manufacturing productivity improvement through …€¦ · automotive ancillary unit pardeep rana...

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International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 11, November 2017, pp. 635–648, Article ID: IJMET_08_11_065

Available online at http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=11

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

© IAEME Publication Scopus Indexed

MANUFACTURING PRODUCTIVITY

IMPROVEMENT THROUGH DMAIC

METHODOLOGY: A CASE STUDY OF

AUTOMOTIVE ANCILLARY UNIT

Pardeep Rana

Research Scholar, UIET, Maharshi Dayanand University, Rohtak, Haryana, India

Prabhakar Kaushik

Associate Professor, UIET, Maharshi Dayanand University, Rohtak, Haryana, India

ABSTRACT

“Tackle the root cause not the effect” a quote by famous Indian industrialist

asserts to analyse causes of problem rather than effects in order to find a solution of

the problem. In industries problems are companions of every process. Present case

study is about problem solving in an automobile ancillary unit manufacturing steering

system. In this paper authors explain the use of various quality tools along with use of

statistics and software to solve problem regarding low productivity level. In the end

organization was able to overcome the problem by improved productivity and

profitability levels. Step by step explanation of the whole case is easily understandable

and can prove to be worthy for industries of similar stature.

Keywords: Productivity Improvement, Problem Solving, Automotive Industry, Quality

Tools and Six Sigma

Cite this Article: Pardeep Rana and Prabhakar Kaushik, Manufacturing Productivity

Improvement through DMAIC Methodology: A Case Study of Automotive Ancillary

Unit, International Journal of Mechanical Engineering and Technology 8(11), 2017,

pp. 635–648.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=11

1. INTRODUCTION

Problems are everywhere but there is a solution to each problem (Sharma 2012; Sharma

2013b). In industries, one has to encounter various problems for example low productivity,

increasing rejection, not meeting the delivery schedules, improper inventory control,

increasing wastage, unmanaged shop floor etc (Sharma & Kadyan 2016b) (Sharma 2013a).

Various case studies have been reported which shows productivity improvement by focusing

on a particular problem solving project. Some uses methodology like six sigma (Edgeman &

Dugan 2008; Falcon et al. 2012; Biswas & Chowdhury 2016; Kaushik et al. 2017; Kaushik &

Manufacturing Productivity Improvement through DMAIC Methodology: A Case Study of

Automotive Ancillary Unit


Mittal 2015b; Kaushik, Prikshit, et al. 2016; Kaushik, Mittal, et al. 2016; Kaushik et al. 2012),

other uses advance product quality planning (APQP) (Mittal, Kaushik, et al. 2010; Sharma &

Kadyan 2016; Mittal, Khanduja, et al. 2010; Mittal et al. 2011; Mittal et al. 2012; Shrotri et al.

2015), quality function deployment (QFD) (Mittal & Kaushik 2011; Akao 1997), quality

circle (Abo-Alhol et al. 2005; Mittal & Prajapati 2014) and many more (Sharma & Kadyan

2015; (Mittal et al. 2017a; Mittal, Tewari & Khanduja 2016; Mittal et al. 2017b; Mittal,

Tewari, Khanduja, et al. 2016; Kaushik & Mittal 2015a; Gupta et al. 2011; Oakland 1990;

Boaden 1997). All implementations have been successful yielding favourable results. Doing

effects accurately and keeping them reliable is the basic idea behind any quality management

technique (Sharma & Kadyan 2015a) (Sharma 2017b) (Sharma 2017a). Present case study

was performed at a firm manufacturing steering system and its parts for major overall

equipment manufacturers (OEMs) across the globe. The organization is located in the state of

Haryana, India. It holds the reputation of being sole supplier of steering system to major

OEMs in India. Industry was facing a major problem in one of the parts of steering assembly.

Five phased Six-sigma DMAIC methodology (Figure 1) is used which includes various

quality tools, some basic statistics and Minitab software for complex calculations and graphs

etc.

Figure 1 Different Phases of Six Sigma and corresponding questions

2. DEFINE

Problem was regarding high rejection rate in manufacturing that part. Part was a shaft having

splines on it. Process mapping (figure 2) shows the steps followed in production of part.

Define• What the customer wants?

Measure• What is the extent of the problem?

Analyse• What are root causes associated with the process?

Improve• What actions can be taken to overcome the problem?

Control

• What is the status of problem after implementing

corrective actions?

Pardeep Rana and Prabhakar Kaushik


Figure 2 Process Mapping

Process starts with centering and facing operation performed on a raw shaft to make it

near perfect size and preparing it for further operation. After that pre roll diameter turning is

performed and at last serration rolling operation is performed. This operation is basically a

rolling operation but the beauty of this operation is to produce splines on the outer periphery

of shaft with ease. Further a process flow diagram (figure 3) was drawn to show the route

followed in manufacturing of the part.

Figure 3 Process Flow Diagram

Figure 4 shows the final part photograph along with serration inspection gauge used by

inspection person for in process inspection. This shaft is used in the rack and pinion assembly

in the steering assembly of the automobile as shown in figure 5 and 6.

Centering &Facing Pre- Roll Diameter Turning Serration Rolling

Process Mapping




Figure 4 Final Part & Serration Inspection Gauge

Figure 5 Steering System Assembly

Figure 6 Serration Roll Shaft in Steering System

Serration Gauge

Rack & Pinion Gear Assy.

Pinion

Serration rolled on outside

diameter

Pinion

(component)



3. MEASURE

After defining the problem, next phase is measure (Jaglan, Pawan; Kaushik, Parbhakar;

Khanduja 2011)(Kaushik & Kumar 2017). In this phase the true extent of the problem is

measured. It was done by constructing appropriate graphical representations. Analysis on the

rejected parts shows the different causes of rejection of shafts. A Pareto chart (figure 7) was

drawn which showed the clear picture. Among various causes of rejection such as serration

mismatch, Major diameter oversize and undersize, Pinion diameter oversize and undersize

and handling damage or marks, serration mismatch came out to be major contributor with 56

% of the total rejection. The average production of the shaft was 72000 shafts per month and

the running rejection parts per million (PPM) was 770 (figure 8). Target was set to bring

down the rejection ppm and achieve six sigma level.

Figure 7 Pareto Chart for Defect Wise Analysis of Rejection

Figure 8 Six Months Rejection Status

A cost analysis in the form of cost of poor quality (COPQ) was also done to highlight the

effect of poor quality in monetary terms (Table 1). It was found that this single part was ill

effecting a revenue by Rs. 28962 per annum. Considering the similar problems in similar

models of other parts made this loss up to Rs. 500000 which is a substantial amount.




Table 1 Cost of Poor Quality for Serration Roll Shaft Rejection

Number of pieces rejected last month (for the part number identified for study) 60

Number of pieces scrapped last month 60

Number of pieces reworked last month 0

Scrap cost/piece 39.85

Rework cost/piece 0

Total scrap cost (Rs. Lakhs) for last month 2391

Total rework cost(Rs. Lakhs) for last month 0

Total Rejection cost (Rs. Lakhs) for last month 2391

Extrapolated Total rejection cost (Rs. Lakhs) for one year 28692

4. ANALYSIS

As the response of the problem is attributing therefore first step was to convert this response

in some measurable terms. For these two terms diameter over pin (DOP) and major diameter

as shown in figure 9 were chosen.

Figure 9 DOP & Major Diameter Measurement Description

8 OK and 8 Not good (NG) parts were collected from the production floor and arranged in

ascending order of the values of above described parameters (Table 2). Paired comparison

was applied and as the table shows that DOP was the major contributor of the problem

because of greater end counts. Table also shows complete segregation of ok and NG parts in

case of DOP and therefore a range of <0.050 was set for the DOP value. Now for finding the

possible causes of higher DOP value a fishbone diagram was generated (figure 10).



Table 2 Paired Comparison for Finding Major Contributor

Figure 10 Fishbone Diagram

With the help of fishbone diagram six causes along with their analysis method were

chosen. These suspected sources of variations are listed in table 3. Choice of analysis method

depends on various factors like type of data available, nature of process/part, applicability etc.

Table 3 SSVs and Analysis Methods

SSV’s PC PPS CS BC CC MVA OS VS FF

HARDNESS

PRE ROLL DIA &RUNOUT

BACKLESH ELIMINATOR

SLIDE REPEATIBILITY

DWELL TIME

THRUST PRESSURE




Legend: PC – Paired Comparison, PPS – Product/Process search, CS – Component

Search, BC – Better vs Current, CC – Concentration chart, MVA – Multi-Vary analysis, OS –

Observation, VS – Variable Search, FF – Full Factorial.

First of all hardness was taken into consideration by paired comparison method. 8 Good

and 8 Bad parts were collected from the production line and arranged in ascending order of

the value of DOP. Here in table 4 the highest and lowest value of hardness was from same

category i.e. bad therefore hardness was not proved to be a cause for the response.

Table 4 Paired Comparison for Hardness

S.No.Hardness

BHN Infrence DOP

1 208.80 Bad 0.108

2 218.30 Good 0.029

3 219.40 Bad 0.182

4 222.20 Good 0.018

5 223.50 Good 0.02

6 228.50 Bad 0.186

7 229.50 Good 0.019

8 231.40 Bad 0.073

9 232.10 Bad 0.162

10 232.20 Good 0.0445

11 235.00 Good 0.025

12 237.00 Good 0.025

13 237.30 Bad 0.117

14 238.00 Good 0.054

15 240.30 Bad 0.128

16 253.90 Bad 0.114

Secondly pre roll diameter & run out were taken into consideration using B & C method.

6 good parts were taken for C category and 6 good parts were taken for B category. A

comparison chart was created as shown in table 5.

Table 5 B vs C for Pre-Roll Diameter Turning and Run Out

After that paired comparison was drawn from the readings of table 5. As evident from

table 6 the total count comes out be 4.5 which is less than 6. Therefore pre roll diameter and

runout were not proved to be the ultimate cause too.



Table 6 Paired Comparison for Pre-Roll Diameter Turning and Run Out

S.No. Pre-Roll Dia RunOut DOP Variation

1 13.86 0.02 0.007

5 13.87 0.02 0.008 Top Count=1.5

12 13.91 0.04 0.008

4 13.86 0.02 0.01

2 13.87 0.01 0.01

11 13.91 0.05 0.01

6 13.85 0.02 0.013

10 13.9 0.05 0.013

3 13.85 0.01 0.014

7 13.89 0.04 0.015 Bottom Count=3

9 13.9 0.045 0.016

8 13.89 0.04 0.017

Now we moved onto our third SSV i.e. backlash eliminator tightening by multi-vary

analysis. Under multi-vary analysis basically three types of variations are checked. These are

categorised as part to part variation, stream to stream and time to time variation. A data

collection scheme was designed as shown in figure 11. Data was collected for a period of two

shifts with 13 time blocks over a set of two rollers (table 7). Minitab software was used to

calculate analysis of variance (ANOVA) where it was clear that the major component of

variation was part to part variation which came out to be 96.43%. The multi-vary response

chart vs time (figure 12) put a stamp on our findings. The main reason for part to part

variation was backlash eliminator loosening. Therefore our main objective was to work upon

the backlash eliminator and eradicate the problem.

Figure 11 Data Collection Scheme for Multi-Vary Analysis

Time

Roller Set

1

Part

1

Roller Set

2

Part

2

Part

3

Part

1

Part

2

Part

3




Table 7 Multi-Vary Analysis for Backlash Loosening

t9t8t7t6t5t4t3t2t1 3t1 2t1 1t1 0t1

r2r1r2r1r2r1r2r1r2r1r2r1r2r1r2r1r2r1r2r1r2r1r2r1r2r1

0.03

0.02

0.01

tim e

response

p 1

p 2

p 3

M ulti-V a r i C ha r t fo r re s po ns e by pa rt - tim ero lls

part

Figure 12 Multi-vary Response vs Time Chart

Roll-1 Roll-2 Roll-1 Roll-2

0.014 0.007 0.004 0.008

0.004 0.009 0.009 0.005

0.007 0.014 0.014 0.012

0.024 0.008 0.024 0.009

0.007 0.016 0.014 0.008

0.011 0.013 0.011 0.019

0.012 0.015 0.017 0.017

0.009 0.006 0.009 0.025

0.016 0.010 0.008 0.007

0.010 0.008 0.003 0.011

0.009 0.021 0.009 0.0140.025 0.013 0.017 0.006

Roll-1 Roll-2 Roll-1 Roll-2

0.013 0.021 0.011 0.022

0.014 0.004 0.007 0.017

0.004 0.005 0.009 0.010

0.008 0.016 0.008 0.018

0.027 0.011 0.020 0.0100.008 0.007 0.018 0.010

0.012 0.005

0.014 0.015

0.006 0.030

Time-2 Time-6

Time-1 Time-5(After

Tighetining Backlesh)

Time-4 (before

Tighetining backlesh)Time-8

Time-3 Time-7

Time-12

Time-11

Time-10

Time-9

Time-13

Nested ANOVA: response versus time, rolls, part

Analysis of Variance for response

Source DF SS MS F P

time 12.000 0.000 0.000 1.510 0.236

rolls 13.000 0.000 0.000 0.435 0.949

part 52.000 0.002 0.000

Total 77.000 0.003

Variance Components

Source Var Comp. % of Total StDev

time 0.000 3.570 0.010

rolls -0.000* 0.000 0.000

part 0.000 96.430 0.007

Total 0.000 0.007



5. IMPROVE AND CONTROL

Rigorous brainstorming sessions were held and the help of each employee associated with the

process was taken before reaching following possible ways of correcting the problem.

1. Frequently tightening of backlash eliminator.

2. To do Preventive Maintenance analysis for backlash eliminator looseness.

3. Interlocking for motor load in the machine for backlash loosening.

Before implementing the corrective measures another group discussion session was held

in which discussion on each above listed option was done. Discussing about first option of

frequent tightening of backlash eliminator it was observed that it is an operator oriented

operation. We have to fully depend on the operator skill and dedication for this option to be a

success. Therefore we could not rely on that option as it is not a poka-yoke. Second option

was to implement preventive maintenance program. This programme was initiated and its

implementation is still in process. The results of this option will be visible in long term

successful running of the programme. Third option was interlocking for motor load in the

machine. Before further discussing on this option it was agreed to test the correlation between

backlash eliminator looseness and motor load. When plotted it showed a strong correlation of

the order of 0.92 (figure 13). Hence it was concluded that by governing motor load (ampere),

the backlash loosening can be controlled.

For determine the motor load value (Ampere) correlation between DOP RANGE & Motor

Load (Ampere) was analyzed (figure 14). Three distinct groups were visible from correlation

graph. Group-1(3.8 amp~4.1amp) with a correlation value of 0.77. Group-2(4.1 amp~4.3amp)

with a correlation value 0.20 and group-3(4.3 amp~4.9amp) with a correlation value 0.58.

After discussing with the persons associated with the process it was agreed to fix the motor

load value between 4.0 ~4.25 Amperes. Before proceeding further a final validation was done

by using Better vs Current method. Three parts each were taken from the production done on

existing and improved parameters. Result showed complete portrayal of the improvement.

Now it was evident that the improved parameters have brought radical results. Therefore the

changes were documented and an editing has been made in the control plan of the process to

eliminate any future ambiguity.

Figure 13 Correlation Analysis of Backlash Eliminator Tightening & Motor Load




Figure 14 Correlation Analysis of DOP Range & Motor Load

6. CONCLUSION

The main focus of this case study is problem solving in industrial environment through six-

sigma implementation and acquiring desired results working as a team. Efficacious

implementation of the corrective actions suggested by the team brought down the rejection

level from 770 ppm to 1.6 ppm. This indeed is a great achievement. The case study also

brought monetary benefits. Estimated cost benefits were calculated to about Rs. 70000 for this

part and when same measures were applied to similar products it arose to Rs. 300000

approximately. Talking about intangible benefits organization was able to achieve employee

motivation, appraisal, personnel development, sense of team work and many similar project

initiation. Authors strongly suggests the execution of such projects throughout the different

working areas in industries across the globe.

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0

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3.75 3.85 3.95 4.05 4.15 4.25 4.35 4.45 4.55 4.65 4.75 4.85

Backlash tightening (Amp)

DO

P R

ange

Group-1 Group-2 Group-3



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