Modelling, Analysis and Improvement of Small Scale Production System through Discrete Event Simulation: A Case Study of Glass Forming Industry

Atul Jain1, Prof. Sanjay Jain2, Dr. P. L. Verma2

1M.Tech Scholar, 2 Associate Professor Department of Mechanical Engineering, Samrat Ashok Technological Institute, Vidisha (M.P), India

Abstract: The application of discrete event simulation tool for modelling, analysis and

improvement in existing industrial manufacturing cell is presented with reference to

required production rate. A case study belonging to glass forming industry is simulated

with the aim of improving specified performance measure related to manufacturing cells

productivity, such as utilization of each station, throughput and takt time. PN-tool (Matlab)

used to analysis the fault in production line and also for further improvement. Experimental

results also validated.

Keywords : Modelling, simulation

1. Introduction

Increasing the intensity of competition in Quality, reliability, speed of innovation, cost of

products and an increasing variety of markets needs place emphasis on higher flexibility,

responsiveness and variability of production capacity [1]. The customer are searching for

different value added instead of standardized products besides are looking for a large

variety of products associated to fast delivery, so there is a mismatch between demand and

capacity from the planning systems, it is time for the Industrial engineer to seek actions

needed for productivity improvement to close the capacity gaps in order the demand of

customer in a timely manner [10]. Decision making for upgrading the specific process as

market demand is a critical activity which can impact on economical aspect of industry.

In small scale industries, the production flow lines are combination of automatic process

and manual process, detection the process in production system, where improvement is

required is a critical task. Analytical techniques, and simulation based techniques are

techniques, used in detecting the defective process of production line. Queuing models and

Markov chains based model categories in analytical techniques. These models are useful

for analysis small plant layout, it can’t use for complex manufacturing system [15].

Simulation modelling and analysis is useful in order to gain insight into complex system, to

achieve the development and testing of new operating or resource policies and new concept

or systems, which live up to expectation of modern manufacturing , before implementing

them and last not least to gather information and knowledge without disturbing actual

system [11,12].

The ability of the simulation software to visualize material flow design increases the

system’s acceptance with the management [2]. As production flow line works on the

principle of discrete event system. Discrete event simulation tools provided more effective

results in terms of performance measures. Manufacturing performance measures often the

capability to reproduce state of manufacturing system, monitor and control the operational

efficiency, drive improvement strategies, verifying manufacturing decision effectiveness

[3, 4]. PN tool Matlab is software which works on discrete event simulation. This software

tool basically works on Petri Net technique of modelling. It has used in determining the

performance measures, (Makespan, mean flow time, maximum flow time, and variance of

flow time) [5].

In this research work a case study is conducted in a Glass forming industry which faces the

problem in problem of tardiness. PN-Tool (Matlab) used for modelling, simulation analysis

of production system and assists in decision taking regarding improvement in production

system.

2. Simulation in manufacturing systemSimulation and modelling is used as decision helping tool; most important feature, which

awakes an interest for simulation, is prospect of working with complex system and

possibility of analysis of the dynamics behaviour of system [13, 14]. Simulation allied with

production system analysis, aiming at performance improvement becomes more relevant in

last decade. Discrete-event simulation is a collection of events that happen in chronological

order and change the system’s state. The state of the system is changed instantly when an

event happens. Discrete-event simulation models are used to study how the system works

during the period of observation [15, 16]. Therefore it is required to use discrete event

software for analysis of production system. There are numbers of software packages, which

works for discrete event simulation like ARENA, WITNESS, PNTool (Matlab). PNTool

(Matlab) is software which works on Petri Net technique, widely used to modelling of

discrete event system [6]. It used to determine the utilization of stations used in modelling

[7, 8]. On simulation, it gives the performance measures which are helpful in decision

making process.

3. Methodology Adopted

Figure 1: Methodology Used for Improvement

The modelling, simulation and analysis is applied here in order to obtain a more precise

detection the part at which improvement is required. PNTool (Matlab) used to design the

virtual model of production system. In PNTool (Matlab), Petri net technique used for

modelling the manufacturing system. The software packages provides the utilization of

each machines, material handling. The queue length, throughput time and mean flow time

can be find from simulation packages. These performance measures are useful to analysis

the bottleneck station present in manufacturing system, material handling system. To

discuss about the alternative solution brainstorming technique is used. The modelling,

simulation technique again used for evaluating the improvement in terms of utilization of

various stations.

A. Petri Net Technique

Petri net are well known for their modeling potential and for their ability to implement

optimization techniques. Karl Petri developed this technique in 1962 for communication

system analysis. Their use has been extended to application like manufacturing.

Problem Identification

Data collection for modelling

Modelling the production system

SImulation Result Analysis

Analysis ofAlternate Solution (Brainstorming)

Implementation

Analysis of Post Improvement

Petri net is a set of node and arc. There are two types of node (place and transition) which

represent the state of system and occurrence of event respectively. In manufacturing

system place would represent operation (e.g., process, transportation, reparation), and

transition symbolize events (Termination of job processing or a machine breakdown). The

firing process includes a tokens flow among places, when transition fires token all input

places are removed and put into output places. A transition can only be fired if it has been

enabled (i.e. there are sufficient token at its input place. Arcs are used as connecting agent

between place and transition.

Figure 2: Tool used for Petri Net Modeling Figure 3 : Firing the Token

Places which are drawn as circles possible states or conditions of the system while transition

which is shown by bars or boxes describe event that modify the system states. The

relationship between places and transition are represented by set of arcs which are only

connectors between places and transition in either direction. The dynamic behavior of

system can be represented using token which graphically appear as a black dots.

Manufacturing system is a discrete event system. So modeling can be done and also by this

we can check various types of analysis before whole system establish.

4.1 Problem Identification (Case study)Industry was facing the problem of less production rate, as it was unable to supply the

finished products at right time hence facing the problem of tardiness. After analyzing the

order delivery report of last three months, tardiness is obtained for 10% products. In such a

competitive market it is not desirable, so there is a need to improve in production line. To

analyze the dynamic behaviour of production line, simulation technique is used.

Figure 4: Production Line

4.2 Data collection for modelling (Pre improvement): The data of 10 working days

considered for modelling the production line. 800 % time of total time is considered as

operation time for actual production, remaining 20 % time being considered for initial

machine setting, labour allowance and other type of allowances. Operation time, material

handling time engaged between stations is indicated in table 1. The size of glass sheet varies

according to order requirement. Average of them is considered for modelling the production

line.

Product Specification Operation Time In Minutes Material Handling Time

Product type

Number of product Cutting Edging Drilling Washing H.T Station

Avg. Time

P 100 12 6 7 4 6 B TO C 1.18Q 75 10 5  - 3.5 6 C TO D 0.75R 50 13 8 4.5 5 7 ON D 2.21S 75 11 10  - 6 8 D TO E 1.20

Average Time 11.41 7.08 6.16 4.54 6.66 E TO F 0.78F TO G 2.023

Table 1: Operation & Material Handling Time (Minutes)

4.3 Modelling Description

The production line split into places and transitions for modelling. Machines, material

handling equipments and processing equipments are considered as places which are indicated

by circles in modelling, Time engaged in various processes considered as transitions which

are indicated by rectangular bar. . The notation used for places, transitions in table is table

no.4 and 5 respectively. Arcs are used as intermediate connecting medium between places

R a w m a t e r ia l 'B '

C u ttin g s ta t io n C

E d g in g S ta t io n

'D '

a ti o n D

D r illin g S t a ti o n E

W a s h in g S ta ti o n 'F '

H e a t T r e a tm e n t

'G 'F in a l

P r o d u c t

and transitions times are allotted to various transitions for their firing. Time distribution

selected as constant. Prepared model is shown in figure no.5

Figure 5: Prepared Model of Production Line

4.4 Analysis of Simulation Result: Results are obtained after simulation in terms of

various performance measures like utilization of station, throughput, throughput time, and

mean flow time per job. These results are shown in table no. 2 and figure no 6.

Figure 6: Utilization of station (%)

Cutting station is consuming higher

percentage of processing time. The

utilization of edging station is much less

as compare to that of cutting station

which indicates that the cutting station is

a bottleneck station hence there is

necessity to deeply investigate cutting

station, henceforth selected for further analysis. In addition to this, mean flow time of a job

is 12.66 minutes which must be less than designed takt time (11.51 minutes) for delivering

product in right time. Time engaged in this process comes out to be 3800 minutes by PN

Other Parameters Value

Mean flow time 12.66

minutes

Throughput time 3800

minutes

Throughput 300

Designed Takt Time 11.51

minutes per

part

Table 2:Result of Simulation (Pre improvement phase)

tool simulation while 3840 minutes as actual time. It is so because the Data collection

process was manually done.

4.5 Analysis of alternate solution & Implementation

The brainstorming sessions are conducted to discuss the problem with experts, operator

and supervisor. After deep analysis of cutting station, two alternative solution found, first

one is One more server of cutting station should install and second one is automated CNC

machine will installed. Automated CNC machine has selected for implementation. This

option makes 4 labours free for other operation.

4.6 Analysis of production line (Post implement) Time consumption in cutting operation is reduced and critical shaped glass can also cut by

CNC machines. One more advantage is reducing in material handling time. There is no

requirement of material handling between cutting station to edging station as outrace of CNC

machine reached to edging machine. Management interested to check amount of

improvement, so again operation time and material handling time has taken and rest of

condition considered same.

Product Specification Operation Time In Minutes Material Handling Time

Product type

Number of product Cutting Edging Drilling Washing H.T Station

Avg. Time

P 100 10 6 7 4 6 B TO C 1.18Q 75 9.2 5  - 3.5 6 ON D 2.21R 50 11.5 8 4.5 5 7 D TO E 1.20S 75 9.5 10  - 6 8 E To F 0.78

Average Time 9.92 7.08 6.16 4.54 F TO G 2.02

Table 3: Operation, material handling time in minutes (Post Implement)

4.7 ` Result of simulation (Post Implement)

The model is simulated for 300 jobs, on simulate the model, utilization of stations comes out to be more as compare to pre-improvement conditions, so now production line seems to be balanced. It can now deliver the product at right time. The designed takt time has achieved. Now mean flow of job is 11.20 which are sufficient of required demand. Makespan also decreased.

Figure 7: Comparison of Utilization index Figure 8: Improvement in Utilization

Utilization of material handling

equipments is increased by 4.78

%. Average utilization of

manufacturing set up is

increased 6.72 %. Production rate is higher than

demand rate. Throughput time for 300 jobs is 3362 minutes. In 10 days working days 2

days 6 hours have saved. In actual condition 300 jobs are manufactured in 3400 minutes.

5. Conclusion: Discrete event simulation used to detection the fault in production line.

Congestion point present in production flow line eliminated by implementing CNC

machine. Small scale industries can be designed their production line as per demand rate

by PN-tool (Matlab) software which shows linearity with actual production rate

Appendix

02468 6.34

4.786.72

Improvement in Utilization (%)

PlantLayout Uti-lization

CuttingEd

ging

Drilling

Washing

Heat Tr

eatmen

t

Materia

l han

dling

0102030405060708090

100

Utilizationof % station (Previous)Utilization % of station (Improve-ment)

Figure 9: Simulated model in PN-Tool(Matlab)

Table 5: Notations Used for transitions in Modelling

Table 4: Notations used for places in Modelling

Reference

Transition PRODUCT Transition PRODUCT Stock to table to gripper PT1

Drilling process PT7

cutting process time PT2

drilling to washing machine PT8

cutting table to edging PT3

washing process PT9

Edging machine PT4

washing to trolly PT10

Edging process PT5

Trolly transfer to H.T. PT11

Trolly to Drilling machine PT6

H.T. Process PT12

PLACE PRODUCT P

CUTTING TABLE PP1TROLLY-1 PP2EDGING MACHINE PP3EDGING PROCESS MACHINE PP4TROLLY-2 PP5DRILLING MACHINE PP6DRILLING PP7TROLLY-3 PP8WASHING MACHINE PROCESS PP9TROLLY-4 PP10H.T. MACHINE PP11H.T PROCESS PP12FINAL POINT PP13

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