J Intell Manuf (2009) 20:637–648 DOI 10.1007/s10845-008-0153-x
Identifying the cause and effect factors of agile NPD process with fuzzy DEMATEL method: the case of Iranian companies Roxana Fekri · Alireza Aliahmadi · Mohammad Fathian
Received: 21 November 2007 / Accepted: 15 July 2008 / Published online: 31 July 2008 © Springer Science+Business Media, LLC 2008
Abstract Although the New Product Development (NPD) process is dynamic in nature, with the dawning of the new millennium the rate of the changes in this process has been increased. Individual customer needs, pressure of global competition, fragmentation of markets into smaller segments, rapid and never ending changes in technological aspects and flexible production, force the NPD teams to introduce new products to the markets as rapidly as possible. In response to these dynamic and unanticipated changes agile NPD is a focus to manufacturing enterprises. Identifying the cause and effect factors of this kind of system can help the NPD managers to show a good view of this process and improve their decision making quality. In this paper first among 32 main items related to agile NPD process, 6 critical success factors are extracted by using the explanatory factor analysis method done on the NPD projects in Iranian companies and then to compensate the vagueness in the complex and dynamic environment of agile NPD and also the uncertain human judgment about the factors affecting this process, Fuzzy DecisionMaking Trial and Evaluation Laboratory (DEMATEL) method is proposed to divide these critical success factors into cause and effect groups.
R. Fekri (B) · A. Aliahmadi · M. Fathian Department of Industrial Engineering, Iran University of Science and Technology, Narmark Street, Tehran, Iran e-mail:
[email protected];
[email protected] A. Aliahmadi e-mail:
[email protected] M. Fathian e-mail:
[email protected] R. Fekri Third floar, 13, Mohandesi Ave, Shariati street, Tehran, Iran
Keywords New product development · Agility · Critical success factors · Explanatory factor analysis · Fuzzy DEMATEL method
Introduction The ability to develop and launch the new product successfully as one of the key determinants of the firm’s competitive advantages is the primary objective of innovation management and one of the most critical tasks and challenging issues that managers face. The competitive situation in manufacturing innovative products for many companies has become more intense in terms of reduced lead-time, cost reduction, increased demand for higher quality and individual products (Chen et al. 2006). To achieve these goals, applying some management issues and techniques such as lean production, alliance strategies, outsourcing, Business Process Reengineering (BPR), Total Quality Management (TQM), concurrent engineering, risk and change management have been suggested and used (Cooper 2003; Cooper and Klienshmidth 1987; Ribbens 2000; Womack et al. 1990; Engardino and Einhorn 2005; Quinn 2000). On the other hand firms which are operating as innovative manufacturers face a host of uncertainties that make it difficult to be reliable in the changing and turbulent environment. Although NPD process contains a tremendous degree of complexity and uncertainty, and this process as a multidisciplinary task, is dynamic in nature (Song and Noh 2006), it has never been more challenging or rewarding than it is today. The importance of reducing time as a competitive weapon to concur on the uncertainties in NPD process has been recognized by many researchers (Calantone and Di Benedetto 2000; Cohen et al. 1996; Gupta et al. 1992; Griffin and Houser 1996; Kessler and Bierly 2002; Swink et al. 2006; Cooper
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2003; Kessler and Chakrabarti 1999; Lukas and Menon 2004, Menon et al. 2002; Nambisan 2002; Tomkovick and Miller 2000; Wheelwright and Clark 1882). Volatile global markets, rapid technological changes and turbulent customer needs and demands make NPD managers to focus on the fast NPD to increase efficiency in this process and reduce the risk of innovation. Furthermore, to become more responsive to the needs of market it is required speed and a high level of maneuverability, which is termed agility. Agility means the firm’s nimbleness to quickly reassemble its technology, employees and management via a sophisticated communication infrastructure in a deliberate, effective and coordinated response to changes of customer demand in the market environment of continuous and unanticipated changes (Kodish et al. 1995). As Christopher (2000) mentioned there is the real difference between the concept of speed (meeting customer demands in the context of shortened delivery leadtime) and agility. Moving quickly is only one of the aspects of agility, which is responding fast and lean to the changes in terms of both variety and volume. As mentioned earlier although reducing the cycle time in NPD process is studied by many researchers, using the agility attributes and dimensions in these projects because of their dynamic and uncertain nature could be more profitable and useful. On the other hand identifying the cause and effect factors affecting the agile NPD process can show a good view of this process to the NPD mangers and help them to make reliable decisions in the uncertain conditions. The method used in this paper for finding these cause and effect factors is Fuzzy DEMATEL method. Fuzzy DEMATEL method is used for solving and modeling some of the complex group decision-making problems like strategic planning, e-learning evaluation and decision making in R&D projects (Wu and Lee 2005; Tzeng et al. 2006; Lin and Wu 2007). The NPD process can be considered a complex system. This process is integrated from the six stages which are related to each other, so all the factors affecting the agility power in each stage of the NPD process, can affect other factors in the other stages. Since the experts express their assessments of the effect of NPD process factors in linguistic terms, using Fuzzy DEMATEL as a useful method in group decision making for dividing these factors to cause and effect groups can help the NPD managers to improve their decision making quality. The rest of the paper is organized as follows: In section two agile NPD and its main items are introduced. In section three, research methodology is presented in two parts; In the first part explanatory factor analysis method and its use for extracting the critical success factors of agile NPD process is studied in details, in the second part of section three the Fuzzy DEMATEL method is used for identifying the cause and effect factors of agile NPD process. In section
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four discussion and some details about the results are mentioned and in the last section the conclusion and some suggestions for future researches will be presented.
Literature review D’avani (1994) defined the agility concept as the ability to detect opportunities for innovation and seize those competitive market opportunities by assembling requisite assets, knowledge and relationship with speed and surprise. In another definition an agile firm is defined as the fast, rapidly, configurable, self-directed, customer and supplier integrated and virtual organization of the future (Dove et al. 1996; Gunneson 1997). In all the agility definitions, the power of exploring, adaptation and responsiveness to the environmental changes are vital factors. As mentioned earlier rapid technological improvement, volatile global markets, never ending customer needs and an increasing rate of innovative products make NPD process more turbulent and unanticipated than ever before. Using agility concept and its dimensions in every stage of NPD process to conquer these unanticipated changes could be profitable and useful. There are different models of NPD process in the literature and this multidisciplinary process is based on the series of development stages (Cooper and Klienshmidth 1987; Crawford and Di Benedetto 2003; Hauser 1993; Peters 1999; Tzokas et al. 2004). In all of these models, six major stages could be seen. These stages are strategic planning or opportunity detection by studying the markets, idea generation and screening, concept development and screening, product design and development, marketing tests and launch of the new product to the market. To achieve the agile NPD using agility dimensions in every stages of NPD process is essential. These dimensions are enriching the customer, leveraging the organizational capabilities, cooperating to enhance competitiveness and mastering the change (Mates et al. 1998). – The meaning of “Enriching the customer” is capturing the competitive market and competence opportunities by considering customer requirements and desires, providing and heightening customer convenience including space, time, speed and personalized convenience. Furthermore, accelerating solutions and product innovations according to customers’ offers, as the source of ideas, should be considered. Customers could contribute in three valuable dimensions for increasing the power of competitiveness. They could make the significant role in achieving markets as the sources of innovative ideas, as co-creators in the development and design products and as users in testing the new product (Nambisan 2002).
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Fig. 1 Using agility concept in the NPD process
Agility Providers: -People - Technology -Innovation -Tools
Agility Drivers Change in:
-Marketplace -Competition -Customer Desire -Social & economical features -Technology
NPD Process: Market study and strategic planning
Idea generation
Agility dimensions: Enriching the customer
Concept development
Leveraging the capabilities
Product development Cooperating to enhance competitiveness Marketing test Mastering the change Launch in the market
Agile NPD capabilities: Responsiveness Competency Flexibility Quickness
– Providing an environment of capacities in which some special capabilities such as customer relationship management, manufacturing management, human resource and information technology management exist, for improving the agility in manufacturing process is vital. This aspect is another dimension of agility concept, which is considered and named as “Leveraging the capabilities” (Sanbarmurthy and Zmud 2004). Furthermore, because of quick changes and innovation in production, the products should be produced with the world class criteria. For achieving these goals and leveraging the capabilities in a manufacturing organization, using some methods such as concentrating on the ability of responsiveness, speed, cost, effectiveness and quality, investing and improving the information infrastructure and empowering them could be profitable. – The third dimension of agility is cooperating with diversified partners in a manufacturing process, which is titled: “Cooperating to enhance competitiveness”. To enhance the power of competitiveness, recognizing potential partners with regard to their special competencies and capabilities, assessing the relationship to the partners continually and frequently, integrating the necessary expertise to achieve the agility goals with regard to different parts of the value-net are vital tasks (Sanbarmurthy and Zmud 2004). – The forth and final dimension of agility concept is “Mastering the change”. In an agile procedure to master
Results of using agility for NPD process: Customer satisfaction of: -Time -Cost -Function -Robustness
changes, the following aspects should be considered (Sanbarmurthy and Zmud 2004) • By strategic insights, managers should sense, anticipate and exploit trends, opportunities and threats. • Recognizing and enriching the capabilities, which are necessary in shaping innovative actions as quickly as possible. • Learning and executing based on useful experiences. Figure 1 illustrates the conceptual model of using agility concept in NPD process. As Zhang and Sharifi (2000) pointed out, there are three main parts for using agility in a process: agility drivers, agility capabilities and agility providers. Agility drivers drive the present situation to the near position to take advantage of the changes in a business environment. These changes in a NPD project are related to customers, competitors, knowledge, new methods of technology, social conditions and unanticipated occurrences. Agility capabilities are the essential abilities that would provide the required strength for responding to the changes. These capabilities are achieved by using the four agility dimensions in each stage of the NPD process. Agility providers are the means by which so called capabilities could be achieved as supposed to be sought for four major areas which as Zhang and Sharifi (2000), mentioned are: People, technology, innovation and tools. The results and benefits of this utilization are responsiveness, speed, competency and flexibility.
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Table 1 The agile NPD items Using the agility dimensions in the phase 1(Strategic planning) 1. Market & customer oriented rather than profit oriented strategies (Cooper and Klienshmidth 1987; Goldman et al. 1995) 2. Clearly defined target market (Song and Noh 2006; Sun and Wing 2005) 3. Top management support of innovation strategies & risk in new product development (Song and Noh 2006) 4. Organizing multi-functional teams to detect and solve managerial and critical problems (Akgün et al. 2005; Lester 1998; Yusuf et al. 1999). 5. Make close relationship with customers & suppliers to find the strategies (Wagner and Hoegl 2006; Walter 2003) 6. Making inter firm collaboration by IT Facilities like LAN and EDI between employees & managers to make the best strategies (Becker et al. 2005) 7. Concentrating on the opportunities and strategies with value added for customers (Norizan and Zain 2004) 8. New product strategies are frequently reviewed and revised (Wheelwright and Clark 1882; Narasimhan et al. 2006) 9. Rapid recognition of new opportunities in the market (Narasimhan et al. 2006) 10. Making dynamic, active, shared & configurable policies (Norizan and Zain 2004) 11. Rapid response to competitors’ actions (Ren et al. 2003) Using the agility dimensions in the phase 2 (idea generation and screening) 12. Choosing the customer focus idea with ethnography and customer historical analogy (Flint 2002; Love and Roper 2001) 13. Choosing global & international oriented ideas and concepts according to WCM criteria (Giffi et al. 1990) 14. Close relationship between the R&D teams and innovation clusters to achieve the best new product ideas (Sher and Yang 2005) Using the agility dimensions in the phase 3 (Concept development and screening) 15. Doing the customer value determination (CVD) process & customer value chain (CVC) process to test the concept of new product (Flint 2002) 16. Using quality management techniques like TQM, QFD, and DFMA for meeting customer needs (Narasimhan et al. 2006; Hart 1993) Using the agility dimensions in the phase 4 (Product design and development) 17. Meet customer needs in design prototype & operations (Narasimhan et al. 2006; Swink et al. 2006) 18. Sharing the information of process between the team project members (Lakemond and Berggren 2006) 19. Using flexible cellular manufacturing (Narasimhan et al. 2006) 20. Product line synergy & integrated product design (Narasimhan et al. 2006) 21. Using concurrent engineering (Narasimhan et al. 2006) 22. Using JIT flow (Narasimhan et al. 2006) 23. Applying CAD, CAT to facilitate adoption change in designing and operation of new product (Becker et al. 2005; Meyer and Utterback 1995; Narasimhan et al. 2006) 24. Using rapid prototyping techniques (Meyer and Utterback 1995; Narasimhan et al. 2006) 25. Using robotics techniques (Meyer and Utterback 1995; Narasimhan et al. 2006) Using the agility dimensions in the phase 5 (Market testing) 26. Emphasizing on accurate marketing tests like pre use testing, alpha, beta and gamma tests after designing the product prototype (Crawford and Di Benedetto 2003) 27. Empowering the cooperation between R&D and marketing teams to evaluate the results of marketing test (Blundell et al. 1999; Garrett et al. 2006; Griffin and Houser 1996; Olson et al. 2001; Song and Noh 2006) 28. Concentrating on early use testing after the production of new product (Crawford and Di Benedetto 2003) 29. Meet market share goals (Narasimhan et al. 2006) Using the agility dimensions in the phase 6 (Launch of the new product in the market) 30. Providing and considering to rich data about partners and competitors to make new (launch) strategies (Norizan and Zain 2004) 31. Accurate forecast of market change to determine the right-launches time and new product commercialization by using IT facilities (Hsieh and Tsai 2006; Chiu et al. 2006; Hauser 1993) 32. Using ERP systems to conquer on geographical, cost and structural barriers in order to catch the markets (Norizan and Zain 2004)
Based on this model to extract the items affecting the agile NPD process, all of the agility dimensions should be used in the stages of the NPD process. Table 1 illustrates these main items which also can be found in the literature of this process.
Research methodology To identify the cause and effect factors of agile NPD process, two main methods are used. First, explanatory factor analysis is used for finding the critical success factors of agile NPD then Fuzzy DEMATEL method is applied to divide them
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into the cause and effect groups. In the first stage a questionnaire was designed and developed based of the 32 agile NPD main items which were mentioned earlier. This questionnaire was mailed to the 280 NPD project managers, NPD consultants and executive managers in Iranian companies, which are the leaders in their business; profitable and innovative in comparison with similar firms and are suitable starters in NPD projects. After finishing the determined time of about 3 weeks, only 155 completed responses were returned. Information of respondents is shown in Table 2. The survey asked the respondents to rate items on a 5-point Likert scale, such with 1 being the lowest rating
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Table 2 The demographic information of respondents Type of NPD project
Frequency
Percent
Chemical Automobile Pharmaceutics Detergents Food and beverage Defense equipment Industrial equipment Total
25 15 23 14 35 18 25 155
16 9.6 14.8 9.03 22.5 11.6 16.1
Number of employees involved in NPD projects 0–100 101–500 501–1,000 1,001–5,000 More than 5,000 Total
49 67 24 11 4 155
31.6 43.2 15.4 7.09 2.5 100
73 45 37 155
47.09 29.03 23.87 100
The respondents’ jobs NPD project manager NPD consultant NPD executive manager Total
means (strongly disagree). Responses of these 32 main items, subjected to the explanatory factor analysis solution, which reduced the observed measurements to 6 factors. After extracting the main critical success factors of agile NPD process the Fuzzy DEMATEL method is used to divide these critical success factors into cause and effect groups. In the next part each of these stages is explained and investigated in details. Explanatory factor analysis for identifying critical success factors of agile NPD The main reason for using explanatory factor analysis method is to reduce the dimensionality of attributed data and cover a smaller number of underlying factors that account for a major amount of variances in the original measurements. By using the Principal Component Analysis technique as the extraction method and Varimax with Kaiser Normalization as the rotation method and by applying SPSS Software, six critical success factors were identified based on the Eigenvalue criterion. These six factors explained 77.73% of variances. Table 3 indicates the Varimax rotation of the critical success factors of agile NPD. Naming these factors extracted from the term that best describes the content domain of the attributes that load labeling on each factor. The names of these six factors, the correlation of the items to each factor, eigenvalues, percentage and cumulative percentage of variance explained are also illustrated in this table. By considering the loading factor higher than 0.6, all 32 items could be acceptable and important. In this study the
internal consistency method is used to test the reliability because of its ease of use and also it is general form of reliability estimation. Cronbach alpha is used as a reliability coefficient in evaluation of the degree to which all the items related within the subset were homogeneous. Typically a reliability coefficient of 0.7 or more is considered adequate (Lattin et al. 2003).The alpha values for the six factor groups ranged from .77 to .95 which are shown in Table 3. With the project management’s principles this range of alpha shows the condition of being consistent (Lattin et al. 2003). The results of explanatory factor analysis reveal that 6 main critical success factors of agile NPD are identified. These main factors are using advanced manufacturing techniques, customer design and development, adaptation to changes and minimizing uncertainty, information-driven and virtual integrated process, market testing and responsiveness and collaborative relationship and participating management style. To identify the cause and effect groups of these critical success factors and because of the dynamic nature of agile NPD process, the Fuzzy DEMATEL method is used. In the next part the stages of this method is explained in details. Using fuzzy DEMATEL method for identifying the cause and effect factors of agile NPD process The DEMATEL method is a comprehensive method for making and analyzing a structured model involving causal relationships between complex factors. Using this method illustrates the interrelations among criteria and applied matrices and digraphs for visualizing the structure of complicated causal relationships. Hence the DEMATEL method can separate the involved criteria of a system or subsystems into the cause and effect groups to assist in making effective decisions (Lin and Wu 2007). On the other hand in the real world decision makers tend to give assessments according to their past experiences and thoughts and also their estimations are often expressed in equivocal linguistic terms. To integrate various experiences, opinions, ideas and motivations of individual decision-makers, it is better to convert linguistic estimations into fuzzy numbers. So integrating fuzzy logic theory and DEMATEL method could be helpful to analyze the complex decision making problems in the real world and fuzzy environment. This method has been successfully applied to group decision making in complex fuzzy environments such as R&D problems (Lin and Wu 2007), global managers’ competencies problems (Wu and Lee 2005) and evaluating the effectiveness of e-learning problems (Tzeng et al. 2006). As mentioned in the first part of the section “Research methodology”, six main critical success factors of agile NPD were extracted by using the explanatory factor analysis method. To divide these critical success factors into cause and effect groups, applying Fuzzy DEMATEL method because of the dynamic and uncertain nature of agile NPD process
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Table 3 Varimax, rotation matrix, eigenvalues and Cronbach alpha for agile NPD critical success factors Factor
Item
1
2
3
4
5 −2.02E-2 −0.112 −7.56E-2 −7.49E-2 −0.180 −9.93E-2 −0.103
6
Alpha (Cronbach)
1. Using advanced manufacturing techniques 19 20 21 22 23 24 25
0.816 0.737 0.797 0.683 0.815 0.839 0.771
0.164 0.201 0.194 0.161 0.139 0.136 0.174
0.212 0.274 0.170 0.165 0.185 0.167 0.277
−0.300 −0.336 −0.161 −0.256 −0.301 −0.316 −0.298
0.686 0.661 0.624 0.707 0.767 0.695
−0.583 −0.508 −0.491 −0.448 −0.430 −0.540
0.245 0.211 0.121 0.273 0.151 0.257
0.489 0.554 0.503 0.523 0.575 0.511
0.661 0.651 0.659 0.631 0.649 0.606
0.133 0.130 9.19E-2 7.54E-2 0.178 0.154
−4.16E-2 −3.53E-2 −0.100 −5.49E-2 −8.27E-3 6.79E-2 −6.8E-2 2.03E-2 −129 −693E-2 3.11E-2 −7.46E-2
−0.272 −0.240 −0.258 −0.187 −0.228
0.174 0.172 0.182 0.150 0.112
0.819 0.819 0.736 0.721 0.796
1.40E-2 8.06E-2 3.96E-2 −6.09E-2 −1.44E-2
−4.93E-2 −9.13E-2 −6.97E-2 1.60E-2 −3.38E-2
0.119 0.214 0.185 0.123 0.239
−0.125 −2.30E-2 −8.91E-2 9.42E-2 −0.113
0.756 0.808 0.775 0.684 0.736
1.23E-2 8.12E-2 1.26E-2 0.104 0.113 0.108 3.93E-2
0.91
2. Customer-oriented design & development 1 0.137 7 0.183 12 7.59E-2 15 0.246 16 0.145 17 9.77E-2 3. Adaptation to changes & minimizing uncertainty 9 −0.290 10 −0.329 11 −0.362 3 −0.316 8 −0.290 13 −0.297 4. Information-driven or virtual integrated process 6 0.369 29 0.358 30 0.367 18 0.398 31 0.412 5. Market sensitivity & responsiveness 2 0.138 29 0.173 26 7.34E-2 27 0.134 28 7.99E-2 6. Collaborative relationship & participating management style 4 −0.232 5 −0.219 14 −0.324 Eigenvalues 6.031 Percentage of variance explained 18.848 Cumulative percentage of variance explained 18.848
1.09E-2 8.43E-3 −9.54E-2 0.116 −2.01E-2 −0.171 9.28E-2 −8.59E-2 −0.186 5.037 4.604 4.119 15.733 14.389 12.872 34.581 48.969 61.841
could be helpful. Generally to apply this method for diving the criteria to cause and effect groups, the common analytical procedure is used, which is described as follows: Step 1: Selecting a committee of experts who have experienced concerning the problem. Step 2: Developing the evaluation criteria and designing the fuzzy linguistic scale. After determining the criteria in this step, the different degrees of influence of a factor on another factor are expressed with linguistic terms which are shown in categorical terms of (Very High, High, Low, Very Low, and No influence). These terms can be demonstrated by the corresponding positive triangular fuzzy numbers. A triangular fuzzy num-
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0.106 2.86E-2 0.131 −8.1E-2 6.73E-2 0.101 8.76E-2 −6.3E-2 0.176 7.87E-3 6.18E-2 −3.77E-2
0.229 0.108 8.93E-2 3.133 9.791 71.632
0.94
0.95
5.51E-2 0.103 0.260 0.158 8.48E-2
0.95
−0.122 −0.145 −0.145 −7.16E-2 1.54E-2
0.88
0.740 0.749 0.767 1.954 6.105 77.73
0.77
ber is defined as z˜ = (l, m, u), where l, m and u are real numbers and l ≤ m ≤ u. The membership function of a triangular fuzzy number is as follows: ⎧ 0, x u, Figure 2 illustrates a triangular fuzzy number in general. The correspondence between the linguistic terms mentioned above and linguistic values is determined in Table 4 and showing these relations as traingular fuzzy numbers is illustrated in Fig. 3. Step 3: Acquiring the assessments of decision-makers.
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µ ( x)
Step 4: Acquiring the normalized direct-relation fuzzy (k) matrix. First consider a˜ i and r (k) as triangular fuzzy numbers as follows:
1 (k)
α˜ i
=
⎛ (k)
z˜ i j = ⎝
n
(k)
li j ,
j=1
0
l
m
u
x
Fig. 2 A triangular fuzzy number z˜ k
r (k)
Linguistic values
Very high influence (VH) High influence (H) Low influence (L) Very low influence (VL) No influence (No)
(0.75, 1.0, 1.0) (0.5, 0.75, 1.0) (0.25, 0.5, 0.75) (0, 0.25, 0.5) (0, 0, 0.25)
µ ( x)
0
0.25
L
0.5
(k) ui j ⎠
(2)
j=1
1≤i ≤n
H
0.75
VH
1
⎡
(k) X˜ ⎢ 11 ⎢ X˜ (k) ⎢ 21 =⎢ ⎢ .. ⎣. (k) X˜ n1
where
x
Fig. 3 Triangular fuzzy numbers for linguistic variables
To measure the relationships between the critical success factors which are demonstrated by C = {Ci |i = 1, 2 . . . n}, the group of the chosen experts (p people mentioned in step 1) was asked to make sets of pair wise comparisons in terms of linguistic terms. Hence fuzzy matrices Z˜ (1) , Z˜ (2) , . . . ., Z˜ ( p) , each corresponding to an expert and with triangular fuzzy numbers are obtained. Fuzzy matrix Z˜ k is called the initial direct relation fuzzy matrix of expert k. Denote Z˜ k as: ⎡ ⎤ (k) (k) 0 Z˜ 12 . . . Z˜ 1n ⎢ ˜ (k) (k) ⎥ . . . Z˜ 2n ⎢ Z 21 0 ⎥ ⎥ ; k = 1, 2, . . . ., P, (1) Z˜ k = ⎢ .. . . .. ⎢ .. ⎥ . . ⎣. ⎦ . (k) (k) Z˜ n1 Z˜ n2 . . . 0
(k) (k) (k) (k) z˜ i j = li j , m i j , u i j . Without loss of generality the z˜ i(k) j = (i = 1, 2, . . . , n) will be regarded as the triangular fuzzy number z = (0, 0, 0), when it is necessary.
(3)
Then the linear scale transformation is used to transform the criteria scales into comparable scales. The normalized directrelation fuzzy matrix of experts denotes as X˜ (k) is given by:
X˜ (k)
VL
j=1
⎞
j=1
Linguistic terms
No
(k)
mi j ,
n
⎛ ⎞ n
(k) = max ⎝ ui j ⎠
Table 4 The correspondence between the linguistic terms and linguistic values
1
n
(k) x˜i j
(k) X˜ 12 . . . (k) X˜ 22 . . . .. .. . . (k) ˜ X n2 . . .
( j)
=
z˜ i j
r (k)
=
(k)
li j
⎤ (k) X˜ 1n ⎥ (k) X˜ 2n ⎥ ⎥ ⎥ ; k = 1, 2, . . . ., P .. ⎥ ⎦ . (k) ˜ X nn (k)
mi j
(4)
(k)
ui j
, , r (k) r (k) r (k)
(5)
As same as the crisp DEMATEL method, we assume at (k) least one i such that nj=1 u i j < r (k) . This assumption is adaptable with the real and practical cases. To calculate the average matrix of X˜ ,which shows the average point of view of all the experts, we use Eqs. 6 and 7 as follows: (1) x˜ ⊕ x˜ (2) ⊕ . . . ⊕ x˜ ( p) (6) X˜ = p ⎡ ˜ X 11 ⎢ ˜ (k) ⎢ X 21 X˜ = ⎢ ⎢. ⎣ .. X˜ n1
X˜ 12 . . . (k) X˜ 22 . . . .. .. . . X˜ n2 . . .
⎤ X˜ 1n p ⎥ (k) x˜ X˜ 2n ⎥ ⎥ ; where x˜i j = k=1 i j .. ⎥ P . ⎦ X˜ nn (7)
Step 5: Establish and analyze the structured model .So to compute the total-relation fuzzy matrix T˜ , we should have to ensure about the convergence of limw→∞ X˜ w = 0. Lin and Wu (2007) that introduced an algorithm of the fuzzy DEMATEL method proved this matter. According to the crisp case we define the total-relation fuzzy matrix as: T˜ = lim ( X˜ + X˜ 2 + · · · + X˜ w ) w→∞
(8)
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⎡
t˜11 ⎢ t˜21 ⎢ T˜ = ⎢ . ⎣ .. t˜n1
t˜12 t˜22 .. . t˜n2
⎤ t˜1n t˜2n ⎥ ⎥ .. ⎥ . ⎦ . . . t˜nn ... ... .. .
tionship between the critical success factors of agile NPD process as follows:
(9)
Step 1: Selecting a committee of NPD experts including 25 NPD project managers, 8NPD consultants and 7 NPD executive managers. Step 2: Developing the evaluation criteria and designing the fuzzy linguistic scale.
where t˜i j = (lij , m ij , u ij ) Matri x lij = X l × (I − X l )−1 Matri x m ij = X m × (I − X m )−1 Matri x u ij = X u × (I − X u )−1
In our case the criteria are the critical success factors of agile NPD, which were extracted by explanatory factor analysis. These critical success factors as mentioned before are C1 (using advanced manufacturing technique), C2 (customer design and development), C3 (adaptation to changes and minimizing uncertainty), C4 (information-driven and virtual integrated process), C5 (market testing and responsiveness) and C6 (collaborative relationships and participating management style). In this step also the different degrees of influence of a factor on the other factor are expressed in five linguistic terms: “Very High, High, Low, Very Low, and No influence” and the corresponding positive triangular fuzzy numbers as mentioned before are shown in Table 4 and Fig. 3.
(10)
Lin and Wu (2007) proved all these formulas. The amount of Matri x li j , Matri x m i j , Matri x u ij and finally matrix T˜ are mentioned above. Step 6: After computing the matrix T˜ , now it is easy to calculate the amounts of D˜ i + R˜ i and D˜ i − R˜ i , where D˜ i and R˜ i are sum of the rows and the sum of the columns of matrix T˜ , respectively. Step 7: In general to convert the fuzzy number N˜ k = (lk , m k , u k ) to the crisp value, Eq. 11 is used as follows: L = min(lk ), R = max(u k ) and = R − L de f N˜ k = L +
×
(m−L)(+u−m)2 (R−l)+(u−L)2 (+m−l)2 (+m−l)(+u−m)2 (R − l)+(u − L)(+m − l)2 (+u − m)
(11) ⎡
(0, 0, 0) ⎢ (0, 0, .25) ⎢ ⎢ (.5, .75, 1) 1 ˜ Z =⎢ ⎢ (.5, .75, 1) ⎢ ⎣ (0, 0, .25) (0, .25, .5)
(.5, .75, 1) (0, 0, 0) (.25, .5, .75) (.75, 1, 1) (.5, .75, 1) (.25, .5, .75)
(.75, 1, 1) (.5, .75, 1) (0, 0, 0) (.75, 1, 1) (.5, .75, 1) (.5, .75, 1)
(0, .25, .5) (0, .25, .5) (.5, .75, 1) (0, 0, 0) (0, 0, .25) (0, 0, .25)
Step 3: Acquiring the assessments of decision-makers. To measure the relationships between the critical success factors; C={Ci |i = 1, 2. . .6} the group of the chosen experts (40 people mentioned in step 1) was asked to make sets of pair wise comparisons in terms of linguistic terms. Hence 40 fuzzy matrices Z˜ 1 , Z˜ 2 , . . . , Z˜ k each is corresponding to an expert and with triangular fuzzy numbers as its elements are obtained. For example matrix Z˜ 1 is as follows: ⎤ (0, 0, .25) (0, 0, .25) (.25, .5, .75) (0, 0, .25) ⎥ ⎥ (0, .25, .5) (0, 0, .25) ⎥ ⎥ (.5, .75, 1) (.75, 1, 1) ⎥ ⎥ (0, 0, 0) (0, 0, .25) ⎦ (.5, .75, 1) (0, 0, 0)
To acquire the causal diagram, the Eq. 11 is used for diffuzification of the amount of ( D˜ i + R˜ i )and ( D˜ i − R˜ i )and convert to ( D˜ i + R˜ i )de f and ( D˜ i − R˜ i )de f , respectively. Step 8: We draw the causal diagram based on the calculations in step 7. Now we use the steps of the procedure to identify the rela⎡
(0, 0, 0) ⎢ (0, 0, .05) ⎢ ⎢ (.1, .15, .2) X˜ 1 = ⎢ ⎢ (, 1, .15, .2) ⎢ ⎣ (0, 0, .05) (0, .05, .1)
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(.1, .15, .2) (0, 0, 0) (.05, .1, .15) (.15, .2, .2) (.1, .15, .2) (.05, .1, .15)
(.15, .2, .2) (.1, .15, .2) (0, 0, 0) (.15, .2, .2) (.1, .15, .2) (.1, .15, .2)
(0, .05, .1) (0, .05, .1) (.1, .15, .2) (0, 0, 0) (.1, .15, .2) (0, 0, .05)
Step 4: Acquiring the normalized direct-relation fuzzy (k) matrix. Consider a triangular fuzzy number (a˜ i ) according to equations 2 and 3 to calculate each direct–relation fuzzy matrix X˜ (k) for each matrix Z˜ (k) . For example for matrix Z˜ 1 , the normalized direct relation fuzzy matrix X˜ 1 can be calculated by Eqs. 2 and 5 as follows: (0, 0, .05) (.05, .1, .15) (0, .05, .1) (.1, .15, .2) (0, 0, 0) (.1, .15, .2)
⎤ (0, 0, .05) (0, 0, .05) ⎥ ⎥ (0, 0, .05) ⎥ ⎥ (.15, .2, .2) ⎥ ⎥ (0, 0, .05) ⎦ (0, 0, 0)
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645
The amount of X˜ (The total normalized direct-relation fuzzy matrix) is calculated by the Eqs. 6 and 7. ⎡ (0, 0, 0) (.09, .14, .19) (.15, .2, .2) (.06, .11, .16) ⎢ (0, 0, .05) (0, 0, 0) (09, .14, .19) (0, .05, .1) ⎢ ⎢ (.1, .15, .2) (.06, .11, .16) (0, 0, 0) (.1, .15, .2) X˜ = ⎢ ⎢ (.1, .15, .2) (.15, .2, .2) (.15, .2, .2) (0, 0, 0) ⎢ ⎣ (0, 0, .05) (.09, .14, .19) (.1, .15, .2) (.09, .14, .19) (0, .04, .09) (.05, .1, .15) (.1, .15, .2) (0, 0, .05) Step 5: The procedure of calculation matrix T˜ (The totalrelation matrix) according to the Equations 8, 9 and 10 is as follows: ⎡
0.017 ⎢ .006507 ⎢ ⎢ .063 matri x lij = ⎢ ⎢ .115 ⎢ ⎣ .017 .008062
⎡
.116 .014 .087 .199 .118 .069
.178 .1 .037 .22 .132 .12
.08 .015 .109 .04 .0106 .021
.015 .052 .017 .127 .018 .091
⎤ .012 .002265 ⎥ ⎥ ⎥ .016 ⎥ ⎥ .156 ⎥ ⎦ .016 .003097
.23 .069 .202 .359 .23 .178
.295 .201 .119 .393 .251 .234
.181 .106 .207 .136 .205 .077
.065 .133 .096 .251 .09 .175
⎤ .036 .021 ⎥ ⎥ .041 ⎥ ⎥ .227 ⎥ ⎥ .041 ⎦ .015
.22 ⎢ .232 ⎢ ⎢ .354 matri x u ij = ⎢ ⎢ .475 ⎢ ⎣ .281 .291
.487 .276 .471 .639 .505 .455
.521 .46 .36 .681 .541 .522
.416 .331 .45 .397 .456 .331
.3 .342 .334 .53 .268 .406
⎤ .21 .182 ⎥ ⎥ .216 ⎥ ⎥ .396 ⎥ ⎥ .221 ⎦ .15
⎡ (.017, .058, .22) ⎢(.0065, .087, .232) ⎢ ⎢(.063, .145, .354) ˜ T=⎢ ⎢(.115, .219, .475) ⎢ ⎣(.017, .058, .281) (.0086, .075, .291)
(.116, .23, .487) (.014, .069, .276) (.087, .202, .471) (.199, .359, .639) (.118, .23, .505) (.069, .178, .455)
.058 ⎢ .037 ⎢ ⎢ .145 matri x m ij = ⎢ ⎢ .219 ⎢ ⎣ .058 .075 ⎡
(.178, .259, .521) (.1, .201, .46) (.037, .119, .36) (.22, .393, .681) (.132, .251, .541) (.12, .236, .522)
Step 6: After computing the matrix T˜ , the amounts of D˜ i + R˜ i and D˜ i − R˜ i are calculated. D˜ i and R˜ i are sum of the rows and the sum of the columns of matrix T˜ respectively. Table 5 illustrates the amounts of D˜ i , R˜ i , D˜ i + R˜ i and D˜ i − R˜ i . Step 7: Now we use the Eq. 11 for diffuzification of the ˜ i − R˜ i and convert to ( D˜ i + amount of D˜ i + R˜ i and D‘ de f de f ˜ ˜ ˜ Ri ) and ( Di − Ri ) respectively. These amounts are illustrated in the Table 6. Then we draw the causal
diagram based on these calculations. Figure 4 illustrates the causal diagram of the criteria. ⎤ (0, 0, .05) (0, 0, .05) (.05, .1, .15) (0, 0, .05) ⎥ ⎥ (0, .035, .085) (0, 0, .05) ⎥ ⎥ (.1, .15, .2) (.15, .2, .2) ⎥ ⎥ (0, 0, 0) (0, 0, .05) ⎦ (.085, .135, .185) (0, 0, 0)
Discussion It is shown in the causal diagram that the critical success factors of agile NPD extracted by using explanatory factor analysis were visually divided into the cause group including C1 , C4 , C5 and C6 , while the effect group was composed of factors C2 and C3 . So, “using advanced manufacturing techniques”, “information -driven and virtual integrated process”, ‘market testing and responsiveness” and “collaborative relationship and participating management style” are cause factors while “customer design and development” and “adaptation to changes and minimizing uncertainty”, are effect factors. By considering to these cause and effect groups, mangers can also determine that all the items related to each critical success factors as are mentioned in the Table 3,also can be divided to the cause and effect items. So these items can be considered as the cause and effect items in the agile NPD process. For example, since the “collaborative relationship and participating management style” is considered as the cause factor, so all the items related to this factor (Table 3), such as “organizing multi-functional teams to detect and solve managerial and critical problems”, “making close relationship (.08, .181, .416) (.015, .106, .331) (.0109, .207, .45) (.04, .136, .397) (.106, .205, .456) (.021, .77, .331)
(.015, .065, .3) (.052, .133, .342) (.017, .096, .334) (.127, .251, .53) (.018, .09, .268) (.091, .175, .406)
⎤ (.012, .036, .21) (.002265, .021, .182)⎥ ⎥ (.016, .041, .216) ⎥ ⎥ (.156, .227, .396) ⎥ ⎥ (.016, .041, .221) ⎦ (.003097, .015, .15)
with customers & suppliers to find the strategies” and “also making close relationship between the R&D teams and innovation clusters to achieve the best new product ideas” can be considered as the cause items in the agile NPD process. This matter is also true for the items related to the effect critical success factor groups. From the causal diagram, valuable cues are obtained that could help the NPD managers in making decisions and prepare a good view from the agile NPD process and introduce
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Table 5 The amounts of D˜ i , R˜ i , D˜ i + R˜ i and D˜ i − R˜ i The critical success factors
D˜ i
R˜ i
D˜ i + R˜ i
D˜ i − R˜ i
C1 C2 C3 C4 C5 C6
(.418, .865, 2.154) (.189, .567, 1.823) (.329, .81, 2.185) (.857, 1.585, 3.118) (.407, .877, 2.271) (.501, .754, 2.15)
(.226, .592, 1.853) (.603, 1.268, 2.833) (.787, 1.493, 3.085) (.371, .912, 2.381) (.32, .788, 2.18) (.205, .381, 1.375)
(.644, 1.457, 4.007) (.792, 1.835, 4.656) (1.116, 2.303, 5.27) (1.288, 2.497, 5.449) (.727, 1.665, 2.451) (.706, 1.135, 3.525)
(−.1.435, .273, 1.928) (−2.644, −.701, 1.22) (−2.756, −.683, 1.398) (1.524, .673, 2.747) (−1.733, .089, 1.951) (−.874, .373, 1.945)
Table 6 The amounts of ( D˜ i + R˜ i )de f and ( D˜ i − R˜ i )de f The critical success factors
( D˜ i + R˜ i
C1 C2 C3 C4 C5 C6
1.81 2.19 2.62 2.79 1.65 2.38
Conclusion and future research
( D˜ i − R˜ i
)de f
)de f
0.24 −0.98 −0.60 1.25 0.11 0.65
D-R 2
1.5
C4
1
C6 0.5
C5
C1
0
0.5
1
1.5
2
2.5
3 C3
-0.5
3.5 D+R
C2 -1
Fig. 4 The causal diagram of agile NPD process
the new product to the markets as rapidly as possible. For example It is shown that the critical factor of information -driven and virtual integrated process (C4 ), with the largest amount of ( D˜ i + R˜ i )de f is the most important cause factor for agile NPD project success and could make the significant role in responding to customer demands. On the other hand the amounts of ( D˜ i − R˜ i )de f for customer design and development (C2 ) shows that this factor with the most negative amount of ( D˜ i − R˜ i )de f is the most important factor of the effect group.
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Fuzzy DEMATEL method as a very useful group decision making tool has been used to transform the complex interactions between the criteria of the problems of practical life into a visible structured model. In this paper this method is proposed and applied to find the cause and effect critical success factors of agile NPD, which have been extracted by the explanatory factor analysis method. The NPD process is a complex system and NPD experts can tend to give assessments of the effect of NPD process factors according to their past experiences and thoughts and also their estimations are often expressed in linguistic terms, so using Fuzzy DEMATEL method can be helpful in identifying the cause and effect groups of critical success factors of this process based on the experts’ opinions. In this paper among 32 main items related to agile NPD process, 6 critical success factors have been extracted. These critical success factors as mentioned before are using advanced manufacturing techniques, customer design and development, adaptation to changes and minimizing uncertainty, information -driven and virtual integrated process, market testing and responsiveness and collaborative relationship and participating management style. After identifying these six critical success factors Fuzzy DEMATEL method was applied for dividing them into cause and effect groups. As mentioned earlier from these six factors, “customer design and development” and “adaptation to changes and minimizing uncertainty” are in the effect group. The remaining four main factors including “advanced manufacturing techniques”, “information-driven and virtual integrated process”, “market testing and responsiveness” and “collaborative relationship and participating management style” are considered as the cause factors. It is also revealed that the critical factor of “information -driven and virtual integrated process” is the most important factor for agile NPD project success and could take the significant role in responding to customer demands while the “customer-design and development” is the critical success factor which can be affected mostly by the other factors.
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Since this study and results is based on the gathered data of experts who worked on the real NPD projects in Iranian companies and also the results are according to the common sense, so these results can be generalized to the other NPD projects. Applying the other methodologies such as Fuzzy Cognitive Map (FCM) to find the model which shows the causal relationships between these critical success factors, also investigating the effects of these critical success factors on the cost, quality and speed of NPD projects in manufacturing enterprises could be considered in future researches. Additionally these two cause and effect groups may be further used to respectively serve as the clusters of cause and effect criteria in a Multiple Criteria Decision Making (MCDM) model such as Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) methods (Saaty, 1990, 1996) for selecting the most optimal alternative.
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