Directory UMM :Data Elmu:jurnal:I:International Journal of Production Economics:Vol67.Issue2.Sept2000:

Int. J. Production Economics 67 (2000) 113}133

Selecting quality-based programmes in small "rms:
A comparison between the fuzzy linguistic approach
and the analytic hierarchy process
Giuliano Noci, Giovanni Toletti*
Department of Economics and Production, Politecnico di Milano, Piazza L. Da Vinci 32, 20133 Milan, Italy
Received 11 March 1998; accepted 27 October 1999

Abstract
Over the last few years, a large number of "rms have implemented TQM programmes in order to introduce e!ective
quality systems and to achieve high-quality products. However, many empirical studies demonstrate that most of the
adopted quality-based programmes did not improve the small "rms' competitiveness and pro"tability. The reasons for
such failures are manifold. Among them, the lack of an operating tool able to support managers in the identi"cation of
quality-based priorities particularly a%icts small "rms. In this paper the authors attempt to suggest an integrated
decisional tool. In particular, according to main characteristics of quality-based programmes, they analyse how MADM
techniques should be used to identify quality-based priorities. ( 2000 Published by Elsevier Science B.V. All rights
reserved.
Keywords: Total quality management; Fuzzy linguistic approach; Analytic hierarchy process; Small "rms; Quality investments'
evaluation

1. Introduction
Over the last few years, a large number of "rms
have implemented TQM programmes in order to
introduce e!ective quality systems and to achieve
high-quality products [1,2]. However, many empirical studies demonstrate that most of the adopted
quality-based programmes did not improve the
small "rms' competitiveness and pro"tability [3}7].
In our opinion such a failure depends on two sets
of reasons:

* Corresponding author. Tel.: #39-02-23992800; fax: #3902-23992720.
E-mail address: giovanni.toletti@polimit.it (G. Toletti)

1. distinctive features characterising small "rms;
2. general problems a!ecting all "rms in the assessment of quality-based programmes.
1.1. Distinctive features characterising small xrms
1. In order to implement TQM initiatives, participation of all the supply value chain actors is
fundamental. In this perspective small and medium
"rms can have some disadvantages because of their
low propensity to collaborate with suppliers and/or

customers. Furthermore, the low bargaining power
characterising small and medium companies does
not allow them to transfer to suppliers and/or
customers the costs related to the adoption of
quality-based investments.

0925-5273/00/$ - see front matter ( 2000 Published by Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 5 - 5 2 7 3 ( 9 9 ) 0 0 1 3 1 - 0

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G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133

2. Small "rms are characterised by some distinctive features. On one hand, they can facilitate the
implementation of quality-based programmes and
on the other, can make it more di$cult. Indeed,
small "rms take advantage, with respect to large
companies, of their higher production #exibility,
but, at the same time, they are subjected to some
peculiar constraints such as limited competencies,

resources, time and data [8,9], reducing their capability in implementing total quality management
programmes.
1.2. Problems awecting all xrms
1. It is not only di$cult to distinguish between
`what is qualitya and `what is nota, but also to
understand the meaning of `gooda and `bada quality [6,10,11]. Such ambiguities make the identi"cation of the quality targets and the measurement of
the achieved performance very complex procedures. Hence, according to the goal setting theory
[12,13], we would say that, in order to achieve the
planned objectives, managers need to identify target indicators that are easy measurable and that are
closely related to results.
2. At the state of the art, there is a lack in
operating tools able to support managers throughout the decisional process (see Fig. 1) [14}16].
Whereas, speci"c and e!ective models aimed
only at evaluating and selecting the most promising

quality-based investment [16}23] do exist, it is
a fact that the pre-selection of the feasible qualitybased alternatives is rather neglected. The problem
is all the more important since the existing models
require a lot of data and a time consuming analysis.
Hence, they are useful only to compare a few quality-related investments, thus requiring a pre-selection of the most interesting alternatives. This a!ects

all the companies, but particularly concerns small
"rms, these being a!ected by the above enlightened
problems. Such "rms are unable to deal with the
wide number of quality investment alternatives
to be considered and, for this reason, they tend
to imitate the quality initiatives of large companies, without properly considering their own
peculiarities.
In the light of these issues, the aim of this paper is
to provide small "rms' managers with an operating
tool able to support the identi"cation of the quality-based alternatives that must be evaluated in
detail with speci"c tools. Hence a "rm, with its
limited resources, would have to consistently
analyse only few options.
Hence, the remainder of the paper consists of
four major sections: (i) Section 2 analyses the problem of the evaluation of TQM investments in
SMEs, identifying a process that has to be followed
for their evaluation; (ii) Section 3 proposes a taxonomy of the data that have to be considered or
collected; (iii) Section 4 describes how it is possible
to rank di!erent quality investments typologies implementing two multi-attribute decision-making
techniques (i.e. fuzzy linguistic approach (FLA) and

analytic hierarchy process (AHP); (iv) and "nally,
Section 5 applies to the real situation in which the
two MADM techniques proposed in order to
evaluate their performance.

2. The evaluation of TQM investments in SMES

Fig. 1. Decisional process for the selection of the priorities in
quality management.

Small "rms too are often unable to identify correctly the quality investment, simply because they
do not have the capability to consider and to evaluate all the di!erent possibilities [16,21]. Indeed,
a large number of such possibilities do exist, whereas
small companies do not have su$cient time, nor
the human and "nancial resources to implement

G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133

a complete evaluation of each of these. Hence, it
would be particularly important to allow SMEs

managers a quick screening of the most promising
alternatives. In this respect we suggest a method for
ranking a set of feasible quality investments typologies in order to enlighten those that would have to
be analysed more carefully (see Fig. 2). Hence, the
"rst step of the managers' decisional process (i.e. the
selection of the possible investment alternatives) is
greatly simpli"ed because their only need is to
identify the speci"c investments to evaluate among
the categories the one with the best rank.
The suggested method consists of three steps
providing managers with an operating tool which
ranks di!erent quality investments:
1. Dexnition of a taxonomy of quality-based investments aimed at properly identifying a set of
alternatives to be evaluated.
2. Identixcation of the "rm's exogenous and endogenous quality priorities. The former are those
priorities exogenous to the "rm's quality system,
depending for instance on the environmental
context, whereas the latter are those priorities
closely related to a "rm's quality de"ciencies. It
is a matter of fact indeed that a "rm can be

in#uenced in its capital budgeting process both
by the competitive context in which it operates
and by the failures of its quality system. Thus, it
is "rst necessary to identify some typical competitive contexts that would allow us to de"ne

115

the di!erent exogenous quality priorities of
a "rm and, then, to model a "rm's quality system
in order to show the possible reasons for the
endogenous ones.
3. Ranking of the di!erent typologies of investments according to the taxonomy of qualitybased investments just introduced and to the
corporate quality priorities that have been identi"ed.
According to this framework, a software tool
aimed at supporting SMEs managers in identifying
their company's quality priorities, has been developed. Such a tool has three main characteristics:
f it has embedded in itself a taxonomy of qualitybased investments, thus limiting the alternatives
to be analysed;
f it has a framework guiding SMEs managers in
the self-evaluation of their "rms exogenous and

endogenous quality priorities;
f it gives managers the possibility of using both
a software (developed by ourselves) allowing the
ranking of TQM investments according to the
FLA, and a software (chosen among the many
available [24]) allowing the same ranking
through the utilisation of the AHP.
It is worth noting that such a software tool is not
able to point out the speci"c investments that
a "rm has to introduce, but it can suggest which
typology of investments has to be more carefully

Fig. 2. The selection of the best quality investments.

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G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133

evaluated, thus providing SMEs managers with
a pre-selection of alternatives to evaluate and

in the process simplify the managers' selection
process.

3. The relevant data
Once the three-step method that has to be followed for the evaluation of TQM investments is
introduced, it is necessary to identify the relevant
data needed to implement it. According to the steps
just described, three main categories of data have to
be considered. The "rst concerns the feasible quality-based investments, whereas the others are
related to the identi"cation of exogenous and
endogenous quality priorities. In order to achieve
this information we have to de"ne:
(1) the set of feasible quality investments;
(2) the competitive context in which the "rm operates;
(3) the "rm's quality system.
3.1. The set of feasible quality investments
Many approaches for classifying quality-based
investments have been suggested. Among them, we
consider one based on two critical variables [16]:
(i) the type of problem under evaluation and (ii) the

"nancial cash outlay needed for the implementation of each investment.
The type of problem under evaluation re#ects the
area of the corporate quality system a!ected by the
investment, making easier the identi"cation of the
speci"c performance in#uenced by each alternative.
It is possible to distinguish between local and
systemic investments: the former aimed at improving
the performance of a speci"c activity or organisational unit of the quality system, while the latter
a!ects the whole corporate quality system. Among
the local investments we have
f
f
f
f
f

engineering or marketing;
quality of supplies;
inspection (both inbound and on line/"nal);
technology;

training.

In the area of systemic investments instead there
are
f investments in product certi"cation;
f investments in quality system certi"cation.
The "nancial cash outlay points out the "nancial
risk of each investment allowing SMEs managers
to choose carefully the approaches more correct for
their evaluation.
It must be noted that, for the objective of this
paper, it is not important to distinguish between
product and process certi"cation, but it is necessary
to separate inbound inspection from on line/"nal
inspection. Hence, we will consider seven investments including the merger of the certi"cation ones
o!set by the separation of inbound and on line/
"nal inspection.
3.2. The competitive context
In order to identify a "rm's exogenous quality
priorities, we have to de"ne the competitive context
in which it operates. To this aim we have to characterise a "rm through both its environmental context and its con"guration.
3.2.1. The environmental context
The environmental context in which a "rm operates can be described by means of three classes of
variables [16]: customer's bargaining power, binding force of regulations and "rm's bargaining
power vs. suppliers.
According to the "rst two variables, we de"ne
two di!erent environments:
(i) compulsory environments (customers' high bargaining power and/or presence of binding force
of regulations): the "rm is compelled to undertake the requested investments just to remain
in the market;
(ii) free environments (customers' low bargaining
power and no binding force of regulations): the
"rm is free to undertake any investment or not,
depending on other motivations.
At a second level, the environmental context can
be described by the "rm's bargaining power vs.
suppliers. This variable clari"es whether a "rm can
(i) induce suppliers to implement programmes

G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133

which allow the "rm to save costs (for instance
those related to stock maintenance) and (ii) execute
design and engineering jointly with suppliers.
In the light of these issues and of main results of
state of the art literature [16] it is possible to
identify under which conditions some qualitybased initiatives appear preferable.
f In compulsory environments all the investments
required by the market must be implemented. This
is, for instance, the case where product and/or
quality system certi"cation is required to compete in some speci"c markets (an example being
the English one). In small "rms, the achievement
of the certi"cation of the quality system could
imply some disadvantages since it decreases the
#exibility of operating procedures which is one
of the most important points of strength of small
companies. Nevertheless, the losses caused by
the impossibility to enter a speci"c market could
overcome those due to lower #exibility.
f In free environments, engineering and/or marketing
investments are a priority. These interventions
have three aims: (i) the proper identi"cation of the
needs of both real and potential customers, (ii) the
correct translation of these needs into project
speci"cations, and, "nally, (iii) product engineering according to the de"ned speci"cations.
f In contexts characterised by a xrm's high bargaining power vs. suppliers, investments in quality of
supplies can be successfully implemented and, at
the same time, engineering investments will be
undertaken. The former can enable the "rm to
reduce (or completely avoid) costs and loss of
time concerned with the purchase of defective
raw materials and/or semimanufactured products. The latter allows the "rm to improve and to
speed up the production process, for instance
through codesign and guarantee of inputs quality.
f In contexts characterised by a xrm's low bargaining power vs. suppliers, testing and inspection
investments are very important. Such investments
limit the absorption of "nancial resources due to
working defective raw materials and components
and, at the same time, improve the company's
economic performance by reducing both (i) the
cost for work in progress/products to be rejected
and reworked and (ii) the cost of lost sales or

117

costs related to customers' complaints due to the
delivery of defective products.
3.2.2. The xrm+s conxguration
The "rm's con"guration has been described in
terms of [16]: managers' competence, level of product quality performed by the "rm with respect to
the competitors' standard, and turbulence of the
market.
Considerations about these variables allow us to
identify under which conditions each quality-based
investment is more suitable.
f In xrms characterised by a low management competence, the adoption of training investments is
a priority. Indeed, successful implementation of
quality-based investments requires a considerable managerial e!ort. For this reason, the introduction of further initiatives is not wise until
managers have gained appropriate skills.
f Firms that achieve a product quality consistent
with competitors should introduce engineering
and/or marketing investments in order to better
satisfy customers' needs and make an increase of
market's share possible.
f Firms which realise low-quality products should
introduce engineering and/or marketing investments, aimed at precisely identifying customers'
expectations.
f In xrms characterised by a high rate of defective
products the adoption of investments in training
(for improving employees' skills), in inspection
(for preventing the use of defective raw materials
and the sale of defective products), and in technology (for obtaining more simply, faster and with
less defects the desired outputs) is dominant.
f In turbulent contexts, investments requiring a small
cash outlay represent a suitable option. In such
markets, the frequent changes in customers' expectations remarkably reduce the life cycle of
both products and equipment. Therefore, these
changes require a continuous innovation of
a "rm's processes and outputs. For these reasons
the reduction of such expenditures could represent a suitable solution.
f In turbulent markets engineering investments can
be considered as a priority. Such investments
make the production process e$cient and

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G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133

#exible as well; thus, they allow the "rm to react
quickly to changes in customer's needs.
3.2.3. The typical contexts
By referring to the six variables describing
a "rm's environmental context and its internal con"guration we can identify some typical contexts. In
this perspective, our analysis can be articulated at
two di!erent levels.
At a "rst level, we underline that: (i) "rms working in compulsory environments have to introduce
all the investments requested by the market and (ii)
non-quality-oriented "rms (i.e. companies which
have not been paying attention to quality systems
and, hence, are not likely to have high-quality competence) have to "rst adopt training investments
before they can implement TQM programmes.
At a second level, in free environments (i.e. markets where binding force of regulations does not
exist), we introduce eight di!erent contexts (see
Table 1) by combining the "rm's bargaining power
vs. suppliers, relative level of product quality with
respect to the competitors' one and market turbulence, in order to identify main investment priorities in each of them. Naturally, the identi"cation of
these priorities is connected to the choice of a speci"c evaluation technique and, for this reason, we will
delay the related analysis in Section 4.
3.3. The quality system
The modelling of the corporate quality system is
requested in order to identify the endogenous quality priorities of the "rm. In this respect we refer to
a state-of-the-art model [14] which de"nes few
subsystems sequentially linked and characterised

by speci"c parameters describing the contribution
of each subsystem to the company's overall quality
performance.
Subsystem 1: Design and engineering. The activities associated with this subsystem can be grouped
into two main categories: (i) determining customers' expectations and (ii) translating such
expectations in to product speci"cations.
Subsystem 2: Testing of raw materials and inbound
inspection. This subsystem is characterised by a "rst
phase, of testing raw materials and semimanufactured products purchased by the "rm, and by a second one, of continuous, or periodic inspection of
the work in process.
Subsystem 3: Process and xnal inspection/testing.
This subsystem is concerned not only with the
company's operations (in particular, it includes also
on-line inspection), but also with the "nal inspection of the "nished products and the testing needed
before introducing them on the market.
Each subsystem is described in terms of physical
variables expressing the quality performance. They
are related to the input, the `statea (i.e. main activities carried out to improve the company's quality
performance) and the output of the subsystem.
Among these variables we are interested in the
following "ve that are independent:
d : representing the quality of the design;
$
d : representing the defectiveness of raw materials;
&
e : representing the e!ectiveness in the identi"ca1
tion of incoming defective components;
d : representing the percentage of defective prod1
ucts achieved in the production department on
account of labour mistakes and equipment
faults;

Table 1
The identi"ed contexts
Firm's bargaining power vs. suppliers
High
Market turbulence

Product quality vs the competitors' one

High
Low

Low
Market turbulence

High

Low

High

Low

2
4

1
3

6
8

5
7

G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133

119

e : representing, at the same time, the e!ectiveness
2
in the on-line identi"cation of defective items
and the e!ectiveness in identifying defective
"nal products.

In this section we will describe how it is possible
to rank the quality investments utilising these two
methods.

The analysis of the performance achieved by the
"rm in each of these parameters could allow us to
identify its endogenous priorities of investment
through:

4.1. TQM investments evaluation with the fuzzy
linguistic approach

f a benchmark (when possible) with the performances achieved by the main competitors on the
same variables aimed at establishing the weakness of the "rm;
f a comparison among the "ve independent variables aimed at ranking the urgency of speci"c
quality investments.

4. Multi-attribute decision-making techniques
The models describing the set of investment alternatives, the competitive context in which a "rm
operates and its quality system represent the basis
of a tool using multi-attribute decision-making
(MADM) techniques in order to achieve a rank of
di!erent investments alternatives.
The choice to utilise such methods is due to
the consideration that conventional "nancial techniques (i.e. discounted cash #ows, or DCF techniques), being incapable of considering those
bene"ts that are di$cult to measure in monetary
terms (i.e. intangible bene"ts), often fail to assess
the e!ectiveness of quality investments and hence
do not correctly support the manager's decision
making [25]. Therefore, various `non-conventionala techniques for the appraisal and selection of
investments have been suggested and can be
grouped into two major categories: modi"ed DCF
and MADM techniques [26]. This article focuses
on the second, because it is our wish to make the
use of linguistic assessments in the investments
evaluation possible.
Among the MADM methods proposed in the
literature, we have considered both the FLA and
the AHP in order to compare the di!erent results
that can be achieved with such techniques
[25,27}40,42,43].

At this level we aim at implementing the FLA
(see Appendix A for a brief general description of
the FLA) according to the model suggested in
Section 3.
4.1.1. The competitive context
As we have seen the evaluation of the competitive context allows managers to determine the
exogenous priorities of a "rm. In operating terms it
requires the decision maker to assess the importance of the seven investments previously described
in each of the eight free contexts.
Hence, we propose an assessment scale of "ve
levels that, even if originally suggested by Liang
and Wang [37] for robot selection, appears suitable
for a wide range of decisional problems (for
instance Rangone and Azzone used it for measuring manufacturing competence). It discriminates
among
f
f
f
f
f

very low priority;
low priority;
medium priority;
high priority;
very high priority.

Such a scale allows us to de"ne the priority of each
investment according to the considerations
previously enlightened (as seen in Table 2).
Now, as prescribed by the FLA, we introduce the
fuzzy numbers corresponding to the assessment
scale mentioned above [37]:
Very low
priority:
VL(0; 0; 0.2)

G

1!5x, 0)x)0.2,
l (x)"
VL
0,
otherwise,

G

5x,

Low priority:
L(0; 0.2; 0.4)

0)x)0.2,

l (x)" 2!5x, 0.2)x)0.4,
L
0,
otherwise,

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G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133

Table 2
The priority of each investment within di!erent contexts
Context
Context 1
Firm's HIGH bargaining power vs. suppliers; HIGH level of
product quality in regard to the competitors' one; LOW turbulence
of the market.

Context 2
Firm's HIGH bargaining power vs. suppliers; HIGH level of
product quality in regard to the competitors' one; HIGH
turbulence of the market.

Context 3
Firm's HIGH bargaining power vs. suppliers; LOW level of
product quality in regard to the competitors' one; LOW
turbulence of the market.

Context 4
Firm's HIGH bargaining power vs. suppliers; LOW level of
product quality in regard to the competitors' one; HIGH
turbulence of the market.

Context 5
Firm's LOW bargaining power vs. suppliers; HIGH level of
product quality in regard to the competitors' one; LOW
turbulence of the market.

Context 6
Firm's LOW bargaining power vs. suppliers; HIGH level of
product quality in regard to the competitors' one; HIGH
turbulence of the market.

Suggested investments

PRIORITY

In
In
In
In
In
In
In

engineering and/or marketing
quality of supplies
certi"cation
inbound inspection
on line/"nal inspection
technology
training

HIGH
HIGH
HIGH
LOW
MEDIUM
VERY LOW
VERY LOW

In
In
In
In
In
In
In

engineering and/or marketing
quality of supplies
certi"cation
inbound inspection
on line/"nal inspection
technology
training

VERY HIGH
VERY HIGH
LOW
LOW
MEDIUM
HIGH
HIGH

In
In
In
In
In
In
In

engineering and/or marketing
quality of supplies
certi"cation
inbound inspection
on line/"nal inspection
technology
training

LOW
HIGH
LOW
MEDIUM
MEDIUM
VERY HIGH
HIGH

In
In
In
In
In
In
In

engineering and/or marketing
quality of supplies
certi"cation
inbound inspection
on line/"nal inspection
technology
training

VERY HIGH
VERY HIGH
MEDIUM
MEDIUM
MEDIUM
HIGH
VERY HIGH

In
In
In
In
In
In
In

engineering and/or marketing
quality of supplies
certi"cation
inbound inspection
on line/"nal inspection
technology
training

MEDIUM
HIGH
VERY LOW
VERY HIGH
HIGH
VERY LOW
VERY LOW

In
In
In
In
In
In
In

engineering and/or marketing
quality of supplies
certi"cation
inbound inspection
on line/"nal inspection
technology
training

VERY HIGH
VERY LOW
HIGH
VERY HIGH
HIGH
HIGH
HIGH

G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133

121

Table 2 (contined)
Context
Context 7
Firm's LOW bargaining power vs. suppliers; LOW level of
product quality in regard to the competitors' one; LOW
turbulence of the market.

Context 8
Firm's LOW bargaining power vs. suppliers; LOW level of
product quality in regard to the competitors' one; HIGH
turbulence of the market.

Medium
priority:
M(0.3;
0.5; 0.7)
High
priority:
H(0.6; 0.8; 1)
Very high
priority:
VH(0.8; 1; 1)

G
G

5x!3/2, 0.3)x)0.5,

l (x)" 7/2!5x, 0.5)x)0.7,
M
5x,
otherwise,
5x!3, 0.6)x)0.8,

l (x)" 5!5x, 0.8)x)1,
H
0,
otherwise,

G

5x!4, 0.8)x)1,
l (x)"
VH
0,
otherwise.

4.1.2. The quality system
Once the exogenous priorities of a "rm is de"ned,
it is necessary to establish the endogenous ones, in
order to evaluate correctly the di!erent quality
investments alternatives.To this aim we need to
undertake four steps:
(i) we need to evaluate the quality-based performances achieved by the "rm with respect to the
"ve variables describing its quality system;
(ii) we have to identify the degree of priority attributed by managers to such variables for
shareholders value creation;

Suggested investments

PRIORITY

In
In
In
In
In
In
In

engineering and/or marketing
quality of supplies
certi"cation
inbound inspection
on line/"nal inspection
technology
training

LOW
VERY LOW
VERY LOW
HIGH
MEDIUM
VERY HIGH
HIGH

In
In
In
In
In
In
In

engineering and/or marketing
quality of supplies
certi"cation
inbound inspection
on line/"nal inspection
technology
training

VERY HIGH
VERY LOW
VERY LOW
VERY HIGH
HIGH
HIGH
HIGH

(iii) we have to establish the expected impact of
di!erent typologies of quality-based investments on the identi"ed quality-related performances;
(iv) we have to determine the endogenous priority
of each investment through a judgement expressed in the same way as that utilised to
describe the exogenous priorities.
In this perspective, we identify:
f "ve types of assessments measuring how di!erent
quality investments can in#uence the performance achieved by the "rm on speci"c variables:
very low in#uence, low in#uence, medium in#uence, high in#uence and very high in#uence;
f "ve categories of weights indicating the importance of each variable referring to the achievement of a "rm's planned results: not important,
little important, moderately important, important and very important.
It is necessary to underline that, whereas the judgements concerning the relations between investments and variables can be established once and for
all, the importance of each variable with respect to
the goals to achieve has to be always rede"ned
depending on the speci"c quality situation of the
"rm analysed.

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G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133

In particular, we think that the investments able
to modify some of the "ve variables describing
a "rm quality system, together with their relative
in#uence are those described in Table 3.
The scale utilised to translate the in#uence of
each investment is the same as previously suggested, whereas we propose a di!erent scale in order to
evaluate the weights describing the consistency of
each quality performance in relation to the a "rm's
overall objective [37]:
Not
important:
NI(0; 0; 0.3)

G

1!10/3x, 0)x)0.3,
l (x)"
NI
0,
otherwise,

G
G
G

10/3x,
0)x)0.3,
Little
l (x)" 5/2!5x, 0.3)x)0.5,
important:
LI
LI(0; 0.3; 0.5)
0,
otherwise,
Moderately
10/3x!2/3, 0.2)x)0.5,
important:
l (x)" 8/3!10/3x, 0.5)x)0.8,
MI
MI
0,
otherwise,
(0.2; 0.5; 0.8)
Important:
I(0.5; 0.7; 1)

5x!5/2,

0.5)x)0.7,

l (x)" 10/3!10/3x, 0.7)x)1,
H
0,
otherwise,

Very
important:
VI(0.7; 1; 1)

G

10/3x!7/3, 0.7)x)1,
l (x)"
VI
0,
otherwise.

The fuzzy numbers describing the investments
evaluation, deriving from this phase of the selection
process, are determined by the sum of the fuzzy
numbers obtained multiplying the relative in#uence of each investment in improving a speci"c
variable by the corresponding weight of that variable in achieving a "rm's quality goals.
For instance, let us consider the evaluation of the
investment in technology in a hypothetical "rm X.
We suppose that, for the management of X, the
weights describing the importance of the "ve quality variables previously introduced are these: d ,
&
little important; e , little important; d , important;
1
1
e , important; d , moderately important. In such
2
$
a situation the fuzzy number describing the importance of the technology investment can be achieved
in this way (see also Table 4):
(0; 0; 0.2)?(0; 0.3; 0.5)=(0.6; 0.8; 1)?(0.5; 0.7; 1)=
(0; 0; 0.2)?(0.5; 0.7; 1)=(0; 0; 0.2)?(0.2; 0.5; 0.8)
"(0; 0; 0.1)=(0.3; 0.56; 1)=(0; 0; 0.2)=(0; 0; 0.16)
"1/4(0.3; 0.56; 1.46)

Table 3
Relations between variable characterising "rm's quality system
and investments
Variable

Related investments

Relative in#uence

d
&

In quality of supplies
In certi"cation

Very High In#uence
Very Low In#uence

e
1

In training
In technology
In inbound inspection

Very Low In#uence
Very Low In#uence
Very High In#uence

d
1

In
In
In
In

Low In#uence
High In#uence
High In#uence
Low In#uence

e
2

In training
Very Low In#uence
In technology
Very Low In#uence
In on line/"nal inspection Very High In#uence

d
$

In engineering/marketing
In technology

engineering/marketing
training
technology
certi"cation

Very High In#uence
Very Low In#uence

"(0.08; 0.14; 0.37)
The above fuzzy number can be translated, according to the scale utilised to describe both exogenous
and endogenous priorities, to the judgement `low
prioritya.
4.1.3. The xnal result
Finally, we need to summarise the results
achieved evaluating the competitive context and
the quality system in order to identify investment
priorities. This requires us to convert all the fuzzy
numbers expressing the priorities of each programme into synthetic indicators capable of rating
the di!erent investments:
f First, such indicators follow from the sum of
given assessments (e.g. it is possible that the
technology investment would have an high

G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133

123

Table 4
Evaluation of the technology investment
Variable

Relative in#uence of the technology investment
(corresponding fuzzy number)

Importance of each variable referring to the achievement of
a "rm's planned results (corresponding fuzzy number)

e
1
d
1
e
2
d
$

Very Low In#uence (0; 0; 0.2)
High In#uence (0.6; 0.8; 1)
Very Low In#uence (0; 0; 0.2)
Very Low In#uence (0; 0; 0.2)

Little Important (0; 0.3; 0.5)
Important (0.5; 0.7; 1)
Important (0.5; 0.7; 1)
Moderately Important (0.2; 0.5; 0.8)

exogenous priority (0.6; 0.8; 1) and a low endogenous one (0; 0.2; 0.4), thus achieving a total
result of (0.3; 0.5; 0.7) corresponding to a medium
priority);
f then, in order to rate real quality-based priorities, managers have to reverse the just obtained
fuzzy numbers into linguistic assessments. In this
perspective, they have, to for each fuzzy number,
(i) calculate the centre of gravity X
G
(X ": x dS/: dS where S is the area included
G
S
S
between the membership function of the fuzzy
number and the x-axis) and (ii) determine the
fuzzy number representing the linguistic value
whose centre of gravity is the nearest to X [31].
G
4.2. TQM investments evaluation with the AHP
Logically, utilising the AHP (see Appendix A for
a brief description), we have to carry out the same
actions characterising the implementation of the
FLA. In this case, however, we will evaluate the
di!erent quality-related investments on the basis of
pairwise comparison judgements, referring this
analysis both to the competitive context in which
a "rm operates and to its quality system.
4.2.1. The competitive context
In order to evaluate the e!ectiveness of the seven
quality investments that we consider in the eight
contexts de"ned in the Section 3.1 it is necessary to
make pairwise comparisons for each di!erent
couple of investments in every context (obtaining in
such a way the weights of the branches of the `Ea
category of Fig. 3).
For instance, if we consider context 1, i.e. a context characterised by a "rm's high bargaining

power vs. suppliers, a high level of product quality
vs. the competitor's one and a low turbulence of the
market, we see that, in such a context, the importance of each quality investment can be evaluated as
summarised in Table 5.
We have to rate in every context the seven quality investments considered by referring to their
higher or lower propensity to support the "rm's
quality goals in that context.
4.2.2. The quality system
Referring to the evaluation of a "rm's quality
performance, we can see that it consists of three
steps.
Step 1 concerns the identi"cation of the contribution that a performance achieved on a particular
variable (d , e , d , e or d ) can o!er to the corpo& 1 1 2
$
rate quality targets (in other words we have to
assign the right weights to the branches of the `Da
category). This step is characteristic of the speci"c
quality situation of a "rm and, for this reason, it is
not possible to provide general considerations. It is
a management task to evaluate the exact contribution of each variable in a "rm's-speci"c context.
Step 2 is related to the identi"cation of the in#uence that the seven quality investments considered
have on the performance of a speci"c variable
(weights of the `Fa category). The relationship between investments and variables are not dependent
on the context and, hence, they can be analysed
once and for all. The results of the pairwise comparisons between the di!erent investments for each
variable are given in Table 6.
Step 3 accomplishes the last pairwise comparison
between the importance of the competitive context
and that of a "rm's quality system with respect to

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G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133

Fig. 3. Analytic Hierarchy Process: the structure.

Table 5
Pairwise comparison judgements and corresponding importance weightings

Eng./mkt.
Supplies
Training
Technol.
Certi"c.
Inb. insp.
Fin. insp.

Eng./mkt.

Supplies

Training

Technol.

Certi"c.

Inb. insp.

Fin. insp.

Rating

1
1
1/7
1/7
1
1/5
1/3

1
1
1/7
1/7
1
1/5
1/3

7
7
1
1
7
3
5

7
7
1
1
7
3
5

1
1
1/7
1/7
1
1/5
1/3

5
5
1/3
1/3
5
1
3

3
3
1/5
1/5
3
1/3
1

0.255
0.255
0.029
0.029
0.254
0.060
0.118

that "rm's planned quality results (i.e. it assigns the
weights of the branches `Aa and `Ba) in order to
achieve the overall suitability rating of the seven
quality investments considered. It is not possible to
establish ex ante the relative importance of the two
branches, because it is strictly dependent on the
external/internal context. For such a reason this
relation too, will be evaluated by SMEs managers.
Once all the weights of the branches are known,
the "nal ranking of the quality investments is immediately clear, according to the rules of the AHP
technique.

5. Application
In this section we aim to compare the results
achieved with the models based on the fuzzy linguistic approach and on the analytic hierarchy
process with respect to a real application. To
this end we considered a small "rm of the Lecco's
engineering district in Italy (called in the paper
PaperMach).
PaperMach originally realised several kinds of
machines for the production of paper handkerchiefs. In early 1990s, it started to diversify its

G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133

125

Table 6
(a) Importance of di!erent investments in order to improve the performance related to d
&
Supplies
Certi"cation
Rating
Supplies
Certi"cation

1
1/9

9
1

0.9
0.1

(b) Importance of di!erent investments in order to improve the performance related to e
1
Training
Technology
Inbound inspect.

Rating

Training
Technology
Inbound inspect.

0.091
0.091
0.818

1
1
9

1
1
9

1/9
1/9
1

(c) Importance of di!erent investments in order to improve the performance related to d
1
Engin./mkt.
Training
Technology

Certi"cation

Rating

Engin./mkt.
Training
Technology
Certi"cation

1
5
5
1

0.083
0.147
0.147
0.083

1
5
5
1

1/5
1
1
1/5

1/5
1
1
1/5

(d) Importance of di!erent investments in order to improve the performance related to e
2
Training
Technology
On line/"n. insp.

Rating

Training
Technology
On line/"n. insp.

0.091
0.091
0.818

1
1
9

1
1
9

1/9
1/9
1

(e) Importance of di!erent investments in order to improve the performance related to d
$
Engin./mkt.
Technology
Rating
Engin./mkt.
Technology

1
1/9

9
1

business toward machines aimed at the production
of paper tablecloths and napkins.
PaperMach exploited the huge network of very
small suppliers characterising the Lecco's district
having in this way a high bargaining power vs.
suppliers. It was also operating in a low turbulence context in which the technology as well
as the customers' needs were consolidated, but
it had a rather important problem of quality a!ecting its new typologies of machines (i.e. those
aimed at the production of paper tablecloths and
napkins).

0.9
0.1

For this reason its management decided to
undertake a TQM investment.
It is a matter of fact that such a situation provided us with the possibility of testing our model in
a real environment managing a real investment
decision. It was also a great opportunity to verify
the user friendliness of the software tool we
developed having in mind SMEs managers.
It appears clear that the situation of PaperMach
can be schematised as in context 3 of Section 3.2
and hence we apply the two methodologies (FLA
and AHP) to such a context.

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G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133

6. investment in engineering and/or marketing: low
priority;
7. investment in certi"cation: low priority.

5.1. Results achieved with the fuzzy linguistic
approach
In Section 4.1 we identi"ed the main investments
priorities of context 3. In order to evaluate TQM
priorities our aim is assigning the judgements referring to the importance of the "ve quality-related
variables in determining PaperMach's quality investment priorities. An interview "rst with the
entrepreneur and then with the operations manager
of the "rm allows us to consider:
d : moderately important;
&
e : moderately important;
1
d : very important;
1
e : little important;
2
d : not important.
$
Starting from these data and remembering the considerations done in Section 4.1, we calculate the
overall fuzzy numbers which summarise, for each
investment, all the assessments and the weights
assigned (all the calculations are reported in Appendix B). In this way, once the fuzzy numbers in
linguistic judgements are translated, we are able to
rank the seven investment alternatives:
f
f
f
f
f

1. investment in quality of supplies: medium/high
priority;
2. investment in technology: medium priority;
3. investment in training: medium priority;
4. investment in inbound inspection: medium
priority;
5. investment in on line/"nal inspection: low/
medium priority;

With respect to this scale we can note that in the
end PaperMach decided to buy a new machine
characterised by a higher product quality, thus
accomplishing the investment ranking second
according to the FLA.
5.2. Results achieved with the analytic hierarchy
process
According to Section 4.2 we can enlighten the
main investments priorities in context 3 (see
Table 7). Then the pairwise comparison judgements
about the importance of the "ve quality variables
considered with respect to the achievement of
a "rm's quality targets are reported in Table 8.
Finally, we have to compare the importance of
the competitive context in which a "rm operates
with that of its quality system. In the case of PaperMach, managers assigned the same importance to
these two elements achieving in this way the weight
of 0.5 for each branch.
In the light of these considerations and, referring
also to the judgements given in Tables 6}9, we can
determine the "nal judgements for every investment and, hence, we can rank the di!erent investment's alternatives:
1. investment in technology: overall judgement
0.3267;
2. investment in training: overall judgement
0.2223;

Table 7
Pairwise comparison judgements and corresponding importance weightings in Context 3

Eng./mkt.
Supplies
Training
Technol.
Certi"c.
Inb. insp.
Fin. insp.

Eng./mkt.

Supplies

Training

Technol.

Certi"c.

Inb. insp.

Fin. insp.

Rating

1
5
5
7
1
3
3

1/5
1
1
3
1/5
1/3
1/3

1/5
1
1
3
1/5
1/3
1/3

1/7
1/3
1/3
1
1/7
1/5
1/5

1
5
5
7
1
3
3

1/3
3
3
5
1/3
1
1

1/3
3
3
5
1/3
1
1

0.036
0.187
0.187
0.393
0.036
0.081
0.081

G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133
Table 8
Pairwise comparison judgements and corresponding importance weightings of the "ve variables describing a "rm's quality
system

d
&
e
1
d
1
e
2
d
$

d
&

e
1

d
1

e
2

d
$

Rating

1
1
5
1/3
1/7

1
1
5
1/3
1/7

1/5
1/5
1
1/7
1/9

3
3
7
1
1/5

7
7
9
5
1

0.164
0.164
0.565
0.077
0.029

3. investment in quality of supplies: overall judgement 0.1673;
4. investment in inbound inspection: overall judgement 0.1076;
5. investment in on line/"nal inspection: overall
judgement 0.0720;
6. investment in engineering/marketing: overall
judgement 0.0545;
7. investment in certi"cation: overall judgement
0.0496.
We can immediately note that the investment in
technology chosen by PaperMach is coherent with
the result of the AHP.
5.3. Implications for practice
In order to compare the e!ectiveness for management of the FLA and the AHP, the two methods
should be analysed in terms of four parameters:
f completeness of the analysis: i.e. the amount of
data considered in the evaluation;
f reliability of the output: how carefully the di!erent
investment alternatives are evaluated;
f ease of use of the technique: the di$culties that
users may incur utilising such methods;
f intuitiveness of the technique: the level of trust
that managers have in such methods because
they are able to understand how they work.
Completeness. Both these techniques refer to the
same data describing, on the one hand, the competitive context in which a "rm operates and, on
the other, its quality system. Hence, they are characterised by the same level of completeness.

127

Reliability. The analysis of the reliability of these
two methods requires us to understand how the
two methods utilise the data in order to achieve the
"nal result and, then, verifying whether the two
methods are able to achieve the same ranking
among investments.
It is a matter of fact that both the techniques are
able to rank di!erent quality investments in order
to help managers throughout the decisional process
of investments evaluation. However, it is also true
that the results achieved are not always the same.
This is, for instance, the case of the situation we
have just analysed.
Really, the di!erences between the rankings are
not overwhelming, i.e. all the investments are in the
same order except the one in quality of supplies.
However, this is a signi"cant exception indeed.
Using the FLA the investment in quality of supplies is ranked "rst, whereas the AHP put it in the
third place.
In our opinion it can be explained analysing how
the two methodologies evaluate the di!erent alternatives. Indeed, even if in order to identify
a weighted mean of the judgements associated with
each investment the adopted techniques would
seem equivalent, their modus operandi is di!erent.
The FLA indeed identi"es the fuzzy number
which de"nes the investment importance by normalising the sum of all the fuzzy numbers expressing the relative priority of that investment. In this
perspective it is clear that, if an investment achieves
excellent results with regard to only one parameter,
whereas it does not perform well referring to the
other quality-related variables, the overall judgement will not be a very good one. This happens
because, the use of an arithmetic mean does not
allow for compensation among criteria.
In the case of PaperMach, for the technology
investment, the low relative importance related to
the variables e , e and d is not su$ciently o!set
1 2
$
by the good results achieved both referring to the
variable d and to the context in which the "rm
1
operates. For such a reason this investment
achieves only a medium priority (see Table 9) and
ranks second, whereas it is ranked "rst by the AHP.
The AHP performs rather di!erently (see Table 10
and Fig. 4). Like the FLA, it sums all the weighted
judgements, but it does not normalise the obtained

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G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133

Table 9
The determination of the technology investment's priority with the FLA
Competitive context

Investment's priority

Corresponding
fuzzy number

3

Very High (0.8; 1; 1)

(0.8; 1; 1)

Quality variables:

System's relative in#uence:

System's importance:

e
1
d
1
e
2
d
$

Very Low (0; 0; 0.2)
High (0.6; 0.8; 1)
Very Low (0; 0; 0.2)
Very Low (0; 0; 0.2)

Mod. Imp. (0.2; 0.5; 0.8)
Very Imp. (0.7; 1; 1)
Little Imp. (0; 0.3; 0.5)
Not Imp. (0; 0; 0.3)
Final result

(0; 0; 0.16)
(0.42; 0.8; 1)
(0; 0; 0.1)
(0; 0; 0.06)
(0.11; 0.2; 0.33)
(0.46; 0.6; 0.67)

Table 10
The determination of the technology investment's priority with
the AHP
Relative
priority

Total
priority

0.3930

0.1965

Competitive Context (weight"0.5)
Quality system (weight"0.5)
e !
0.091*0.164
1
d "
0.417*0.565
1
e #
0.091*0.077
2
d $
0.100*0.029
$

0.0149
0.2356
0.0070
0.0029
0.2604

0.1302
0.3267

!Values derived from Table 6(b) and 8.
"Values derived from Table 6(c) and 8.
#Values derived from Table 6(d) and 8.
$Values derived from Table 6(e) and 8.

result and, hence, it is able to correctly emphasise
the importance of one characteristic in spite of the
poor performance the investment can achieve on
other variables (i.e. if an investment achieves an
excellent performance referring to one parameter, it
will also have a good "nal evaluation if it does not
perform well according to the other variables).
In the light of these issues, the major di!erence
between the proposed techniques is that:
f the AHP favours the investments that achieve at
least a good performance in one of the evaluation criteria;

Fig. 4. The determination of the technology investment's priority with the AHP.

f the FLA prefers those investments that achieve
equilibrate performance in all the parameters
evaluated, not being able to isolate the e!ect of
only one point of excellence.
For this reason the FLA penalises investments,
such as the technology one, that could in#uence
more variables. Indeed, when such investments are
not speci"cally aimed at improving all the variables
that could in#uence, it is rather frequent that they

G. Noci, G. Toletti / Int. J. Production Economics 67 (2000) 113}133

will achieve poor performance in, at least, one of
them. In contrast, the AHP does not overemphasise
the performances of marginal importance, giving
instead the right weight to the most relevant ones.
Let us consider for instance two investments
A and B. The "rst allows a "rm to achieve a high
improvement in the variable d and a little im1
provement in the variable e . The second, on the
2
contrary, has e!ect only on the variable d , that is
1
highly improved. It appears clear that, ceteris
paribus, A is the better investment because it allows
managers to achieve the same results of B on d ,
1
ensuring also a little improvement on e . The AHP
2
is able to rank precisely these investments, whereas
FLA, weighting the performance achieved by B on
the two variables, assigns a better rank to B.
According to these issues the AHP performs
better because it allows us to evaluate more carefully the di!erent investments. In contrast, the fuzzy
linguistic approach gives too much importance to
the less signi"cant performances, achieving, in this,
not always achieving correct results.
Ease of use. Both the techniques are very easy to
use, but we think that the FLA would