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An Anatomy of a Decision-Support System for Developing and Launching Line Extensions Author(S): Morris A

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An Anatomy of a Decision-Support System for Developing and Launching Line Extensions Author(S): Morris A An Anatomy of a Decision-Support System for Developing and Launching Line Extensions Author(s): Morris A. Cohen, Jehoshua Eliashberg and Teck H. Ho Source: Journal of Marketing Research, Vol. 34, No. 1, Special Issue on Innovation and New Products (Feb., 1997), pp. 117-129 Published by: American Marketing Association Stable URL: http://www.jstor.org/stable/3152069 . Accessed: 15/04/2013 10:19 Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp . JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. American Marketing Association is collaborating with JSTOR to digitize, preserve and extend access to Journal of Marketing Research. http://www.jstor.org This content downloaded from 128.91.110.146 on Mon, 15 Apr 2013 10:19:14 AM All use subject to JSTOR Terms and Conditions MORRISA. COHEN,JEHOSHUA ELIASHBERG, and TECKH. HO* For most firms producing fast-moving consumer packaged goods, line extension is central to their new product development (NPD) strategy. The authors present a decision-support system for managing the NPD process in this industry, which explicitly evaluates the financial prospects of new line extension concepts. The system developed is based on an in- depth analysis of 51 new product projects launched over a three-year period at a major food manufacturer. It embodies historical knowledge about the productivity of the firm's NPD process and captures some key research and development resource inputs that can affect this productiv- ity. It also provides shipment forecasts at various stages of the NPD process and thus can be used at new product project review gates to evaluate line extension concepts systematically. Finally, the system also can be used to improve the practice of the NPD process by enabling its users to take a product line perspective, using incremental sales evalua- tion, and by facilitating cross-functional and inter-project learning. Although the system has been developed specifically for a packaged food company environment, its underlying design principles are generic and applicable to a wide range of industries. An Anatomy of a Decision-Support System for Developing and Launching Line Extensions For most industrialand consumer productfirms, success- dustry,characterized by a high rate of productobsolescence ful new products are engines of growth for sales and prof- and a narrowwindow of marketopportunity, depends on the itability.Although there is consensus about the strategicim- interactionof a numberof factors, includingthe level of cap- portance of new product development (NPD), no universal ital investmentto supportdevelopment (e.g., use of comput- formula for success has been prescribed.Firms in different er-aideddesign and engineering), the productivityof the en- industrieswrestle with differentcompetitive imperatives and gineeringresource (i.e., how fast it can improveproduct per- formulatedifferent strategiesto succeed in the marketplace. formance),the numberof engineeringhours allocated to dif- In Cohen, Eliashberg,and Ho (1996), we present an ana- ferent researchactivities in the NPD process, an appropriate lytical model for managing the trade-offs between a target choice of a targetlevel for productperformance, and time to level of product performanceand the time to market, two market. critical factors for success in high-technology industries The challenges faced by firms in the packaged goods (e.g., computers, packaged software). Because objective (e.g., food, detergents)industry are somewhat different.Us- measures for product performance can be determined in ing an in-depthanalysis of 51 new productslaunched over a these industriesfairly easily, the primaryobjective of a de- three-yearperiod at a major packaged goods company, we sign team becomes launching a product with sufficiently observed that it is often impossible to develop objective high performance in the shortest time possible. We also measuresof productperformance. Consequently, the drivers show that the ability to achieve success in this type of in- of success of NPD for packaged goods are different from those in the high-technologyindustry. Firms cannot perfect- determine customer wants ex ante for *Morris A. Cohen is the Matsushita Professor of Manufacturingand ly (especially truly Logistics, Operations & Information Management Department, and new products),so they must invest relatively more time and JehoshuaEl iashberg is Professorof Marketing,Operations and Information effort as they move throughthe design process to capturethe Management, Department of Marketing, Wharton School, University of "voice of the customer,"so that the new product will be Pennsylvania.Teck H. Ho is Assistant Professor of Operationsand Tech- more likely to meet customers' needs (Akao 1992). nology Management,Anderson School of Management,University of Cal- ifornia, Los Angeles. Therefore,the critical input resources are those that help to identify,and laterto influence, customers'wants. We have Journal of MarketingResearch 117 Vol. XXXIV (February 1997), 117-129 This content downloaded from 128.91.110.146 on Mon, 15 Apr 2013 10:19:14 AM All use subject to JSTOR Terms and Conditions 118 JOURNAL OF MARKETINGRESEARCH, FEBRUARY 1997 found that these inputs include the level of experienceof the Figure 1 new product design team leader and the amount of cus- THEPS2'S OVERALLSYSTEM DESIGN tomer-basedinformation collected during the NPD process. In addition,a significant amountof promotionand advertis- dollars ing must be invested to launch such products suc- New Product Function Button Invoked cessfully. Life Cycle of Support System Model (Equation The difficulty and risk associated with determiningcus- Number) tomers' needs have promptedmany firms in the fast-moving consumergoods industriesto rely heavily on line extensions for stimulating demand for their brands. Indeed, a recent study of leading consumer packaged goods companies re- Resource Product vealed that only 5% of new product introductionsare new Scenario Performance Analysis Inprovement brands,6% are brandextensions, and the remaining89% are Function line extensions (Aaker 1991).1 Many believe that line exten- (2.1-2.2) sions will continue to be central to NPD strategy in con- sumerpackaged goods markets(Aaker 1991). Therefore,the importance of effectively managing the line extension is the lower risk in- Promotion & process quite high. Moreover, despite Multi-stage Forecasting volved in line feel that their NPD Advertising Forecasting Functions extensions, managers Spending process is successful less than 50% of the time (Group EFO (2.3) Limited 1994). One way to improve the NPD process is to rely on deci- sion-support systems (DSSs). The use of computer-based supportsystems to improvedecision making in marketingis Competitive, Cash Flow Cumulative Pricing, & Cos Analysis Profit Function not new. Several successful of DSSs have Assumptions implementations (2.7) been reported,supporting such activities as marketingmix (Little 1970, 1975), sales calling (Lodish 1971), retailing (Lodish 1981), consumer promotion (Keon and Bayer commercializationand communicationof new Cumulative Profit 1986), prod- Break-even Time ucts (Rangaswamy et al. 1987), new product launching (Choffray and Lilien 1986; Ram and Ram 1989), and mar- ket trendsdetection (Schmitz, Armstrong,and Little 1990). We describe a DSS designed for packaged goods manu- istics similar to those of existing products.The process of facturersto improvethe evaluationand selection of new line developing it, in cooperationwith the company,has already extension concepts, the allocation of the necessary re- led to changes in the company's NPD procedures.We con- sources, and the promotion of links between research and clude by discussing the actual and likely future impacts of development (R&D) and marketingdecisions. The DSS is the system on the company, its managerialand researchim- model-based and comparable to ADBUDG (Little 1970), plications, and the conditions necessary for its successful CALLPLAN(Lodish 1971), and BRANDAID (Little 1975). implementation. Several empirical relationships, which can be developed OVERALLSYSTEM DESIGN from company historical recordsof line extension launches, are embeddedinto the system. These empiricalrelationships Description of the System are used to predict the impact of alternativeresource input The DSS we developed is called Product Portfolio Sup- mix choices on anticipatedproduct performance and to gen- port System (PS2). The PS2 design is based on data available erate sales volume forecasts at various stages of the NPD in the company and supports marketresearch, brand man- process. As a result, the system can help improve the selec- agers, R&D managers, and manufacturing managers tivity of the NPD process so that inferior concepts can be throughoutthe NPD process. The basic shell of the system screened out early on, therebyenabling superiorconcepts to was created in Hypercard2.0 V2. be developed successfully (Kotler 1994; Wind 1982). In Figure 1, we indicate the overall.design of PS2 and the We illustrate how the system can be used to support a way it supportsresource allocation, as well as selection and
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