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ARTICLE IN PRESS Int. J. Production Economics 107 (2007) 179–189 www.elsevier.com/locate/ijpe A DFX and concurrent engineering model for the establishment of a new department in a university Ching-Chow Yanga, Shun-Hsing Chena,b,, Jiun-Yan Shiaua a Department of Industrial Engineering, Chung-Yuan University, Taiwan, ROC Department of Industrial Engineering and Management, Chin-Min Institute of Technology, Taiwan, ROC b Received 11 July 2005; accepted 29 August 2006 Available online 1 November 2006 Abstract The natural focus of concurrent engineering (CE) and design for X (DFX), as commonly used by manufacturing industry, is on product design or new service development. The present study applies the DFX technique in a CE environment to the planning and design of a new department in a university, and thus develops a comprehensive model for such an undertaking. The model identifies two stages in the overall process: the planning stage and the design stage. The planning stage includes four dimensions, whereas the design stage includes 11 dimensions. The dimensions are interdependent; indeed, the dimensions cannot be implemented separately and sequentially. The model must be implemented in a CE environment. A case study is then presented in which a department of leisure management at a university is established using the model described. The implications of the case study and the final conclusions of the paper are then presented. r 2006 Elsevier B.V. All rights reserved. Keywords: Concurrent engineering (CE); Design for X (DFX); New service development (NSD); University 1. Introduction The twenty-first century is the era of the globalized knowledge economy for a wide range of business activities, including university education. As the educational market has become liberalized, Taiwan has recently introduced reforms in educational policy. These reforms have been especially marked since the entry of Taiwan to the World Corresponding author. Department of Industrial Engineering and Management, Chin-Min Institute of Technology, 110 HsuehFu Road, Tou-Fen, Miao-Li 35145, Taiwan, ROC. Tel.: +886 3 7605723; fax: +886 3 7605724. E-mail address: k872790@yahoo.com.tw (S.-H. Chen). Trade Organization (WTO). The rapid growth in university education has changed the nature of the Taiwanese educational sector from its original model of elite education to one of mass education and, subsequently, to a system of universal education (Ministry of Education Statistical Department, 2004). However, these changes have created imbalances between supply and demand in university education, leading to a reduction in educational quality. As international competition in educational services has become more intense, many countries have invested enthusiastically in university education in an effort to maintain their international competitiveness. Taiwan is no exception. To adapt to the strong competition that has accompanied 0925-5273/$ - see front matter r 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2006.08.009 ARTICLE IN PRESS 180 C.-C. Yang et al. / Int. J. Production Economics 107 (2007) 179–189 membership of the WTO, Taiwan must immediately improve the quality of its university education (Chen and Ho, 2003). The provision of education is a service industry characterized by a high degree of interpersonal contact (Chase, 1978; Katouzian, 1970); therefore, any exploration of the management of the education system must begin with a consideration of its service-industry attributes. However, despite the importance of the service sector, empirical research on new service development (NSD) is still sparse (Bullinger et al., 2003), and the studies that have focused on NSD (Alam and Perry, 2002; De Brentani, 1995) have largely neglected its application in the educational sector. This relative lack of attention is both surprising and a matter for concern specially in view of the fact that service design in education has been identified as a crucial factor determining educational quality (Oplatka, 2004). In particular, when planning and designing new departments in universities, few suitable models are available for reference in designing integrated models that are appropriate to practical requirements (Bullinger et al., 2003). Universities must take care in planning new departments and satisfying their customers. Kanji and Tambi (1999) have noted that university customers include students, staff, parents, businesses, and government. To meet the demands of these customers in a competitive market, universities must promote themselves as offering highquality education. In pursuit of this objective, unsatisfactory departments are frequently dissolved to allow new departments to introduce novel curricula, advanced technologies, first-class teaching, and improved service quality. This encourages able students to enroll and enables the university to provide graduates who meet modern recruitment criteria. This process of renewal and improvement is important to universities in the modern competitive environment. If planning and resources are insufficient, the process will fail to deliver satisfactory outcomes, thus leading to a lack of student enrollment and, ultimately, to adverse affects on the reputation and financial success of the university. In the services sector in general, many servicedesign methods are available; however, these have seldom been used in the design and development of the education sector. Many studies have reported on the implementation of such methods as quality function deployment (QFD) and concurrent engi- neering (CE) in manufacturing industries and in NSD in general (Han et al., 2004; Stehn and Bergström, 2002; Kumar and Midha, 2001; Koufteros and Marcoulides, 2006). However, QFD is more complicated and less convenient than ‘design for X’ (DFX) (Hsiao, 2002). DFX emphasizes the consideration of all design goals and related constraints in the early design stage (Kuo et al., 2001) and allows the rationalization of services, associated processes, and systems (Huang and Mak, 1997). Effective utilization of DFX and CE in NSD can concurrently improve quality, costs, and cycle times (Dowlatshahi, 2001a, b; Huang and Mak, 1997). Against this background, the present study applies the DFX technique in a CE environment to the problem of establishing a new department in a university. 2. Literature review 2.1. CE Prasad (1996) defined CE in the following terms: ‘‘concurrent engineering is a systematic approach to the integrated, concurrent design of products and their related process, including manufacture and support’’. In manufacturing, CE is predominantly used in product design (Dowlatshahi, 1996, 1997), and product life-cycle (Dowlatshahi, 2001a). The advantages of the use of CE are (Dowlatshahi, 1992, 1997):            reduction in product development cycle time; avoidance of costly future redesigns; reduction in duplication of effort; better communication and dialogue; more efficient operations and higher productivity; overall cost savings; elimination or reduction of product recalls; lower maintenance costs; more reliable products; better customer satisfaction; and improved bottom line. CE impinges on several factors in the establishment of new department in a university including customers’ demands, competitive advantage, market attractiveness, financial resources, and the quality of execution of the whole process of establishing a new department. To consolidate these dimensions, it is therefore important that a new ARTICLE IN PRESS C.-C. Yang et al. / Int. J. Production Economics 107 (2007) 179–189 department be designed in a CE environment. With scrupulous planning and design, the new department will not consume valuable resources unnecessarily, and will simultaneously meet the demands of customers. 181 (Dowlatshahi, 1997, 2000); logistics (Dowlatshahi, 1996, 1999); product life-cycle (Dowlatshahi, 2001a); and product safety and reliability (Dowlatshahi, 2001b). As Dowlatshahi (1992) has demonstrated, all aspects of DFX are integrated and can proceed simultaneously (see Fig. 1). 2.2. DFX 2.3. New service development (NSD) DFX was developed in the late 1970s (Kuo et al., 2001). Since the late 1990s, hundreds of papers have been published pertaining to the DFX applications in manufacturing (Rosairo and Knight, 1989; Kuo et al., 2001) and it is widely used in the development of new products (Huang and Mak, 1997; Kuo et al., 2001). DFX is a general term; ‘X’ can represent assembling, manufacturing, quality, and so on. The exact nature of the variable ‘X’ in any instance defines the focus of a DFX tool. ‘X’ has two parts X ¼ x+bility. The suffix ‘-bility’ corresponds to the performance matrices (Huang and Mak, 1997). The ‘x’ part represents one or more business processes corresponding to one or more life cycles in product development (Huang and Mak, 1997). However, there have been few studies in the literature on the application of DFX in the design of a new department in a university. DFX emphasizes consideration of all design goals and related constraints in the early design stage (Kuo et al., 2001). As such, DFX represents a suite of contemporary service-development techniques that can effectively be applied in service development to achieve concurrent improvement in quality, cost, and time to market. The technique allows the rationalization of services, associated processes, and systems (Huang and Mak, 1997). DFX has been applied in: purchasing (Dowlatshahi, 1992); product design in a designer–buyer–supplier interface Design for Marketability Design for Procurability Design for Cost Design for Schedulability The ability of a service organization to remain competitive in today’s technologically dynamic and market-driven environment is largely dependent upon the quality, cost, and timing of new service offerings (De Brentani, 1995; Dowlatshahi, 1997). A service organization needs to provide new services of high quality at low cost at the right time for customers. Many quality problems are recurrent and, to a great extent, these result from shortcomings in the development of new services (Edvardsson, 1992; Juran, 1992). In recent decades, manufacturing industries have developed many models, methods, and tools for the development of quality new products such as the spiral model (Bullinger et al., 2003), QFD, CE, business process re-engineering (BPR), and DFX. However, few of these are used in the design and development of service organizations (Bullinger et al., 2003), including those in the educational sector (Friel, 2000). Effective utilization of CE and DFX to new service design and development will result in concurrent improvement in production innovation, quality, costs, and cycle times (Dowlatshahi, 2001a; Huang and Mak, 1997; Koufteros et al., 2001; Pillai et al., 2002). The core of the NSD process cycle is the ‘service concept’—which involves the service system, technology, people, tools, and the organizational context. Process Design Design for Reliability and Maintainability Production Design Design for Manufacturing planning and Control Design for Safety and Liability Design for Qualit Design for Logistic and Environment Fig. 1. DFX in the CE environment framework (Source: Dowlatshahi, 1992). ARTICLE IN PRESS 182 C.-C. Yang et al. / Int. J. Production Economics 107 (2007) 179–189 3. Model for establishment of a new department in a university New service design and development involves the following steps (Alam and Perry, 2002; Edvardsson, 1997; Johnson et al., 2000; Murdick et al., 1990):      concept creation; planning and analysis; design; testing and pilot run; and performance measurement. The present study applies the NSD concept in developing a model for the establishment of a new department in a university. The model integrates the above steps into two stages: planning and design. These two stages are discussed further below. 3.1. Planning stage To best meet consumers’ requirements of a product (service) from a design perspective, the physical elements of the product (service) requirements being linked to consumers’ perception of the product (Aitken et al., 2003; Lai et al., 2006.). Therefore, the planning stage consists of four dimensions (see Fig. 2):    confirmation of customer requirements; analysis of competition; strategic decisions; and Confirmed customer requirements Confirmed new department vision and mission Planning stage Competition analysis Strategic decisions Design of evaluation system Design of student recruitment Design of marketability planning Design of administration support Design of space planning Design of financial planning Design stage Design of teacher employment Design of education quality Design of physical/ technical facilities Design of teaching/ service process Design of curriculum planning Fig. 2. Framework of university’ new department with the DFX in the CE environment. ARTICLE IN PRESS C.-C. Yang et al. / Int. J. Production Economics 107 (2007) 179–189  confirmation of new department’s vision and mission. Each of these is discussed below. 3.1.1. Confirmation of customer requirements The most important customers of educational organizations are students and staff (Kanji and Tambi, 1999), and this stage ensures that the target customers are recognized. Customer interviews, customer surveys, and focus group methods are used to ascertain and confirm customer demands. 3.1.2. Analysis of competition As noted above, competition among universities is becoming increasingly fierce. Strength, weakness, opportunity, threat (SWOT) analysis is therefore applied to analyze the university operations. The analysis reveals resources and competitiveness in relation to other institutions in terms of demographics, economic environment, political and legal environment, sociocultural environment, technological environment, and global environment. The purpose of this analysis is to allow the university to make use of its strengths, modify any weaknesses, master the available opportunities, and exclude any threats (Yang, 2004). 3.1.3. Strategic decisions The analysis of the competition (above) allows the university to determine the departments that should be established to meet customer demand. By adopting an appropriate strategy, organizations can avoid potential problems and risks (David, 2001), and thus achieve competitive advantage. In contrast, incorrect decisions can cause inefficiencies and cost increases ultimately eroding organizational competitiveness (De kluyver, 2000; Quinn, 1980). 3.1.4. Confirmation of new department’s vision and mission The new department requires an appropriate mission and vision to promote the reputation of the university, and to enhance cooperation and teamwork among staff and students. An organizational mission is a statement of the reason for the existence of that organization (Kaplan and Norton, 2001; Niven, 2002), whereas a vision provides a blueprint that points to the future development of the organization (Kaplan and Norton, 2001; Niven, 2002). Such a vision is usually expected to establish 183 a framework of teamwork, resources, and support structures. 3.2. Design stage In investigating the design stage, the present study used interviews and/or a questionnaire survey administered to 92 administrative executives or deans of several universities in Taiwan. The questionnaire used Likert-style scales from 1 to 7 to measure responses (with 1 representing very unimportant to 7 representing very important). The original questionnaire developed for the study included 14 dimensions that received very low scores (mean value o6). These were eliminated from the final questionnaire, which contained 11 dimensions. The study then explored the parallel, and interactive relationships among these dimensions. The objective was to reorganize each practice in terms of the principles of DFX, and then to propose an integrated model of a new department in a CE environment. As a result of the above empirical evidence and analysis, the dimensions can be summarized as follows (see Fig. 2):            design design design design design design design design design design design of of of of of of of of of of of student recruitment; financial planning; marketability planning; teacher employment; education quality; curriculum planning; teaching/service process; physical/technical facilities; space planning; administration support; and evaluation system. Each of these is discussed below. 3.2.1. Design of student recruitment Students are the key customers of a school (Kanji and Tambi, 1999), and student tuition fees dominate the financial income of a university. This aspect of the design includes entry standards, enrollment requirements, class sizes, and student numbers. Class sizes and the number of students in a department should be approbated by the Ministry of Education (MOE) of Taiwan, and so must be planned in advance. ARTICLE IN PRESS 184 C.-C. Yang et al. / Int. J. Production Economics 107 (2007) 179–189 3.2.2. Design of financial planning Universities must have adequate financial resources such that its departments can implement the institution’s mission and vision. This aspect of the design includes the cost of libraries and facilities, staff salaries, and personnel expenses. 3.2.3. Design of marketability planning This dimension includes the design of marketing methods, marketing expenses, marketing the implementation unit, and the arrangement of personnel. 3.2.4. Design of teacher employment This dimension includes the deployment of teachers, ensuring teaching skills, determining teacher numbers, and deciding teachers’ salaries. 3.2.5. Design of education quality This dimension includes determining teacher/ student ratios, deciding teaching hours, implementing plans for teaching improvement, and arrangements for education quality assessment. 3.2.6. Design of curriculum planning This dimension includes curriculum development, designing overall credits (including compulsory and optional subjects), and curriculum planning and evaluation. 3.2.7. Design of teaching/service process This dimension includes the standardization and simplification of teaching procedures, the determination of customer requirements and expectations, establishing the service level and quality standards specifications, and designing a monitoring-andcontrol system for teaching/service process quality. 3.2.8. Design of physical/technical facilities This dimension includes the design of a friendly environment, facility layout, interior decoration, flow of people, and design of physical surroundings. 3.2.9. Design of space planning This dimension includes the design of space for classrooms, learning, libraries, and facilities, and the allocation of research rooms for use by teachers. 3.2.10. Design of administration support This dimension includes the design of an administrative framework, the framing of teacher and student satisfaction surveys, and planning of administration facilities. 3.2.11. Design of evaluation system This dimension includes the design of performance-measurement systems and performance-measurement indicators. If these dimensions are carefully planed and designed, erroneous planning and investment decisions will be avoided. 3.3. Summary of planning and design stages As shown in Fig. 2, a carefully considered planning stage supports the design stage. The dimensions of each are interdependent and cannot be implemented separately or sequentially. Indeed, if attempts are made to implement the dimensions separately or sequentially, a lack of communication will lead to confusion and wasted effort (Anumba et al., 2002). In a properly organized CE implementation, each department offers its suggestions thus providing the best framework in which to undertake group decision-making. It is apparent that the model must be implemented in a CE environment, and that the DFX and CE aspects must be fully integrated. 4. Case study Ming-Hsin University of Science and Technology (MHUST) is a private university situated in northern Taiwan. The university was founded in 1966, and now has 17 faculty departments, 9 research centers, 17,000 students, and 588 staff members. The present paper took MHUST as an empirical case study to exemplify the successful implementation of the theoretical model described above. The case study presents the establishment, in 2001, of a new teaching department in leisure management within MHUST. 4.1. Implementation of planning stage It will be recalled (see Section 3.1) that the planning stage of the model consists of the following steps:     confirmation of customer requirements; analysis of competition; strategic decisions; and confirmation of new department’s vision and mission. ARTICLE IN PRESS C.-C. Yang et al. / Int. J. Production Economics 107 (2007) 179–189 The implementation of these steps in the case study is described below.  4.1.1. Confirmation of customer requirements Taiwan has beautiful scenery and the Taiwanese government is promoting tourism. However, many health clubs and amusement parks lack high-level managerial talent. Many people are employed in the leisure industry, but there is a scarcity of universities offering appropriate education in leisure management. MHUST assessed this situation and decided, in view of government policy and the demands of students and enterprises, that a new integrated teaching department should be established in the field of leisure management. 4.1.2. Analysis of competition and strategic decisions In this case study, the second and third steps in planning competition analysis and strategic decision-making are considered simultaneously. MHUST undertook an internal and external environmental analysis and established that only two Taiwanese universities had departments of leisure management (DLM). It was therefore felt that establishing a DLM at MHUST had enormous potential in terms of attracting students. Furthermore, it was noted that MHUST is located near the Hsinchu Science Park. This means that the region surrounding the university has a high concentration of well-paid professional employees who work under significant pressure. These people and their families require suitable leisure activities, and it was therefore considered worthwhile to support the development of a local leisure industry by setting up a DLM in the local area. 4.1.3. Confirmation of new department’s vision and mission The MHUST defined the mission and vision of the new department as described below. The mission of the new department was defined as: To train polite and well-presented service personnel who: (i) are concerned about society; (ii) have humanistic attitudes; (iii) possess the ability to think independently; and (iv) are able to combine the theory and practice of leisure business. The vision of the new department was defined as:  To develop a leisure operation with Taiwanese characteristics pottery art, tea art) and local resources (for example, forest in accordance (for example, environmental and coast) to 185 popularize healthy and meaningful leisure activities. To promote the ability of students to obtain employment by establishing multi-dimensional and diversified leisure operations as the core curriculum of the new DLM and management and planning as the supplementary curriculum of the new DLM. 4.2. Implementation of design stage It will be recalled (see Section 3.2) that the design stage of the model consists of the following steps:            design design design design design design design design design design design of of of of of of of of of of of student recruitment; financial planning; marketability planning; teacher employment; education quality; curriculum planning; teaching/service process; physical/technical facilities; space planning; administration support; and evaluation system. The implementation of these steps in the case study is described below. 4.2.1. Design of student recruiting The new department decided to recruit students for five classes every academic year, with each class containing 50 students. In Taiwan, class sizes and the number of students in a department must be approved by the Ministry of Education (MOE) of Taiwan, and these must therefore be planned in advance. With respect to entrance requirements, it was decided that students would apply for admission and then pass an admission examination. It was also decided that the number of enrollments each year would be adjusted according to demand for leisure-operation personnel. 4.2.2. Design of financial planning Salaries of teachers are determined by MOE regulations. The following budgetary figures were determined:    personnel expenses: NT$740,000,000 scholarship funds: NT$49,000,000; and purchase of instruments and equipment: NT$7,000,000. ARTICLE IN PRESS 186 C.-C. Yang et al. / Int. J. Production Economics 107 (2007) 179–189 4.2.3. Design of marketability planning Design of marketability planning included:       the drawing-up of a budget (NT$2,000,000) for marketing (to be carried out by the enrollment department); taking part in school admission fairs frequently; promoting the new department in high schools; and marketing using relevant media to enhance the reputation of the new DLM. 4.2.4. Design of teacher’s employment It was decided that the department would employ 16 full-time teachers—15 senior staff members with doctorates and one lecturer without a doctorate. In addition, the new department would utilize the services of several part-time teachers with practical experience in various aspects of leisure management, including:      management of leisure centers; physical-health activities management of natural resources; services provision; and pottery, tea art, and so on. 4.2.5. Design of education quality To improve education quality, the staff/student ratio was set at 1:25. Scholarships were to be granted to excellent students, and guidance and assistance were to be provided for weaker students to encourage all students to work hard. In addition, an early-warning system of grades was instituted to avoid students being expelled due to bad grades. Finally, a system of counseling assistance was to be provided to monitor students who were facing psychological difficulties and to provide them with appropriate advice and spiritual guidance. 4.2.6. Design of curriculum planning It was decided to base the curriculum planning design on 131 credit points (88 in the compulsory curriculum and 43 in the optional curriculum). This was to include:    leisure agriculture (organic agriculture, leisure fishery, leisure farming); leisure art (pottery art, gardening, tea-art culture); leisure sports (practice of sports, water-leisure sports, physical-health management); leisure and natural resources (forest leisure operations, coastal leisure development, hot springs management); and leisure for those with special needs (children, women, the elderly). Practical training in enterprises was to be provided during two summer vacations to ensure that practical experience was gained before graduation. 4.2.7. Design of teaching/service process A questionnaire surveying administration and teaching was to be issued during each semester to identify the expectations and satisfaction of students. This questionnaire was to be the basis of assessment of progress in the new department’s teaching/service process. In addition, the Internet was to be utilized as an information resource and a means of network teaching. Finally, cooperation with leisure enterprises was deemed to be essential to:      identify demand for graduates; achieve timely modification of courses and teaching methods; identify purchases of appropriate equipment; meet the demands of all customers; and estimate overall effectiveness of the new department. 4.2.8. Design of physical/technical facilities It was decided to provide the following facilities:          a laboratory for physical-health management (providing equipment for sports testing and physical fitness); a video conference room (providing for a capacity of 150 persons); multimedia rooms; a reading room; a pottery art room; a tea-art room; a case-study room; a rhythmic gymnastics room; and a teachers’ study room. 4.2.9. Design of space planning In designing physical and technical facilities, it was decided that there should be sufficient space for accommodation and leisure activities for the projected number of students, with allowance for space ARTICLE IN PRESS C.-C. Yang et al. / Int. J. Production Economics 107 (2007) 179–189 for an increased number of students if demand led to an extension of the department. 4.2.10. Design of administration support It was decided that all service processes should be computerized to reduce manpower and to promote service effectiveness. ISO-9000 standard operations procedures were to be established to enable teachers and students to be aware of the complete operational procedure of the department. In addition, arrangements were made for all instruments and equipment to be regularly maintained in good order to maintain the highest standards of teaching and research. 4.2.11. Design of evaluation system It was decided to establish an effectivenessmeasuring system of the administration and teaching. A questionnaire was designed to survey teaching quality and satisfaction of students during each semester. This was to include an assessment of individual teachers and staff members to ensure optimal performance. It was decided to establish the following performance indicators:     customer satisfaction: 90%; registration rate: 100%; e-teaching service: 70%; and performance evaluation of department: top 5. 4.3. Summary of case study The theoretical model presented in this study applies the DFX technique while taking account of every dimension in the CE environment. By implementing this model in the establishment of its new department of leisure management, MHUST achieved the following apparent benefits:     The department registration rate reached 100% in the three years since the model was first implemented in 2001. The performance of the department ranked first in the university on all measures (including student satisfaction, overall performance assessment, and so on). The department’s mission and vision were enunciated and communicated clearly, thus facilitating effective shared efforts among students and staff in achieving departmental goals. In accordance with enterprise demand and government policy, the new department has  187 overcome deficiencies in the training of leisure managers, and has successfully met the demands of leisure enterprises. By emphasizing a combination of theory and practice, students have acquired relevant theory from university classes and practical skills through experience of working in business. (In this respect, it should be noted that higher education in Taiwan is notably deficient in combining theory and practice effectively.) There are many service design theories and models are very plenteous, but each method has its own advantages and disadvantages. In Failure Mode and Effect Analysis (FMEA), DFX and QFD are most familiar applications for product design (Chen and Yang, 2004). FMEA is concerned with identifying the ways in which a product can fail and the effects of such failures. It also provides alternative solutions to prevent failures (Dowlatshahi, 2001b). But, the FMEA method is unsuitable for using this case study. QFD, a known tool is also often applied in service design and development, but statistical analysis, decisions in each service items correlation and calculation of its weight value are necessary completed. Therefore, QFD is more complicated and less convenient than ‘DFX’ (Hsiao, 2002). Traditional product designs or service designs only consider such design factors as cost, quality, manufacture and reliability (Dowlatshahi, 2001b), however, could allow for the inclusion and trade-offs among such design factors as: marketability, education quality, space planning and financial planning. These factors will be adjusted according to the points of view of the industry or the organization. Therefore, the case study applies DFX in a CE environment very cautiously to consider every key factor. The use of DFX in a CE environment has ensured success in the planning and design of the new department. If DFX is implemented separately from a CE environment, communication problems invariably arise which increase costs and decrease the ultimate service quality. The incorporation of a CE environment in the overall process ensures simultaneous consideration of service design and process design, thus, improving service quality and reducing re-design cost (Yan and Wu, 2001). The use of DFX in a CE environment, strictly planned and designed, has ensured that the implementation of the model in the case study has been complete and efficient. For the university, the ultimate result is that the institution ARTICLE IN PRESS 188 C.-C. Yang et al. / Int. J. Production Economics 107 (2007) 179–189 has attracted excellent teaching staff and outstanding students. The model has thus promoted the wider reputation of the university in the community and has ensured its financial health. 5. Conclusion Competition in the higher education sector is becoming increasingly fierce. In such an environment, universities must learn from private enterprise in emphasizing excellent quality, low costs, and high efficiency. Higher education institutions also need to apply modern management methods such as total quality management (TQM), balanced scorecard (BSC), and Six Sigma. These integrated methods can promote efficiency and effectiveness, and thus maintain the growth and financial health of the institutions concerned. In addition, these methods also promote the wider reputation of the institution in the community. The present study has addressed these matters by integrating two scientific approaches the DFX technique and the theory of CE to improve educational quality and management performance. The establishment of a new department must take into account many interdependent dimensions simultaneously. 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Stehn, L., Bergström, M., 2002. Integrated design and production of multi-storey timber frame houses—production effects caused by customer-oriented design. International Journal of Production Economics 77, 259–269. Yan, J.H., Wu, C., 2001. Scheduling approach for concurrent product development process. Computers in Industry 46, 139–147. Yang, C.C., 2004. Strategy Creates Advantages. Chinese Productivity Center, Taipei. CONCURRENT ENGINEERING: Research and Applications Overcoming the 90% Syndrome: Iteration Management in Concurrent Development Projects David N. Ford1,* and John D. Sterman2 1 Department of Civil Engineering, Texas A&M University, College Station, TX 77843-3136, USA 2 Sloan School of Management, Massachusetts Institute of Technology, 50 Memorial Drive, E53-351, Cambridge, MA 02142 USA Abstract: Successfully implementing concurrent development to reduce cycle time has proven difficult due to unanticipated iterations. We develop a dynamic project model that explicitly models these interactions to investigate the causes of the ‘‘90% syndrome,’’ a common form of schedule failure in concurrent development. We find that increasing concurrence and common managerial responses to schedule pressure aggravate the syndrome and degrade schedule performance and project quality. We show how understanding of and policies to avoid the 90% syndrome require integration of the technical attributes of the project, the flows of information among participants, and the behavioral decisionmaking heuristics participants use to respond to unanticipated problems and perturbations. Key Words: concurrent development, concurrent engineering, iteration, rework, cycle time, project management, system dynamics. 1. Introduction Developing products faster has become critical to success in many industries, whether the product is an office building, software package, or computer chip. Calls for faster product development have simultaneously taken on the sacred tone of a mantra and the volume of a brass band [34]. Perhaps with good reason. Cycle time reduction is considered crucial to success by many researchers (e.g., [34,41]) and practitioners (e.g., [29,33]). Developing products faster than competitors can increase market share, profit, and long-term competitive advantage [29,33,41]. In response many firms have shifted from sequential to concurrent development (aka Integrated Product Development or Fast Track development). Large reductions in cycle time can be realized by applying concurrent development [4,9,31,41,42]. Despite some successes, implementing concurrent development has proven difficult for many [30,41]. These failures arise in part because cycle time reduction through concurrent development increases process and organizational complexity [8,26,41]. Concurrent methods often increase the frequency and number of information transfers among project phases [7,8]. More tasks are begun with *Author to whom correspondence should be addressed. E-mail: DavidFord@tamu.edu incomplete or preliminary information, increasing iteration. Management policies have not generally improved to address the effects of increased complexity created by concurrent development. Many explanations have been suggested for the concurrent development implementation challenge. Backhouse and Brookes [4] suggest implementation fails due to mismatches among a development organization’s people, controls, tools, processes and structure, and the organization’s need for efficiency, focus, incremental change, radical innovation, and proficiency. Other researchers focus on the disaggregation of work into smaller pieces [35,43] and mismatches between attributes of the technology and the degree of overlapping employed [25]. Still others focus on activity sequencing [22,35], coordination caused by overlapping activities [21], and information transfer [6,25]. In this paper we develop a formal model to explore how concurrent process structures can cause a particular form of development project schedule failure, the 90% syndrome. We show how development processes such as overlapping of activities, activity durations, and delays in the discovery of rework requirements can create unplanned iteration, delays, higher costs, and lower quality. We explore policies that can help improve project performance. In the companion paper ([19], this issue), we use the model to explore the interactions between the process structure of concurrent projects and behavioral responses of developers and managers. Volume 11 Number 3 September 2003 1063-293X/03/03 0177–10 $10.00/0 DOI: 10.1177/106329303038031 ß 2003 Sage Publications 177 178 D. N. FORD 2. AND The 90% Syndrome J. D. STERMAN inter-phase iterations suggest the importance of the late discovery of unanticipated rework. One common concurrent development problem is the ‘‘90% syndrome.’’ The syndrome describes a project that reaches about 90% completion on schedule but then stalls, finally finishing after about twice the originally projected duration. A senior manager in one company we worked with described their experience as ‘‘The average time to develop a product is 225% of the projected time, with a standard deviation of 85%. We can tell you how long it will take, plus or minus a project’’ [16]. The syndrome is common in many industries including software, construction, consumer electronics, and semiconductors [2,12,24]. Consider the following example from our fieldwork with a leading semiconductor firm, describing an ASIC (Application Specific Integrated Circuit) project we call Rattlesnake. The original schedule called for a 34 week project, with a smooth, single-pass flow of work through product definition, design, layout, mask preparation, prototype fabrication, prototype testing, manufacturing process design, and production rollout—no iterations were anticipated. The project appeared to progress smoothly, though somewhat more slowly than planned, apparently completing 79% of the project scope by the original deadline. However, prototype testing revealed major problems, requiring an unplanned iteration with revisions in the design. Tests of the second prototype found still more problems, requiring another major iteration. The project was finally completed in week 81, more than twice the original schedule (see Figure 1 in [19], this issue). The slow progress experienced late in the project is typical of the 90% syndrome, and the unplanned Product Definition 3. The Development Project Model To investigate the impacts of the interaction of physical and information processes with managerial decision-making we built a dynamic project simulation model that integrates several constraints usually treated separately: 1. Characteristic Process Durations: Development activities require minimum times to be performed regardless of the resources allocated to the activity. 2. Development Activity Sequencing: Development activities occur in a specific sequence within phases. 3. Dynamic Information Requirements: Development phases are constrained by information dependencies within and among project phases. These dependencies vary with phase progress and management decisions. 4. Work Release Policies: Work is often not released as it is completed, but in discrete packets. Policies governing packet size and release timing strongly affect the availability of information across project phases. 5. Coordination: When released work is discovered to require rework developers in the originating and discovering phases must coordinate prior to revising the work. Our model simulates the performance of a multiplephase development project. Each phase is represented by Prototype Testing Reliability & Quality Design Network Legend Development Phase Products of Development Phase Supplying Phase Return Errors for Rework Product Design Prototype Reliability Definition Testing & Quality Receiving Phase Product Definition X X Design X X X X Prototype Testing X X X X X X X Reliability & Quality Figure 1. A project phase network and its corresponding Design Structure Matrix. Iteration Management in Concurrent Development Projects a generic structure, which is parameterized to reflect a specific stage of product development such as preparing construction drawings, writing software, or testing prototypes. The unit of measure for development work is the ‘‘task’’ or work package, an atomic piece of work. Examples include writing a line of code or installing a steel beam. When tasks within a phase are heterogeneous the unit of work can be defined as the average amount an experienced person can accomplish in a given interval (e.g., an hour or day). Information concerning the quality of completed tasks is generated through testing or quality assurance efforts. Tasks may require rework because they were done incorrectly or because the work or information they were based on was itself erroneous or has changed. The model is a system of nonlinear differential equations. The model is based on existing product development theories and our field studies of development projects. For example, the development process structure is based on theories of project constraints and resources [32] and previous dynamic project models including [1,10,17]. Because no closedform solutions are known, we simulate the system’s behavior. The full model is available at in the Vensim simulation language (see ). 3.1 Modeling Work and Information Flows The flow of work and information among phases defines the network structure of the project. Figure 1 shows a simple but common example. The links shown in Figure 1 represent several forms of interphase interaction, including: . Work progress in which supplying phases provide development products or other information to receiving phases. These flows are shown by the solid arrows. . Work inherited by receiving phases from supplying phases may require rework (either mandatory due to errors or optional for improvement). Inheriting work containing errors or requiring rework corrupts work done by the receiving phases. When corrupted work is discovered it is reported to the phase responsible for the problem so it can be reworked. These information flows are shown by the dashed arrows in the project network. . Rework requires coordination between the phase that discovered the change requirement and the phase that generated and must correct the work. Coordination must occur prior to the revision of the work. Coordination is an activity in individual phases (boxes) generated by the reporting of problems requiring rework (dashed arrows). 179 The information flows among phases in the model are bi-directional, as in the Design Structure Matrix (DSM) approach [13,35,36]. The DSM identifies bi-directional dependencies between phases in which the activity initiated first (upstream) receives products from the activity initiated later (downstream) as well as the more traditional dependence in the general direction of work flow. Figure 1 also shows the DSM corresponding to the project network in the diagram. Several iterative loops are created by the bi-directional dependencies among phases. Our model allows any DSM to be represented by specifying the number of phases and the dependencies among them. All development processes are constrained by the physical and information relationships among the activities and phases within a project. These constraints include development activity durations and precedence relationships, information dependencies leading to iteration [36], the availability of work [17], coordination mechanisms [22], the characteristics of information transferred among development phases [25], and the number, skill, and experience of project staff [2]. These processes and policies can interact to constrain progress. Consider the erection of the structural steel skeleton for a ten story building. Each member (the columns, beams, and bracing) must be installed, inspected, and corrected if the installation is found to be defective. These activities can only occur in a specific order: install, inspect, approve or discover a problem, rework, and re-inspect; when no further problems are found the work is approved and released so other work dependent on that task can proceed (e.g., installation of floors, walls, etc.). Management policies such as the number of floors that must be approved prior to release also affect progress and information availability downstream. Figure 2 shows the states of work within a single development phase in the model and the flows of work among them. As work is first completed it enters the stock of work awaiting quality assurance (QA). If it passes QA (either because it is correct or the need for changes is not detected), it is approved and enters the stock of approved work. When sufficient work has been approved, a package is released, adding to the stock of work released to other phases or customers. The release package size is a management decision and is conditioned by characteristics of the phase. For example, in semiconductor development the vast majority of the design code must be completed prior to release for a prototype build since almost all the code is needed to design the masks. In other development settings managers have broad discretion in setting release package sizes. Work found to require changes must be resolved through coordination with the phase responsible for the problem. Classic examples include designers working with marketers to refine ambiguous product 180 D. N. FORD AND J. D. STERMAN Notification and Return Rate Coordination rate Work needing Coordination Work known to need Rework Discovery of Needed Rework Rework Rate Work needing Initial Completion Work needing Quality Assurance Approval Rate Work Approved Release Rate Work Finished and Released Initial Completion Rate Figure 2. Work flows within a single development phase. specifications and manufacturing engineers meeting with designers to explain why parts cannot be built as specified in the drawings. After coordination resolves disputed issues, these tasks move to the stock of work to be changed and are subsequently reworked, then returned to quality assurance for re-inspection. Quality assurance is imperfect, so some tasks requiring rework can be missed and are erroneously approved and released. Such rework requirements may be discovered later by another phase—if not, they remain embedded in the product, to be discovered by the customer. When the phase reports problems, the affected tasks are moved from the stock of work considered finished to the coordination backlog, then eventually reworked. For example, a test phase may discover a short circuit across two layers in a prototype chip. If the error is traced to the design, test engineers must work with the designers to specify the location and characteristics of the short circuit. The designers then must rework, recheck and rerelease the design, followed by layout, masking, prototype fabrication, and retesting of the new chip. The probability of error detection depends on a variety of behavioral factors and management decisions. The probability of detecting problems declines as the information available is less current, complete, and accurate. High overlap between dependent phases in concurrent development means many tasks are done on the basis of specifications and components that are unavailable or changing. Our fieldwork shows that activities such as quality assurance, testing, documentation, and validation commonly suffer under concurrent development as development activities are overlapped. For example, we asked engineers in a large manufacturing firm how they accommodated the schedule compression and concurrency created by the organization’s official product development process: ‘‘The technology may not be ready before alpha phase. Sometimes we have no choice, we just have to put something in’’ — Development Engineer ‘‘We might accept a lower level of maturity [in the prototype]. Maybe maturity isn’t a good word. We might accept a lower level of design representativeness than we would like’’ — Engineering Manager ‘‘Often, we’ll put [early prototype] parts in a [later prototype] . . . There’s no getting around it as long as we have to go fast’’ — Program Manager Schedule compression also biases workers towards getting their tasks done, even when that means spending less time on validation and quality assurance. An engineer in the organization above admitted that ‘‘We might not be able to finish the part, finish the FMEA’s [Failure Modes and Effects Analysis], etc. We’ll do the FMEA’s, but they won’t be as thorough as they would [be] otherwise.’’ Another commented: ‘‘We haven’t done the FMEA yet. We’ll probably do it for beta [second prototype], but not for alpha [first prototype]. We just don’t have time to do it.’’ These quotes illustrate a phenomenon we find repeatedly in our fieldwork: The greater the degree of concurrence, the greater the schedule pressure, and the greater the gap between resources available and resources required, the less effort is devoted to quality assurance and the lower the effectiveness of that effort. We capture these effects parsimoniously by assuming the probability of detecting the need for rework declines as the degree of concurrence increases. 3.2 Modeling the Speed of Development Activities and Concurrence The rates at which development activities are performed depend on two types of constraints: resources and processes. Obviously, progress can be constrained by inadequate resources—too few workers, insufficient worker skill and experience, or insufficient supporting infrastructure (such as CAD/CAM systems). A variety of models explore how underestimating project scope, overestimating productivity, delays in the discovery of errors, or unexpected changes in customer requirements Iteration Management in Concurrent Development Projects can cause resource shortages that lead to delays, cost overruns, and quality problems (e.g., [2,10,37 Ch. 2.3]). In this paper, however, we seek to show how the structure of a development process interacts with managerial decision making to contribute to the 90% syndrome, even when resources are ample. If so, throwing more people and money at a project in trouble will have low leverage; effective policies will require changes in project structure and management policies. Even when resources are ample, progress can be constrained by the interdependencies among phases and tasks. As an example, consider again the erection of the steel skeleton for a building. Each steel member must be installed (base work), and inspected (quality assurance). If an error is found, the affected supervisors and skilled trades must work together to devise a plan to remedy it (coordination) before the error can be corrected (rework). For any given technology, a certain minimum amount of time is required for each of these activities even when resources such as laborers and cranes are ample. Further, certain tasks cannot be started or completed until others are done. For example, the steel members for the upper floors cannot be installed until the beams and girders for lower floors are in place. These constraints are captured in our model through concurrence relationships. The function relating how much steel for upper floors can be placed to the progress of lower floors defines an intraphase concurrency relationship (the constraint arises within the steel erection phase). Analogously, interphase concurrence relationships answer the question ‘‘How much work can we now complete given the work released by the phases upon which we depend?’’ For example, the erection of the steel for an office building depends on the release of construction drawings by the design phase and the progress of foundation work (among others). These constraints require two interphase concurrence relationships: one describing how much of the steel can be erected based on the release of construction drawings, and another describing how much steel erection can proceed based on the state of the foundations. Either of these interphase relationships might constrain steel erection. Each interphase concurrence relationship describes the fraction of a phase’s total scope that can be done based on the fraction of work released by a supplying phase. Interphase concurrence relationships characterize the dependencies among the off-diagonal terms in the Design Structure Matrix. They are potentially nonlinear, allowing our model to capture changes in the degree of dependence among phases as a project evolves. For example, ASIC designers may be able to develop certain standard elements of the design (memory registers, data bus) with early information about customer requirements, but may be unable to continue until full specifications for the required functionality are released. Concurrence relationships are characteristic features of a project’s network structure and must be estimated for each project. We tested our model against the behavior of a medium-sized chip development project at a major U.S. semiconductor firm (the Python project) [18]. Ford and Sterman describe the protocol used to elicit these concurrence relationships from project personnel and provide examples. Figure 3 shows four expert estimates of the interphase concurrence relationship between the product definition and design phases of the Python project. The product definition phase develops product architecture and specifications based on the Python chip’s market and target performance. The designers use these specifications as the basis for the detailed design embodied in the software code used to 100 e epr ve tati sen gR etin Fraction Available for Initial Completion by Design Phase (%) gic ate Str rk Ma r tA ct ite ch uc od Pr Design Manager r igne Des 0 0 181 Fraction Released by Product Definition Phase (%) 100 Figure 3. Four estimates of the interphase concurrence relationship between the product definition and design phases. 182 D. N. FORD AND J. D. STERMAN planned progress 12 weeks after the project began. However after development began the decision was made to design the Python chip in two components instead of one, resulting in two design releases and therefore two jumps in performance. Like the Rattlesnake project, Python suffered from the 90% syndrome. The project remained close to the original schedule through week 20 and was 73% complete by the original deadline. Progress then slowed from 1.8% per week to 0.9% per week, and the project was ultimately completed 77% late (week 69 vs. 39). We drew on our fieldwork and prior research (e.g., [11]) to estimate the parameters for the Python case. Ford and Sterman [18] describe the protocol used to elicit the concurrence relationships and provide examples from the Python project. Figure 5 shows planned and actual project performance as simulated by our model. Planned progress simulates management’s plan for a single design release, their assumption of no interphase iteration, and overestimation of productivity. Experienced managers expect iteration but are often required to use ‘‘stretch objectives’’ in planning because management believes they keep motivation and pressure to perform high. Python project developers repeatedly described the unrealistic optimism used to plan projects, including the assumption of little or no rework. The model simulation (Figure 5) closely matches actual project behavior (Figure 4) and recreates the 90% syndrome experienced by the Python project. Differences between our simulation and the project’s actual behavior are largely due to resource constraints omitted from the model, specifically staffing problems created by the unanticipated iterations. The similarity between Figures 4 and 5 indicates that even projects staffed by skilled personnel with ample resources can experience the 90% syndrome, solely as a function of the informational and physical dependencies created by concurrency. lay out individual components on the silicon. Each estimate in Figure 3 describes the mental model of a participant concerning the question ‘‘How much design work can be completed based on the fraction of the product definition work that has been completed, approved and released?’’ Interestingly, the two ‘‘upstream’’ people (the marketing representative and the product architect) believed the ‘‘downstream’’ people (the design manager and designer) could, and presumably should, begin their work quite early, when few product specifications have been released, while those downstream believed their work could only progress when a majority of the specifications were available. These differences in mental models had led to conflict and delay in prior development projects. The explicit description of these mental models initiated and facilitated discussions for improving the organization’s development processes. 3.3 Model Testing The model was tested for structural and behavioral similarity to actual development projects using standard methods [20,37, Ch. 21]. Model structure was based on product development project processes and organizations as described in the literature and derived from our fieldwork. We examined the ability of the model to replicate the experience of the Python project described above. Python applied all the major precepts of concurrent development including overlapping phases and cross-functional teams. The organization was well trained in concurrent development practices [15,40]. Figure 4 compares Python’s original schedule and actual performance, developed from records of the phase durations and completion dates (e.g., by counting lines of code in each version of the design code). As is common in semiconductor development, and verified in our interviews, the design phase planned to release its work in a single large package, generating the jump in 100 Cummulative Progress (percent) 90 80 70 60 50 40 30 20 Planned Progress 10 Actual Progress . 0 0 5 10 15 20 25 30 35 40 45 50 55 60 Time (weeks from project start) Figure 4. Planned and actual progress of the Python project. 65 70 75 183 Iteration Management in Concurrent Development Projects 100 Cummulative Progress (percent) 90 80 70 60 50 40 30 20 Simulated planned progress 10 Simulated actual progress 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 Time (weeks from project start) Figure 5. Planned and actual progress of the Python project as simulated by the model. 4. Implementing Concurrent Development Processes To examine the impact of concurrence on schedule performance, we simulated the model with different levels of concurrence. We vary all inter- and intraphase concurrence constraints. We preserve start time constraints such as cases where a phase cannot start until at least some work has been released by a supplying phase. Figure 6 shows the impact on project duration, iteration, and quality of varying the degree of concurrence from fully sequential (100% of the base case) to highly overlapped activities (þ150% of the base case).1 As expected, large reductions in the degree of concurrence cause sharp increases in duration (Figure 6). As overlapping decreases some phases delay the start of their work well after the point at which the supplying phases have completed theirs. Increasing concurrency reduces duration, but with sharply diminishing returns: a 50% increase in overlap compared to the base case reduces duration by 22%, while another 50% increase cuts duration only another 6%, and improvement essentially ceases beyond that point. Figure 6 also shows total work effort relative to total project scope, defined as the cumulative number of tasks completed (both initially and through rework), and is a proxy for project cost.2 1 A stretch factor of þ50% more concurrence means each phase can now do 75% of its work at the point where it could have done 50% in the base case; a stretch factor of 50% means the phase could do only 25% of its work at the point where it could do 50% in the base case. In implementing different levels of concurrence we preserve constraints, where they exist, that, e.g., phase j cannot begin its work until phase i has released as least some of its work. Such constraints mean the ‘‘stretch’’ factor is nonlinear. The web-appendix provides full documentation. 2 Project cost is actually the sum of the tasks completed by each phase weighted by the unit cost of tasks in each phase. To preserve confidentiality we report total work–not cost, implicitly assuming the unit cost of tasks in each phase are equal. Note that in the base case, total work effort is 55% greater than project scope due to the impact of rework (if all tasks were completed perfectly with no need for changes work effort would equal project scope). Interestingly, increasing concurrence decreases total work effort—a 50% increase in concurrence cuts total work effort from 1.55 to 1.27 times the project scope. One might argue that total work effort should rise with increasing concurrence since more work must be redone when errors are discovered. This effect does occur, but is overwhelmed by another, less intuitive impact: increased concurrence increases the average iteration path length by delaying the discovery of the need for rework to phases farther from the generating phase. In an iteration cycle, rework requirements are passed from the discovering phase to the originating phase. After coordination, changes are made to the flawed work in the originating phase and to contaminated work in all affected phases. The reworked tasks are re-inspected, rereleased and arrive at the location of its discovery again. For example, a test phase may discover an error in the chip and trace it to the design. Test engineers notify and coordinate with the designers to specify the location and characteristics of the flaw. The designers then must rework, recheck and rerelease the design, followed by changes in layout, tape out, masking, and prototype fabrication. The cycle is completed when testing of the redesigned prototype begins. High concurrency means downstream activities carry out a substantial portion of their work before they (or any other phase) have a chance to detect and correct errors. In essence, the downstream phases outrun the discovery of inherited rework requirements. From the preceding it appears that increasing concurrence both speeds the project and cuts project costs. However, the third graph in Figure 6 shows the cost of concurrency: project quality drops significantly. In the base case, the number of uncorrected errors 184 Log (Duration/Base Case) D. N. FORD Total Tasks Done/Project Scope J. D. STERMAN Project Duration 5.0 3.0 1.0 0.8 -1.0 -0.5 0.0 0.5 1.0 (Concurrence – Base Concurrence)/Base Concurrence 1.5 Work Effort 1.6 1.4 1.2 1.0 -1.0 Errors Remaining/Project Scope AND -0.5 0.0 0.5 1.0 (Concurrence – Base Concurrence)/Base Concurrence 1.5 0.08 Errors Remaining at Completion 0.06 0.04 0.02 0.00 -1.0 -0.5 0.0 0.5 1.0 (Concurrence – Base Concurrence)/Base Concurrence 1.5 Figure 6. Impact of varying concurrence (internal and external). released to the customer is 5% of total project scope. These uncorrected errors include both outright defects (where the product does not function as designed) and instances where the design does not correspond to customer requirements. In the chip development context such errors include features the customer wanted that are not available (e.g., power consumption is too high), features that do not function as designed (e.g., a certain combination of inputs gives a fatal error), or design attributes that cause low manufacturing yield. Increasing concurrence 50% raises errors remaining at project completion to 6.7% of project scope— a 34% increase over the base case. At the same time that increasing concurrence delays the discovery and correction of errors, it also increases the likelihood of releasing tasks requiring rework. As concurrence increases, the information, technologies, and components of each phase relies upon as the basis for its work are necessarily less complete, less accurate, and more ambiguous. The number of tasks requiring rework grows while at the same time the ability of personnel in each phase to detect these problems falls, increasing the number of tasks released with errors and thus the chance that needed rework will not be discovered and corrected before the project is completed. The simulations show a strong tradeoff between schedule and quality performance. Increased concurrency interacts unfavorably with the delays in the discovery of rework needs. The greater the overlap, the more work is completed and released before rework requirements can be detected, leading to more unplanned iteration. Greater concurrence increases the vulnerability of a project to delays in discovering rework and increases the fraction of work requiring such changes. The result is lower suitability to customer requirements and lower product quality. Iteration Management in Concurrent Development Projects 5. Conclusions In this paper we use a dynamic model of development projects to describe, quantify, and simulate how physical and information processes interact with managerial decision making to constrain progress and cause project overruns. We have shown the critical role of iteration cycles in explaining the 90% syndrome. Our research suggests that an effective strategy addresses the managerial behaviors that cause iteration cycles to constrain progress. Iteration cycles can delay projects by being more in number, longer in the distance which information must travel, slower in traversing that distance, and occurring later than possible. Researchers have proposed process designs to manage iteration cycle number, speed, length, or timing. For example Terwiesch et al. (1998) recommend ‘‘a fast process of problem detection, problem solving and engineering change implementation’’ to increase iteration cycle speed. They suggest ‘‘loosening the coupling (dependence) between development activities’’ and improving the accuracy of preliminary information, both of which reduce the number of cycles. McAllister and Backhouse (1996) suggest redesigning work flows to reduce the number of iteration paths in a project network. However increased concurrence works against all these recommendations. Our work has several implications for concurrent development research. The model and simulations demonstrate that effective modeling of development processes must include the structure of information dependencies to explain problematic project behaviors. More specifically, the role of iteration cycles in the 90% syndrome demonstrates the need for explicitly including iteration. Future research can identify and test metrics that relate iteration to different forms of project and phase performance and how specific iteration features constrain progress. Most interesting and relevant for managers, the model can be used to study the interaction of the process structure described in this paper and the behavioral decision rules used by engineers and managers under pressure to meet aggressive deadlines (see [19], this issue). In that paper we show how common behaviors such as concealing the need for rework from managers and colleagues interacts with the structure of concurrent development programs to intensify the 90% syndrome, lower product quality, and undercut the benefits of increased concurrency. We argue that sustained improvements in project performance require integration of both the physical and informational structure of concurrent development processes with the behavioral decision rules of the engineers and managers who work within them. 185 Acknowledgments The authors thank the Organizational Learning Center and the System Dynamics Group at the MIT Sloan School of Management and the Python organization for financial support. Special thanks to the members of the Python team for their interest, commitment, and time. References 1. Abdel-Hamid, T. and Madnick, S. (1991). Software Project Dynamics, An Integrated Approach, Prentice-Hall, Inc: Englewood Cliffs, NJ. 2. Abdel-Hamid, T. (1988). Understanding the ‘‘90% Syndrome’’ in Software Project Management: A Simulation-Based Case Study, The Journal of Systems and Software, 8: 319–330. 3. Adler, P.S., Mandelbaum, A., Vien, N. and Schwerer, E. (1995). From Project to Process Management: An Empirically-based Framework for Analyzing Product Development Time, Management Science, 41(3): 458–484. 4. Backhouse, C.J. and Brookes, N.J. (1996). Concurrent Engineering, What’s Working Where, Gower, Brookfield, VT: The Design Council. 5. Bohn, R. (July–Aug 2000). Stop Fighting Fires, Harvard Business Review, 78(4): 83–91. 6. Browning, T. (Oct. 1999). Sources of Schedule Risk in Complex System Development, Systems Engineering, 2(3): 129–142. 7. Clark, K.B. and Fujimoto, T. (1991). Product Development Performance, Strategy, Organization, and Management in the World Auto Industry, Boston, MA: Harvard Business School Press. 8. Clark, K.B. and Fujimoto, T. (1989). Reducing the Time to Market: The Case of the World Auto Industry, Design Management Journal, v. 1(1): 49–57. 9. Componation, P.J., Utley, D.R. and Armacost, R.L. (1999). Prioritizing Components of Concurrent Engineering Programs to Support New Product Development, Systems Engineering, Oct 4, 1999, 2(3): 168–176. 10. Cooper, K.G. (1980). Naval Ship Production: A Claim Settled and a Framework Built, Interfaces, 10(6): 20–36. 11. Cooper, K.G. and Mullen, T.W (1993). Swords and Plowshares: The Rework Cycle of Defense and Commercial Software Development Projects, American Programmer, 6(5). 12. DeMarco, T. (1982). Controlling Software Projects, New York: Yourdon. 13. Eppinger, S.D., Whitney, D.E., Smith, R.P. and Gebala, D.A. (1994). A Model-Based Method for Organizing Tasks in Product Development, Research in Engineering Design, 6: 1–13. 14. Ettlie, J.E. (1995). Product-Process Development Integration in Manufacturing, Management Science, 41: 1224–1237. 15. Ford, D.N. (1995). The Dynamics of Project Management: An Investigation of the Impacts of Project Process and Coordination on Performance, Doctoral Thesis, Cambridge, MA: Massachusetts Institute of Technology. 16. Ford, D.N., Hou, A. and Seville, D. (1993). An Exploration of Systems Product Development at Gadget Inc. Report D-4460, System Dynamics Group, Sloan 186 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. D. N. FORD AND School of Management, Cambridge, MA: Massachusetts Institute of Technology. Ford, D.N. and Sterman, J.D. (1998a). Dynamic Modeling of Product Development Processes, System Dynamics Review, 14(1): 31–68. Ford, D.N. and Sterman, J.D. (1998b). Expert Knowledge Elicitation to Improve Formal and Mental Models, System Dynamics Review, 14(4): 309–340. Ford, D.N. and Sterman, J.D. (2003). The Liar’s Club: Concealing Rework in Concurrent Development, Concurrent Engineering: Research and Applications (this issue). Forrester, J and Senge, P. (1980). Tests for Building Confidence in System Dynamics Models, TIMS Studies in the Management Sciences, 14: 209–228. Haddad, C.J. (1996). Operationalizing the Concept of Concurrent Engineering: A Case Study from the U.S. Auto Industry, IEEE Transactions on Engineering Management, 43(2): 124–132. Hauptman, O. and Hirji, K.K. (1996). The Influence of Process Concurrency on Project Outcomes in Product Development: An Empirical Study of Cross-Functional Teams, IEEE Transactions on Engineering Management, 43(2): 153–178. Joglekar, N.R., Yassine, A.A., Eppinger, S.D. and Whitney, D.E. (2001). Performance of Coupled Development Activities with a Deadline, Management Science, 47(12): 1605–1620. Kiewel, B. (January 1998). Measuring Progress in Software Development, PM Network, Project Management Institute, 12(1): 29–32. Krishnan, V. (1996). Managing the Simultaneous Execution of Coupled Phases in Concurrent Product Development, IEEE Transactions on Engineering Management, 43(2): 210–217. Krishnan, V., Eppinger, S.D. and Whitney, D.E. (1995). A Model-Based Framework to Overlap Product Development Activities, Management Science, 43: 437–451. Loch, C.H. and Terwiesch, C. (1998). Communication and Uncertainty in Concurrent Engineering, Management Science, 44(8): 1032–1048. McAllister, J. and Backhouse, C. (1996). An Evolving Product Introduction Process in Backhouse, C. and Brookes, N. (eds.) Concurrent Engineering, What Works Where. Gower. Brookfield, VT. Meyer, C. (1993). Fast Cycle Time, How to Align Purpose, Strategy, and Structure for Speed, New York: The Free Press. Moffat, L.K. (1998). Tools and Teams: Competing Models of Integrated Product Development Project Performance, Journal of Engineering Technology and Management, 15: 55–85. Nevins, J.L. and Whitney, D. (1989). Concurrent Design of Products & Processes, A Strategy for the Next Generation in Manufacturing, New York: McGraw-Hill. Noreen, E., Smith, D. and Mackey, J. (1995). The Theory of Constraints and its Implications for Management Accounting, Great Barrington, MA: North River Press. Patterson, M.L. (1993). Accelerating Innovation, Improving the Process of Product Development, New York: Van Nostrand Reinhold. Rosenthal, S.R. (1992). Effective Product Design and Development, Homewood, IL: Business One Irwin. Smith, R.P. and Eppinger, S.D. (1997a). A Predictive Model of Sequential Iteration in Engineering Design, Management Science, 43(8): 1104–1120. J. D. STERMAN 36. Smith, R.P. and Eppinger, S.D. (1997b). Identifying Controlling Features of Engineering Design Iteration, Management Science, 43(3): 276–293. 37. Sterman, J.S. (2000). Business Dynamics, Systems Thinking and Modeling for a Complex World, New York: Irwin McGraw-Hill. 38. Sterman, J.D. (1994). Learning in and about Complex Systems, System Dynamics Review, 10(2–3): 291–330. 39. Terwiesch, C., Loch, C.H. and De Meyer, A. (2002). Exchanging Preliminary Information in Concurrent Engineering: Alternative Coordination Strategies, Organization Science, 13(4): 402–419. 40. Voyer, J., Gould, J. and Ford, D.N. (1997). Systematic Creation of Organizational Anxiety: An Empirical Study, Journal of Applied Behavioral Science, 33(4): 471–489. 41. Wheelwright, S.C. and Clark, K.B. (1992). Revolutionizing Product Development, Quantum Leaps in Speed, Efficiency, and Quality, New York: The Free Press. 42. Womack, J.P., Jones, D. and Roos, D. (1990). The Machine that Changed the World, The Story of Lean Production, New York: Rawson Associates. 43. Zirger, B.J. and Hartley, J.L. (1996). The Effect of Acceleration Techniques on Product Development Time, IEEE Transactions on Engineering Management, 43(2): 143–152. Biographies David N. Ford David N. Ford, PhD, P.E. is an Assistant Professor in the Construction Engineering and Management Program in the Department of Civil Engineering, Texas A&M University. He researches development project strategy, processes, and resource management. Dr. Ford earned his PhD from MIT and Master and Bachelors degrees from Tulane University. He has over 14 years of engineering and project management experience. John D. Sterman John D. Sterman is the Jay W. Forrester Professor of Management at the MIT Sloan School of Management and Director of MIT’s System Dynamics Group. His most recent book is Business Dynamics: Systems Thinking and Modeling for a Complex World. International Journal of Project Management 20 (2002) 49±57 www.elsevier.com/locate/ijproman Managing project risks: a case study from the utilities sector Paul Elkington, Clive Smallman * University of Cambridge, Trumpington Street, Cambridge CB2 1AG, UK Received 20 January 2000; received in revised form 19 June 2000; accepted 13 July 2000 Abstract We examine the project risk management practices in a British utility, which manages its information systems and business change projects using the Prince2TM method. This method has greatly increased the success rate of projects run within the company, but has little in the way of directing Project Managers in handling project risk. We review current project risk management literature. We then explore the current usage of risk management in the utility's projects, and determine the e€ect of risk management on project success. We conclude by outlining recommendations for improving projects run in the utility and elsewhere. # 2001 Elsevier Science Ltd and IPMA. All rights reserved. Keywords: Project management; Risk management; Utilities; Case study 1. Introduction In the last decade, British utility (water, power, and telecommunications) companies have seen an unprecedented change to their businesses, a direct result of their shift from the public to the private sector. The manner in which utilities manage such change is increasingly via change programmes. These are either a large set of changes to just business processes and computer systems, or changes to company culture and the attitude of its sta€. The majority are a mix of both. With signi®cant strategic change being implemented by these programmes, project and programme management is becoming increasingly important to the companies' survival, and much e€ort and resource are being put into ``professionalising'' the project approach undertaken. The ``less predictable'' nature of projects makes them riskier than day to day business activities. Hence, risk management is an integral part of project management and most large companies put substantial resources into the management of business risk. However, there is evidence that a culture of risk management may not ®lter down into every level of a company [1]. Consequently, companies do not capitalise upon operational sta€'s * Corresponding author. Tel.: +44-1233-766592; fax: +44-1223339701. E-mail address: c.smallman@jims.cam.ac.uk (C. Smallman). detailed knowledge of business processes, and it is likely that many potential risks are not even noticed [2]. We present a study of project risk management practice in a British utility (henceforth `the Utility'). 2. Project management practices in the Utility Until privatisation, the Utility was the monopoly supplier and distributor of a public good throughout a large region of Britain. Since ¯otation and the deregulation of their businesses, the Utility has diversi®ed in an attempt to attract new customers, whilst retaining a strong presence in its traditional markets. Immediately after privatisation, engineers, many of whom had joined at sta€ level, dominated the Utility's senior management. This encouraged a culture in which small multi-skilled teams e€ected infrastructure maintenance; that is through projects. These ran using the knowledge and experience of the engineers, rather than formal methods of project management. Risk (usually only technical systems risk) was considered, but mainly on an ad hoc basis. Risk management consisted of overengineering the infrastructure (using high speci®cation components where lesser ones would have suced) to e€ectively build technical risk out, but at massively increased costs. The engineering side of the business utilised informal project management; the rest did not. There were no 0263-7863/01/$20.00 # 2001 Elsevier Science Ltd and IPMA. All rights reserved. PII: S0263-7863(00)00034-X 50 P. Elkington, C. Smallman / International Journal of Project Management 20 (2002) 49±57 formal project teams and the deliverables from the work were not always clear. The direction of process work was largely ad hoc, as the leader of the work usually had to continue their other duties alongside the changed work. Such `Project Managers' rarely had training or a ¯air for directing project work. Not surprisingly, many `projects' that incorporated business change failed to deliver the bene®ts that were expected. 2.1. The development of a project ethos In the early 1990s, the Utility was advised to undertake large business changes by using speci®c programmes, and to use projects under that programme structure. This improved the control and overall direction of the business changes, and more bene®ts were realised than was previously the case. However, following the failure of a major business programme failed in the mid-1990s, senior management decided that programme management in the Utility needed to be more `professional'. The aim was to ensure that future programmes had the best sta€ available and that training in Programme Management for senior sta€ was provided. As part of this training, it was realised that the Project Manager level of sta€ in the programmes were key to the success of each project and therefore the programme, and that this level of sta€ must also receive formal training and quali®cations in Project Management. The project management method, Projects in Controlled Environments 2 (Prince2) [3] was chosen, as it was a generic project management method. Nearly 100 sta€ were trained in the method, including members of the Information Systems Division (ISD) Programme Services section, which also recruited experienced programme managers. The section manages some business change programmes and o€ers advice to the rest of the company. risk is acceptable, and if not, what actions can be undertaken to make the risk acceptable. The options of which action can be taken to make the risk acceptable are: . prevention, where countermeasures are put in place to stop the threat or problem from arising, or to prevent it from having any impact on the project or business; . reduction, where actions either reduce the likelihood of the risk developing, or limit the impact to acceptable levels; . transfer of the risk to a third party, for example by taking out an insurance policy or a penalty clause; and . contingency, where actions are planned and organised to come into force as and when the risk occurs. Risk management is the second phase of the Prince2 management of risk framework. Its objective is to integrate the risks identi®ed in the risk analysis stage into the project management. This is achieved through: planning the countermeasures identi®ed in the risk analysis stage; identifying and allocating resources to carry out the risk avoidance work; monitoring against the plans that the actions are having the desired e€ect on the risks; and controlling to ensure that the planned events actually happen. Other methods identi®ed also seem to follow the same broad approach to risk management. Page [4] writes that risk management should be broken into four stages, that of comprehensive risk identi®cation of all sources of risk, objective analysis of their signi®cance, planning appropriate responses and the management of those responses. 3. Risk management in projects 3.1. Risk identi®cation In Prince2, risk is categorised into two types. Project risk is de®ned as threats directly to the project, such as supplier issues, organisational issues and resource issues. Business risks are those that may a€ect the delivery of the bene®ts to be gained from the project, for example the risk that the business case will become invalid due to changes in the market in which the company operates. The process of managing risk begins with risk analysis, which is designed to pick up and gain detail on both business and project risks, and consists of: Risk identi®cation appears to be the least mentioned of the risk techniques. It is, however, the most important stage of risk analysis, as no work can be done on risks that no one has discovered. Risk identi®cation requires divergent thinking on the part of the project manager, to identify potential risks at each stage of the project, but this investigation is easier if guidelines are set. Chapman and Ward [5] state that risk identi®cation is both important and dicult, and that it calls for `some creativity and imagination'. The identi®cation process can be made more ecient if the skills and experience of others can be harnessed. They recommend directed thinking approaches, such as interviews of individuals or groups, brainstorming or using checklists. Overall, they attempt to put more detail into the method of identifying risks. However, unless it is carefully examined . risk identi®cation to determine potential risks; . risk estimation to determine the importance of each risk, based on its likelihood and impact; and . risk evaluation, which decides whether the level of P. Elkington, C. Smallman / International Journal of Project Management 20 (2002) 49±57 51 and broken down as above, it appears complex, and this is its main failing. The CCTA's [6] approach is a more detailed version of that in Prince2. However, the process is, again, very technical and structured: set the proper context and perspective for the analysis; gather information on risks; classify risks based on their causes. The CCTA [6] approach is procedurally precise, and answers the question `how do I identify risk?' However, it does not necessarily o€er users the right information or the whole picture, and does not mention the imagination or creativity necessary for e€ective risk identi®cation. The process directs the project manager to use product or activity-based planning, and then to look at the risk of each product. The weakness is that risks may not be based on products of the project. Chapman and Ward [5] note that project managers should also be aware of `positive' risks. Most experienced project managers focus on the risk of late delivery, overspend and poor quality in the project products, but early delivery can also cause signi®cant problems. Even products that are not on the critical path for the project can cause problems if they are delivered early. The CCTA [6] take the process further by recommending that the estimation phase is an iterative one, and that the estimates should be clari®ed and improved on an ongoing basis. Again, Chapman and Ward [5] appear to have thought through the mechanics of the assessment of the likelihood of a risk occurring. However, as with risk identi®cation his process is highly technical. Chapman and Ward [5] suggest the use of incremental scenario planning to determine both the likelihood and impact of a risk. This means determining the maximum and minimum impacts of the risk, and then using incremental steps to decide the impact of scenarios between the maximum and minimum impact. The same approach is then used to assign a probability to each scenario. The approach also encourages several passes of each stage, to re®ne the thought process. Chapman and Ward [5] describe methods of probability assessment that will improve the estimates made above, these include fractile, relative likelihood and probability distribution functions. 3.2. Risk estimation The CCTA [6] take a three-step approach to the evaluation stage of the risk management process: Risk estimation is the Prince2 term for determining how important the risk is, (potential impact), and what the likelihood is of the risk occurring. The CCTA [6] further de®ne the estimation process to be the likelihood, consequence and timing of the risk. It appears that writers do not want to approach this part of risk management. Dembo and Freeman [7] discuss the philosophy of risks and on how to decide if a risk is worth taking, but always seems to start by stating a probability associated with a risk. They have the `luxury' of operating in the world of ®nancial risk, where vast statistical databases instruct probability. Project managers operating outside of ®nance, facing operational risks, constantly try to decide the chances of an identi®ed risk occurring. Some use historical project data, and others use unwritten past experience, but in many cases, the likelihood of a risk occurring is derived by means of an educated guess. Prince2 o€ers no advice to project managers on risk estimation. However, it appears to recognise the diculty project managers face, as it recommends that the risk register only has the three bands of high, medium and low likelihood of occurrence, and does not expect an accurate appraisal. The area of assessing the impact of the risk on the project has even less advice, but simply identi®es that some measure should be made. The CCTA [6] advise that the project manager should assess the qualitative likelihood of the risk occurring, but does not o€er up any methods by which to do it. The `consequences' section does, however, introduce the notions of time-delimited risks and time-expiring risks. 3.3. Risk evaluation 1. Assess the risks against risk indicators and determine the acceptability of each. This step, unlike Prince2, suggests that risks may be `grouped' and have their impact assessed together. 2. Generate alternative paths of action for risks that do not meet the acceptability criteria. This step is e€ectively the ®rst stage of the Prince2 method of determining what action to take with the risk. This step also recommends that the project manager should return to the classify risk and cause step if necessary for further information. 3. Sort risks into ®nal order of priority and crossreference to the identi®ed risk reduction options. The ®nal step sets the actions that the project manager will take to manage the risk. Chapman and Ward [5] view the evaluation stage as central in the risk management process. Throughout, they emphasise that the stages should be used, as necessary, to improve the information on a risk and its management. Looping back into other phases of the analysis will be necessary to clarify and reassess the risks. This approach is more detailed than the Prince2 or the CCTA [6] methods, and identi®es four speci®c steps to the evaluation process: 1. Select an appropriate subset of risks. This is where risks are grouped into subsets that...
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Article 1
A DFX and Concurrent Engineering model for Establishment of anew Department in a
University
Changes in the education sector from a model of the elite to a model of mass education has
created unprecedented imbalances between supply and demand for university education which
has contributed to a reduction in education quality in Taiwan and many other countries as well.
This has created a need for improving Taiwan education.
The design model X’ (DFX) have been found to be both convenient and less complicated in
comparison with other services sector designs. By effectively utilizing DFX and CE
concurrently, quality, costs, and cycle times are improved. This study summary showcases the
application of these designs to the problem of developing a new department in a university
Literature Review and Case Study
CE – concurrent engineering involves designing products in an integrated, concurrent, and
systematic approach.
DFX – tries to emphasize the consideration of the design goals and constraints of earlier design
stages in the development of new products.

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Model for designing a new department
The development involves the application of the NSD concept which carries two stages:


Planning Stage – involves making the design's physical elements i...


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