Directory UMM :Data Elmu:jurnal:I:International Journal of Production Economics:Vol69.Issue2.Jan2001:

Int. J. Production Economics 69 (2001) 177}191

The development of a decision support tool for the selection of
advanced technology to achieve rapid product development
Athakorn Kengpol*, Christopher O'Brien
Manufacturing Engineering and Operations Management, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
Received 26 March 1998; accepted 27 January 2000

Abstract
In a highly competitive market, product design, manufacturing and distribution strategies may change frequently and
rapidly. The challenge for a company is not only how to continue to maintain a technically advanced and competitive
product but also how to reduce the design, development and manufacturing time in line with demands of the market. This
paper outlines a decision support tool to assess the value of investing in Time Compression Technologies (TCTs) to
achieve rapid product development. It presents a proposed data structure to monitor the e!ectiveness of a decision, and
a decision model which consolidates quantitative and qualitative variables through the use of the Analytic Hierarchy
Process (AHP), Cost/Bene"t and statistical analyses. ( 2001 Elsevier Science B.V. All rights reserved.
Keywords: Neutraline pro"tability; Decision-making e!ectiveness; Analytic hierarchy process

1. Introduction
Throughout the 1970s and 1980s product developers focused on production quality and achieving
minimum production cost within long product life

cycles. As distinct from those decades, in the 1990s
product developers began to shift their focus onto
a new competitive weapon } time, which dominates
other factors (for example, product development
cost, production cost or logistic cost) in planning
the launch of new products. This new focus results
from the reduction in product life cycles, customers'
need for more choices, and global competition from
the `start up companya, etc. Companies which
launch a product to market faster than their competitors generally get greater market share. Time-

* Corresponding author.
E-mail address: epxak@nottingham.ac.uk (A. Kengpol).

to-market, therefore, is particularly crucial in such
highly competitive markets where product designs,
manufacturing and distribution strategies change
rapidly. At present no market place is too remote to
be accessed by companies from anywhere in the
world. A very small `start up companya can have

their products or services promoted via the Internet
to all around the globe at very low cost. To compete and survive within this environment, established companies have to deliver the right
product/service for the right market, at the right
cost in the right time. The challenge for the company is not only how to continue to maintain
a technically advanced and competitive product
but also how to reduce the design, development
and manufacturing time in line with demands of the
market. As a result, they need new technologies to
assist them compress the development time to get
products to the market more quickly than their
competitors.

0925-5273/01/$ - see front matter ( 2001 Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 5 - 5 2 7 3 ( 0 0 ) 0 0 0 1 6 - 5

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A. Kengpol, C. O'Brien / Int. J. Production Economics 69 (2001) 177}191

In practise, the owner or board of a company

(called the `decision makera) are often reluctant to
make large investments in new types of development technology because they may lack the means
to analyse these new technologies or the know-how
to put a value on reducing development time which
can be used to justify such investments. Conventional techniques can be well established from past
data but decision makers need to know what data
is required and how to manipulate those data to
justify new technologies. Although justi"cation can
be approached on "nancial terms it is necessary
also to justify intangible aspects. The objective of
this present research, the development of a decision
support tool for the selection of advanced technology to achieve rapid product development, addresses these complex requirements.
There is a need to structure a decision support
tool (as illustrated in Fig. 1) that can integrate
models for:
f Quantifying the impact of the value of reducing
development time (or Cost/Bene"t Analysis):
This model provides needed data and manipulation methods for decision makers to explore the
trade-o! between the usual development time
and shortened development time.

f Measuring decision-making e!ectiveness: This
model can lead to a computation of the `prob-

ability of product successa. A decision maker can
trace past data to predict the success of a new
product launch using logistic regression analysis.
f Assessing common criteria used by decision
makers in evaluating TCT and methods of relating
criteria to alternatives: The criteria include
Cost/Bene"t Analysis and Probability of Product
Success, plus other tangible and intangible criteria.
The above decision models are supported by data
from a Feedback Data Model and Data Bank.
The Data Bank is where all manipulated data are
kept and communicated in a structured way to the
Cost/Bene"t Analysis Model, Decision-Making
E!ectiveness Model, Common Criteria Model and
Feedback Data Model. The Data Bank communicates not only with these models but it is also a place to
keep data on new technology to be assessed in order
to justify the value of reducing development time.

The Feedback Data model is a concrete data
structure of selected technology implementation
which contains and manipulates product launch
and product success data. The required data for
calculation of pro"tability and trend in product
success data against the e!ectiveness of decision
making on investment in technology can be
achieved in this model. The result obtained from
this model is kept in the Data Bank and used to
support and update better analysis in each model.
The Decision Making model can identify a set of
criteria from the Common Criteria model (tangible
and intangible), evaluate criteria with the data from
the Cost/Bene"t Analysis model and Decision
Making E!ectiveness model using the Analytical
Hierarchy Process (AHP) and choose the most
appropriate technology to implement.

2. Literature review
The work of a number of researchers is described

below:
2.1. The selection of advanced technology and
decision support tools

Fig. 1. Decision support tool.

Many researches have been carried out to apply
decision support tools to assist decision makers

A. Kengpol, C. O'Brien / Int. J. Production Economics 69 (2001) 177}191

select the most appropriate advanced technology.
For example, Riddle and Williams [1] state that
technology selection is the process of determining
which (new or old) methods, techniques, and tools
satisfy criteria re#ecting a speci"c target community's requirements. Selection requires several capabilities: the ability to identify a set of candidates to
be considered, the ability to evaluate (either comparatively or in isolation) the candidates, and the
ability to choose from amongst the candidates
based upon the evaluations. They also examine
technology selection as the key to technology improvement and transfer. It is the critical "rst step in

improving practice and it can identify the need for
new acquisition, integration, propagation techniques, and perhaps even suggest the general nature
or operational details of these techniques.
Yap and Souder [2] develop a systems model to
encompass the analytical aspects of the technology
selection decision, the impacts of behavioural and
organisational processes on these decisions and integration between these aspects and various external environment factors.
Some researches, such as that by Yurimoto and
Masui [3] propose complex decision support tools,
but Swann and O'Keefe [4] reported that simple
decision support tools are more readily trusted and
used by "rms as sophisticated tools may mislead
managers. It would seem sensible, therefore, to try
to build a sophisticated tool that is simple to understand, whose workings are highly visible.
Primrose and Leonard [5] state that a decision
support tool should:
f
f
f
f

f

be accessible to engineers,
able to evaluate any investment,
include all factors or criteria,
adhere to established accounting principles,
give veri"able results acceptable to accounting
and "nancial managers.

The "rst requirement for the construction of a decision support tool is to select the candidate method
to be used for evaluation from amongst the various
advanced technologies, and for this the Analytical
Hierarchy Process (AHP) is chosen [6}10]. The
second requirement de"ning, `the criteriaa, will be
explained in Section 2.2.

179

As in practical approaches, various software
tools and methodologies had grown so rapidly that

a project manager was left with a confusing array of
choices, the US Air Force awarded a contract to
Boeing Aerospace Company to address the problem. The stated objective of the Speci"cation Technology Guidelines contract was to organise existing
information or requirements and design technologies into a Guidebook that could be used by USAF
technical managers in selecting appropriate
methodologies for future projects [11]. As an
example of the real-world needs to have a decision
support tool for selection of advanced technologies.
Pandy and Kengpol [12] applied a multi-criterion
decision method for selecting the best possible automated inspection device for use in #exible manufacturing systems.
In summary there is a need to have a decision
support tool that is practical, simple to understand,
and a range of capabilities to choose from amongst
advanced technologies.
2.2. Criteria for the selection of advanced technology
Not many researches deal directly with the criteria for the selection of advanced technology, almost all of them deal with decision-making theory.
Souder [13], Baker and Freeland [14] report some
strengths and limitations of a number of quantitative R&D selection and resource allocation models.
Their list of criteria and characteristics presented
are useful as a reference guide.

Forman [15] argue that the executive decision
makers are involved in establishing goals and criteria, and integrating information relevant to the
goals and criteria. Therefore, they need to have
a guide criterion that allows them to structure and
incorporate subjective as well as objective factors,
and incorporate their expertise as well.
The most interesting criteria for selection of advanced technologies come from Liberatore [16,17]
He presents a criterion and subcriteria for project
proposal evaluation. Four Categories are listed:
Manufacturing Criteria, Technical Criteria, Marketing and Distribution Criteria, and Financial
Criteria. Dyer and Forman [18] go further by presenting several di!erent criteria in many judgement
projects within an AHP decision process. Criteria

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A. Kengpol, C. O'Brien / Int. J. Production Economics 69 (2001) 177}191

for selecting the most promising new product, selecting the best South American Oil Pipelines
route, allocation of funds for helping the dean to
improve a school's e!ectiveness, and selecting the

best retail site are presented. Their overall conclusions support the criteria for project proposal
evaluation of Liberatore [16,17].
2.3. Time compression technologies (TCTs)
The advanced technology in this research can be
referred to as time compression technology. Time
compression has come a long way since Stalk and
Hout's [19] seminal work on the concept. Bhattacharya and Jina [20] report that since then, `Time
Compressiona has received increasing attention
from academics and practitioners both in analysing
the way businesses operate and in remodelling the
associated processes to render them more e!ective.
Time Compression Technology (TCT) can mean
any technology that can improve a design and
manufacturing process to achieve better quality in
a shorter period. One example of TCT is Rapid
Prototyping (RP) technology which can be applied
to shorten design and development time. In conventional technology, we need to have some time
for preparation (machining) of a prototype model
from a Computer Aided Design (CAD) "le but RP
technology uses Stereolithography methods or
other RP methods of generating 3D forms directly
from a CAD "le. Therefore, engineers can visualise,
verify, iterate, optimise or even fabricate the objects
within a shorter period with better quality.
Voss [21] carries out an investigation of the
introduction of Advanced Manufacturing Technologies (AMTs). It reported that less than one in
six companies obtained a real competitive advantage from any given AMT, the suggestion being
because of most companies' lack of understanding
of their real goal.
Many published papers have been based upon
the implementation of TCTs in real business. In
a survey conducted by United Research [22], interviews were conducted with 500 executives and
mangers of technology-based companies looking at
the problems involved in implementing time-based
management in new product development. The
study showed that changes in the design are

required after the development process begins,
because of inadequate concentration on the manufacturing or procurement processes early in the
design stage. These changes cause schedule delays
and increased costs in the development programme. Beesley [23] argues that decision making,
especially at the upstream concept development
stage of the programme, is probably the largest
cause of lost time. More than 80% of executives in
the survey were unaware or did not understand
how the product development process or elements
of the process worked.
Many researchers refer to the Mckinsey & Co.
report [24] which revealed that product launch
delays of 6 months reduce their product's pro"tability by one-third over its life cycle [25,26]. Another
study showed that a 20% decrease in time-to-market could increase NPV of a new automobile model
by 350 million dollars [27]. Crawford [28] and
Cooper [25] argue that the conclusion from
Mckinsey & Co. are probably overstated because
they were taken out of context: for example they
used atypical data with a very highly dynamic market situation (20% annual market growth, 12%
annual price erosion and 5 year product life). Furthermore, no evidence has been found about the
computation of the Mckinsey & Co. report.
2.4. Cost/benext analysis
In new and advanced design/development technology investments may be considered for several
reasons, amongst these are to add capacity, to replace an old and obsolete equipment and particularly to reduce product development time so that
a company can release a new product to the market
before competitors. In theory Finnie and Sizer
[29], Middaugh and Cowen [30], and Sizer [31] all
suggest evaluating the projected future cash #ows.
The most frequently recommended appraisal
method are Net Present Value (NPV) in which the
future value of projected cash #ows are discounted
back into current money terms for evaluation [32],
and Internal Rate of Return (IRR) in which the rate
of discount when applied to the project cash #ows
produces a zero NPV [33,34]. In reality, Reinertsen
[24] from Mckinsey & Co. stated that he found the
average experienced managers neither use such

A. Kengpol, C. O'Brien / Int. J. Production Economics 69 (2001) 177}191

techniques nor have a good sixth sense of what the
economics of development really are in business.
Such misunderstandings and miscalculations are
very important because they cause many wrong
decisions which lead to lost pro"tability. As timeto-market is the most important issue in highly
competitive markets at the moment, companies
need to quantify the impact of reducing design and
development time which can be used to justify
investments in new TCT technology. Moreover,
a company also needs to know how to quantify
their probability of success after implementing new
TCT technology.
2.5. The measure of decision-making ewectiveness
Investment in new TCT technology is a strategic
decision. Therefore, some model is needed to monitor the results of decisions on investment in TCT
technology projects and feed them back to a Data
Bank via a Feedback Data Model (as illustrated in
Fig. 1) for future guidance on decisions, called
`Measure of Decision Making E!ectivenessa.
A logistic regression model may be applied to assess the `Probability of Product Successa which
directly relates to the success of the investment in
new TCT technologies.
Success is de"ned as the achievement of something desired, planned or attempted. While "nancial return is one of the easily quanti"able
industrial performance yardsticks, it is far from
the only important one. Failure is part and parcel of the learning process that eventually results
in success [35}37].
Not all new products succeed and are well recorded
in the marketplace. Many researchers for example,
Crawford [38], Booz-Allen & Hamilton [39],
Cooper [40,41], Hultink and Robben [42] report
that the failure rates are between 30% and 40%
and some are not recorded in a structured way. For
these reasons it is not surprising that researchers
have started to study these dimensions of success
[40,43,44,36,37]. Traditionally new product success
has been measured in "nancial terms and market
impact [45,42].
A logistic regression model utilises past data to
estimate probability of future success. Neter and

181

Wasserman [46] explain that the "tting of a transformed logistic response (logit) function
g(x)"b #b x is relatively simple when there are
0
1
repeat observations at each x value, e.g. where x is
the budget spent on TCT technology implementation, b , b are constant number. The probability
0 1
of product success (n(x)) can be obtained from the
following equations.
The "tted response function (b , b ) has been
0 1
obtained by maximising the likelihood:
logit g( (x)"b #b x.
0
1

(1)

It also can be transformed back into the original
units:
eb0 `b1 x
n( (x)"
1#eb0 `b1 x

(2)

or equivalently to
1
.
n( (x)"
1#e~(b0 `b1 x)

(3)

Based upon (1) and (3), if we know g( (x) we can
easily compute the probability of product success
n( (x).
Some researchers have used a logit model in the
"nance "eld. For example Jain and Nag [47] applied logit models as a forecast generator in their
decision support model for investment decisions in
new "nance ventures. They attempt to improve the
quality of the decision by integration of quantitative and qualitative data. Prosser and Nickl [48]
applied logistic regression to consider the interorganizational e!ects of Electronic Data Interchange
(EDI) from a transaction cost perspective.
Based upon the above it may be possible to relate
the probability of product success to the investment
in TCT technologies.

3. Proposed research model
The Decision Support Tool as illustrated in
Fig. 1, proposes the integration of a Cost/Bene"t
Analysis model, a Decision-Making E!ectiveness
model, and a Common Criteria model to choose
from amongst TCT technologies.

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A. Kengpol, C. O'Brien / Int. J. Production Economics 69 (2001) 177}191

3.1. Cost/benext analysis model
Although there are precise analysis models such
as NPV or IRR, these techniques are not necessarily more useful to decision making than a simple
calculation of cumulative pro"ts. Particularly for
product life cycle of 5}6 years or less, which is the
case for many `hi-techa products in the market.
Before quantifying and carrying out a sensitivity
analysis of compressing the development time, it is
necessary to set up a neutraline pro"tability model
as illustrated in Appendix Table 3. Any sensitivity
analysis can then be compared to that neutraline
model. The Neutraline Pro"tability Model is the
anticipated cash #ow using the illustrative data for
current technology and business practise. Therefore
all data in this model are simply illustrated "gures
in the given example. In practise, a company needs
to adjust its own speci"c data to obtain accurate
results for a speci"c product. The software using to
develop this model is Microsoft Excel.
Referring to The Neutraline Pro"tability Model
in Table 3 of the Appendix, the TCT technology is
assumed to have 2 years for development and implementation, and another 5 years to produce the
product. The Neutraline Pro"tability Model is
composed of 3 sections: Revenues, Expenses and
Pro"ts.
In the Revenues section, the projections of starting price and average selling price are proportionally decreased by market competition and
technology performance. For example in the electronic industry, the price of a disk drive may drop
approximately 20%/year, semi-conductor unit
price may decrease 25}30%/year, or mechanical
systems go down by 2.5%/year [49,32]. For simplicity, we use a 5% annual drop which eventually
causes the business to discontinue their product by
the end of 5 years because the Return on Revenues
are too low or there is no pro"tability. The Unit
sales are projected as a function of both market
capability and growth, and market share. As illustrated in Fig. 2, Total Sales dramatically increase
from year `0a to the peak in year `3a and then drop.
In the Expenses section, the projection of startup cost and unit cost can be drawn. The unit cost is
composed of labour cost, material cost, machining
and processing cost. Although typically the learn-

Fig. 2. Annual pro"t and total sales.

Fig. 3. Cumulative PBT.

ing curve leads to decreasing unit cost, labour cost
is approximately increased 5% annually plus
a slight increase in material cost. For simplicity,
a 2.5% annual increase in unit cost is used. In
`Operating Expensesa engineering cost or product
development cost is one of the most important
factors of the Neutraline Pro"tability model, because it consists of Capital Expenditure (CAPEX)
in new technology including equipment, installations etc. and Operating Expenditure (OPEX) in
training cost, set up cost, etc. This cost will rapidly
drop after the product launch. Marketing cost, general and admin. costs also have to be accounted.
Typically, marketing cost (i.e. advertising, promotion campaign, etc.) and general and admin. cost
(i.e. stationery, o$ce equipment, etc.) are proportionate to total sales. In the example 15% of total
sales is estimated for marketing cost and 5% of
total sales for general and admin. cost. The annual
operating expenses can then be computed.
In the Pro"ts section, the Pro"t Before Tax
(PBT) as illustrated in Fig. 2 and cumulative PBT
which is illustrated in Fig. 3 can be computed based
upon revenues section and expenses section.

A. Kengpol, C. O'Brien / Int. J. Production Economics 69 (2001) 177}191

Fig. 2 compares Annual Pro"t and Total Sales,
from the beginning year, which has negative pro"t,
through to the year the product is discontinued.
Fig. 3 illustrates the Cumulative PBT: the result
from the Neutraline Pro"tability Model is a Cumulative Pro"t Before Tax (PBT)"C10 793 693 which
is the pro"t for the whole life cycle of the product.
This cumulative PBT is the Neutraline and against
which sensitivity analysis is applied to obtain the
value of shorter product development time.
In Appendix Table 3, the market share is set at
10% every year, engineering cost at C550 000 annually in the development years and C20 000 each
production year, to obtain C10 793 693 Cumulative
Pro"t Before Tax (PBT) which is the Neutraline. In
Appendix Table 4, if the product is delayed by
6 months the demand in year `0a and the market
share would be down to 3% and in other years
would be down to 9% due to competitors taking
market share. This causes very severe decrease in
Cumulative PBT C2 536 513 or (!23.5%). On the
other hand in Appendix Table 5, if the company
realises that they could not launch their product in
time, so they decide to invest another 50% over
development budget in TCT technology (for
example RP) for 2 development years so that they
could still launch their product on schedule, they
would reduce their loss of Cumulative PBT from
the Neutraline to only C550 000 or !5.1%. In
particular, in Appendix Table 6 if at the beginning
the company decides to invest in TCT technology
another 50% over development budget for 2 development years, they would shorten the introduction
time to market by 6 months and the company can
increase market share. The cumulative PBT would
go up to C1 149 369 or #10.6% from the Neutraline Model. From below Table 1 (Trade-o! decision table) and Fig. 4 (Cumulative PBT impact), all
conditions are compared with the Neutraline
Cumulative PBT. Monthly impact is the approximate per month, within 6 months in each case. If the
company launches the product 6 months late, they
would lose C422 752 per month. On the other hand,
if the company could shorten development time by
6 months with 50% over budget, they could earn
an extra C191 562 per month. The pro"t window
in the 2 development years can also be compared:
previous engineering budget (Tables 3 and

183

Table 1
Trade o! decision table
Conditions

Cumulative
PBT

Neutraline

C10 793 693

Delay

C8 257 180

6 months
On Schedule

Cumulative Monthly impact
PBT Impact (within 6 month)
0

*

!C2 536 513 !C422 752
!23.5%

C10 243 693

Over Budget

!C550 000

*

!5.1%

by 50%
Shortened
6 months

C11 943 062

C1 149 369

C191 562

10.6%

Over 50%

Fig. 4. Cumulative PBT impact.

4)"C2]550 000 per year"C1 100 000 per 2 years.
New engineering budget (Table 6)"C2]825 000
per year"C1 650 000 per 2 years. Total development investment increases"C550 000. A shortened
development time by 6 months earns higher pro"ts
of"C1 149 369. Therefore, the company earns a
total pro"t of C1 149 369!550 000"appx.
C600 000 per product. Based upon these calculation, it is worth the company paying for shorter
product development time.
3.2. Decision-making ewectiveness model
In reality a company needs to update the data in
the above pro"tability model by referring to the
data from the Feedback Data Model via the Data
Bank so that the company can precisely monitor
their pro"tability. In addition to the Neutraline
Pro"tability Model and above sensitivity analysis

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A. Kengpol, C. O'Brien / Int. J. Production Economics 69 (2001) 177}191

it is proposed to investigate to what extent it may
be possible to calculate the Probability of Product
Success based upon an analysis of historical data.
For example referring to Section 2.5 the "tted
response function (b , b ) has been obtained from
0 1
Eq. (1).
logit g( (x)"b #b x.
0
1
in which x is the budget spent on each TCT technology. Probability of product success n( (x) can be
achieved by Eq. (3).
1
.
n( (x)"
1#e~(b0 `b1 x)
Therefore, it is necessary to know
1. How much budget has been spent on TCT in the
past?
2. How many TCT projects have been implemented?
3. How many TCT projects succeeded?
Based upon the many papers presented in Section
2.5, these required data are not well recorded in
companies. Therefore, companies need to structure
these data and try to record the budget spent for
TCT technology with the success/failure rate of the
product. Currently these records may not be available in the company, or only available in unorganised records, but a company can try to structure
their new product records in order to estimate the
probability of product success.
Given adequate data, a company must be able to
estimate that an investment of (say) C25 000 for new
TCT technology may have a Probability of Success
n( (x)"63% (based upon past performance). The
analysis can be performed by a statistic software
package such as SPSS.
Such information may be useful in guiding the
selection of the appropriate techniques to meet
given criteria.
3.3. Common criteria model
A hierarchy of common criteria and subcriteria is
prepared (as illustrated in Fig. 5) which comes from

Fig. 5. Common criteria.

the literature, and discussions with companies.
These criteria are in the context of how they relate
speci"cally to TCT and have both tangible and
intangible criteria.
The Accuracy criteria for example may vary depending on the requirement. For example, investing in RP, accuracy depends on whether the work is
needed for fabrication, visualisation or tooling, etc.
The Stage of Technology Development criteria
means the stage of maturity of the TCT technology.
All criteria need to be prioritised in line with the
company requirements. Therefore it is necessary to
know how companies prioritise these criteria which
can then be structured in the Feedback Data model
and kept in the Data Bank so that the company can
match its TCT selection to its priorities. AHP and
the Expert Choice Software can be used to structure this prioritisation.

4. Pilot study and some analysis of responses
To gather the data needed by the above model,
interviews are being conducted with many companies. A pilot study has been carried out with only
5 companies to establish data on such area as their
approach to Cost/Bene"t analysis, whether they
keep records on product development and, for
example, the target and actual budget spent for

A. Kengpol, C. O'Brien / Int. J. Production Economics 69 (2001) 177}191

185

Table 2
Tabulated data and some typical responses
Company

Employees

Turnover
(mC)

Type of product

Compute value of time to
launch new product

Concrete measure
of new product
success

Common
criteria
(major)

1
2
3
4

165
15
35
4500

8.5
1
4
300

No
No
Yes, but not well structure
No

No
No
No
No

Match
Match
Match
Match

5

400

30

Plastic packaging
Sport equipment
Rapid prototyping products
Health care
and cosmetic
Personnel hygiene

Yes, but not well structure

No

Match

development of a product, target and actual date to
the launch of a product, etc.
Data in the companies and some typical responses are illustrated in Table 2. Some companies
have never tried to compute the value of time to the
launch of their product, although they launch their
product to the market later than target many times!
Typically all companies use cost/bene"t analysis
but none of them use this analysis in order to
analyse the impact of product launch before or
after the target date. No company has any concrete
measure of the success or failure of a new product,
but they would be eager to manage their records to
enable a calculation of `Probability of Product
Successa. We found that the proposed criteria
match with all companies with minor modi"cation
to suit with their speci"c TCT technology. Not
surprisingly, the high priority criteria are Cost/Bene"t Analysis and Probability of Product Success
but more surprisingly is that Method of Payment is
put as one of the high priority criteria because of
the potential e!ect of purchasing new technology
on cash #ow. Information on Market share and size
of the market are very important for all companies
and their marketing department estimate this information from past data and market trends. All
companies re-estimate and survey market share
and size of the market every 6 months. All companies place emphasis on unit cost, particularly
labour cost which is rising annually against a selling price which is annually decreasing. The healthcare and cosmetic company and the sport
equipment company estimate their marketing, general and admin. budget based upon total sales,

whereas the rapid prototyping company budgets
instead for a certain "xed amount.

5. Conclusion and further study
Although, the data gathered from the pilot study
are not su$cient to represent general business
trends in analysing the impact of the value of time
on product development, it has nevertheless
thrown up some interesting responses:
f Companies have reacted favourably to the Neutraline Pro"tability Model with its trade-o!
table, and are looking to apply them to their own
Cost/Bene"t Analysis. The model will be re"ned
following interviews with many more companies.
f In general, inadequate data currently exists within companies to generate a Probability of Product Success. However, the models have made
companies aware of their need for better records
to improve decision making. The model will be
developed to assist companies in breaking down
the existing barriers to obtaining data.
f Companies' criteria for choosing from amongst
technologies tend to be similar but with different emphasis according to their speci"c
products.

Appendix A
As implied in the text we provide the Tables 3}6
in this appendix.

186

Table 3
Neutraline pro"tability model
Year
2

3

4

5

C2000

C1900

C1805

C1715

C1629

C1548

Market capability and growth (units)
Market share
Unit sales

20 000
10%
2000

40 000
10%
4000

80 000
10%
8000

120 000
10%
12 000

90 000
10%
9000

50 000
10%
5000

Total revenues or total sales
Cumulative revenues

C4 000 000
C4 000 000

C7 600 000
C11 600 000

C14 440 000
C26 040 000

C20 577 000
C46 617 000

C14 661 113
C61 278 113

C7 737 809
C69 015 922

C1000

C1025

C1051

C1077

C1104

C1131

C2 000 000
C2 000 000
50.0%

C4 100 000
C3 500 000
46.1%

C8 405 000
C6 035 000
41.8%

C12 922 688
C7 654 313
37.2%

C9 934 316
C4 726 796
32.2%

C5 657 041
C2 080 768
26.9%

C20 000
C1 140 000
C380 000
C1 540 000

C20 000
C2 166 000
C722 000
C2 908 000

C20 000
C3 086 550
C1 028 850
C4 135 400

C20 000
C2 199 167
C733 056
C2 952 223

C20 000
C1 160 671
C386 890
C1 567 562

Revenues
Average selling price
Beginning price

C2000

Expenses
Unit cost (including labour cost)
Beginning cost
C1000

!1

0

!5%/yr

#2.5%/yr

Cost of goods sold
Gross margin (C)
Gross margin (percents)
Operating expenses
Engineering cost
Marketing cost 15% of total sales
General and admin. cost 5% of total sales
Total operating expenses

C550 000

C550 000

C550 000

C550 000

C200 000
C600 000
C200 000
C1 000 000

Total expenses
Cumulative expenses

C550 000
C550 000

C550 000
C1 100 000

C3 000 000
C4 100 000

C5 640 000
C9 740 000

C11 313 000
C21 053 000

C17 058 088
C38 111 088

C12 886 539
C50 997 626

C7 224 603
C58 222 229

C1 000 000

C1 960 000

C3 127 000

C3 518 913

C1 774 574

C513 206

C1 860 000

C4 987 000

C8 505 913

C10 280 486

C10 793 693

25.8%

21.7%

17.1%

12.1%

6.6%

Proxts
Pro"t before tax (PBT)

!C550 000

!C550 000

Cumulative PBT

!C550 000

!C1 100 000

Return on revenues (PBT/total revenues C)

!C100 000
25.0%

Cumulative total revenues (C)
Cumulative gross margin (C)

C69 015 922
C25 996 877

Cumulative PBT

C10 793 693

Neutraline

A. Kengpol, C. O'Brien / Int. J. Production Economics 69 (2001) 177}191

1

!2

Table 4
Pro"tability model when product launch is delayed by 6 months
Year
1

2

3

4

5

C2000

C1900

C1805

C1715

C1629

C1548

Market capability and growth (units)
Market share
Unit sales

20 000
3%
600

40 000
9%
3600

80 000
9%
7200

120 000
9%
10 800

90 000
9%
8100

50 000
9%
4500

Total revenues or total sales
Cumulative revenues

C1 200 000
C1 200 000

C6 840 000
C8 040 000

C12 996 000
C21 036 000

C18 519 300
C39 555 300

C13 195 001
C52 750 301

C6 964 028
C59 714 330

C1000

C1025

C1051

C1077

C1104

C1131

C600 000
C600 000
50.0%

C3 690 000
C3 150 000
46.1%

C7 564 500
C5 431 500
41.8%

C11 630 419
C6 888 881
37.2%

C8 940 884
C4 254 117
32.2%

C5 091 337
C1 872 691
26.9%

C20 000
C1 094 400
C342 000
C1 456 400

C20 000
C2 079 360
C649 800
C2 749 160

C20 000
C2 963 088
C925 965
C3 909 053

C20 000
C2 111 200
C659 750
C2 790 950

C20 000
C1 114 245
C348 201
C1 482 446

!2
Revenues
Average selling price
Beginning price

C2000

0

!5%/yr

#2.5%/yr

Cost of goods sold
Gross margin (C)
Gross margin (percents)
Operating expenses
Engineering cost
Marketing cost 15% of total sales
General and admin. cost 5% of total sales
Total operating expenses

C550 000

C550 000

C550 000

C550 000

C200 000
C192 000
C60 000
C452 000

Total Expenses
Cumulative expenses

C550 000
C550 000

C550 000
C1 100 000

C1 052 000
C2 152 000

C5 146 400
C7 298 400

C10 313 660
C17 612 060

C15 539 472
C33 151 532

C11 731 835
C44 883 366

C6 573 783
C51 457 149

C148 000

C1 693 600

C2 682 340

C2 979 828

C1 463 167

C390 246

C741 600

C3 423 940

C6 403 768

C7 866 935

C8 257 180

24.8%

20.6%

16.1%

11.1%

5.6%

Proxts
Pro"t before Tax (PBT)

!C550 000

!C550 000

Cumulative PBT

!C550 000

!C1 100 000

Return on Revenues (PBT/total revenues C)

12.3%

Cumulative total revenues (C)
Cumulative gross margin (C)

C59 714 330
C22 197 190

Cumulative PBT

C8 257 180

Cumulative PBT

C10 793 693
!C2 536 513

Neutraline
!23.5%

187

Pro"t lower

!C952 000

A. Kengpol, C. O'Brien / Int. J. Production Economics 69 (2001) 177}191

Expenses
Unit cost (including labour cost)
Beginning cost
C1000

!1

188

Table 5
Pro"tability model when product introduction is on schedule but development cost over budget by 50%
Year
1

2

3

4

5

C2000

C1900

C1805

C1715

C1629

C1548

Market capability and growth (units)
Market share
Unit sales

20 000
10%
2000

40 000
10%
4000

80 000
10%
8000

120 000
10%
12 000

90 000
10%
9000

50 000
10%
5000

Total revenues or total sales
Cumulative revenues

C4 000 000
C4 000 000

C7 600 000
C11 600 000

C14 440 000
C26 040 000

C20 577 000
C46 617 000

C14 661 113
C61 278 113

C7 737 809
C69 015 922

C1000

C1025

C1051

C1077

C1104

C1131

C2 000 000
C2 000 000
50.0%

C4 100 000
C3 500 000
46.1%

C8 405 000
C6 035 000
41.8%

C12 922 688
C7 654 313
37.2%

C9 934 316
C4 726 796
32.2%

C5 657 041
C2 080 768
26.9%

C20 000
C1 140 000
C380 000
C1 540 000

C20 000
C2 166 000
C722 000
C2 908 000

C20 000
C3 086 550
C1 028 850
C4 135 400

C20 000
C2 199 167
C733 056
C2 952 223

C20 000
C1 160 671
C386 890
C1 567 562

!2
Revenues
Average selling price
Beginning price

C2000

0

!5%/yr

#2.5%/yr

Cost of goods sold
Gross margin (C)
Gross margin (percents)
Operating expenses
Engineering cost
Marketing cost 15% of total sales
General and admin. cost 5% of total sales
Total operating expenses

C825 000

C825 000

C825 000

C825 000

C200 000
C600 000
C200 000
C1 000 000

Total expenses
Cumulative expenses

C825 000
C825 000

C825 000
C1 650 000

C3 000 000
C4 650 000

C5 640 000
C10 290 000

C11 313 000
C21 603 000

C17 058 088
C38 661 088

C12 886 539
C51 547 626

C7 224 603
C58 772 229

C1 000 000

C1 960 000

C3 127 000

C3 518 913

C1 774 574

C513 206

C1 310 000

C4 437 000

C7 955 913

C9 730 486

C10 243 693

25.8%

21.7%

17.1%

12.1%

6.6%

Proxts
Pro"t before tax (PBT)

!C825 000

!C825 000

Cumulative PBT

!C825 000

!C1 650 000

Return on revenues (PBT/total revenues C)

25.0%

Cumulative total revenues (C)

C69 015 922

Cumulative gross margin (C)

C25 996 877

Cumulative PBT

C10 243 693

Cumulative PBT
Pro"t lower

!C650 000

C10 793 693
!C550 000

Neutraline
!5.1%

A. Kengpol, C. O'Brien / Int. J. Production Economics 69 (2001) 177}191

Expenses
Unit cost (including labour cost)
Beginning cost
C1000

!1

Table 6
Pro"tability model when product introduction is shortened by 6 months with 50% over budget
Year
1

2

3

4

5

C2000

C1900

C1805

C1715

C1629

C1548

Market capability and growth (units)
Market share
Unit sales

20 000
15%
3000

40 000
11%
4400

80 000
11%
8800

120 000
11%
13 200

90 000
11%
9900

50 000
11%
5500

Total revenues or total sales
Cumulative revenues

C6 000 000
C6 000 000

C8 360 000
C14 360 000

C15 884 000
C30 244 000

C22 634 700
C52 878 700

C16 127 224
C69 005 924

C8 511 590
C77 517 514

C1000

C1025

C1051

C1077

C1104

C1131

C3 000 000
C3 000 000
50.0%

C4 510 000
C3 850 000
46.1%

C9 245 500
C6 638 500
41.8%

C14 214 956
C8 419 744
37.2%

C10 927 748
C5 199 476
32.2%

C6 222 745
C2 288 845
26.9%

C20 000
C1 254 000
C418 000
C1 692 000

C20 000
C2 382 600
C794 200
C3 196 800

C20 000
C3 395 205
C1 131 735
C4 546 940

C20 000
C2 419 084
C806 361
C3 245 445

C20 000
C1 276 739
C425 580
C1 722 318

!2
Revenues
Average selling price
Beginning price

C2000

0

!5%/yr

#2.5%/yr

Cost of goods sold
Gross margin (C)
Gross margin (percents)
Operating expenses
Engineering cost
Marketing cost 15% of total sales
General and admin. cost 5% of total sales
Total operating expenses

C825 000

C825 000

C825 000

C825 000

C200 000
C900 000
C300 000
C1 400 000

Total expenses
Cumulative expenses

C825 000
C825 000

C825 000
C1 650 000

C4 400 000
C6 050 000

C6 202 000
C12 252 000

C12 442 300
C24 694 300

C18 761 896
C43 456 196

C14 173 192
C57 629 389

C7 945 063
C65 574 452

C1 600 000

C2 158 000

C3 441 700

C3 872 804

C1 954 031

C566 527

C2 108 000

C5 549 700

C11 376 535

C9 422 504

C11 943 062

25.8%

21.7%

17.1%

12.1%

6.7%

Proxts
Pro"t before tax (PBT)

!C825 000

!C825 000

Cumulative PBT

!C825 000

!C1 650 000

Return on revenues (PBT/total revenues C)

!C50 000
26.7%

Cumulative total revenues (C)
Cumulative gross margin (C)

C77 517 514
C29 396 565

Cumulative PBT

C11 943 062
C10 793 693

Neutraline

Pro"t higher

C1 149 369

10.6%

189

Cumulative PBT

A. Kengpol, C. O'Brien / Int. J. Production Economics 69 (2001) 177}191

Expenses
Unit cost (including labour cost)
Beginning cost
C1000

!1

190

A. Kengpol, C. O'Brien / Int. J. Production Economics 69 (2001) 177}191

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