The development of Ford Escort, Chrysler LH, and. Chrysler Neon is reportedly to have cost their companies $5 billion, $1.5 billion and $1.3 billion respectively.
Journal of Intelligent Manufacturing (1999) 10, 231±244
Arti®cial intelligence and expert systems applications in new product developmentÐa survey S . S U B B A R AO , A B R A H A M N A H M , Z H E N G Z H O N G S H I , X I AO D O N G D E N G and A H M A D S Y A M I L Department of Information Systems and Operations Management, College of Business Administration, The University of Toledo, 2801 West Bancroft Street, Toledo, Ohio 43606, USA
The authors review and categorize the research in applications of arti®cial intelligence (AI) and expert systems (ES) in new product development (NPD) activities. A brief overview of NPD process and AI is presented. This is followed by a literature survey in regard to AI and ES applications in NPD, which revealed twenty four articles (twenty two applications) in the 1990±1997 period. The applications are categorized into ®ve areas: expert decision support systems for NPD project evaluation, knowledge-based systems (KBS) for product and process design, KBS for QFD, AI support for conceptual design and AI support for group decision making in concurrent engineering. Brief review of each application is provided. The articles are also grouped by NPD stages and seven NPD core elements (competencies and abilities). Further research areas are pointed out. Keywords: Arti®cial intelligence, expert systems, knowledge-based systems, new product development
1. Introduction Arti®cial intelligence (AI), as a discipline, has grown enormously during the past decade. AI applications today span the realm of manufacturing, consumer products, ®nance, management and medicine. Implementation of the appropriate AI technique in an application is often a must to stay competitive. Truly pro®table AI techniques are often kept secret. Expert systems (ES) and knowledge-based systems (KBS) applications have been proli®c, impacting many areas of decision making in industries, especially in manufacturing like scheduling, production planning, plant layout, advanced manufacturing processes, purchasing and materials. Today's manufacturing organizations compete not only on the factory ¯oor, but also in the product development arena (Abegglen and Stalk, 1985; Stalk, 1988; Nonaka and Takeuchi, 1995). A competitive advantage can be built through superior manufac0956-5515 # 1999
Kluwer Academic Publishers
turing but sustaining it over time requires skills in developing a continual stream of new products. Shorter product life cycle, more demanding customers, and globalization of market make new products not just desirable for a company, but mandatory (Kuczmarski, 1988). Not only is product development becoming more central to meeting the increasingly specialized demands of customers, it can also have a powerful impact on manufacturing productivity and quality. For example, setup times are determined not only by process design, but also by product design. 70±80% of the cost of the product is determined during the design stage. The same is true for product quality. Poor product design causes many defects on the production ¯oor (van Dierdonck, 1990). Product development also absorbs a huge amount of money. The development of Ford Escort, Chrysler LH, and Chrysler Neon is reportedly to have cost their companies $5 billion, $1.5 billion and $1.3 billion respectively. While maintaining or improving product
232 quality, companies are forced to reduce product development cost to improve their pro®tability. Thus, the ability to improve the product development processÐto drive new products from idea to market faster, with less cost and fewer mistakesÐis the key to the competitiveness of a manufacturing company. Given the complexity of the product development process, and the key role product development plays in determining the down-stream manufacturing activities, arti®cial intelligence and expert systems can play an important role in manufacturing by reducing product development time, improving quality and reducing costs. Thus AI and ES applications in new product development can possibly confer competitive advantage to manufacturing companies. In this paper, the authors review the research in applications of AI and ES in new product development activities, categorize and summarize the research as well as point to future areas for research. 2. New product development Product development is a process by which an organization transforms data on market opportunities and technical possibilities into information assets for commercial production (Clark and Fujimoto, 1991). There are two major approaches to product developmentÐthe traditional sequential process and the more recent concurrent/simultaneous/parallel process.
Fig. 1. Stage gate system.
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Product development can be viewed as a production process in which a sequence of operations are performed in successive stages and can be described by a stage gate system (Cooper, 1990). A stage gate system divides the product development process into a predetermined set of stages. Each stage (phase) consists of a group of prescribed and related activities. Between each stage, there is a quality criteria checkpoint or gate to ensure that the product passes certain criteria before entering the next stage. The stages are where the work is done; the gates ensure that the quality is suf®cient. Gates are staffed by senior managers. Stage-gate systems recognize the idea that the product development should be organized and conducted systematically, in a logical sequence and under tight control. Figure 1 is an example of a stage gate system from Northern Telecom, a Canadian company similar to AT&T (Rosenthal and March, 1991). To remain competitive, more and more companies are being required to compress the product development process. The focus of time-based literature is on compressing product development stages and therefore the product development cycle time because the most positive impact on pro®ts from time-compression activities comes from new-product development (Blackburn, 1991). Reducing product development cycle time can be achieved by concurrent engineering, which is the practice of involving teams of functional specialists to plan product and process activities
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Fig. 2. Concurrent engineering.
simultaneously. The key practice of concurrent engineering is ``parallel'' scheduling of activities or overlapping product development stages (not sequential as in a stage system) (Takeuchi and Nonaka, 1986). Figure 2 is an example of concurrent engineering in a car company (Clark and Fujimoto, 1989). Somewhat close to concurrent engineering in essence but different in practice is quality function deployment (QFD). QFD is a system for designing a product or a service based on customer wants, involving all members of the supplying organization. As such, it is a conceptual map for interfunctional planning and communication (Lynch and Cross, 1991). It may be de®ned as elaborate charts to translate customer wants into product characteristics and product characteristics into fabrication and assembly requirements. In this way ``the voice of the customer'' is deployed throughout the company (Garvin, 1988).
study and creation of computer systems that exhibit some form of intelligence. Intelligence is a system that can learn new concepts and tasks; reason and draw useful conclusions about the world around us; understand a natural language; and perceive and comprehend a visual scene (Patterson, 1990). Typical research areas of AI include problem solving and planning, expert systems, natural language processing, robotics, computer vision, neural networks, genetic algorithms and machine learning (Krishnamoorthy and Rajeev, 1996). Case-based reasoning, rough set theory and intelligent agent are the recent emerging areas. Table 1 summarizes these areas and their research focuses. In the next section we present a classi®cation and review of the literature on applications of arti®cial intelligence (AI), expert systems (ES), and knowledge-based systems (KBS) in new product development.
3. Arti®cial intelligence
4. Arti®cial intelligence in new product development
Although there are diverse de®nitions of arti®cial intelligence, the essence of these de®nitions is the same: AI is a discipline that is concerned with the
The research in the intersection area of arti®cial intelligence and new product development is comparatively new. The authors found 24 articles
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Table 1. Areas of research in AI Area
Research focus
References
Problem solving and planning Expert systems
Deals with systematic re®nement of goal hierarchy, plan revision mechanisms and a focused search of important goals. Deals with knowledge processing and complex decision-making problems.
Hewitt, 1971
Knowledge based systems
Generation expert systems characterized by two approaches: combining multiple models and reasoning techniques and using knowledge-level approaches for designing systems. Natural language Areas such as automatic text generation, text processing, machine Processing translation, speech synthesis and analysis, grammar and style analysis of text etc. Robotics Deals with the controlling of mechanical equipment to manipulate or grasp objects and using information from sensors to guide actions etc. Computer vision Deals with intelligent visualization, scene analysis, image understanding and processing and motion derivation. Learning Deals with research and development in different forms of machine learning. Genetic algorithms These are adaptive algorithms that have inherent learning capability. They are used in search, machine learning and optimization. Neural networks Deals with simulation of learning in the human brain by combining pattern recognition tasks, deductive reasoning and numerical computations. A problem-solving strategy based on the similar past problem-solving Case-based reasoning experience to help the designer to exploit the useful details for application to a particular similar case. Rough set theory New mathematical tool to deal with vagueness and uncertainty. Intelligent agent Computational systems that inhabit some complex, dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed.
(reporting 22 applications) in the 1990±1997 period; among them, 19 have been published since 1994. The research in this ®eld is nascent and growing. The research in the area can be categorized as in Table 2. As can be seen, expert system (ES) and knowledgebased system (KBS) have found greater use compared to some of the other AI methods like genetic algorithm, case-based reasoning or agents. The application areas have been in product and process design, project evaluation, conceptual design (early stage of product development), quality function deployment and group decision making as in concurrent engineering. In what follows, we brie¯y review the various papers classi®ed by the area of application.
Feigenbaum, 1977; Newell and Simon, 1976; Shortliffe, 1976 David et al., 1993 Hayes-Roth and Lesser, 1977 Engelberger, 1980 Minsky, 1975 Smith et al., 1977 Holland, 1975; Goldberg, 1989 Selfridge, 1959 Hammond, 1986; Kolodner, 1987; Riesbeck and Schank, 1989 Pawlak et al., 1995 Maes, 1995
4.1. Project evaluation In the new product development process, gaining top management support and suf®cient resources to accelerate the development cycle is not easy, due to the magnitude of resource commitments and new product failure rates (Liberatore and Stylianou, 1995). Therefore, being able to consistently and rationally evaluate and justify go/no-go decision making for each new product development project becomes extremely desirable from both top management as well as project manager's point of view. Liberatore and Stylianou (1995, 1994) offer a modeling framework that merges knowledge-based
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Applications in new product development Table 2. Arti®cial Intelligence applications in new product development Category
Area of application Description
References
Expert decision support systems (EDSS)
NPD project evaluation
Attempts to support new product development team or management team in making a strategic go/nogo decisions in various stages of NPD process.
Knowledge based systems (KBS)
Product and process design
Efforts to support designers in making decisions regarding to design improvements and selecting the near optimal design, especially by means of providing them with a timely feedback in regard to manufacturability and economic aspect of each product and process design.
Quality function deployment (QFD)
Liberatore and Stylianou, 1995, 1994; DI Benedetto et al., 1994; Cohen, 1997 Ssemakula and Cloyd, 1994; Darwish and EI-Tamimi, 1996; You et al., 1994; Syan, 1994; London et al., 1992; Lai, 1993; Park and Khoshnevis, 1993; Kamrani, 1996; Zhang and Lu, 1990 Braun, 1990
An attempt to support the process of designing and manufacturing of a quality product based on customer requirements, using KBSs throughout the process of QFD. Descriptions of AI usage in the early stage of Wang et al., 1994; designing process. Hague et al., 1996; Santillan-Gutierrez and Wright, 1996; Bracewell et al., 1996; Maher et al., 1995, 1997 Ullman and Herling, Attempts to support group decision making among 1996; Bahter et al., multiple experts participating in concurrent 1994; Oh and Sharpe, engineering through the usage of various AI 1996; Chen and techniques and by providing awareness of Gaines, 1997 changes made in others' web sites.
Conceptual AI ( problem design solving strategies, genetic algorithms, case-based reasoning, agents)
Group decision making in concurrent engineering
expert systems (ES) and decision support systems (DSS) technology with management science methods for the evaluation of new product development projects. Based on this model, they have created an expert decision support system for new product development project evaluation, called PRAS ( project assessment system). In using this system, the project that is to be evaluated is presumed to be at the stage on the development cycle where a major resource commitment is required to ``prove'' the product concept moves from preliminary development and testing to scale-up, where piloting or either small-scale or contract manufacturing is required. Their system is composed of ®nancial assessment system (FAS), customer satisfaction assessment
system (CSAS), technology assessment system (TAS), marketing assessment system (SMAS), manufacturing assessment system (MAS), and the master control system (MCS), which connects all sub modules of the system and provides the ®nal result. It is reported that the users have expressed satisfaction and commitment to the systems developed, and have indicated that the systems offer them a competitive advantage. Liberatore and Stylianou have promised for more detailed measurements concerning decision quality and cycle time reduction in future research. NEWPRODEX, created and reported by Di Benedetto et al. (1994), is designed to screen industrial product concepts, identify potential ®nancial successes and diagnose opportunities for concept
236 enhancement to improve its chances of success on the international marketplace. The authors have based their expert system development effort on a theoretical model called ``Integrative Model of the New Product Development Process,'' ®rst developed by Calantone and Di Benedetto (1988). In practice, NEWPRODEX receives user's input with regard to various constructs described in the model. The user, or the decision maker, is lead through the process by three ``managers'' with which the system is composed of. First, the Pro®le Manager leads the user to construct a pro®le for both the ®rm and the new product, including the success-failure-rating (SFR) for the new product. Second, the Financial Manager helps the user to analyze on the ®nancial implications of selected actions and strategies. It has three related components: a spreadsheet, interactive graphics, and a database. Third, the Strategy Implications Manager. This is where the user can reap the maximum bene®t. Using this component, the user can determine at a glance the strengths and weaknesses of the new product development project. The user can also examine the impact of every change he may make on the ®rm/product pro®le, thus being able to ®gure out the necessary actions needed to lead a new product development project into a success. In effect, NEWPRODEX system brings together the knowledge gained from combined experience of many managers in similar industries, and the situation-speci®c knowledge and experience of the system user. Although the system depends on the accuracy of the input provided by the user, the appealing feature of the system is that it allows the user to try a wide range of sales and cost predictions to help assess the risks involved in the new product project. Cohen, Eliashberg, and Ho (1997) present a decision support system for managing NPD process in fast-moving consumer packaged goods, which explicitly evaluates the ®nancial prospects of new line extension concepts. The system is based on an in depth analysis of 51 new products launched over a three-year period at a major food manufacturer. It embodies historical knowledge about the productivity of the ®rm's NPD process and captures some key research and development resources' input that can affect this productivity. 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
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practice of the NPD process by enabling its users to take a product line perspective, using incremental sales evaluation, and by facilitating cross-functional and inter-project learning. Its underlying design principles are generic and applicable to a wide range of industries. 4.2. Product and process design Braun (1990) states that expert systems have proven useful in many areas, their greatest potential lying in their ability to better manage industrial processes. They are well suited to assist in automation, especially in automating complex procedures and controls, and in supporting computer-integrated manufacturing (CIM) environments. As if to con®rm his claim, our literature survey reveals that knowledge based systems and expert systems in product and process design evaluation is the area in which the majority of the work is done. In an environment where ®rms are pressured for timely deliverance of high quality products, concurrent engineering is becoming more important tool to accomplish this goal. Ssemakula and Cloyd (1994) claim that in order to accomplish full implementation of the concept of concurrent engineering, ®rms have to deal with a very important, practical obstacle, which is the variability of the production system parameters in the real world. To this end, they have developed a dynamic process planning system (DYNACAPP) in the context of ¯exible manufacturing environment. The system is divided in two parts: The standard process planner (SPLAN), and the dynamic process planner (DPLAN). The SPLAN would take the input from CAD ®le, and determine the machining operations required to manufacture the proposed design, together with the appropriate process parameters, based on the gross but static capabilities of the manufacturing shop: and select the speci®c cell(s) to be employed. The primary concern in this stage is the overall feasibility and ef®ciency of the generated plan in light of the general resources of the shop; and the ®rst decision of SPLAN would be whether the part can be made in-house or not. If the required machining operations are capable to process in house, than SPLAN prepares a general process plan for the part in question, performs a cell selection and downloads the plan to DPLAN. DPLAN is a celloriented process planner, and its purpose is to validate the process plan selected by SPLAN from a variety of
Applications in new product development
other viewpoints, especially from the availability and projected loading of appropriate machines, operators, jigs, ®xtures, and material handling devices. Darwish and El-Tamimi (1996) developed a knowledge based system for casting process. Casting process appears to be an appropriate domain for knowledge systems for many reasons, including the fact that no one person can realistically be expected to know or remember all aspects, and there is a large discrepancy between the best and worst designs. Darwish and El-Tamimi have created an expert system that is intended to place specialist information at the designer's ®ngertips, and link design, manufacturing, and production. Based on required casting characteristics, the proposed expert system produces a list of candidate processes to produce a particular part. These choices help the designer in identifying alternatives early in the design process. You et al. (1994) also report on the development of an expert system for casting design evaluation. The casting process is heavily experience-oriented and casting design is an iterative task between casting designers and foundry experts. There is a lot of waste in resources and time during the design-manufacturing cycle when a part is being engineered. Initial estimates of manufacturability are valuable to the designer as this avoids costly redesign efforts. The important aspects of this paper are: (1) a new symbolic representation based on pattern representation and sweep representation for describing three-dimensional objects, (2) a method to reason about local shape characteristics based on symbolic descriptions, and (3) a modi®ed threedimensional thinning algorithm to aid the decisionmaking process in evaluating the global casting soundness. The system developed, named EXCAST, was implemented successfully at Cummins Engine Company, Columbus, IN, with an interactive CAD environment on a Sun workstation 3/60 UNIX using Sun Common-LISPand the AutoCad graphics package. Syan (1994) describes the development of an expert system for surface treatment and coating selection assistance in product design. The system created starts its consultation with the hypothesis that all 30 generic coating and treatment technologies are equally suitable for the particular consultation. As the user supplies information about the particular application, the system uses the rules in the knowledge-base to reject any generic coating and treatment technologies that are unsuitable. The system goes through ®ve
237 solution stages: (1) operating constraints, (2) processing constraints, (3) geometrical constraints, (4) topographical constraints, and (5) economic constraints. Sub-conclusions are reported by the system after each one of the ®ve stages of the system knowledge has been applied. The user has the option of rejecting any proposed subsolution if the user so wishes. The ®nal conclusions are reported in two forms: the ®rst group of recommendations which are ``certain'' deductions, and the second group which are the ``possible'' recommendations. These have numbers of ``maybe'' responses allocated to them as an indication of possible uncertain factors that may rule out these conclusions. Further development envisaged by Syan is to integrate the treatment and coating selection with the materials selection process, which would provide a more comprehensive materials and surface engineering assistance to the designer. The expert cost and manufacturability guide (ECMG) of London et al. (1992) is an expert system designed to provide mechanical engineers with ®rstorder manufacturing cost estimates and manufacturability feedback very early in the design process, during preliminary design. The ECMG is designed to be a general, customizable tool, applicable to a variety of manufacturing processes and organizations. The Guide consists of two software package, allowing it to achieve the desired level of customizability. The ®rst package, ECMG, is used by a mechanical engineer to derive cost estimates of preliminary designs and to understand the manufacturability factors in¯uencing the design. By using ECMG, the engineer can examine the effect of design trade-offs on cost and manufacturability. The second package, ECMGEdit, provides the tools necessary for ``lead users'' to make substantive changes and additions to the knowledge base, or ``cost models,'' utilized by ECMG during an analysis session. ECMGEdit provides a knowledge base manager for maintaining the rules, relations, and equations stored in a cost model, and structured editors for the various elements of the ECMGEdit language. Lai (1993) presents an expert design system, the knowledge based design for assembly (KBDA), for product assembly analysis and re®nement. The assembly function, when it is not taken into consideration in design process, usually leads to tremendously long development cycle and waste of man power. The design for assembly is thus becoming in demand. The application of knowledge based systems for this work is, Lai insists, one of the most
238 promising approaches among the many other techniques proposed in recent years. KDBA performs analysis for system products and provides advises for design re®nement. The system is written in PROLOG. It implements heuristic design rules acquired from actual practice and the knowledge given in the assembly handbook. Park and Khoshnevis (1993) describe a computeraided process planning system that serves as a tool for concurrent design of prismatic parts and their manufacturing processes. The system, which they called Real-time Computer-aided Process Planner (RTCAPP), provides timely manufacturing cost feedback to the designer for each added design feature, enabling the designer to select the least cost design alternatives that meet the desired product functionality requirements. On completion of the design process, RTCAPP provides a complete process plan for the entire design. RTCAPP is an effective concurrent engineering tool that can be used for simultaneous design of parts and their manufacturing processes. The prototype of RTCAPP was programmed in Sun Common Lisp and its performance result is reported to be quite promising. Kamrani (1996) presents the development of a methodology for a group technology knowledgebased system that will support the associativity analysis required for integrated product design and process planning. He states that a `feature,' de®ned as a speci®c design functionality from a designer's point of view, can illustrate a certain manufacturing process from a manufacturing point of view. Thus in a featurebased design environment, features could be used to illustrate associativity between both design and manufacturing using `standard features.' He also points out that an extensive literature survey has revealed that very few approaches are aimed at the development of a methodology for coding and classi®cation of parts into part families, based on both design and manufacturing attributes and their conformance with DFM (design for manufacturing) methodology. The developed KB process planning system provides the designer with: (1) the rules associated with the analysis of the design data, (2) the rules associated with process prediction and associativity analysis, and (3) the GT-database structure for supporting the developed rule-based system, among several others. Zhang and Lu (1990), recognizing the importance of combining manufacturing and management sys-
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tems for machining operation planning, present a new methodology for the evaluation of economic aspects in an operation plan. To ensure that the quality of machined parts satis®es the required speci®cations, the manufacturing system acts as an alternative generator that provides meaningful and practical plans. Through cost analysis, the variable, ®xed, and total costs associated with the machining operation are quantitatively determined. The management system, which functions as an evaluation mechanism, then selects the optimal plan based on the de®ned goal. Zhang and Lu had applied the proposed methodology in the framework design of an expert system. The program establishes a sequence of machining operation planning and searches for the optimal plan.
4.3. Quality function deployment Braun (1990) states that ``expert systems will become more important tools as U.S. industry moves from limited manufacturing quality control to more comprehensive product development quality control in which many people, including many not in quality departments, work on QC teams. Quality function deployment (QFD) is a good representative of this approach and seems especially compatible with today's expert system.'' The basic QFD tool is the product-planning matrix or means/ends matrix, which is also frequently referred to as the ``house of quality.'' This matrix, according to Braun, ®ts nicely with the equivalent premise/conclusion matrix frequently used in preparing data for expert systems. The matrix format, in turn, leads directly into the if-then rule structure of an expert or knowledge base system. In addition, a network of related systems can be used to tie the various parts of a total QFD plan together and to provide the disciplined environment of a formal, yet very ¯exible, framework. Different knowledge base systems, as shown below, can be applied in the course of QFD, and can be linked in a network to exchange information. Basic QFD knowledge base systems KBS 1 KBS 2 KBS 3
Help translate customer requirements into product speci®cations Assist in quality product design based on product speci®cations Assist in optimal process design based on product design and speci®cations
Applications in new product development KBS 4 KBS 5
Translate product and process design into quality manufacturing De®ne ®nal test procedure and criteria to ensure product quality
Cost evaluation knowledge base systems
KBS 6 Prepare cost projection based on customer requirements and speci®cations KBS 7 Prepare cost estimate based on actual product and process design KBS 8 Prepare ®nal product cost based on actual manufacturing costs (Braun, 1990)
The fact that these KBSs can be developed in a modular format makes it easy for the ®rm to carry out a progressive, evolutionary upgrading strategy, with the ®nal goal of turning it someday into a full-¯edged, integrated decision-making expert system. 4.4. Conceptual design According to Wang et al. (1994), conceptual design is a very important target in computer aided design (CAD), but it is also a very dif®cult target to accomplish. Computers have been used extensively in areas such as optimization, ®nite element analysis, reliability design, computer graphics and simulations, but there have been relatively few applications in the conceptual design stage. Wang et al. applied the DAER (design-analysis-evaluation-redesign) model, a simple but practical model for conceptual design. Based on this model, they developed a problemsolving strategy, which is composed of ®ve stages. Each stage in the problem solving strategy combines numerical calculation (such as mathematical modeling, optimization and scheme analysis) with symbolic reasoning (knowledge representation and model handling as well as scheme evaluation) to accomplish the objectives in every stage. The system they designed is named conceptual design expert system for transmission (CDEST). Hague et al. (1996) acknowledge the fact that the highest return on investments made during various stages of product development is recorded during the design stage. For this reason, conceptual engineering design phase is viewed as the most critical and determinant factor in product competitiveness. Product developers must, at an early design stage, take into account all the life-cycle concerns such as manufacturing, reliability, marketing and distribution. There is a pressing need for the development of a
239 methodology to support the evaluation of conceptual design alternatives from multiple life-cycle facets (design, manufacturing and assembly) throughout the generation of alternative solutions. This requires access to any necessary down-stream information throughout the early design stages. ``Co-Designer,'' a computer-aided conceptual design tool is developed to meet these needs. Applying techniques from machine learning, such as rote learning and parameter adjustment learning, Co-Designer learns design problem-solving processes by recording the user interaction, such as actions and decisions, and storing them as decision trees. These are extended, improved (``®ne tuned'') every time the system is used. The parameter adjustment learning technique is intended to enable Co-designer to learn by experimentation (by doing) and to give preference to commonly used design alternatives. The stored decision trees are used to provide the user, when required, with a view of the consequences of a particular decision. Santillan-Gutierrez and Wright (1996) also state that during the development of a product, the conceptual and embodiment stages are critical. Modeling the conceptual stage is dif®cult, because it is a continuous process of evaluation and satisfaction of different criteria, dealing with often vague and imprecise information. They proposed an approach which is aimed at helping the designer during the end of the conceptual stage. It is based on the use of genetic algorithms (GA) and more traditional techniques for locating groups of promising solutions. The ®rst part of the program was still under development at the time of publication. The system is based on GENESIS (genetic search implementation system, developed by Geffenstrette, as public domain software). Bracewell et al. (1996) describe an integrated suite of software systems called Schemebuilder. It emphasizes the integrated set of software tools used in converting a qualitative description of a scheme into a quantitatively de®ned one whose performance can then be simulated and whose general layout can be drawn. Arguably, the single most compelling reason for an engineering enterprise to make the paradigm shift to concurrent engineering is to reduce as much as possible the time-to-market for a product. To reduce the lead time further, it is sensible that designers be provided with greater computer support at the conceptual design stage. The Schemebuilder project addresses this need by providing an integrated design
240 workbench for the designer to take an ``expression of'' need and developing it into schemes very quickly which can then be quanti®ed and evaluated to assess the impact of the decisions that have been made. This has been achieved by adopting a unifying representation via bond graphs for modeling of designs and supported by a set of integrated design tools made up of knowledge based systems, simulation packages, CAD packages and hypermedia systems. Maher et al. (1995, 1997) describe CASECAD, a multimedia case-based reasoning system for design. CASECAD is a domain-independent design system based on an integration of case-based reasoning (CBR) and computer-aided design (CAD) techniques. CASECAD employs a memory organization scheme that partitions memory into model-based memory and case memory. Model-based memory provides generalized knowledge about the design domain as well as an organizational schema for case memory. Case memory is a multimedia representation of design episodes using an object-oriented representation of design variables and text descriptions, CAD drawings to illustrate the geometric of design cases and graphical illustrations of behaviors of design cases. CASECAD provides a multimedia design case library browser that allows designers to view and compare relevant past design cases in both symbolic and graphical modes. Case retrieval can be based on the required function, behavior and/or structure of the new design. Once a set of suitable cases are retrieved from the case library, the designer can navigate the retrieved cases in the multimedia environment in order to select the most applicable case for the current situation. The designer can then modify the text and graphic descriptions of the selected case for the new design context if needed. This is made possible by a text editor for the object-oriented representation, a CAD system for the CAD drawings, and a drawing program for the graphic representation of behaviors. The current version of CASECAD is considered a design aiding system as opposed to an autonomous design system. 4.5. Group decision making in concurrent engineering Ullman and Herling (1996) describe a new method, called the engineering decision support system (EDSS) that aims to support engineers in making design decisions early in the process. Alternative
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designs, after being put into the system, are evaluated by multiple designers. The system then summarizes and gives feed back to each designer. The completeness of the evaluation for the team or any individual can be readily reviewed. The EDSS described above is still in development stage at the time of publication. Bahler et al. (1994) state that negotiation plays a key role in concurrent engineering, where experts from many disciplines with diverging viewpoints need to cooperate at many stages of product development. Design advice tools can assist in negotiation by making their critiques conveniently available to all members of the product development team. Fully automated negotiation involves computerbased agents and a system that is able to provide its own compromise protocols, which can provide alternative ways to resolve con¯icts. In partially automated negotiation, by contrast, the compromise proposals are supplied by clients, which can be either humans or other computer-based systems, and the negotiation system provides only support that encompasses the detection of potential con¯icts involving several clients, and the collection and evaluation of compromise proposals. Bahler et al. argue that partially automated negotiation, and more generally partially automated design, is a more appropriate paradigm in the development of design advice systems. Oh and Sharpe (1996) describe how Schemebuilder, discussed by Bracewell et al. (1996), can be used also for managing con¯icts among designers in an interdisciplinary design environment. There are essentially two ways to manage con¯icts: (1) avoid them or minimize their occurrence by designing them away, and (2) resolving them as they occur. These two approaches are used in Schemebuilder, in which the authors employed a mix of formal and informal methods to realize these approaches. The former is in the form of analytical methods like multicriteria decision making tools and the latter is in the form of heuristically driven rules, established protocols or conventions and negotiation techniques. Oh and Sharpe have taken the approach that the tools and mechanisms they provide in Schemebuilder are more supportive in nature than automating; for, in Schemebuilder, the ultimate decision must be made by the designer when the situation calls for it. Chen and Gaines (1997) discuss awareness issues in collaborative group work. One of the problems of
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Applications in new product development
supporting scienti®c collaboration on the Internet is to maintain awareness among remote research partners, in which the activities that have occurred in one location can affect those in another. Such mutual awareness is an important issue for supporting taskoriented collaborative projects of research groups or organizations. They introduce CHRONO, an HTTPD server-side system that generates chronological listings of web pages that have been changed recently at speci®c sites, as a tool to support collaborative work within group members or among multiple groups. The system has been set up for the Department of Computer Science at the University of Calgary since March 1995, on an experimental basis. Individual group members who used the system stated that
indeed the system provided chronological awareness for them at both group and organizational level. Most of them found such chronological awareness-support service helpful to them in keeping themselves informed about works of others. 5. Discussion and conclusion In addition to the stage-gate model describing the NPD process, a review of the literature dealing with Al applications in NPD reveals a number of activities in NPD process like developing plans, schedules, identifying the value chain, tracing and analyzing risk factors, team working, clarifying product concept,
Table 3. Classi®cation of Al applications by NPD stages and core elements NPD stages Core elements Phase Review Process
Core Team
Structured Development Process Product Strategy Technology Management Design Techniques & Development Tools
Cross Project Management
Strategic Opportunity Opportunity planning identi®cation development
Product/service creation
Liberatore and Stylianou, 1994, 1995; Di Benedetto et al., 1994; Cohen et al., 1997 Oh and Sharpe, Ullman and 1996 Herling, 1996; Bahler et al., 1994; Chen and Gaines, 1997 Saemakula and Cloyd, 1994; Braun, 1990
Darwish and Wang et al., El-Tamimi, 1996; 1994; Hague You et al., 1994; et al., 1996; Santillan-Gutierrez Syan, 1994; London et al., and Wright, 1996; 1992; Lai, 1993; Bracewell et al., Park and Khoshnevis, 1996; Maher et al., 1993; Kamrani, 1995, 1997 1996; Zhang and Lu, 1990
Product/service Life cycle introduction management
242 determining market requirements, life cycle timing management, and resource and portfolio management. Integrating these activities with the elements of the stage gate model, we can classify the NPD process into the following stages: (1) strategic planning, (2) opportunity identi®cation, (3) opportunity development, (4) product/service creation, (5) product/service introduction, (6) life cycle management (Noori et al., 1997). To successfully work in these stages of NPD, organizations should have appropriate competencies such as phase review process, core teams, structured development process, product strategy, technology management, design techniques and development tools and cross project management (Jenkins et al., 1997). Al applications to NPD process and activities can be viewed along two dimensionsÐNPD stages and NPD core elements (competencies and abilities). Table 3 shows the distribution of the research in Al applications to NPD along these two dimensions. As we can see from the table, most of the existing Al applications (13) are in the design techniques and development tools. Of them, ®ve are in the opportunity development stage, and eight in product/service creation stage. There are no papers reporting Al applications in product strategy, technology management and cross project management. Further, along the NPD stage dimension, there are no Al applications reported for strategic planning, opportunity identi®cation, product/service introduction and life cycle management. It is in these stages of NPD, especially in the front end stages of opportunity identi®cation and opportunity development, where more research is needed. As pointed out earlier, review of Table 1 ``Areas of Research in Arti®cial Intelligence'' reveals that there is much scope for applying the newer tools of Al like case based reasoning, genetic algorithms, intelligent agents, rough set theory, etc. to NPD processes. More research into and development of Al to QFD, decision making in concurrent engineering and conceptual design areas will bene®t NPD. Expert systems, knowledge-based systems and other Al techniques and methodologies are still relatively new, since they are developments of the last two decades. This paper reviewed ®ve areas of Al applications in the context of new product development. We identi®ed the popular areas of research in Al applications in NPD and have pointed out the need for further work in other areas of NPD. One thing seems
Rao et al.
to be for sure: We will be seeing a growing usage of ES, KBS and other Al methodologies for the purpose of developing economic, high quality products in a timely manner.
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