An expert system for feasibility assessment of product development

Expert Systems With Applications, Vol. 7, No. 2, pp. 291-303, 1994 Copyright © 1994 Elsevier Science Ltd Printed in the USA. All rights reserved 0957-4174/94 $6.00 + .00

Pergamon

An Expert System for Feasibility Assessment of Product Development JACKY AKOKA AND BERNARD LEUNE I~coleSup~rieuredes Sciences~nomiques et Commerciales(ESSEC),Cergy,France

ALEXIS KOSTER San Diego State University,San Diego,CA

Abstract--This article presents an expert system used for assessing the chances of success of a new product. This system was successfully used in a commercial environment for the development of a pharmaceutical product. This system is not limited to specific aspects of a specific product nor to a particular domain. The domain considered comprises economic, financial, and marketing aspects. The expert system encompasses relationships among the many factors of the decision-making process across these domains. This approach is a dynamic one that, beyond today's choices, will help with future decisions.

It can be associated with a growth strategy of new market development, building of market share, or with a strategy of holding a market. It can be proactive or reactive. Acquisition was often preferred to new product development by many large U.S. firms during the 1980s. An enterprise can be involved in several strategies at the same time with different products. Moreover, a given strategy for a product can be achieved by very different means. This article is concerned with the growth strategy of new product development and the description of an expert system to help in this process. We can view this expert system as just one "subexpert" system in the total information system of the enterprise. At a higher-level, expert systems could be used to advise on what strategy to choose, on selecting a portfolio of products, and so on. At a lower level, expert systems could be used in various specialized areas of finance (Holsapple, Tam, & Whinston, 1988), such as capital budgeting (Myers, 1988), in advertising strategies (McCann, 1986; McCann & Gallagher, 1991), and in accounting (Thierauf, 1990).

1. NEW PRODUCT DEVELOPMENT

1.1. Growth Strategy and New Product Development The life-cycle of a product consists of three phases: development, maturity, and decline. These three phases correspond to three specific strategies for the enterprise, growth, consolidation, and disengagement, respectively. Figure 1 illustrates these strategic choices and the strategic objectives that follow from these choices. In the consolidation strategy, the enterprise searches to optimize the revenues generated by a product currently marketed. This objective can be achieved in various ways, including increase in productivity and close scrutiny of financial activities. This strategy produces short-term results. In the fierce competitiveness of the global economy, and with the rapid changes caused by technology, it could present severe problems for the long-term. Disengagement can be the logical consequence of a consolidation strategy for a product, or it can result from the decision to concentrate on different products or activities. A well-managed disengagement strategy may actually generate profits. New product development is itselfa strategic choice by management, which involves other strategic choices.

1.2. New Product Development: A High-Risk Activity The design, development, and marketing of a new product is a high-risk activity. In 1980, it took several years and between two and six million dollars to bring a major new product to market (Urban & Hauser,

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1980). A recent survey indicates an average development cost of 231 million dollars for new drugs based on new chemical entities (DiMasi, Hensen, Grabowski, & Lasagna, 199 l). In some industries one new product out of five will be successful. Some studies indicate that over 70% of expenses for the development of new products is spent on unsuccessful projects (Booz, Allen, & Hamilton, 197 l). Fast improving technology and fast changing customer needs add even more uncertainty to the chances of success of new products (Bridges, Coughlan, & Kalish, 199 l). What makes new product development so risky is the large number of factors and variables that can affect the outcome of a project as well as the complexity of the relationships among them. Values of some of these variables may be difficult to assess; some may be subject to rapid changes, and final outcome may be very sensitive to small parameter changes. Those data are often classified as external or internal data. External data, also called environmental data, include many economical data, such as economic growth indicators, rate of inflation, various interest rates, various market trends, foreign exchange rates, price of raw materials,

technological factors, political, social, and legal factors, competition, and so on. Internal data are data relative to the enterprise, for example various financial ratios, assets, liabilities, production costs, sales prices, and product portfolio. In contrast to external data, managers have some degree of control over the values of internal data. A number of techniques have been proposed and used for deciding on new product development. These techniques usually involve statistical analysis on some of the data aforementioned and use of management science models (Abell & Hammond, 1979; Holak, Parry, & Song, 1991; Pessemier, 1982). The variety and number of data analyzed and the depth of analysis can vary greatly from one technique to another. Experts' knowledge in those schemes is considered late in the process. Decisions sometimes are made and new products developed without the help of any formal techniques. This has been the cause of costly failures (Urban & Hauser, 1980). Given the complexity of the decision-making process, the propensity to omit some or all of the steps of formal techniques or to misuse them because of per-

Feasibility Assessment of Product Development sonal biases, and the existence of recognized experdses, which are not always tapped, an expert system appears as a good tool to help in that decision-making process. The rest o f this article describes an expert system developed to assess the feasibility of a new pharmaceutical product. The generality of criteria used by that system makes it possible to use it for the development of other types of products.

2. D E C I S I O N - M A K I N G PROCESS IN N E W PRODUCT DEVELOPMENT The model presented here confiders three fundamental aspects: finance, marketing, and economy. Table 1 shows key elements of the decision-making process and the main variables affecting these key elements. A more complete model would involve other elements, for example technical and human resources. 2.1. Financial Feasibility Financial resources, current and projected, are analyzed to determine if they can support the financial requirements of the project. Financial elements related to marketing, such as market entry costs (advertising and distribution), are included in this analysis. The impact of environmental factors, such as the cost o f certain raw materials and interest rates, are also considered here. Other important financial factors include: • overhead expenses, operating costs • direct and indirect labor costs • labor productivity, investment productivity • capital structure of the business, in particular permanent assets--liquid assets, including debts, - - net worth indebtness--long-term and short-term debts, indicating financial independence - - current assets, which indicate whether the enterprise can meet its short-term obligations These financial factors are indicative o f the enterprise capacity to engage in this new activity. Other financial factors include: • total cost, including cost of raw materials, direct and indirect labor costs, and overhead expenses • productivity--personnel productivity and investment productivity • cost o f the new product--this cost depends on price of raw materials used in its fabrication and on various factors of the economic environment, on labor costs and overhead expenses • selling price of the new product, mainly based on total cost, but also on competitors' prices, and strategy (market penetration, market dominance, or short-term profit) • sales, based on price and quantity sold

293 • earnings, before and after taxes, before and after dividend distribution 2.2. Marketing Feasibility Marketing feasibility incorrectly conducted (or worse, not conducted at all) may be one of the leading causes of new product failure. An example is wrong perception of consumer needs and of the market (Cooper, 1975). This may also be the phase where personal biases have the largest impact. The following elements are factored in the decision process: • Competition analysis. One will determine the number of competitors, the leading competitors and their market shares, the existing brands for the product, the type of market (monopolistic or free market), the competitors' capacity to react financially, technologically, and through their marketing know-how and structure. • Market entry barriers. One will determine the cost and time to penetrate the market, in particular the financial requirements for product advertising and distribution. • Product characteristics. Is the product a completely new product, is it a variant of an existing one, does it bring a new or improved functionality, is it based on a better technology? • Market demand for the product. One will determine the global trends of the market, the trends for this type of product, the existing opportunities due to changes in competition and in market structure. • Marketing capacity of the business. Are the business distribution channels appropriate, or is a new structure needed? The sales force must be evaluated. Given financial and market constraints, what will be the advertising medium chosen? How will the message be perceived by the prospective consumers? • Projected product sales. From the market feasibility analysis, one may assess the sales of the product in quantity and price. 2.3. Environmental Feasibility The evaluation of the impact o f environmental (i.e., macroeconomic) factors on a new product is critical. Although this evaluation is difficult and often incomplete, it is better than no assessment at all. As a French product is discussed, some of the elements considered here could have a lesser impact if it were developed in a different country like the United States. Nevertheless, the product discussed here is intended for worldwide distribution. The United States is considered an important market for this product. This makes the environmental analysis even more complex. The French Administration tends to support industries that it judges important to French economy, prestige, and national security. For example, the computer industry,

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Z Akoka ~ a~ TABLE 1 Key Variables in the Decision-Making Process

Key Elements Product structure Growth and capacity for profit

Technology Investment

Marketing

Competition

Outlook

Government policies and economy

Labor climate Consumers Human resources

the electronics industry, and the automobile industry have received heavy support from the French government. This involvement may take the form of direct or indirect government subsidies, preferential tax laws, and preferential orders by state-owned companies.

Factors product characteristics purpose of product customers past trends future trends important demand factors market segment factors subjacent technology opportunity in technology role of technology in product success cost of acquisition cost of divestiture depreciation methods role of investment in product success long-term and short-term debts sales tools and methods distribution network competitive edge of product role of marketing in product success role of advertising, advertising methods market shares market concentration market segmentation characteristics of successful enterprises trends in competition financial and marketing characteristics of competition reactive capacity of competition number of competitors and competing brands trends in demand trends in market structure trends in technology key factors for success French inflation industrial policies economic policies French gnp French interest rates French trade balance exchange rate FF vs $US oil price US trade balance US GNP US economic policies industry trend overall trend buying behavior earnings buying power job market motivation creativity productivity satisfaction impact of personnel on success

Government involvement in the USA usually is more modest. It often stems from the application of laws and regulations (Koster & Parker, 1990) or sometimes the relaxation in the application of regulations by regulatory federal or state agencies.

Feasibility Assessment of Product Development France has a national health insurance system. The national health insurance agency regulates the price of pharmaceutical products. It also decides the rate of reimbursement to patients. Therefore, its decision will have a critical impact on the success of a new pharmaceutical product in France. In the United States the Food and Drug Administration approves new pharmaceutical products after a complex and time-consuming process. Pressure groups (environmentalists, consumer advocate groups, and religious groups, to cite some recent examples) may have a substantial marketing impact by applying political pressure on the Federal Government to speed up that process or by trying to forbid the marketing of the product. The following environmental elements are considered: • French administration support for industry. Is the administration policy one of active support, neutrality, or disengagement? • Labor climate. Are labor-management relations good, or is there a risk of labor unrest? • Economy. The construction of an econometric model is possible in a stable environment. A very rich knowledge base is required to get an assessment, even approximate, of the economic environment short-term impact. Moreover, there are a number of reciprocal interactions among the various parameters that must also be factored in: French GNP, US GNP, French international trade balance, prime rate, rates of inflation in France and in the United States, foreign exchange rates, U.S. economic policy. 3. DEVELOPMENT AND DESCRIPTION OF THE EXPERT SYSTEM

3.1. Development of the Expert System The project personnel consisted mainly of two kinds of experts: staff members of the pharmaceutical company and two knowledge engineers. They realized the project, including installation and training of users, in about 8 months, taking about 8 man-months. The breakdown of the various activities is as follows: • problem definition, knowledge acquisition, choice of knowledge representation and development t o o l 4 months • programming of the knowledge base and of related modules--2 months • integration, testing, and installation--2 months Those figures are approximate, because the personnel participating in the project were also involved in other activities and because the use of prototyping to develop the project made it harder to keep accurate records of the time spent on the various phases.

3.2. Description of the Expert System The dependencies among the various parameters affecting financial feasibility, environmental feasibility,

295 and marketing feasibility are depicted in Figures 2, 3, and 4. These dependencies actually can be represented by trees, which are themselves subtrees of the tree depicting the overall strategic objectives shown in Figure I. Although this is not a decision tree in the usual sense, it is similar to one in its structure. Therefore, a rulebased expert system was judged appropriate for representing the knowledge. The expert system was realized using the expert system shell M.1 (Mockler, 1989). M.l is a rule-based system with a backward chaining inference engine. It runs on IBM-PC and compatible microcomputers. It offers certainty factors. By default, certainty factors in M. 1 are based on an algebra similar to the algebra developed for MYCIN (Buchanan & Shortliffc, 1984). However, M.1 permits the knowledge engineers to override these computation rules and to define their own rules for computing certainty factors (Gallagher, 1988). Given the special properties of the problem domain, this latter solution, described next, was chosen for the expert system. The variables used in the premises and conclusions of the rules in the knowledge base take their values on ordered domains of symbolic values, such as weak, average, strong or strongly decreasing, decreasing, flat, increasing or strongly increasing. One can easily remark the similarity to the enumerated data type first introduced by the programming language Pascal. Moreover, the value of a variable appearing in the conclusion of a rule in the knowledge base is a monotonic function of each variable appearing in the premise of the rule. These observations permit the mapping of the domains of these variables to numeric values into a numeric interval. The interval 21-100 was chosen here to conform with M. l, which usually discards facts with certainty factors in the range 0-20. Moreover, these numeric values are also used as values of the certainty factors for these variables. Table 2 gives a partial list of the symbolic values of some variables with their associated certainty factors. Combining symbolic values and numeric calculations on certainty factors offers some advantages: 1. Reduced size of the knowledge base. 2. User-friendliness of symbolic values. The use of symbolic values increases the readability of the knowledge base and facilitates its maintenance. 3. Modeling power. Various mathematical functions can be used to compute the certainty factor of the conclusion. Because several numeric values can be chosen for a given symbolic value, the fuzziness of symbolic data can be reduced, and a finer analysis can be provided. The overall structure of the knowledge base is represented by dependency diagrams (Mocklcr, 1989) shown in Figures 5, 6, and 7. Variable names appear in rectangles. The rules appearing directly below the rectangle are rules that are used to evaluate the variable (that is, the variable appears in the conclusion of these rules).

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Variables appearing in the premises of these rules are shown below these rules (but not necessarily directly under the rules). A triangle under a variable indicates that the value of the variable is obtained through interactive dialogue with the user. 4. THE EXPERT SYSTEM AT WORK 4.1. Expert System Advice The appendix shows the first part of a sample session with the expert SYStem,during which the expert system

requests various data from the user. An excerpt of the expert system assessment of the product, together with its evaluation of a number of important parameters is shown next: Project feasibility: acceptable cf 56 Results: average cf 56 Economic environment: good cf 71 Earnings: critical cf 41 Sales: average cf 58 Selling price: critical cf 48 Competitors' position: strong cf 44

TABLE 2 Mapping Certainty Factors to Symbolic Values

Certainty Factors Rule Variables

100-90

Environment Labor_climate Economy PNB Trade_balance Currency Project_Feasibility

favorable good good growth positive decrease very good

89-70 good stable stable low_growth slightly_positive slight_decrease good

69-50 ok ok ok stable balanced stable acceptable

49-30 critical unstable unstable low_decrease slightly_negative slighLdecrease doubtful

29-21 risky risky bad decrease negative increase very_doubtful

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Investment level: low cf 35 Permanent assets: low utilization cf 85 Indebtness: good cf 85

Quantity sold: average cf 67 Product demand: low cf 46 Distribution: ok cf 62 Advertising effectiveness: appropriate cf 95

4.2. Analysis of the Expert System Evaluation The project feasibility is found acceptable (cf 56) by the expert system. The evaluation of earnings as critical (cf4 l) indicates that the enterprise will not get enough profit on this product. The selling price of the product is critical (cf 48), dependent on actual cost (average cf 51 ) and competitors' position. A decrease in costs and overhead could result in a decrease of the product cost. Given the strong (cf 44) competitors' position, it

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can be expected that they will act to keep their market share. This enterprise will likely increase its market share by taking consumers away from the weakest competitors. Market entry cost is very high (cf 25). The costs incurred by the top three competitors for each percentage point of market share are 0.862 million FF and 0.445 million FF in sales force and advertising, respectively. These figures represent 66% and 34% of the marketing investment. To gain 1% market share,

the enterprise will need to spend 1.3 million FF in salesforce and 0.7 million FF in advertising. The financial health of the enterprise is good: very low short-term debts (cf 95) and low long-term debts (cf 80). Permanent assets are underutilized (cf 83). The ratio of capital equity to debts is high (cf 80). Therefore, the enterprise has a high ratio of financial independence (1.31), and it can easily raise money to finance its advertising campaign for the new product. Last, we note that the market demand is average (cf

Feasibility Assessment of Product Development

60). The product demand is low (cf 46) due to its low differentiation from existing products (cf 33). 4.3. Expert System Assessment and Actual Results The assessment of the project feasibility by the expert system (acceptable cf 56) was validated in two ways. First, this assessment was very close to the value arrived at by the more traditional techniques used in marketing: marketing research, forecasting and diffusion models, and test marketing. Second, most of the values of the various factors of the project provided by the expert system differ only slightly from the actual results for the product after 7 months on the market. The use of the expert system provides a number of comparisons and explanations useful to understand what is actually happening. In particular, it sheds light on critical factors, for which corrective actions can be proposed. An important discrepancy between the expert system and actual results lies in the market share likely to be reached after 1 year: only 1% instead of the objective of 1.5% predicted by the expert system. This weak value reflects both the assessment of the salesforce as mediocre (cf45) and of the productivity assessed as average (cf 62) by the expert system. Moreover, the proposed ratio of investments in salesforce and advertising (66% vs. 34%), which is the ratio used by the top three competitors differs greatly from the actual ratio (95.5% vs. 4.5%) for this product. That could be a major reason for the lower share market, in spite of the fact that the actual overall marketing investment is much greater for this enterprise than for the three competitors: 3.3 million FF versus 1.3 million FF. More analysis is needed to explain this discrepancy. The results of this analysis could then be incorporated in the expert system knowledge base. 5. DIRECTIONS FOR FURTHER DEVELOPMENT Developed as a prototype, this expert system is used by the enterprise. Comparisons of the expert system assessment with actual market data for the product developed should permit to improve the expert system and provide a better model with respect to the three major components: financial, marketing, and environmental. Whereas the financial aspect seems to require little improvement, both the marketing and environmental components will require further analysis, from which new values for existing parameters, new parameters, and new rules will be developed and included in the expert system. Although this expert system is directly usable for other pharmaceutical products, some parts of its knowledge base may have to be adjusted to the actual product and to the actual market. A first direction for a greater generality of the expert system is to develop

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a framework in which the knowledge base could be structured to permit its adaptation to various products and markets. In this approach, one could view the knowledge base as consisting of three components. First, a "kernel" knowledge base would embody the knowledge common to the development of various products. The rules of the expert system presented here that are relative to the financial feasibility and some rules relative to the environmental feasibility and marketing feasibility would be likely parts of the kernel. Second, we could have a library of small modules. Each module could contain the knowledge necessary for various types of products and intended broad markets. Third, as a specific product is developed, knowledge specific to it would have to be formalized in a specialized "layer." There will also be some need to adapt the kernel and library modules to the terminology used by the industry for the market considered. The final version of the expert system would then be assembled from the kernel, selected library modules, and the specialized layer. The proposed approach embodies two concepts: the concept of generic expert system and the concept of reusable modules in expert systems. The kernel knowledge base contains the generic knowledge, and the specialized layers contain the pragmatic knowledge (Reitmann, 1991). The various modules of the three components of the knowledge base can then be viewed as reusable components (Liebowitz, 1991). This structuring of the knowledge base would facilitate knowledge base updating. To increase even more this capability, an automated knowledge acquisition tool would be a useful addition to the system (Liebowitz, 1991; Marcus, 1988). Such a system would be part of a decision support system, in order to manage the operations needed to assemble a complete expert system, as well as to provide other facilities. Whereas the expert system provides useful advice on the appropriateness of a new product development, management science-based models will still be needed when more precise figures are required, such as in the search of an optimal solution. The integration of expert systems with decision management systems has been actively investigated in the past few years (King, 1990; Klein & Methlie 1990; O'Leary & Watkins, 1990), in particular integration with operations research (Turban & Trippi, 1990). Simulation components should also be available to test the advice of the expert system (Balmer, Goodman, & Doukidis, 1989). Data should be made available to the expert system either interactively or from spreadsheets and databases (Gallagher, 1988; McCann & Gallagher, 1991). Various architectures of such DSS have been discussed. In the proposed DSS, the augmented expert system would be a component at the same level as the model base management components and the DBMS (Turban, 1992).

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6. C O N C L U S I O N The experiment described here was positive. The project was successfully used on a pharmaceutical product. As expected, there were differences between the actual results and the ones predicted by the expert system. The analysis o f these differences should improve the model. The basic model should be useful in different businesses and for different products. Needed changes will have to be brought to the knowledge base to reflect the new conditions. The system offers an e n v i r o n m e n t for product development that is flexible and easy to use. It allows the explicit tracing o f its line o f reasoning. This is easier to understand than the m o r e traditional m a n a g e m e n t science models. The proposed system considers a large n u m b e r o f factors right away. In fact, the model design forced by the technology is in itself an important factor. It provides the system user a good understanding o f a complex reality. Finally, the system can be used in various "what-if" scenarios. Built on recognized expertise in the enterprise, which can be enriched along the years, it gives the enterprise an important competitive edge. T o be truly effective, this system will need a builtin facility permitting the updating o f its knowledgebase to adapt it to various products and markets. A n intelligent decision support system should constitute the framework permitting this type o f changes as well as providing access to m a n a g e m e n t science-based quantitative models, and to data contained in spreadsheets and data bases. New information systems technology is a critical aspect o f the competitiveness o f enterprises. Expert systems such as this one permit an enterprise to treat the knowledge o f its personnel as a critical resource that can be stored a n d effectively used. M a n y efforts are still needed to build better models o f that knowledge, to learn to acquire, organize it and update it in an efficient way, and to integrate it in the overall information systems o f the enterprise.

DiMasi, J.A., Hansen, R.W., Grabowski, H.G., & Lasagna, L. (1991). Cost of innovation in the pharmaceutical industry. Journal of Health Economics, 10(2), 107-142. Gallagher, J.P. (1988). Knowledge systems for business-integrating expert systems & M1S. Englewood Cliffs, NJ: Prentice-Hall. Holak, S.L., Parry, M.E., & Song, X.M. (1991). The relationship of R&D/sales to firm performance: An investigation of marketing contingencies. Journal of Product Innovation Management, 8(4), 267-282. Holsapple, C., Tam, K.Y., & Whinston, A. (1988). Adapting expert system technology to financial management. FinancialManagement, 17(33), 12-22. King, D. (1990). Modelingand reasoning: Integratingdecision support with expert systems. In J. Liebowitz (Ed.), Expert systems for business and management (pp. 51-76). Englewood Cliffs, NJ: Prentice-Hall. Klein, M., & Methlie, L. (1990). Expert systems: A decision support approach. Reading, MA: Addison Wesley. Koster, A., & Parker, S. (1990). Regulation management and logic programming. Software--Practiceand Experience, 20(1), 80-107. Liebowitz, J. (1991). Institutionalizing expert systems: A handbook for managers. Englewood Cliffs, NJ: Prentice-Hall. Marcus, S. (Ed). (1988). Automating knowledge acquisitionfor expert systems. Boston: Kluwer Academic. McCann, J.M. (1986). The marketing workbench. Homewood, IL: Dow Jones-Irwin. McCann, J.M., & Gallagher, J.P. (1991). Databases and knowledge in merchandising. New York: Van Nostrand Reinhold. Mockler, R.J. (1989). Knowledge-basedsystems for management decisions. Englewood Cliffs, NJ: Prentice-Hall. Myers, S.C. (1988). Notes on an expert system for capital budgeting. Financial Management, 17(33), 24-31. O'Leary, D.E., & Watkins, P. (1990). Expert systems and decision support systems in auditing. In J. Liebowitz(Ed.), Expert systems for business and management (pp. 77-99). EnglewoodCliffs, NJ: Prentice-Hall. Pessemier, E. (1982). Product management. New York: Wiley. Reitman, W. (1991). Managing the development of generic expert system products. In D.A. DeSalvo& J. Liebowitz(Eds.),Managing artificial intelligence& expert systems (pp. 146-169). Englewood Cliffs, NJ: Prentice-Hall. Thierauf, R.J. (1990). Expert systems infinance and accounting. New York: Quorum Books. Turban, E. (1992). Expert systems and applied artificial intelligence. New York: Macmillan. Turban, E., & Trippi, R. (! 990). Integrating expert systems with operation research: A conceptual framework. Expert Systems with Applications, 1, 335-343. Urban, G.L., & Hauser, J.R. (1980). Design and marketing of new products. Engiewood Cliffs, NJ: Prentice-Hall.

REFERENCES Abell, D.F., & Hammond, J.S. (1979). Strategic market planning. Englewood Cliffs, NJ: Prentice-Hall. Baimer, D., Goodman, D., & Doukidis, G. (1989). Integratingexpert systems and simulation for decision support. In G. Doukidis, F. Land, & G. Miller (Eds), Knowledge-based management support systems (pp. 134-160). New York: Wiley. Booz, Allen & Hamilton. (1971). Management of new products. New York: Author. Bridges, E., Coughlan, A.T., & Kalish, S. (1991). New technology adoption in an innovative marketplace: Micro- and macro-level decision making models. International Journal of Forecasting, 7(3), 257-370. Buchanan, B.G., & Shortliffe, E.H. (Eds). (1984). Rule-based expert systems. Reading, MA: Addison-Wesley. Cooper, R.G. (1975). Why new industrial products fail. Industrial Marketing Management, 4(2), 315-326.

APPENDIX: A S A M P L E S E S S I O N W I T H T H E EXPERT SYSTEM: D E S C R I B I N G THE PRODUCT AND DATA The following is a small excerpt of a session with the expert system. For simplification, the actual screens are not shown. Typically, users are provided a list of possible values of a variable, from which they choose the correct one. Q. A. Q. A. Q. A.

What is your main objective? Product development. For what industry? Pharmaceutical Industry. Is the product ethical? Yes.

Feasibility Assessment of Product Development Q. What is the class of the product? A. Vasculoprotector. Q. What is the rate of reimbursement of this class of products by the national medical insurance agency? A. 40%. Q. Is the enterprise part of a larger entity? A. No. Q. What is the degree of stability of the industry? A. Stable cf60. Q. How is the labor climate? A. Good cf95. Q. What is the administration policy toward this industry? A. Keep at current level cf 60. Q. What is the GNP trend? A. Stable cf 60. Q. What is the inflation trend? A. Slow decrease cf 80. Q. Will the product have a positive or negative effect on the "trade balance"? A. Slightly positive cf 80. Q. What is the trend of the currency for the country involved in the previous question? A. Stable cf60. Q. What is the cost of raw materials in relation to competitors? A. Average of 60. Q. What is the value of long-term debt? A. 5363000 FF (authors' note: close to 1 million US dollars). Q. What is the value of short-term debt? A. 350000 FF. Q. What will be the contribution of the product to general expenses? A. Average cf60. Q. How many brands do exist? R. 80.

303 Q. What is the market share of the top three competitors? A. 35.8%. Q. How many competitors do offer a similar product? A. 1. Q. What is financial strength of the competitors? A. Very good cf 25. Q. What is the reactive capacity of the competitors? A. Very strong cf 25. Q. What is the technological position of the competitors? A. Average cf60. Q. What is the market entry cost (that is, what is the amount of advertising of the top three competitors)? A. 16 Million FF. Q. How long did it take them to get 35.8% of the market? A. 36 months. Q. What part of the market do you want to reach in 1 year?. A. 1.5%. Q. What part of the market do you want to reach in 3 years? A. 5%. Q. What will be the short-term trend of the market? A. Stable cf60. Q. What will be the long-term trend of the market? A. Stable cf 60. Q. What will be the mid-term trend of the market? A. Stable cf 60. Q. How do you assess the enterprise salesforce in relation to the competition? A. Mediocre cf 45. Q. How do you evaluate the enterprise distribution structure? A. Adapted to product cf 95. Q. How will your message be perceived by your targeted consumer?. A. Precise 85. Q. Given your advertising budget, how appropriate is the selected advertising media? A. Adapted cf 95.