*Corresponding author.
E-mail address:[email protected] (A. Kengpol).
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 devel-opers 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 com-petitors generally get greater market share.
Time-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 companyacan have their products or services promoted via the Internet to all around the globe at very low cost. To com-pete and survive within this environment, estab-lished companies have to deliver the right product/service for the right market, at the right cost in the right time. The challenge for the com-pany 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.
Fig. 1. Decision support tool.
In practise, the owner or board of a company (called the`decision makera) are often reluctant to make large investments in new types of develop-ment 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. Conven-tional 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 techno-logy to achieve rapid product development, ad-dresses 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 manipula-tion 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 communic-ates 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
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 commun-ity's requirements. Selection requires several capa-bilities: the ability toidentifya set of candidates to be considered, the ability toevaluate(either com-paratively 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 im-provement and transfer. It is the critical"rst step in improving practice and it can identify the need for new acquisition, integration, propagation tech-niques, 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 in-tegration between these aspects and various ex-ternal 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 under-stand, whose workings are highly visible.
Primrose and Leonard [5] state that a decision support tool should:
f be accessible to engineers, f able to evaluate any investment, f include all factors or criteria,
f adhere to established accounting principles, f give veri"able results acceptable to accounting
and"nancial managers.
The"rst requirement for the construction of a deci-sion 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.
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 prob-lem. The stated objective of the Speci"cation Tech-nology Guidelines contract was to organise existing information or requirements and design technolo-gies 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 au-tomated inspection device for use in#exible manu-facturing 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 cri-teria for the selection of advanced technology, al-most all of them deal with decision-making theory. Souder [13], Baker and Freeland [14] report some strengths and limitations of a number of quantitat-ive 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 cri-teria, 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.
for selecting the most promising new product, se-lecting 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 con-clusions 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. Bhat-tacharya 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 con-ventional 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 Tech-nologies (AMTs). It reported that less than one in six companies obtained a real competitive advant-age 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], inter-views 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 manu-facturing or procurement processes early in the design stage. These changes cause schedule delays and increased costs in the development pro-gramme. 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" tab-ility by one-third over its life cycle [25,26]. Another study showed that a 20% decrease in time-to-mar-ket 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 mar-ket situation (20% annual marmar-ket growth, 12% annual price erosion and 5 year product life). Fur-thermore, no evidence has been found about the computation of the Mckinsey & Co. report.
2.4. Cost/benext analysis
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 time-to-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 moni-tor 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 as-sess 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 some-thing desired, planned or attempted. While" nan-cial return is one of the easily quanti"able industrial performance yardsticks, it is far from the only important one. Failure is part and par-cel 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
Wasserman [46] explain that the"tting of a trans-formed logistic response (logit) function
g(x)"b
0#b1xis relatively simple when there are
repeat observations at eachxvalue, e.g. wherexis the budget spent on TCT technology implementa-tion,b
0,b1 are constant number. The probability
of product success (n(x)) can be obtained from the following equations.
The "tted response function (b
0,b1) has been
obtained by maximising the likelihood:
logit g((x)"b
0#b1x. (1)
It also can be transformed back into the original units:
n((x)" eb
0`b1x
1#eb0`b1x (2)
or equivalently to
n((x)" 1
1#e~(b0`b1x). (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] ap-plied 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 quantitat-ive and qualitatquantitat-ive data. Prosser and Nickl [48] applied logistic regression to consider the interor-ganizational 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
Fig. 2. Annual pro"t and total sales.
Fig. 3. Cumulative PBT.
3.1. Cost/benext analysis model
Although there are precise analysis models such as NPV or IRR, these techniques are not necessar-ily 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 im-plementation, 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 start-ing price and average sellstart-ing price are propor-tionally decreased by market competition and technology performance. For example in the elec-tronic 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 simpli-city, 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 illus-trated in Fig. 2, Total Sales dramatically increase from year`0ato the peak in year`3aand then drop. In the Expenses section, the projection of start-up 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-ing curve leads to decreaslearn-ing 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 Expensesaengineering cost or product development cost is one of the most important factors of the Neutraline Pro"tability model, be-cause it consists of Capital Expenditure (CAPEX) in new technology including equipment, installa-tions etc. and Operating Expenditure (OPEX) in training cost, set up cost, etc. This cost will rapidly drop after the product launch. Marketing cost, gen-eral and admin. costs also have to be accounted. Typically, marketing cost (i.e. advertising, promo-tion campaign, etc.) and general and admin. cost (i.e. stationery, o$ce equipment, etc.) are propor-tionate 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.
Table 1
Trade o!decision table
Conditions Cumulative Cumulative Monthly impact PBT PBT Impact (within 6 month)
Neutraline C10 793 693 0 *
Delay C8 257 180 !C2 536 513!C422 752
6 months !23.5%
On Schedule C10 243 693 !C550 000 *
Over Budget !5.1%
by 50%
Shortened C11 943 062 C1 149 369 C191 562
6 months 10.6%
Over 50%
Fig. 4. Cumulative PBT impact.
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 Cumu-lative 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 an-nually in the development years andC20 000 each production year, to obtainC10 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`0aand 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 PBTC2 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 devel-opment 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 toC1 149 369 or#10.6% from the
Neutra-line Model. From below Table 1 (Trade-o! deci-sion table) and Fig. 4 (Cumulative PBT impact), all conditions are compared with the Neutraline Cumulative PBT. Monthly impact is the approxim-ate per month, within 6 months in each case. If the company launches the product 6 months late, they would loseC422 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
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
develop-ment investdevelop-ment 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 calcu-lation, it is worth the company paying for shorter product development time.
3.2. Decision-making ewectiveness model
Fig. 5. Common criteria.
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
0,b1) has been obtained from
Eq. (1).
logit g((x)"b
0#b1x.
in whichxis the budget spent on each TCT techno-logy. Probability of product success n((x) can be achieved by Eq. (3).
n((x)" 1
1#e~(b0`b1x).
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 imple-mented?
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 avail-able in the company, or only availavail-able in unor-ganised 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
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 de-pending on the requirement. For example, invest-ing 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 struc-ture this prioritisation.
4. Pilot study and some analysis of responses
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)
1 165 8.5 Plastic packaging No No Match
2 15 1 Sport equipment No No Match
3 35 4 Rapid prototyping products Yes, but not well structure No Match
4 4500 300 Health care
and cosmetic
No No Match
5 400 30 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 re-sponses 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/Be-ne"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 in-formation from past data and market trends. All companies re-estimate and survey market share and size of the market every 6 months. All com-panies place emphasis on unit cost, particularly labour cost which is rising annually against a sell-ing price which is annually decreassell-ing. The health-care and cosmetic company and the sport equipment company estimate their marketing, gen-eral 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
Neu-traline 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
with-in companies to generate a Probability of Prod-uct 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 dif-ferent emphasis according to their speci"c products.
Appendix A
Table 3
Neutraline pro"tability model
Year
!2 !1 0 1 2 3 4 5
Revenues
Average selling price C2000 C1900 C1805 C1715 C1629 C1548 Beginning price C2000 !5%/yr
Market capability and growth (units) 20 000 40 000 80 000 120 000 90 000 50 000
Market share 10% 10% 10% 10% 10% 10%
Unit sales 2000 4000 8000 12 000 9000 5000
Total revenues or total sales C4 000 000 C7 600 000 C14 440 000 C20 577 000 C14 661 113 C7 737 809 Cumulative revenues C4 000 000 C11 600 000 C26 040 000 C46 617 000 C61 278 113 C69 015 922
Expenses
Unit cost (including labour cost) C1000 C1025 C1051 C1077 C1104 C1131 Beginning cost C1000 #2.5%/yr
Cost of goods sold C2 000 000 C4 100 000 C8 405 000 C12 922 688 C9 934 316 C5 657 041 Gross margin (C) C2 000 000 C3 500 000 C6 035 000 C7 654 313 C4 726 796 C2 080 768 Gross margin (percents) 50.0% 46.1% 41.8% 37.2% 32.2% 26.9%
Operating expenses
Engineering cost C550 000 C550 000 C200 000 C20 000 C20 000 C20 000 C20 000 C20 000 Marketing cost 15% of total sales C600 000 C1 140 000 C2 166 000 C3 086 550 C2 199 167 C1 160 671 General and admin. cost 5% of total sales C200 000 C380 000 C722 000 C1 028 850 C733 056 C386 890 Total operating expenses C550 000 C550 000 C1 000 000 C1 540 000 C2 908 000 C4 135 400 C2 952 223 C1 567 562
Total expenses C550 000 C550 000 C3 000 000 C5 640 000 C11 313 000 C17 058 088 C12 886 539 C7 224 603 Cumulative expenses C550 000 C1 100 000 C4 100 000 C9 740 000 C21 053 000 C38 111 088 C50 997 626 C58 222 229
Proxts
Pro"t before tax (PBT) !C550 000 !C550 000 C1 000 000 C1 960 000 C3 127 000 C3 518 913 C1 774 574 C513 206
Cumulative PBT !C550 000 !C1 100 000 !C100 000 C1 860 000 C4 987 000 C8 505 913 C10 280 486 C10 793 693
Return on revenues (PBT/total revenuesC) 25.0% 25.8% 21.7% 17.1% 12.1% 6.6%
Cumulative total revenues (C) C69 015 922 Cumulative gross margin (C) C25 996 877
Cumulative PBT C10 793 693 Neutraline
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Year
!2 !1 0 1 2 3 4 5
Revenues
Average selling price C2000 C1900 C1805 C1715 C1629 C1548 Beginning price C2000 !5%/yr
Market capability and growth (units) 20 000 40 000 80 000 120 000 90 000 50 000
Market share 3% 9% 9% 9% 9% 9%
Unit sales 600 3600 7200 10 800 8100 4500
Total revenues or total sales C1 200 000 C6 840 000 C12 996 000 C18 519 300 C13 195 001 C6 964 028 Cumulative revenues C1 200 000 C8 040 000 C21 036 000 C39 555 300 C52 750 301 C59 714 330
Expenses
Unit cost (including labour cost) C1000 C1025 C1051 C1077 C1104 C1131 Beginning cost C1000 #2.5%/yr
Cost of goods sold C600 000 C3 690 000 C7 564 500 C11 630 419 C8 940 884 C5 091 337 Gross margin (C) C600 000 C3 150 000 C5 431 500 C6 888 881 C4 254 117 C1 872 691 Gross margin (percents) 50.0% 46.1% 41.8% 37.2% 32.2% 26.9%
Operating expenses
Engineering cost C550 000 C550 000 C200 000 C20 000 C20 000 C20 000 C20 000 C20 000 Marketing cost 15% of total sales C192 000 C1 094 400 C2 079 360 C2 963 088 C2 111 200 C1 114 245 General and admin. cost 5% of total sales C60 000 C342 000 C649 800 C925 965 C659 750 C348 201 Total operating expenses C550 000 C550 000 C452 000 C1 456 400 C2 749 160 C3 909 053 C2 790 950 C1 482 446
Total Expenses C550 000 C550 000 C1 052 000 C5 146 400 C10 313 660 C15 539 472 C11 731 835 C6 573 783 Cumulative expenses C550 000 C1 100 000 C2 152 000 C7 298 400 C17 612 060 C33 151 532 C44 883 366 C51 457 149
Proxts
Pro"t before Tax (PBT) !C550 000 !C550 000 C148 000 C1 693 600 C2 682 340 C2 979 828 C1 463 167 C390 246
Cumulative PBT !C550 000 !C1 100 000 !C952 000 C741 600 C3 423 940 C6 403 768 C7 866 935 C8 257 180
Return on Revenues (PBT/total revenuesC) 12.3% 24.8% 20.6% 16.1% 11.1% 5.6%
Cumulative total revenues (C) C59 714 330 Cumulative gross margin (C) C22 197 190
Cumulative PBT C8 257 180
Cumulative PBT C10 793 693 Neutraline
Pro"t lower !C2 536 513 !23.5%
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Pro"tability model when product introduction is on schedule but development cost over budget by 50%
Year
!2 !1 0 1 2 3 4 5
Revenues
Average selling price C2000 C1900 C1805 C1715 C1629 C1548 Beginning price C2000 !5%/yr
Market capability and growth (units) 20 000 40 000 80 000 120 000 90 000 50 000
Market share 10% 10% 10% 10% 10% 10%
Unit sales 2000 4000 8000 12 000 9000 5000
Total revenues or total sales C4 000 000 C7 600 000 C14 440 000 C20 577 000 C14 661 113 C7 737 809 Cumulative revenues C4 000 000 C11 600 000 C26 040 000 C46 617 000 C61 278 113 C69 015 922
Expenses
Unit cost (including labour cost) C1000 C1025 C1051 C1077 C1104 C1131 Beginning cost C1000 #2.5%/yr
Cost of goods sold C2 000 000 C4 100 000 C8 405 000 C12 922 688 C9 934 316 C5 657 041 Gross margin (C) C2 000 000 C3 500 000 C6 035 000 C7 654 313 C4 726 796 C2 080 768 Gross margin (percents) 50.0% 46.1% 41.8% 37.2% 32.2% 26.9%
Operating expenses
Engineering cost C825 000 C825 000 C200 000 C20 000 C20 000 C20 000 C20 000 C20 000 Marketing cost 15% of total sales C600 000 C1 140 000 C2 166 000 C3 086 550 C2 199 167 C1 160 671 General and admin. cost 5% of total sales C200 000 C380 000 C722 000 C1 028 850 C733 056 C386 890 Total operating expenses C825 000 C825 000 C1 000 000 C1 540 000 C2 908 000 C4 135 400 C2 952 223 C1 567 562
Total expenses C825 000 C825 000 C3 000 000 C5 640 000 C11 313 000 C17 058 088 C12 886 539 C7 224 603 Cumulative expenses C825 000 C1 650 000 C4 650 000 C10 290 000 C21 603 000 C38 661 088 C51 547 626 C58 772 229
Proxts
Pro"t before tax (PBT) !C825 000 !C825 000 C1 000 000 C1 960 000 C3 127 000 C3 518 913 C1 774 574 C513 206
Cumulative PBT !C825 000 !C1 650 000 !C650 000 C1 310 000 C4 437 000 C7 955 913 C9 730 486 C10 243 693
Return on revenues (PBT/total revenuesC) 25.0% 25.8% 21.7% 17.1% 12.1% 6.6%
Cumulative total revenues (C) C69 015 922
Cumulative gross margin (C) C25 996 877
Cumulative PBT C10 243 693
Cumulative PBT C10 793 693 Neutraline
Pro"t lower !C550 000 !5.1%
A.
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Pro"tability model when product introduction is shortened by 6 months with 50% over budget
Year
!2 !1 0 1 2 3 4 5
Revenues
Average selling price C2000 C1900 C1805 C1715 C1629 C1548 Beginning price C2000 !5%/yr
Market capability and growth (units) 20 000 40 000 80 000 120 000 90 000 50 000
Market share 15% 11% 11% 11% 11% 11%
Unit sales 3000 4400 8800 13 200 9900 5500
Total revenues or total sales C6 000 000 C8 360 000 C15 884 000 C22 634 700 C16 127 224 C8 511 590 Cumulative revenues C6 000 000 C14 360 000 C30 244 000 C52 878 700 C69 005 924 C77 517 514
Expenses
Unit cost (including labour cost) C1000 C1025 C1051 C1077 C1104 C1131 Beginning cost C1000 #2.5%/yr
Cost of goods sold C3 000 000 C4 510 000 C9 245 500 C14 214 956 C10 927 748 C6 222 745 Gross margin (C) C3 000 000 C3 850 000 C6 638 500 C8 419 744 C5 199 476 C2 288 845 Gross margin (percents) 50.0% 46.1% 41.8% 37.2% 32.2% 26.9%
Operating expenses
Engineering cost C825 000 C825 000 C200 000 C20 000 C20 000 C20 000 C20 000 C20 000 Marketing cost 15% of total sales C900 000 C1 254 000 C2 382 600 C3 395 205 C2 419 084 C1 276 739 General and admin. cost 5% of total sales C300 000 C418 000 C794 200 C1 131 735 C806 361 C425 580 Total operating expenses C825 000 C825 000 C1 400 000 C1 692 000 C3 196 800 C4 546 940 C3 245 445 C1 722 318
Total expenses C825 000 C825 000 C4 400 000 C6 202 000 C12 442 300 C18 761 896 C14 173 192 C7 945 063 Cumulative expenses C825 000 C1 650 000 C6 050 000 C12 252 000 C24 694 300 C43 456 196 C57 629 389 C65 574 452
Proxts
Pro"t before tax (PBT) !C825 000 !C825 000 C1 600 000 C2 158 000 C3 441 700 C3 872 804 C1 954 031 C566 527
Cumulative PBT !C825 000 !C1 650 000 !C50 000 C2 108 000 C5 549 700 C11 376 535 C9 422 504 C11 943 062
Return on revenues (PBT/total revenuesC) 26.7% 25.8% 21.7% 17.1% 12.1% 6.7%
Cumulative total revenues (C) C77 517 514 Cumulative gross margin (C) C29 396 565
Cumulative PBT C11 943 062
Cumulative PBT C10 793 693 Neutraline
Pro"t higher C1 149 369 10.6%
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191
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