Degree of Uncontrollable External Factors Impacting to NPD

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1 Degree of Uncontrollable External Factors Impacting to NPD Seonmuk Park, 1 Jongseong Kim, 1 Se Won Lee, 2 Hoo-Gon Choi 1, * 1 Department of Industrial Engineering Sungkyunkwan University, Suwon , Republic of Korea 2 College of Business Administration Dongguk University-SEOUL, Seoul , Republic of Korea roidin@skku.edu, comgram@skku.edu, swlee94@dongguk.edu, hgchoi@skku.edu *:Corresponding author Abstract: The success of new product development (NPD) project is heavily dependent upon both internal and external factors arising during the development stages. Therefore, these factors should be managed for the entire stages with scientific or logical ways. This procedure is required for a firm to expand strategically its competitive advantages by developing the successful products in less cost, time and technical risks. Although both internal and external factors are the risks affecting to NPD project performances, the internal factors can be controllable and the external factors are very difficult to be controlled by a firm. This study focuses on the uncontrollable external factors to search, collect and classify, and suggests a fuzzy method to quantify the degrees of them impacting against NPD projects. In this study, forty external factors are selected, and classified into four different success factor groups: product-level, customer-level, firm-based level and financialperformance level. Each external factor is exclusively independent, but it affects inclusively to each group. Also, this study determines the integrated impact degree for each group that can be utilized for better strategic decision-making throughout the life of NPD projects. Key-Words: New Product Development (NPD), Uncontrollable external factors, Success factor groups, Impact degrees, Fuzzy method, Integrated impact degree 1 Introduction About 80% of NPD efforts have failed before project completion and more than 50% of the efforts have made no returns on the investment of money and time [1] due to extremely complex development process and business decision-making process. These processes can be characterized by three different types of uncertainties in either internal or external forms: environmental, technical and product. The environmental uncertainties include economic shift changes, inflation rate changes, interest rate changes, oil price changes, commodity price changes, government regulation changes, etc. The technical uncertainties include customer requirement changes, technical innovation speeds, short product-life cycle, etc. Some examples of the product uncertainties are required product function changes, market changes, globalization speeds, competition toughness in price and quality, product differentiation, product liability, etc. Therefore, a firm should develop various effective and efficient strategies, plans, decision-making methodologies to search, collect, analyze and respond against these uncertainties in a timely manner. This effort needs a lot of resources such as time and budget. More uncertainties or risk mean lower probability of success for NPD projects. This study focuses on the uncontrollable external factors only. Many existing studies have dealt with the internal factors that can be measurable or controllable, not the external factors even though they have recognized the importance of analyzing the external factors. In other words, the impact of the internal factors can be measureable by various statistical methods or other scientific ways with past records and experiences. However, it is hard to analyze the external risks to be occurred unexpectedly due to the complexity and randomness. In this study, a quantitative model is proposed to measure systematically the degrees of the external factors impacting to NPD projects. The model based on fuzzy theory can provide the primitive guidelines for a project life as GO/NO-GO criteria in either early design stage or middle stages. For this purpose, this study first searches in the previous studies, collects possible external factors from them. Then, the factors are classified by the success factors to be used as evaluation criteria if a project can be declared as a success or failure. It means that the entire external factors can be grouped into four success factors on the basis of product-level, ISBN:

2 customer-level, firm-based level and financialperformance. 2 Reviews of Relevant Studies The relevant studies show that both internal and external factors affect the project s success. However, few studies have dealt with the relations between external factors and NPD project success. Brown and Eisenhardt contend that the environmental factors have been neglected from considerations for deciding the project success [2]. Ulrich and Eppinger present that those factors of competitors, customers, markets and macroeconomic environment should be considered for better decision-making on project management by the entire firm [3]. Miller presents six different types of uncontrollable factors: politics and government regulations, macro-economic factors such as natural calamities, inflation rate, and interest rate, competition factors in industry, resources and industrial structures, product market, and demand patterns. These factors influence on related markets and industries as negative aspects [4]. Werner et al. verifies Miller s classification from realistic views and redefines them as environmental uncertainties[5]. Crowford and Di Benedetto presents that various uncertain factors such as market entrance hardness and technical constraints influence on product life-cycle and profits, and classifies them into project success factors [6]. Although there are other studies that investigate the success factors for NPD projects, their studies are usually based on the internal factors. Ernst summarizes many previous studies to extract common success factors [7]. Also, Balachandra and Friar present that the success factors affect NPD projects either positively or negatively based on the characteristics of projects [8]. 3 External Factors in NPD projects 3.1 Collection and Classification of External Factors An external factor is defined as an uncontrollable variable that affects success of a NPD project in this study. The external factor can be divided into different types based on environmental characteristics such as dynamism, hostility, technical complexity, industrial life cycles and differences [9-11]. The characteristics of each external factor can be classified by factor definition. Forty independent external factors related to NPD projects are finally selected from more than 100 external factors after removal of redundant definitions. Each factor has its own definition and environmental characteristics [6][8][12-17]. Both definition and characteristics are utilized in classification. That is, each factor is further classified with respect to environment, market, and technical [8] as shown in Table Success Factor Measures Either success or failure of a NPD project can be evaluated by different points of views of a firm. For example, a new product can be regarded as a successful one from customer views in terms of its price and quality. However, the success of the same product can affect negatively to organization in terms of team leadership. In this study, four different views are set to classify each external factor as follows [18]: 1. PL (Product-Level Measure): The external factors that affect market entrance speed, technical performance satisfaction, product specification and its performances, level of innovation, quality, development cost, etc. These factors include NPD processes, number of competitors in existing markets, availability of resources such as people, budget and technologies availability, easiness of manufacturability achievement, etc. 2. CL (Customer-Level Measure): The external factors that affect high customer satisfaction, high acceptance level of customer requirements, targeted market share and its growth percentage, revenue growth rate, etc. These factors include number of potential customers, level of customer satisfaction, profit goal, market share goals, length of product life-cycle, targeted market positions, changes in product repurchasing power, etc. 3. FB (Firm-Based Measure): The external factors affect the entire firm such as technologies, patents and other precious knowledge properties owned by a firm, strategic fit with business, business environment, investment for facilities, etc. 4. FP (Financial-Performance Measure): The external factors that affect a firm s profit increase, high profit margin goals, high profitability goals, etc. These factors include break-even point or time against total amount of investment for NPD projects, internal rate of return (IRR), return on investment (ROI), etc. Some example factors are listed in Table 1 along with the characteristics and four different success factor groups. It should be noted that many external factors affects inclusively to multiple number of groups. ISBN:

3 4 Quantification Model 4.1 Impact Degree of External Factor A firm should be capable of estimating accurately and quantitatively the external factor in terms of its seriousness or the impact degree, and be prepare for better strategies or plans to respond against it. Project manager or decision maker can utilize the degree as a visible guideline to decide GO/NO-GO more effectively and efficiently on a project at the entire development stages. The impact degree of an external factor is the numeric amount of either negative or positive influences on NPD project in this study. As described in the previous sections, all external factors are classified into four different success factor groups. Therefore, those degrees related to the number of external factors within each success factor group can be integrated together to determine total impact degrees. 4.2 Membership Functions in Fuzzy Set Theory The impact degree can be strong or weak depending on the project difficulty, internal and external factors surrounding a firm, the project progress made thus far, etc. This means that the impact should be measured or evaluated by scientific methods and predicted for future purposes. Usually, subjective methods have been used to measure and evaluate the impact with experts. However, this method is too flexible to yield consistent results although an expert s evaluation is still essential for better decision-making in risk analysis. For lowering the expert s subjectivity, the AHP (analytic hierarchy process) method can be used. This method is a structured technique that provides a comprehensive and rational framework for complex decision-making problems [19]. However, this method needs significant amount of response time for respondents or experts to answer all questions. Especially, the method is almost not useful if there are many elements to be compared them in pair. This study deals with forty factors (elements) to be evaluated for their significances or impact degrees. Fuzzy set theory [20] used in this study reduces the computational time and is capable of handling those external factors. Regarding fuzzy concepts, the membership function and defuzzification are required to obtain a quantitative value for a specific decision. The most common membership function is a triangular function. A specific membership function can be defined and selected for each external factor by experts. The triangular function shape should be differently defined in terms of scale, area and slope for each factor. The experts should be careful for defining the membership function of each factor because its impact degree is changed by business environment surrounding a firm and NPD project characteristics such as project difficulty. Also, the membership function shape of each factor may be modified or adjusted for each success factor group. Table 1 Classification of external factors by success factors Factor Type a External Factor b Success Factor Measures PL CL FB FP E Availability of Resource, Raw materials O O O M/T Client Acceptance O O O M Competitive Environment O O O O M Effects on Other Business O E Exchange Rate O O M Existence of a Potential Demand O O M Few Competitors O O E Government Regulations O O E/M/T Risk Distribution O O E Inflation Rate O O E Interest Rate O O M Competitor Analysis O O O O M Number of End-User O O E Political/Social Factors O O M Rate of New Product Introduction O O M Price Competition O O O O M Probability of Commercial Success O O T Probability of Technology Success O E/T Product Liability O O a. External factors related to environmental (E), market (M), technical (T) b. This table presents some example factors only. The actual number of factors considered in this study is 40. ISBN:

4 For example, the factor Price Competition affects inclusively PL, CL, FB and FP as shown in Table 2. This factor may have different membership functions or different impact degree within each success factor group. Therefore, the experts should modify the membership function carefully. In this study, forty membership functions are initially defined and each function may be modified further on the basis of the success factor group. Unfortunately, 160 membership functions at maximum would be needed due to this modification. Each NPD project is affected differently by both internal and external factors with specific membership functions. For the case of internal (controllable) factors, it would be relatively easy to develop the membership functions in contrast to external factors because a firm may have a plenty of related methods, knowledge and experiences to handle them effectively and efficiently. For the case of external (uncontrollable) factors, they would affect many projects on common basis like inflation rate and oil price. Therefore, it would be relatively easy to evaluate the impact degree to the projects, but difficult to determine the membership functions. The membership function value obtained from the union operator is still a fuzzy value, and thus requires a defuzzification process to convert the output from the fuzzy rules into a scalar, or nonfuzzy value. There are several defuzzification methods such as the clipped center of gravity method, mean of maximum method, and maximum criterion method. The first of these methods has been regarded as the most powerful for defuzzification in which the centroid of the trapezoidal area is calculated [1]. 4.3 Calculation of Integrated Impact Degree Every external factor is inclusively related to the success factors and is classified into one of four different success factor groups shown in Table 2. After the impact degree of each external factor is quantified by using its membership function, it can be synthesized with impact degrees of other external factors within a success factor group. This value is defined as the integrated impact degree as shown in Eq. (1). Eq. (1) is a multiplication form, not an additive form because the study assumes that multiple number of external factors can be simultaneously occurred, and their impact degrees are multiplicative. However, the integrated impact degree should be finalized by either decision maker or project manager through verification and validation test. where : integrated impact degree for success factor i (i=1 for PL, i=2 for CL, i=3 for FB, i=4 for FP) : impact degree of external factor j with respect to success factor i; n: total number of external factors in success factor i 5 An Illustrated Example As shown in Table 1, four sample factors among forty external factors are considered to illustrate how to obtain the integrated impact degree. Four steps are required for this purpose. Although the impact degree is quantified by the fuzzy set theory, some inputs are required from experts, especially in Steps 1 and 2. All values appeared in the table are assumed, not from realistic observations. Step 1: Defining membership functions: As described in Section 4.2, each external factor has its own membership function defined by experts at evaluating the factor. Also, the membership function would be modified further due to different impact within each success factor group. An example is given in Fig. 1 as a triangular function. Each triangle can be defined by its own shape in terms of scale, area, slope, and decision criteria (minor, low, moderate, high, and very high). This example actually needs twelve membership functions: one for Effects on Other Business, four each for Availability of Resource, Raw Materials and Competitive Environment, and three for Client Acceptance. However, four functions are used only under the assumption of identical function being applicable for each external factor within each success factor group. Step 2: Evaluation of each external factor: This step needs for the experts to evaluate each external factor with a value between 0 and 1 that influences the success factor group. Table 2 presents the results of the experts evaluation for each factor. Again, all the values are assumed. Step 3: Defuzzification process The experts evaluation is still a fuzzy value, and should be converted into a scalar by using a defuzzification method. The clipped center of gravity method is utilized in this study. Figs. 2 and 3 show the fuzzy process based on a union operator when the impact degree of an external factor is ISBN:

5 given by the experts. Table 3 presents the defuzzification results. Fig. 3 The centroid of the trapezoidal area using a clipped center of gravity method Fig. 1 A membership function example Table 2 The evaluation results External Factors Success Factors PL CL FB FP Effects on other business Availability of resources Client acceptance Competitive environment Table 3 Defuzzification results for external factors External Factors Success Factor Measure PL CL FB FP Effects on other business Availability of resources Client acceptance Competitive environment Fig.2 Fuzzy process based on an union operator Step 4: Calculation of integrated impact degree The impact degrees of the external factors within four success factor groups are obtained in Step 3. Then, the integrated impact degree for each success factor group can be decided easily by using Eq. (1). That is, As a result, four sampled external factors affect the success factor, Firm-Based Measure most significantly in this illustrated example. The above procedure can be applied for each NPD project whenever some external factors should be considered in a specified time. At product development stages, project managers can respond to those factors with better alternatives by referring the quantified impact degrees, and can conclude if a given project should be continued or dropped. 6 Conclusions The key contributions of this study are as follows: 1. forty external factors that affect NPD project life are collected from the previous studies. 2. The collected factors are further classified into four success factor groups because either success or failure of a NPD project should be evaluated differently on the basis of product, customer, firm, and finance-performance. All the external factors are classified into the appropriate group. Then, a multiplicative model is suggested to determine the integrated impact degree. ISBN:

6 3. The impact from an external factor can be measured various methods such as subjective method and AHP. However, these methods have some disadvantages hard to overcome in terms of unambiguity, response time, structure, etc. Fuzzy method is selected for this study because of better accuracy. This study can provide either decision maker or project manager a logical method to measure or predict the impact from the external factors for NPD projects. However, this study needs some critical works further. First, the number of membership functions required in this study is very high under the fuzzy method. Also, it needs in-depth research to define each membership function. To overcome these drawbacks, different approaches such as Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO) should be considered to resolve the number of membership function problem, and multidisciplinary research method should be performed for accurate definition of a membership function. Second, a NPD project is also influenced by the internal (controllable) factors. Those factors would affect the project difficulty. Therefore, a synthesized model should be developed by combining the impact degrees from both factors. Finally, the system suggested by this study should be applied to realistic cases for verification and validity. References: [1] H. G. Choi, J. O. Ahn, Risk analysis models and risk degree determination in new product development: A case study, Journal of Engineering and Technology Management, Vol.27, 2010, pp [2] S. L. Brown, K. M. Eisenhardt, Product development: past research, present findings, and future directions, Academy of Management Journal, Vol.20, No.2, 1995, pp [3] K. T. Ulrich, S. T. Eppinger, Product design and development, third edition, McGraw-Hill, New York [4] K. D. Miller, A framework for integrated risk management in international business, Journal of International Business Studies, Vol.23, No.2, 1992, pp [5] S. Werner, L. E. Brouthers, K. D. Brouthers, International risk and perceived environmental uncertainty: The dimensionality and internal consistency of Miller`s measure, Journal of International Business Studies, Third quarter, [6] M. Crawford, A. Di Benedetto, New product management, eighted, McGraw-Hill, New York, [7] H. Ernst, Success factors of new product development: a review of the empirical literature, International Journal of Management Reviews, Vol.4, Issue.1, 2002, pp [8] R. Balachandra, J. H. Friar, Factors for success in R&D projects and new product innovation: a contextual framework, IEEE Transactions on Engineering Management, Vol.44, No.3, [9] D. Miller, P. H. Friesen., Strategy making and environment: The third link, Strategic Management Journal, Vol.4, 1983, pp [10] G. J. Covin, D. P. Slevin, Strategic management of small firms in hostility and benign environments, Vol.10, 1989, pp [11] G. G. Dess, D. W. Beard, Dimensions of organizational task environments, Administrative Science Quarterly, Vol.29, 1984, pp [12] R. Balachandra, J. A. Raelin, When to kill that R&D project, Research Management, Vol.27, No.4, 1984, pp [13] J. K. Pinto, S. J. Mantel, The cause of project failure, IEEE Transaction on Engineering Management, Vol.37, No.4, 1990, pp [14] D. E. Carter, Evaluating commercial projects, Research Technology Management, Vol.25, No.6, [15] D. B. Merrifield, Selecting projects for commercial success, Research Management, Vol.24, No.6, [16] R. G. Cooper, Identifying industrial new product success: Project NewProd, Industrial Marketing Management, Vol.8, 1979, pp [17] R. G. Cooper, E. J. Kleinschmidt, New products: What separates winners from losers?, Journal of Product Innovation Management, Vol.4, 1987, pp [18] A. Griffin, A. L. Page, An interim report on measuring product development success and failure, Journal of Product Innovation Management, Vol.10, 1993, pp [19] R. W. Saaty, The analytic hierarchy process-what it is and how it is used, Mathematical Modeling, Vol.9, Issues.3-5, 1987, pp [20] L. A. Zadeh, Fuzzy sets, Information and Control, Vol.8, 1965, pp ISBN: