The two major challenges that industries are facing today are continuous improvement in productivity and quality of the product. Bosch Production System (BPS) - one of the leading manufacturers in Diesel system equipments, has been successfully employing the efficient statistical tool ‘Shainin® ’ for the root cause identification and Design of Experiments (DOE) techniques for analysis and optimization of the quality related issues. Shainin® is popular being a simplest and effective tool to employ in solving the manufacturing related problems. The present paper deals with one of the quality issues resolved by using Shainin® methodology in Diesel systems plant, Bosch Ltd., Bengaluru.

1. Proceedings of the 2nd
International Conference on Current Trends in Engineering and Management ICCTEM -2014
17 – 19, July 2014, Mysore, Karnataka, India
200
ROOT CAUSE ANALYSIS OF CONTROL ROD FRICTION PROBLEM IN
DFIP USING SHAININ®
APPROACH
Bhaskara P1
, Raghuveer Prasad2
, K M Subbaiah3
, G L Shekar 4
1, 3, 4
(Dept. of Industrial & Production Engineering, The National Institute of Engineering,
Mysore-570 008, Karnataka, India)
2
(Dept. of Quality Management & Methods, Bosch Ltd., Adugodi, Bengaluru-560 030, Karnataka, India)
ABSTRACT
The two major challenges that industries are facing today are continuous improvement in productivity and
quality of the product. Bosch Production System (BPS) - one of the leading manufacturers in Diesel system equipments,
has been successfully employing the efficient statistical tool ‘Shainin®
’ for the root cause identification and Design of
Experiments (DOE) techniques for analysis and optimization of the quality related issues. Shainin®
is popular being a
simplest and effective tool to employ in solving the manufacturing related problems. The present paper deals with one of
the quality issues resolved by using Shainin® methodology in Diesel systems plant, Bosch Ltd., Bengaluru.
Keywords: Shainin®
Methodology, Root Cause Analysis (RCA), Component Search, RedX.
1 INTRODUCTION
Diesel engines have become the most popular power packs for heavy duty vehicles and equipments such as
trucks, tractors, passenger vehicles, gensets, etc. as the diesel fuel is one of the most efficient and energy dense fuels
available today. Nevertheless, the diesel engine has several great advantages, the quality production and maintenance of
critical components of engine system has become yet a challenging task. Diesel Fuel Injection Pump (DFIP) System- the
heart of the diesel engine is one such critical system [1]. Nowadays, the quality of the product has become the dominant
criteria to acquire the global market. BPS is the leader in quality production by deploying advanced quality measures in
its manufacturing processes and thus, satisfying the customer. It has been possible through continuous improvement and
proactive quality maintenance techniques like Shainin®
System, Failure Mode Effect Analysis (FMEA), Six Sigma, etc.,
in the production processes [2]. The quality of the product may be quantified in terms of money (INR), First Pass Yield
(FPY), part per million (ppm), etc. There is a need to employ a simpler and efficient tool along with the traditional seven
quality tools in order to achieve the Six-sigma quality in manufacturing industries. Since the customer complaint, line
stop kind of issues are having crucial impact on productivity, cost control and customer satisfaction parameters.
1.1 Root Cause Analysis Techniques
Increasing trend towards proactive and reliability centered maintenance has wide scope for the development of
advanced and simpler investigation techniques to handle the manufacturing related problems [3]. In this regard Loss
Causation Mode and RCA methods such as Fishbone diagram, Shainin®
DOE, Why-Why analysis, Failure tree analysis
etc., have been very effectively used in the industries [4].
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 5, Issue 9, September (2014), pp. 200-205
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2. Proceedings of the 2nd
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17 – 19, July 2014, Mysore, Karnataka, India
201
1.1.1 Shainin®
approach for problem solving
Shainin®
system could be effectively used for solving the problems with the theory that “there is a dominant
cause of variation in the process output that defines the problem” and it is analogous to Pareto principle of ‘vital few and
trivial many’ or ‘20/80 principle’ [5]. The categorization of the problem done in order to choose the best effective
techniques such as Component search, Paired comparison, Concentration chart, Strategy diagram etc. Classification of
the problem that is GreenY (as termed in Shainin® system) is done as Property, Profile, Defect or Event. Event sub-
categorized as destructive or malfunction event.
2. APPROACH AND EXPERIMENTATIONS
The problem solving methodology follows the discipline of Focus-Approach-Converge-Test-Understand-Apply-
Leverage, which has been discussed in detail in the following sections.
2.1 Focus
Since the focus of project in the business point of view is about 4% FIPs produced are rejected and sent to
rework during control rod’s dynamic friction check point during functional audit.
Fig. 2.1: Illustration of Problem Statement (GreenY)
The illustration of problem identification and definition are shown in Fig. 2.1. The control rod has been pulled
by hand to check its dynamic frictional condition. Frictionless control rod will reach back with a positional tolerance
limit of -0.09 to 0.35mm. the check is done on forward and backward movement of control rod namely F8 and T3
checkpoints. The additional cost of rework is the reason for taking up the project as priority. The project objective is to
identify and eliminate the Root cause (RedX-Shainin® term) which is causing ‘Control rod friction during idling
condition of the FIP’ or ‘The reduction of the T3/F8 check point rejection rate’.
2.2 Approach
The effective utilization of the statistical tool and quickness of the root cause identification relays on the
selection of samples with two extreme characteristics, that is WOW (worst of worst) and BOB (best of best). The type of
problems that is tackled is apparently limited to variation problems. The generic form of problems is a certain quality
characteristic (which can be a variable or an event) has a probability distribution which does not meet the demands. Here
the sample which gives value close to mean is BOB and which is towards either of the extremes is WOW.
Fig. 2.2: GreenY Categorization as ‘Malfunction event’

3. Proceedings of the 2nd
International Conference on Current Trends in Engineering and Management ICCTEM -2014
17 – 19, July 2014, Mysore, Karnataka, India
202
The defined GreenY is a ‘Malfunction event’ as shown in Fig. 2.2. It is difficult to analyze or use a technique to
identify the RedX since the problem identification has been done on functional audit bench as a whole assembly of FIP.
Hence, the assembly has to be divided in to sub-assemblies that is pump housing and governor cover sub assembly. This
variety of problems could be efficiently handled primarily by doing validation of measurement system through
Component search-0, followed by Component search, Stage-I and Stage-II and Paired comparison.
2.3 Converge
Converging towards the RedX causing the GreenY has been achieved by Component search technique. The
solution tree for the GreenY to determine the RedX is illustrated in Fig. 2.3.
Fig. 2.3: First Level Solution Tree
Primary step during converge process is to recheck the measurement system based on which problem has been
formulated. This can be done by Component search, Stage-0 exercise. The result of the exercise is shown in Fig. 2.3 and
Fig. 2.4.
Fig. 2.4: Results of Component Search exercises, a).Stage-0, b).Isoplot
The Isoplot shows that Discrimination ratio is <6, since the analysis of measurement system is very critical due
to the unavoidable noise factor that is vibrations during measurement and these measurement variation can not be ruled
out. So, the study has been proceeded towards the Component search, Stage-I and II and result of which shown in Fig.
2.3 and Fig. 2.5.
Component search, Stage –I exercises are done to investigate that the RedX is in the process of assembly or in
the component itself. Two times dismantling and re-assembly will ensures whether problem persists even after older
process effect is completely eliminated. In this case it is found that BOB and WOW remain unaltered. So the proceeding
with Component search, Stage-II. Doing swap between two sub-assemblies-governor cover and pump housing of the
DFIP assembly among two extreme samples. It is found that characteristics have swapped along with the governor cover
as shown in Fig.2.5.

4. Proceedings of the 2nd
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17 – 19, July 2014, Mysore, Karnataka, India
203
Fig. 2.5: Component Search, Stage –I and II
Converging on to the first level root cause that is Governor Cover (GC), study has to be further focussed on the
critically assembled components of the GC (Fig. 2.6). in governor cover again the component search is to be done by
considering suspected set of linkages which may be the probable causes for GreenY. Here, Tension lever set tightness or
the sticky linkages are the probable causes. Since Component search, Stage-II has clearly shown that tension lever set has
given dominant swap in BOB & WOW characteristics, while, for the other linkages fewer times swap was found (Fig.
2.7). Further, continuing the CS to GC spring swap, it has been found the RedX factor, while others components are
PinkX, Pale PinkX factors (Shainin® term for root causes with lesser influence for GreenY) and hence, sticky linkages
have been temporarily eliminated from the study (Fig. 2.3).
Fig. 2.6: Second Level Solution Tree
Fig. 2.7: GC Level- Component Search, Stage-II

5. Proceedings of the 2nd
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17 – 19, July 2014, Mysore, Karnataka, India
204
3. RESULTS AND DISCUSSION
3.1 Test
The confirmation test of the found out RedX has been done by Paired Comparison tool. Comparison of GC
spring length has given 10 count for 5 pairs of sample.
Table 1: Paired Comparison Result for GC Spring
Governor
Cover
Governor spring
L0 = 51±0.5
WOW BOB
Pair 1 50.7 ▼ 50.98
Pair 2 50.68 ▼ 50.96
Pair 3 50.98 ▼ 51.32
Pair 4 50.76 ▼ 51.34
Pair 5 50.96 ▼ 51.38
The length of springs could be clearly differentiated between BOB and WOW samples as shown in Table 1. All
the WOW springs are having length towards the lower limit of the specification. Hence, the identified root cause is
confirmed as the RedX.
3.2 Understand
From the results of component search and paired comparison GC spring length has been found as the RedX. The
spring is connected to tension lever followed by GC linkages and hence to Control rod. Any stickiness or frictional
condition in any of the links connected to control will reflect in its dynamic friction test (T3/F8) performance and hence,
on the idling condition behaviour of the FIP and engine. The spring length more found to be problematic as the shorter
length spring is induced with prior tension in the lever (as illustrated in Fig.2.9) , which does not allow the control rod to
reach back to its original position during test. Whereas the GC springs with length more that is towards the higher
specification of the limit has freely moving tension rod, which allow the control rod to function freely.
Fig. 2.8: Pre-tension Effect of GC Spring Length Less and More
The inspection of a batch of GC spring length has shown the distribution pattern of spring length. The mean of
the specification has to be 51mm, but the current status is it is skewed towards the lower specification with the mean
value of 50.72mm (Fig. 2.9). And the probability of spring length fall out of specification is also about 15%. Hence, the
chance of a control rod fall sticky because of this reason is also more.
Fig. 2.9: Current GC Spring Length Distribution

6. Proceedings of the 2nd
International Conference on Current Trends in Engineering and Management ICCTEM -2014
17 – 19, July 2014, Mysore, Karnataka, India
205
It has been proved in the ‘Current versus Better’ analysis that, pumps with governor covers having better spring
length that is, length towards its higher specification limit (51-51.5mm) have not failed in idling condition check point
during calibration due control rod friction as shown in Fig. 2.10. This RedX has been confirmed as the major contributor
for the control rod friction problem from the governor cover part.
Fig. 2.10: Current versus Better Analysis for Spring Length
4. CONCLUSIONS
Following conclusions have been drawn from the experimental result and discussions.
• The governor cover main spring length variation has been found as the RedX causing control rod friction
problem.
• Design optimization in spring length is needed to set right the GreenY and reduce the rejection rate of FIP at
idling condition check point during calibration.
• It is suggested in the new design that, either GC spring length tolerance has to be reduced to 0.5mm that is
51+0.5mm or shifting the mean value to 51.5±0.5mm from 51±0.5mm, which can possibly reduce the problem
occurrence and rejection rate.
• Shainin®
has found to be simple and efficient statistical tool which can give clue about the most unsuspected
design variation also.
Currently done analysis in governor cover assembly can possibly reduce the rejection percentage from 4% and
hence, the rework rate also. However, entire fuel injection pump assembly analysis, disciplined quality manufacturing of
components, awareness about the quality management and robust measurement techniques are much essential to achieve
and sustain the lower or zero rejection rates during the calibration of the same.
5. REFERENCES
[1] Robert Bosch GmbH, Bosch Automotive Handbook (SAE International, 5th
edition, 2000).
[2] Jeroen de Mast and Joran Lokkerbol, An Analysis of the Six Sigma DMAIC Method from the Perspective of
Problem Solving, Int. J. Production Economics, Vol.139, 2012, 604 –614.
[3] Akashdeep Howladar and Denis Cavallucci, Analysing Complex Engineering Situations Through Problem Graph,
Procedia Engineering, Vol.9, 2011, 18–29.
[4] Sunil Sharma and Anuradha R Chetiya, Simplifying the Six Sigma Toolbox through Application of Shainin®
DOE Techniques, Vikalpa, Vol. 34/01, Jan-Mar 2009, 13-19.
[5] Claus Emmelmann and Juan Pablo Calderón Urbina, Analysis of the Influence of Burst-Mode Laser Ablation by
Modern Quality Tools, Physics Procedia, Vol.12, 2011, 172–181.