEP 5. Verification Simulation Model

STEP 6. Developing L8 design using Minitab Software

STEP 7. Modeling 8 experiments on Arena Simulation Software

STEP 8. Execution of modeled experiment and collection of output statistics

STEP 9. Statistical Analysis of output data using Minitab Software to generate main effect plots

STEP 10. Analysis and Interpretation of responses from main effect plots to arrive at optimum layout

design

4.3 Application of Taguchi design

Using L8 Taguchi design, the 128 alternatives were confounded to 8 models as listed in table 4 below

Table 4 : L8 Taguchi design

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7

Model 1 G1 G2 G3 G4 30 Manual Current Layout

Model 2 G1 G2 G3 UG4 60 SemiAuto Suggested Layout

Model 3 G1 UG2 UG3 G4 30 SemiAuto Suggested Layout

Model 4 G1 UG2 UG3 UG4 60 Manual Current Layout

Model 5 UG1 G2 UG3 G4 60 Manual Suggested Layout

Model 6 UG1 G2 UG3 UG4 30 SemiAuto Current Layout

Model 7 UG1 UG2 G3 G4 60 SemiAuto Current Layout

Model 8 UG1 UG2 G3 UG4 30 Manual Suggested Layout

4.4 Conducting the Experiments

The above eight experiments along with the „As-Is‟ model were modeled on Arena Simulation Software

version 14.0. A sample model is shown in the appendix. All the process times and transportation times

were also modeled. Each model was run for duration of 1000 hours i.e. 5 months with 25 working days.

5. Analysis and Interpretation

The performance metrics received from the Simulation runs on Arena Simulation Software provided us

with data for each model. Based on the performance metrics and their factor weights as assigned in

section 3.4 the following results were obtained as show in table 5.

Table 5 : Model-wise performance metrics results

Model Output WIP

*Total

Savings

(Labor +

Transport)for

in Rs

Capacity

Utilization

Weighted

Index in

1000

points

M1 7405 683.21 165855 74 1315

M2 7368 786.01 141143 74 1187

M3 7412 721.6 105934 73 1075

M4 7361 791.66 44356 66 799

M5 7332 814.93 80688 63 918

M6 7415 701.89 53242 69 873

M7 7358 798.93 93625 72 999

M8 7404 726.44 40000 68 812

* Total Saving = Labor Cost Savings + Transport Cost Savings

Labor Cost Savings

Labor Cost is Rs 40 per hour so for 1000 hours = 1000*40=Rs 40,000

for each instance of grouping, labor cost reduces by Rs. 40,000

Using Minitab Statistical Software the following Main Effect Plots were generated as seen in figure 1.

Figure 1 : Main Effects Plot for Means

From the above Main Effects Plot for Means we observe that factors 1,2,3 and 4 are highly significant.

This signifies that grouping sequential where skill requirement is not too high will significantly improve

the system. Factors 1,2,3 and 4 have direct impact on the cost savings as the cost of 4 labors i.e. Rs.

1,60,000 will be saved during the 5 months that the simulation was run. We also observe that by reducing

the batch size of the bottleneck process (factor 5) i.e. heat treatment by oven considerably improves the

system. This is because by reducing the batch size by half, the queuing time reduces dramatically and so

does the Work in Progress. Hence, it is suggested to the company that they should use two smaller ovens,

each having half the capacity of the current single large oven. Factor 6, level of automation for internal

transport has a major impact on the system as it causes a twofold effect, firstly, due to automation using

gravity conveyors and a manual forklift the transportation time reduces, secondly, lesser casual labor is

required to carry out transportation and hence leads to cost savings. Although Factor 7, layout option

shows insignificant variation, it is because the machines are located closer together in the suggested

layout that leads to the applicability of grouping at the four stations and also leads to transportation

savings in terms distance and reduces wasteful moves. It is observed that the output for all 8 models

remains almost the same. This is level is kept keeping in mind the balance between demand and supply.

Producing beyond this level is not in the best interest of the company. If need be to increase production,

overtime can allowed or two shifts can be operated.

The responses indicate that the optimum combination is

F1=G1; F2=G2; F3=G3; F4=G4; F5=30; F6=Semi Auto; F7=Suggest

Using Taguchi Technique, the performance metrics for best alternative were calculated and are listed

below in table 6 in comparison with the existing „As-Is‟ layout

Table 6 : Comparison of As Is/existing system to optimum system

Sl.

No

Output

variable

Unit of

Measure

As-is

layout

To be

layout

% of

Improvement

Remarks

1

Weighted

Index in 1000

points

Points

score 605.5 1389 129

Highly Significant

Improvement

2 Output Numbers 7351 7412 1

Purposely kept nearly

constant as the company

faces marketing

challenges to sell beyond

current demand. Can

operate in overtime or in

two shifts if demand

increases,

3 WIP Numbers 806 699 13 Significant Reduction

4

Total Savings

in Rs Rupees -3077 184288

Yearly savings

of Rs 442291.2

per shift

operation

Significant

5

Capacity

Utilization Percentage 62 78 26

Significant

6. Implication

Data presented in table 6 implies that it is possible for the company to reduce labor staffing levels,

improve capacity utilization and reduce Work in Progress without disturbing the output level. One critical

point to be observed is that the above results have been obtained by causing minimal structural change

and without disturbing most heavy equipment like centre lathes.

7. Conclusion and Scope of application

A innovative integrated methodology to solve Product/Line layout problem where there is variability in

processing different models of the same part family leading to variation in processing time and where

multi-skilling is not available at every work station can be accommodated and be solved with high

accuracy.

The same methodology can be applied to any system where optimization is sought. Based on the

stakeholders‟ requirements, appropriate factors must be selected and a Simulation model must be

developed and responses must be analyzed.

References

a) Anu Maria, (1997), “Introduction to Modeling and Simulation”, Proceedings of the 1997 Winter

Simulation Conference

b) K. Balakrishnan, S. Anand and David Kelton, (2000), “Integrating simulation and design of

experiments to identify factors for layout design”, M.Sc. Thesis, University of Cincinnati

c) Mason, R. L., R. F. Gunst, and J. L. Hess, (2003), “Statistical Design and Analysis of Experiments.”

John Wiley & Sons Publication.

d) R. Jayachitra and P. S. S. Prasad, (2010), “Design and selection of facility layout using simulation and

design of experiments”, Indian Journal of Science and Technology, Vol. 3 No. 4

e) S. Nahmias, (2005), “Production and Operations Analysis”, 3rd

Edition, McGraw-Hill, New York. pp.

561- 573

f) S Ramakrishnan, P Tsai, K Srihari and C Foltz, (2008), “Using Design of Experiments and Simulation

Modeling to Study the Facility Layout for a Server Assembly Process”, Proceedings of the 2008

Industrial Engineering Research Conference

g) W. David Kelton, Randall P. Sadowski and Deborah A. Sadowski, (1998), “Simulation with

Arena”, McGraw- Hill

Appendix

Figure 2 depicts the Arena Simulation Model for Model Experiment 1

Figure 2 : Arena Simulation Model for Model Experiment 1