reduction of operator’s loading and unloading …...lean systems are used in industries on the...

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International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 10, October 2017, pp. 207–216, Article ID: IJMET_08_10_025

Available online at http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=10

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication Scopus Indexed

REDUCTION OF OPERATOR’S LOADING AND

UNLOADING TIME USING LEAN SYSTEMS

FOR PRODUCTIVITY IMPROVEMENT

A. Gnanavelbabu

Department of Industrial Engineering, CEG Campus,

Anna University, Chennai, Tamilnadu, India

P. Arunagiri, G. Bharathiraja, V. Jayakumar and V. Velmurugan

Department of Mechanical Engineering, Saveetha School of Engineering,

Saveetha University, Chennai, Tamilnadu, India

ABSTRACT

The objective of this paper is to implement the concepts of lean production in

terms of waste elimination, in the matter of identification of waste time, factors

leading to it and eventually reduce\eliminate the same to provide guidance to the

industry. The research describes an approach to wait time waste of operator manual

loading and unloading time measurement. It is uniformly applied and creates results

that can be compared from one production line to another. The wait time waste

elimination method is a common approach for gathering and combining data to

support identification and elimination of time waste in the industrial lifecycle of a

product unit. The lean production system is to determine an efficient production

process through the removal of waste and to implement a flow. Wastes of various

types are seen in the manufacturing area. One among them is the time taken for

loading and unloading of work piece. Reduction in time waste will improve the each

production line and in an overall improvement in the number of components produced

per shift. Industrial data analysis has been carried out for calculating the current time

taken and improving the production process by setting the optimal time to get the

required output in terms of increase in the number of components produced per shift.

Keywords: Waiting time, Lean production, Optimised time, Allocated time, Actual

time.

Cite this Article: A. Gnanavelbabu, P. Arunagiri, G. Bharathiraja, V. Jayakumar and

V. Velmurugan, Reduction of Operator’s Loading and Unloading Time using Lean

Systems for Productivity Improvement, International Journal of Mechanical

Engineering and Technology 8(10), 2017, pp. 207–216.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=10

Reduction of Operator’s Loading and Unloading Time using Lean Systems for Productivity

Improvement


1. INTRODUCTION

Lean systems are used in industries on the basis of the requirement of the relevant lean tools

and techniques. Every firm uses specific lean tools to reduce the waste and increase the

number of components produced over a period of time. Roberto Panizzolo (1998) has worked

on lean production model by 27 excellent firms. Their study suggests that the implementation

of lean production principles and the perspective analysis shifted from operations

management to relationships management. Suggestions for future research have been

indicated through the empirical findings. Tu et al (2006) stated that the review of absorptive

capacity and develop the reliable instrument to measure the impact of organization. Fawaz

Abdulmalek & Jayant Rajgopal (2007) have made a study o the impact of lean principles

adopted in a large steel mill. The VSM tool was used to solve the problems in the firm.

Moreover a new simulation model has been developed for lead time and inventory reduction.

Richard Lindeke et al. (2009) state that the Temporal Think Tank (TTT) brings round all to

work as a team to develop their decision making skills to incubate new process or products

and after some time the individual are return to original position to implement the learned

procedure. Tarcisio Abreu Saurin & Cleber Fabricio Ferreira (2009) has made an assessment

of the impact of LPS (Lean Production System) on working conditions in a harvester

assembly line of an American-owned plant in Brazil. They have grouped the data collected as

work content, work organization, continuous improvement and health and safety. The

working conditions were fairly good and had improved after the introduction of LPS.

Krisztina Demeter & Zsolt Matyusz (2011) have focused on improvement of the inventory

turnover performance using lean systems. Firms that widely apply lean systems have high

inventory turnover than those that do not rely on lean systems. The data were analysed using

International Manufacturing Strategy Survey (IMSS). Kuhlang et al. (2011) have suggested a

methodical approach connecting the VSM and Method Time Measurement (MTM) and offers

new advantages to reduce the lead time and increase productivity. Arnout Pool et al. (2011)

affirm that the cyclic schedules fit in a lean improvement approach for the semi-process

industry. The cyclic schedules help to improve production quality and supply-chain

coordination and discrete event simulation. It is useful tool in facilitating a participative

design of a cyclic schedule. Noor Azlina et al. (2012) found a company that had implemented

green lean total quality information management have seen good financial outcomes in

Malaysian automotive industries. Stewart Robinson et al. (2012) have used discrete event

simulation and lean care approaches for the improvement of process and service delivery in

health care systems. Richard turner & Ju Ann Lane (2013) list the specific applications of the

concepts to support coordination of systems engineering activities with large scale system

acquisition, development and evolution. Sharma Neha et al. (2013) have studied application

of the lean systems leading to the continuous production process with a focus of the steel

industry. Sukhwinder Singh Jolly (2013) have reviewed the role of lean manufacturing

for various organization, lean manufacturing techniques and the benefits achieved through

lean systems. Matt & Rauch (2013) have analyzed the suitability of existing lean methods in

small size enterprises in Italy. Through a case study the difficulties in the implementation

stage and how to eradicate the same were discussed. Keitany & Riwo Abudho (2014) have

identified the impact of lean towards tools, quality, employee perceptions, and challenges.

Findings suggest that lean can improve using latest technology, customer involvement, reduce

resistance, challenges faced while adopting lean in Kenyan industries. Daryl Powell et al.

(2014) focus on the production line of main leaf spring. Analysis of the current map with the

experts and a future map is designed to propose a different way to reduce lead-time and to

increase productivity/ output. Michael Dwianto Nirwan & Wawan Dhewanto (2015) support

focus of lean startup methodology on agile testing and learning cycle for validating

A. Gnanavelbabu, P. Arunagiri, G. Bharathiraja, V. Jayakumar and V. Velmurugan


hypotheses. The various barriers for implementation of lean in Indonesian environment and

the success rate of lean in United States environment has been discussed.

2. METHODOLOGY

The purpose of the methodology is to achieve the objective of the work. It is a guideline to

analyze wait time using the eight step Practical Problem Solving method. The step by step

process as elaborated below was used to find out the operators manual wait time reduction.

The 8 step problem-solving methodology was used for crank case cell in Greaves Cotton (P)

Ltd, Ranipet, India. The step by step process involved in the eight step PPS methodology is

listed below. xt into it.

A) Clarify the Problem

In the current situation, the number of components produced per shift was focused for the

entire sections crank case cell. The number of the components manufactured in ideal

condition was determined. There was a wide gap between the allocated time and actual time.

B) Break down the Problem

The process involved in the operators manual loading and unloading operation time was

carefully studied. Duration of inactivity or time wasted on unnecessary efforts was identified.

C) Set the Target

The current level of production was observed and production level considered ideal for the

optimal time was set. The aim of the target setting was to increase the output from current

level to targeted level.

D) Analyze the Root Cause

Fish bone diagram was used to identify the various causes for the reduction of loading and

unloading time during the production process as shown in Figure 1 below.

Figure 1 Fish bone diagram to find loading and unloading time

E) Develop Countermeasures

1. The operator’s loading and unloading time for each process was tabulated.

2. The total operator allocated and actual time for each level has been calculated.

3. Graphs were plotted between the number of operations Vs Operators Allocated time,

Actual time and optimized time of crank case cell.

4. The actual processing time per shift in crank case cell were calculated.

5. The optimized operators loading and unloading time per shift in crank case cell were

calculated.

Mapping the

current

production

process.

Calculate the

loading and

unloading time

for each

operation.

Calculate the

optimized time for

each loading and unloading

operation.

Setting the optimised

time for the loading and

unloading .

Increase the number

of components

produced per shift .

Reduction of

loading and

unloading time

during production

process

environment.


Improvement


F) Implementation of the Countermeasures

The various activities carried out in various sections were mapped. The section mapping gave

a clear idea of material movement from the first machine to the last machine till it was

converted into the finished product. The implementation of the reduction in operators loading

and unloading time was been carried out in the various sections. The results were monitored

and increase in the number of components per shift was calculated.

3. PROCESS MAPPING OF PRODUCTION AREA FOR CRANK CASE

CELL

Mapping the production process in Greaves cotton (P) Ltd for different sections of the

production system was done. The various areas in the production system are crank case cell

was first taken up and mapping was done mapped based on the various operations carried out

in this section and the movement of the material from machine 1 to machine 18 was carefully

studied. The movement of material is shown in Figure 2 below.

Figure 2 Process mapping of crank case cell

A) Operators Loading and Unloading Time MUDA Analysis in the Crank Case

Cell

For each set of machines from machine 1 to machine 16, the total allocated time, and actual

time were calculated and the optimized time was set to find the ideal output. The graphs were

plotted for the allocated, actual and the optimized manual operators loading and unloading

time for each operation as shown in the Figure 3 below.

Figure 3 Number of operation carried out Vs allocated time, actual time and optimized time for crank

case cell

Machine

2

Machine

1

Machine

4

Machine

3

Machine

5

Machine

6

Machine

7

Machine

8

Machine

9

Machine

10

Machine

11

Machine

12

Machine

13

Machine

14

Machine

16

Machine

15

Machine

17

Machine

18



B) Monitoring the Process and Results for Operator’s Allocated, Actual and

Optimized Time in Crank Case Cell

The time calculation has been carried out for allocated processing time per shift in crank cell.

The actual and optimized processing time can be calculated accordingly. The allocated time

for loading and unloading was 30 seconds. During the actual production, the time taken was

very much less when compared to the allocated time. The operators loading and unloading the

time taken was approximately 20 – 25 seconds. To achieve a optimum output, an optimized

time of 20 seconds was set for loading and unloading and output parameters were calculated

with reference to this time. The total time saved per shift was 39 minutes. The average time

for producing one component was 3 minutes and the number of components produced in the

optimized time was 161 per shift and 483 components per day. The current performance,

modified performance and the time were noted after re-modification of the system calculated

which indicated the improvement in the productivity. The simulated results using the Pro

model software also indicates that there is improvement in the number of components after

modifying the optimized operator manual loading and unloading time.

C) Simulation Carried out for Allocated Time Using Pro model Software in

Crank Case Cell

Single capacity locations are those that have the capacity for handling one job at a time.

Multi-capacity locations are those that involve handling more than one job at a time. Here, in

the case, taken up single capacity locations were the machines on the shop floor and multi-

capacity locations were the pallets, inspection deck, rack etc. The first set of machines include

a Vertical Machine Centre (VMC)1-VMC 8, a total of 8 machines and the second set included

VMC 9- VMC 16. The VMC was simulated as Lathe due to non-availability of options in the

Pro model software for all the simulated results.

Figure 4 Location utilization for allocated time in the crank case cell

For the first set of machines, the location utilization was 100% from start to end of the

schedule. For the second set, only 33% was utilized at the start of the operation. Starting hour

1, there was an increase in the utilization rate from 33% to 64%. This gradually decreased

during hour 2 to 54% and in hour 3 to 52 %.The utilization remained constant at hour 3 and

hour 4. At hour 5 there was an increase in utilization to 62% which was constant at hour 6

also. At the end of the operation at hour 7 the utilization decreased from 62 % to 56% which

ended the operation schedule as shown in Figure 4 above.


Improvement


Figure 5 Simulated results for allocated time in the crank case cell

Figure 5 above indicates the various factors that were considered by the software during

the simulation. First consideration of the entity states graph, indicated the current status of the

jobs under process. In other words, it can also be called as work in process status. As

mentioned earlier, the researcher has considered one type of job for crank case cell. Here,

product 1 was completed by 23%, of effort. Similarly, the total output from the system was

104 from type 1. The average time in the system was 3.81 hours for one product and average

was 0.88 hours for the specific operation. In multi-capacity location state the pallets for part

storage were considered. Here 100% utilization was seen for the pallets and emptiness is nil.

Figure 6 Machine utilization for allocated time in the crank case cell

The machines processing the jobs were under the single capacity location. The first set of

machines included VMC 1-VMC 8 utilized fully 100% during the 8-hour schedule. The

second set of machines included VMC 9-VMC 16 which utilized only 55% during the

operation and 45% of the machines remained idle as shown in Figure 6 above.



D) Simulation Carried out for Actual Time Using Promodel Software in Crank

Case Cell

Here in the case, single capacity locations were the machines on the shop floor and multi-

capacity locations were the pallets, inspection deck, rack etc. The first set of machines

included VMC 1-VMC 8, of 8 machines in all were there in the first set. The second set

included VMC 9- VMC 16 as shown in Figure 7 below.

Figure 7 Location utilization for actual time in the crank case cell

For the first set of machines, the location utilization was 100% from start to end for the

schedule. For the second set, at only 31% was utilized at the start of the operation. From hour

1 there was an increase in the utilization rate from 31% to 64%. This gradually decreased

during hour 2 to 58% and in hour 3 to 56 %.The utilization remained constant at hour 3 and

hour 4. At hour 5, there was an increase in utilization to 58% and the utilization for hour 6

was 64%. This remains constant at hour 7 also.

Figure 8 Simulated results for actual time in the crank case cell


Improvement


Figure 8 indicates the various factors that were considered by the software during the

simulation. To start with, consideration of the entity states graph, indicates the current status

of the jobs being processed. In other words, it can also be called as work in process status. As

mentioned earlier, it was considered as one type of job for crank case cell. Presently Product 1

is completed by 23%, similarly, the total number of product from the system were 112

products for type 1. The average time in the system was 4.01 hours for one product and the

average time of operation was 0.87 hours. In the multi-capacity location state, the pallets for

part storage were considered. Here 100% utilization was seen for the pallets and emptiness is

nil.

Figure 9 Machine utilization for actual time in the crank case cell

The machines processing the jobs come under the single capacity location. The first set of

machines included VMC1-VMC 8 which was utilized 100% during 8-hour schedule as shown

in Figure 9 above. The second set of machines included VMC 9-VMC 16 which was utilized

only at 55 % during the operation. 45% of the machines remained idle. The Pro model

software simulated results indicated an increase in the number of components. Both the

calculated and simulated results show positive with the outputs

E) Standardize and Share Success

Various steps were taken over a period of time for monitoring the operator manual loading

and unloading time. Material movements were carefully monitored. Man and material

movement considered unnecessary were avoided by setting the optimized time for the loading

and unloading. The setting of the optimized time periodically increased the daily output. The

output from the crank case cell showed a marginal change per shift.

4. CONCLUSIONS

Today lean faces a major challenge for implementation in industries. But many industrial

organizations have started adopting various techniques for ensuring productivity improvement

and to stay in the market. Process mapping has been done for the crank case cell and

individual process mapping has been done for all the above sections. The problem was solved

using the 8 step PPS methodology. In crank case cell , the number of output per shift before

optimization was 148 whereas the number of output per shift after optimization was 161

components per shift. The number of components increased from 444 to 483 components in

crank case cell. The output increase in the crank case cell was found. The types of lean waste

analyzed were waiting time reduction, motion, transportation and over processing. The lean

tools used are concepts of Process mapping, 8 step problem-solving methodologies,



elimination of waste, Set up time reduction. Here increase in the output in the industrial data

analysis results after setting the optimized loading and unloading time increases the number of

components per shift. The results indicate that there is impact of lean systems for the

productivity improvement in automobile industries.

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Improvement


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