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Standardisation of quality and reliability tests in the auto-parts industry: a structured approach concerning thermal systems

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Abstract

In the automotive industry, first-tier suppliers play an important role, as they often establish long-term partnerships with multiple car-makers for developing and supplying complete car modules. One of the conditions underlying these partnerships is the quality and reliability of car modules. To achieve it, car-makers generally require multiple tests, often on 100% of the parts subcontracted. The number of tests required can be very high, even for modules with a relatively low level of customisation. Also, the configuration of the tests can vary significantly from one car-maker to another, even for the same test typologies. The aim of this paper is to present a decision-support tool for the standardisation of quality and reliability tests, which uses some already available information on previous tests (e.g. about their effectiveness, cost and simplicity of execution) and involves experts both from the supplier's and car-makers' staff. Test standardisation is guided by a simple procedure based on two steps: (i) grouping the tests required by different car-makers into typologies of homologous tests, with a similar protection level in terms of product and quality reliability, and (ii) determining the most appropriate configuration for each test typology. The description of the methodology is based on a real case-study concerning a worldwide supplier of thermal systems.
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Standardization of quality and reliability tests in the auto-parts industry: a
structured approach concerning thermal systems
Fiorenzo Franceschini1 and Domenico Maisano2
1 fiorenzo.franceschini@polito.it; 2 domenico.maisano@polito.it
Politecnico di Torino, DIGEP (Department of Management and Production Engineering),
Corso Duca degli Abruzzi 24, 10129, Torino (Italy)
Abstract
In the automotive industry, first-tier suppliers play an important role, as they often establish long-
term partnerships with multiple car-makers, for developing and supplying complete car modules.
One of the conditions underlying these partnerships is the quality and reliability of car modules. To
achieve it, car-makers generally require multiple tests, often on 100% of the parts subcontracted.
The number of tests required can be very high, even for modules with a relatively low level of
customization. Also, the configuration of the tests can vary significantly from a car-maker to one
other, even for the same test typologies.
The aim of this paper is presenting a decision-support tool for the standardization of quality and
reliability tests, which uses some already available information on previous tests (e.g., about their
effectiveness, cost and simplicity of execution) and involves experts both from the supplier’s and
car-makers’ staff. Test standardization is guided by a simple procedure based on two steps: (i)
grouping the tests required by different car-makers into typologies of homologous tests, with a
similar protection level in terms of product and quality reliability, and (ii) determining the most
appropriate configuration for each test typology.
The description of the methodology is based on a real case-study concerning a worldwide supplier
of thermal systems.
Keywords: Quality and reliability test, Auto-parts, First-tier supplier, Car module, Standardization, Test
effectiveness, Thermal systems.
1. Introduction and problem definition
Since several decades, outsourcing plays a strategic role in the automotive industry (Franceschini et
al. 2003). Most of car-makers tend to build long-term partnership alliances with a relatively limited
number of first-tier suppliers, who are gaining more and more responsibility in the development of
entire car modules (e.g., engines, transmissions, braking systems, seats, tyres, etc.) and their
integration in the final product (Aláez-Aller and Longás-García 2010).
This tendency, accelerated by the recent socio-economic crisis, pushed suppliers in joining forces
either through mergers, acquisitions and joint ventures, so as to establish highly specialized and
2
efficient organizations serving a large number of car-makers (Schaede, 2010, Tsu-Ming, Fan-Yun &
Kai-I, 2013).
For simplifying the design and manufacturing stage without compromising product customization,
auto-parts suppliers generally develop a relatively small number of multifunctional
modules/platforms (Minhas et al. 2011). From the perspective of car-makers, ordering a complete
module reduces the number of parts to be outsourced and thus the time of assembly, quality control
cost, labour and administrative cost.
In the after-sales service, car-makers generally collect the so-called Voice of the Customer (VoC)
(Franceschini 2002; Sireli et al. 2007; Mavridou et al., 2013), to have an indication on the customer
satisfaction with the full “package” (i.e., the final product plus additional services, such as
maintenance program, roadside assistance, etc.). This information is strategic for car-makers
oriented at developing new products or improving the existing ones according to the real customer
requirements (van Driel and Dolfsma 2009). Sharing this information with suppliers, at least those
of the most “strategic” modules, is an important issue for consolidating partnerships. From the
perspective of suppliers, this constant flow of information is essential to guide the quality
improvement of the parts subcontracted, in accordance with the philosophy of “continuous
improvement” (Delbridge and Barton 2002 ).
Another condition to reinforce the partnership between suppliers and car-makers is the quality and
reliability of modules, which can have a very strong impact on the customer’s quality perception of
the final product. For example, a survey of an Italian car-maker showed that the majority of
customer complaints, relating to city-cars, concerned the performance of the heating-ventilating-
and-air-conditioning (HVAC) unit (Bassotto et al. 2005)! For achieving reliability, car-makers
generally require several tests, which should be carried out by suppliers often on 100% of the parts
supplied (Zhiqiang, Yuejun , Xiaole, 2013).
This paper will focus on a case-study concerning quality and reliability tests on thermal systems
(e.g., electric compressors, HVAC units, radiators, etc.) produced by an important worldwide
supplier, with a plant based in Northern Italy. For reasons of confidentiality, the company will be
kept anonymous and hereafter denominated with the acronym DTS. DTS supplies a large number of
car-makers, such as Fiat, General Motors, PSA, Renault, Volkswagen, etc., and, by tradition, gives
great importance to the product reliability.
It is worth noting that a scarcely debated issue in the scientific literature is that of the great variety
of tests required by car-makers to their suppliers. This variability is twofold:
1. In terms of test typologies. The total number of test typologies (i.e., groups of tests aimed at
testing the same function/attribute) can be very high, especially for parts subject to prolonged
3
and continuous use. In addition, similar tests can be considered as important by some car-makers
and neglected by others.
2. In terms of test configurations. For tests of the same typology, parameters (e.g., number of
cycles, temperature, pressure, etc.) can vary significantly from a car-maker to one other. The
practical implication is that tests of the same typology may be more or less effective, expensive
or simple to execute, depending on the configuration requested by car-makers.
The variety of test typologies and configurations can be large even for parts, such as thermal
systems, with a relatively low level of customization. This apparent paradox is explained by the fact
that car-makers generally develop their test practices individually. This generates a certain
“affection” for the practices in use and a consequent reluctance towards the introduction of possible
changes (Pil and MacDuffie 1999). Several existing techniques and procedures can be used for
assessing the capability of suppliers to (i) perform the tests imposed by a car-maker and (ii)
manufacture parts that satisfy these tests as much as possible; one of the most popular is the
Production Part Approval Process (PPAP), developed by the Automotive Industry Action Group
(AIAG) as part of the Advanced Product Quality Planning (APQP) manual (AIAG, 2006;
Franceschini et al., 2011). On the other hand, suppliers can hardly play an active role in reducing
test variety because of the lack of unified standards defining tests univocally and thoroughly. As a
result, managing quality and reliability tests may be complicated for multiple reasons:
Need for different types of test beds, some of which dedicated to just a few tests.
Flexibility of the operators, who must be able to switch from one configuration to one other (on
single or multiple test beds) without making mistakes.
Risk of biased conclusions about the actual reliability of the parts investigated, due to the fact
that different test configurations can be more or less effective; for example, a part passing the
test by one car-maker could not pass that by another one.
Operating costs likely to grow.
The previous considerations highlight the need for reducing the variety of tests in a rational way.
The objective of this paper is the introduction of a simple standardization procedure based on two
main steps: (i) grouping the tests required by different car-makers into typologies of homologous
tests, with a similar protection level in terms of product reliability, and (ii) determining the most
reasonable and appropriate configuration for each test typology.
The proposed procedure uses the results of previous tests and the opinion of experts – i.e., engineers
and/or technicians – both from the supplier’s and the car-makers’ staff.
The remainder of this paper is organized in two sections. Sect. 2 illustrates in detail the
standardization procedure, providing an application example to reliability testing on radiators
4
produced by DTS. The concluding section summarizes the original contribution of the manuscript
and discusses the advantages and limitations of the proposed procedure.
2. Methodology
Tab. 1 summarizes the phases of the proposed procedure, which are described individually in the
following subsections. The description is based on a case-study concerning tests on radiators
supplied by DTS to four worldwide car-makers (CM1 to CM4). For reasons of confidentiality, car-
makers are kept anonymous.
Phase denomination Input Output Subjects involved
2.1 Identification of test typologies Technical specifications
concerning the tests required
by the car-makers
List of the test typologies, with
their individual configurations
A team of experts on
reliability tests from DTS
staff
2.2 Determination of the importance
level of test typologies
Questionnaires submitted to
experts
Judgements defined on a
5-level ordinal scale
Experts on reliability tests
both from the staff of
DTS and that of each car-
maker
2.3 Comparison of the alternative
configurations (for each individual
test typology)
- - -
2.3.1 Definition of judgements
relating to each configuration
Results of previous reliability
tests and questionnaires
submitted to experts
Judgements defined on 5-level
ordinal scales (concerning
effectiveness, cost, simplicity
of execution)
A team of experts on
reliability tests from DTS
staff
2.3.2 Selection of the most suitable
configuration
Judgements resulting from
phases 2.2 e 2.3.1
Selection of a configuration for
each test typology
A team of experts on
reliability tests from DTS
staff
Tab. 1. Typical phases of the test standardization procedure, specifying input/output data and subjects involved.
2.1 Identification of test typologies
One of the most delicate phases of the procedure is the classification of the tests imposed by various
car-makers into groups of homologous tests. Consistently with the definition of reliability, i.e., “the
ability of a system or component to maintain its functions/attributes under stated conditions for a
specified period of time” (O’Connor 2002), homologous tests should be focused at testing the
maintenance of similar functions/attributes (e.g., corrosion resistance, sealing, etc.). Unfortunately,
this classification is complicated by the fact that there is no standard to define the set of
functions/attributes of a generic system uniquely. We take the liberty to clarify this issue through a
similarity between the concept of measurement and that of reliability test.
A measurement is an operation for estimating an attribute of a real entity (e.g., the length of an
object), using an appropriate instrument (e.g., a tape, a calliper, a laser interferometer or an echo
sounder). Results of measurements obtained by different instruments can be compared since they
are linked to the same reference unit (e.g., in the case of length measurements, the meter). This link
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originates from the instrument calibration process, which establishes a connection between the
measurement result and the reference unit by an unbroken metrological traceability chain
(JCM200:2012 2012). Of course, the results of measurements performed using different instruments
may differ in several aspects, such as accuracy, cost, simplicity of execution, etc..
On the other hand, reliability tests can be viewed as special measurements for assessing the ability
of a component to maintain a certain function/attribute over time. Even considering the same
function/attribute, there can be different instruments (test beds) and procedures (configurations of
test parameters) to test it, as evidenced by the variety of tests suggested by different car-makers.
Unfortunately, the results of tests performed with different instruments and/or procedures are not
easy to compare for at least two reasons: (i) the difficulty in identifying the functions/attributes of a
system uniquely, and (ii) the lack of standard references for establishing the conditions in which
evaluating the maintenance of these functions/attributes.
The large variety of tests imposed by various car-makers is also reflected by their denominations: in
most cases, car-makers use acronyms or reference numbers referred to internal procedures.
A possible way to overcome these limitations (at least partially), allowing comparisons among tests
suggested by different car-makers, is to create typologies of homologous tests. This activity can be
carried out by a team of experts, consisting of engineers and/or technicians with a deep experience
and knowledge of the tests of interest.
Tests of the same typology will differ in several aspects, such as effectiveness – defined as the
ability of the test to reveal the maintenance of a certain function/attribute, in a realistic operational
context – cost, simplicity of execution, etc.. We are aware that the definition of test typologies is a
subjective operation. However, the fact that it is carried out by a team of multiple experts represents
a partial guarantee for obtaining reasonable results.
Tab. 2 lists the test typologies defined by the team of experts from DTS staff. It can be seen that test
typologies are variegated; about half of them are required by the majority of car-makers but only 9
out of 28 are shared by all of them. Also, there are several tests required by few or even individual
car-makers. Those requested by unique car-makers (highlighted in gray in Tab. 2) were not taken
into account.
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Ref. no. Test typology denomination CM1CM2 CM3 CM4
T1 Bursting test    
T2 Drain packing    
T3 Draincock    
T4 External corrosion (salt spray)    
T5 External corrosion (severe wastewater analysis)    
T6 Fluid cooler heat exchange    
T7 Functional characteristics    
T8 General characteristics    
T9 Internal cleanliness    
T10 Internal corrosion    
T11 Leak    
T12 Long life coolant resistance    
T13 Low Temperature    
T14 Performance measurement    
T15 Phys./Chem./ Environm./Mech.    
T16 Pollution    
T17 Pressure cap wear    
T18 Pressure cycle durability    
T19 Pressure resistance    
T20 Resistance to fastening dowels    
T21 Resistance to fluid attack    
T22 Resistance to gravelling    
T23 Resistance to painting    
T24 Rubber seal    
T25 Temperature endurance    
T26 Thermal cycle durability    
T27 Vacuum    
T28 Vibration durability    
Tab. 2. List of the test typologies concerning radiators manufactured by DTS, sorted alphabetically by their
denomination. Test typologies required and non-required by each of the car-makers (CM1 to CM4) are
respectively marked by the symbols “” and “”. The test typologies highlighted in grey are required by unique
car-makers and therefore will not be taken into account in the rest of the analysis.
2.2 Determination of the importance level of test typologies
The level of importance of a test typology depends on the negative effects, which may originate
from the loss of the function/attribute investigated. This judgement may change from a car-maker to
one other. For example, test typology “T11–Leak” is regarded as very important by the totality of the
car-makers, because leakage from the radiator can rapidly lead to compromising its main function
of cooling the car engine. Instead, some car-makers consider the typology “T10–Internal corrosion”
as important, while others do not.
This judgement was collected by questionnaires submitted to experts in reliability tests, both from
the DTS’ and car-makers’ staff. Experts from the car-makers were engineers and/or technicians
dealing with DTS for technical issues about the tests of interest. Judgments were collected for DTS
and each of the four car-makers separately.
To make judgments as simple as possible, it was adopted a 5-level ordinal scale (see the second
column in Tab. 3). The category N/A (not applicable) was assigned to car-makers not requiring the
test typology of interest.
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Level Importance
(of a test typology)
Effectiveness
(of a configuration)
Cost
(of a configuration)
Simplicity
(of a configuration)
1 Not at all important Not at all effective Very high cost Not at all simple
2 Low importance Low effectiveness High cost Low simplicity
3 Medium importance Medium effectiveness Medium cost Medium simplicity
4 High importance High effectiveness Low cost High simplicity
5 Very high importance Very high effectiveness Very low cost Very high simplicity
N/A Not applicable Not applicable Not applicable Not applicable
Tab. 3. Definition of the 5-level scales used for evaluating (i) the importance of a test typology and (ii) the
effectiveness, cost and simplicity of execution of the relevant test configurations.
In order to facilitate the formulation of judgments, we provided respondents with the results of a
previous Failure Mode, Effects, and Criticality Analysis – FMECA (Bouti and Kadi 1994) on the
radiator, which identified and prioritized the main failures.
It is reasonable to assume that the major test typologies are those investigating the maintenance of
functions/attributes potentially affected by the most critical failures. Tab. 4 illustrates the results of
the questionnaires for each test typology. The most important typologies at global level are those
requested by a large number of car-makers and those with relatively high importance judgements.
For each test typology, it is possible to determine the median1 level of importance:
)( i
ImedianI
~
, (1)
being Ii the importance levels assigned by experts from DTS and each of the car-makers (if
applicable). Precisely, subscript

ADTSi , where
4321 CM,CM,CM,CMA indicates
groups of experts from the subset of car-makers requiring the test typology of interest. E.g., the test
typology T1 is required by CM3 and CM4, but not by CM1 and CM2, therefore A={CM3, CM4}.
Test
ref. no.
I
I
~
DTS CM1CM2CM3CM4
T1 4 N/A N/A 5 4 4
T3 1 1 2 1 N/A 1
T4 3 5 3 4 4 4
T5 3 4 4 5 5 4
T9 2 1 1 3 1 1
T10 1 4 3 1 1 1
T11 4 4 5 5 5 5
T13 3 2 1 2 N/A 2
T14 5 5 5 3 2 5
T15 1 N/A N/A 1 1 1
T18 5 5 5 4 4 5
T23 3 N/A N/A 2 4 3
T26 5 4 5 5 5 5
T27 4 5 N/A N/A 3 4
T28 3 5 5 5 2 5
Tab. 4. Judgements of experts from DTS and four car-makers (CM1 to CM4) on the importance of the test
typologies in Tab. 2.
I
~
is the median the importance values relating to each test typology.
1 Using the average value as a central tendency indicator may be inappropriate since Ii values are defined on an ordinal
scale (Stevens 1946).
8
~
will be used in the next stages of the procedure (see the last column of Tab. 4). For simplicity, it
was assumed that judgements by the groups of experts from DTS and each of the car-makers have
the same relevance.
It can be noticed that, even for tests of the same typology, there can be significant differences
between the judgements by different respondents. This is probably the result of their specific
experience on previous tests.
2.3 Comparison of the alternative configurations
2.3.1 Definition of judgments relating to each configuration
In this phase, the attention is focussed on the configurations imposed by different car-makers for
each of the test typologies selected in Sect. 2.1. For the purpose of example, Tab. 5 reports the
configurations concerning to the test typologies “T1–Bursting test” and “T26–Thermal cycle
durability”.
Test typ. CM1 CM2CM3CM4
T1 N/A N/A Fill radiator with test fluid;
Increase pressure at
4bar/min, up to 3.5bar;
Hold this pressure for 30s.
Pressure (1.5*inlet pressure);
Increase pressure at 0.1bar/s, up to
3.5bar;
Hold this pressure for 300s;
Ambient temperature: 23±5 °C.
T26 No. of cycles: 1000;
Coolant temperature: from 0°C
to 100±2°C;
Pressure: 130±10kPa.
No. of cycles: 7000;
50% water 50% coolant as
medium;
Coolant temperature: from
20 °C (30 s max) to 90 °C
(2 min) and to 20°C (2 min)
with flow rate 40 l/min.
No. of cycles: 1000;
Cycle rate: 7 cycles/h;
Coolant temperature: from
-30 °C to 100 °C;
Pressure: 1.3 bar.
No. of cycles: 2500;
Pre-conditioning: 2 h at 20 °C;
Ambient temperature: 23±5 °C;
High temperature of coolant:
113 °C;
Low temperature of coolant: 23 °C;
Switch duration between high and
low temperature phase: 5 s;
Tab. 5. Configurations of the test parameters for test typologies “T1–Bursting test” and “T26–Thermal cycle
durability”, from the perspective of four car-makers (CM1 to CM4).
For each of these configurations, different aspects were investigated. The first one is the test’s level
of effectiveness in detecting possible abnormalities of the part in maintaining its functions/attributes.
The survey was carried out by submitting questionnaires to a team of DTS experts, already involved
in the activities described in Sects. 2.1 and 2.2.
Again, judgements were defined on a 5-level ordinal scale (see the third column in Tab. 3). In
general, it was assumed that the most effective tests tend to be severe/conservative, generating a
significant amount of “false positives”, i.e., parts that did not pass the test, while being functionally
acceptable (in statistical terms, a greater type-I error). Therefore, very high levels of effectiveness
are justified only for test typologies of high importance, for which it can be reasonable to minimize
the probability of “false negatives” (in statistical terms, the type-II error), i.e., parts with
deteriorated function(s)/attribute(s), which passed the test. Tab. 6 shows the resulting judgements
9
(see the column “Eff”, for each car-maker).
Respondents were subsequently asked to judge the level of cost and simplicity of execution of each
configuration. Cost, which generally depends on test time and hourly cost of equipment/operator(s),
is quite simple to estimate. On the other hand, simplicity – which may depend on the complexity of
test set-up, risk of human error, operators’ degree of familiarity with the equipment, etc. – is more
difficult to quantify. These judgements were defined on two 5-level scales (see the fourth and fifth
column in Tab. 3). The scale related to cost is “reversed”, so that low and high levels have a
negative and positive connotation respectively. Tab. 6 shows the resulting judgements (see the
columns “Cost” and “Simpl” for each car-maker).
Test
I
~ CM1
CM2
CM3
CM4
Selected
Ref. No. Eff Cost Simpl Eff Cost Simpl Eff Cost Simpl Eff Cost Simpl config.
T1 4 N/A N/A N/A N/A N/A N/A 3 2 2 5 3 2 CM4
T3 1 3 1 3 2 2 2 1 3 3 N/A N/A N/A CM3
T4 4 3 2 2 4 4 2 5 4 2 5 2 2 CM2
T5 4 4 1 2 5 2 1 5 1 2 4 1 1 CM1
T9 1 3 2 1 1 1 3 1 1 1 3 3 1 CM2
T10 1 4 5 5 2 5 5 1 3 4 2 5 5 CM3
T11 5 3 4 3 5 4 4 5 5 5 4 3 3 CM3
T13 2 1 2 1 1 1 1 1 1 2 N/A N/A N/A CM2
(
1
)
T14 5 4 4 2 2 3 2 3 2 4 3 2 4 CM1
(
1
)
T15 1 N/A N/A N/A N/A N/A N/A 1 3 4 3 2 1 CM3
T18 5 4 5 5 3 4 5 4 5 3 4 5 4 CM1
(
1
)
T23 3 N/A N/A N/A N/A N/A N/A 5 2 3 4 1 1 CM4
T26 5 3 3 4 5 4 5 4 4 3 5 3 5 CM2
T27 4 2 1 1 N/A N/A N/A N/A N/A N/A 5 1 1 CM4
T28 5 4 1 1 4 1 2 4 2 2 3 1 1 CM3
(
1
)
(1) In this case, Effi<I
~for all the alternative configurations; as a result, Eq. 2 can not be applied. The selected configuration is the one
with max(Effi).
Tab. 6. Judgments of experts from DTS about the degree of effectiveness (Eff), cost and simplicity of execution
(Simpl) of the test configurations proposed by any of the car-makers (CM1 to CM4). The last column shows the
configuration selected according to the procedure described in Sect. 2.3.2.
2.3.2 Selection of the most suitable configuration
Among the possible configurations, the “best” is selected according to the procedure illustrated in
the flowchart in Fig. 1.
As shown, in the case there are two (or more) configurations that satisfy the condition
min(Effi | Effi
~
), (2)
being iA, i.e. the subset of car-makers requiring the test typology of interest, the selection
continues by applying a lexicographic order based on cost and simplicity of execution. In the
unlikely event of a further tie, the final decision would be determined manually by the team of
expert.
10
Identify the configuration(s) with
Collection of judgements concerning the
alternative test configurations
NO
YES
I
~
|min
ii
EffEff
Is the solution univocal (i.e., no ties)?
NO
YES Is the solution univocal (i.e., no ties)?
Among the joint winners, identify
the one(s) with max(Simpl
i
)
NO YES Is the solution univocal (i.e., no ties)?
Manual choice of the best
configuration by the team of experts
End
Among the joint winners, identify
the one(s) with max(Cost
i
)
Fig. 1. Flowchart depicting the procedure for selecting the “best” configuration, for a certain test typology.
The last column in Tab. 5 reports the configurations selected applying the previous procedure.
For the purpose of example, as regards T9, two are the configurations satisfying Eq. 2: CM2 and
CM3. Since these two alternatives have the same cost level (i.e., 1), the selection is determined by
simplicity of execution, which is higher for CM2 (i.e., 3) with respect to CM3 (i.e., 1).
The logic of selection seen above is based on several assumptions:
The best configuration is not defined “from scratch”, instead it is selected among those imposed
by the car-makers. Defining the parameters of a test is actually a very delicate operation because
of the multiplicity of factors (e.g. as regards radiator: number of cycles, temperature, pressure,
composition of coolant, etc.), which may affect its effectiveness. These factors and their possible
interactions should be examined rigorously by experimental plans (Box et al. 1978). It was
assumed that the test configurations were defined by the car-makers following this approach.
It was assumed that test effectiveness and severity, i.e., the probability to generate “false
positives”, go hand in hand. The fact that the selected configuration should have a level of
effectiveness as close as possible to that of
~
prevents from selecting (i) tests that are too severe
with respect to their relatively low importance, or (ii) tests that are not very effective, despite
their relatively high importance. The authors are aware that, in some cases, this assumption may
not be realistic. For example, there could exist very effective configurations with relatively low
incidence of “false positives”. When, on the basis of its experience, the team of expert feels that
this hypothesis should be relaxed, one could select the configuration satisfying the condition:
11
max(Effi). (3)
Also, Eq. 3 could be used when there is no configuration satisfying Eq. 2, because Effi <
~
Ai (see tests T13, T14, T18 and T28 in Tab. 6).
Among the three types of judgements (effectiveness, cost, simplicity) related to the
configurations, it was implicitly assumed the ordering Eff > Cost > Simpl (symbol “>” means
“preferred to”). However, the technique based on lexicographic ordering could be replaced by
more complex techniques, such as Multi-Criteria Decision-Making (MCDM) methods
(Franceschini et al. 2007; Köksalan et al. 2011).
3. Final remarks
This work focused on the problem of the standardization of reliability tests for auto-parts suppliers.
This problem originates from (at least) two reasons: (i) in general there are no unified standards
defining exhaustive and univocal sets of tests, and (ii) any car-maker requires a set of tests, with ad
hoc configurations deriving from their specific experience and work practices.
The proposed procedure is a first attempt to address this problem in a simple and economic way. A
more elegant and sophisticated approach would be that of designing new optimal configurations, in
terms of effectiveness, through rigorous design of experiments (DoE). Unfortunately, the price to
pay would be too high because of the large number of experiments required. On the contrary, the
proposed technique exploits a large amount of information already available (i.e., results of
previous tests) and the expertise of engineers and/or technicians from suppliers and car-makers.
The procedure was applied in DTS on a number of thermal systems, such as radiator, HTVC, heater
core, etc., focussing on the test configurations imposed by several worldwide car-makers. The
example presented in this paper illustrated the philosophy behind the procedure.
Thanks to its simplicity and low cost, the procedure was judged by DTS staff as very useful and
easy to implement. For this reason, it will be extended to other components manufactured by the
company. The proposed methodology can be considered as a decision-support tool for rationalizing
the management of reliability tests for auto-parts suppliers, which is complementary to other
procedures, such as the AIAG’s APQP/PPAP (AIAG, 2006).
The proposed approach has some limitations, summarized as follows:
Test standardization is internal with respect to a specific supplier, since it depends on the degree
of expertise of engineers/technicians, the information regarding previous tests, the available
equipment (test beds) and the variety of tests imposed by car-makers. As a consequence, the
application of the procedure to different suppliers could lead to different results, even
considering homologous parts.
12
Several phases of the procedure are subjective, such as the interpretation of the results of
previous tests or the formulation of judgments. To avoid disputes, these phases should be carried
out in a transparent manner, involving technical staff with a certain expertise on reliability tests,
both from suppliers and car-makers.
The procedure can be applied to auto-parts with a relatively low degree of customization, where
comparing tests related to similar product models is not hasty.
Standardized tests may be rejected by some car-makers, who are “attached” to their
configurations. However, the results of the proposed procedure may be used for persuading the
most reluctant car-makers to accept standardized tests, as they will probably be more effective,
cheaper and simpler than other ones.
Acknowledgements
Authors gratefully acknowledge the contribution of Giridharan Sundar and the company staff of DTS, in
developing the proposed methodology.
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