Recent years, since problems with respect to atmosphere pollution hasten countries to accentuate green-related policy regarding the sustainable energy, the lithium-iron phosphate (LiFePO4) battery has been appealed to the world. However, more and more firms invest the LiFePO4 batteries production that has launched a fierce competition. Successful new product development (NPD) processes have been considered the key for LiFePO4 battery firms to increase their competitive advantage. Firms must make correct decision faster due to the rapid development of technology and the decreasing product life cycle. This study proposes a hybrid multiple criteria decision making (MCDM) model based on the literature review and consultation with the experts, interpretive structural modeling (ISM), and fuzzy analytic network process (FANP) for evaluating various strategies for NPD. First of all, reviewing of literature and meeting with the experts are used to screen factors and select the criteria. Then, an ISM is managed to determine the feedback and interdependency of those factors in a network. Finally, a fuzzy theory is applied to resolve the linguistic hedges and an ANP is adopted to obtain the weights of all the factors. A case study is undertaken to validate the model in a Taiwanese company that provides professional packing and design for lithium-iron phosphate battery.
It has been a global issue to make the world a low carbon place supported by sustainable energy due to the limited oil storage and the global warming. For this reason, people have been looking for effective and environment friendly energy storage devices for the sustainable energy to solve the problems. For this reason, the lithium-ion (Li-ion) batteries (LIB) have been attracting wide attention, lithium-iron phosphate (LiFePO4) especially. Even though many types of Li-ion batteries such as lithium cobalt oxide (LiCoO2), lithium manganese oxide (LMO), and lithium nickel manganese cobalt oxide (NMC) are common in consumer electronics, lithium-iron phosphate (LiFePO4) has been deemed the most promising one for the next generation Li-ion battery due to its inherent merits such as low cost, low toxicity, long cycle ability, and high safety. LiFePO4 battery firms have developed a number of lightweight, high energy density products including portable devices, power tools, and electric vehicles. LiFePO4 batteries are replacing the lead acid and other batteries which have been historically used. It seems that the LiFePO4 battery industry is currently one of the most valuable industries in Taiwan. However, since more and more firms invest huge amount of money in the LiFePO4 batteries, they are facing an extremely competitive and cost-cutting war. The increasing demand for LiFePO4 batteries has led the firms and academics to stress on new product development (NPD) projects concerning the environmental topics to gain and maintain a competitive edge. However, not all of the NPD projects could reach the business goal and keep the competitive advantage. The strategies of proposed projects must be precisely assessed to see if they meet the business’s objectives before they move forward. From John Goodenough’s research group published literature in 1997 to present, continuous efforts have been improving the performance of LiFePO4. However, although plenty of researches provide solutions for LiFePO4 batteries, a discussion on evaluation of new product development projects for LiFePO4 batteries is rare.
The first significant step to evaluate strategies of new product development projects for LiFePO4 battery firms is to recognize the critical success factors (CSFs). In today’s fast-paced, fiercely competitive world, identifying the critical success factors of new product development projects separates the successful LiFePO4 batteries firms from their competitors. The critical success factors are high-level criteria for the firms to make qualified strategies. However, it is not easy for firms to assess those critical factors because numerous problems could affect the evaluation of NPD projects. For example, time in developing new products is one of the essential factors to reach the ultimate business purpose because of the decreasing life circle of products and the rapid growth of technology. Besides, recent years, due to the depleting fossil fuel and global warming threats, environmental issues prompt firms to design green-related products and encourage them to adopt green energy for NPD projects, particularly for the energy companies. In addition to time and environmental reasons, many more aspects need to be incorporated into the NPD projects assessment including finance, technology, design, manufacture, team work, and marketing. Unfortunately, not all of factors are aligned with the ultimate profit goal. Managers need to choose right strategies in the NPD projects, policies, and practices to provide reasonable assurance and to avoid unpredictable impact on the business. Thus, the LiFePO4 batteries firms have to look for a systematic way to fast response to those demands so as to appropriately judge the critical success factors of NPD project and precisely make strategies.
Since the demand for effective energy storage devices is increasing, the LiFePO4 battery with outstanding performance is believed to be the most promising candidate for the sustainable energy. LiFePO4 battery firms have to seek beneficial investments such as international cooperation, new product development, and equipment update in order to survive in this highly competitive global economy. New product development is deemed the most difficult among the investments. In general, the cost of new product development, the number of developing team member, and the duration of a project are in direct proportion. Besides, firms need to pay for equipment and tools for manufacturing. It is not easy for firms to develop an unflawed product. It actually takes a great deal of time and money to successfully develop a new product. New product development projects give the managers challenges on decision making, regulation change, manufacturing details, time stress, market economy, producing schedule, customer needs, member diversity, and team work. As a result, an efficient assessment model for new product development projects is the first priority solution in the LiFePO4 battery industry.
Although it is hard to find studies in the field of evaluating NPD projects for LiFePO4 batteries, researchers have employed various methodologies for firms in many other industries. One of the most applied approaches to solve the problems of performance assessment with numerous factors is using multiple criteria decision making (MCDM) methods. The analytic hierarchy process (AHP) has been widely recognized as a distinguished decision making method in a variety of fields pertaining to a multiple criteria decision making problems. Although the AHP has become a popular application for performance assessment, each individual criterion is assumed to be independent. In order to deal with the limitation of AHP, the analytic network process (ANP) method has been largely used to solve the interdependency issue. The ANP approach replaces the AHP with a network that allows for complicated relationships among criteria as well as subcriteria. Current multiple criteria decision making models may be considered to help the LiFePO4 battery managers solve their problems. However, existing models lack a holistic solution. As a result, a wrong and misjudged decision may cause unpredictable impact on the firms. According to the analysis and comparison with above methods, it is vital for researchers to use advantaged method for creating an evaluation model.
The members of decision makers working on the new product development projects are usually formed by a group of managers. If they make wrong decisions, it will cause a great loss to enterprise in finance. For this reason, the chance of wrong decisions can be decreased to the lowest if a suitable assessment model is provided in advance for new product development projects. The increasing demand for LiFePO4 batteries has launch an extremely competition. It is not allowed the firms to hesitate to choose NPD projects. The main purpose of this study is to help the LiFePO4 battery managers precisely evaluate the critical success factors in NPD projects and efficiently make correct decisions. In order to achieve the research objective, this study would propose a synthetic MCDM appraisal model using a step by step method. At first, literature reviews and expert opinions are employed to identify the criteria of NPD projects. After getting done the critical success factors selection, an interpretive structural model (ISM) is managed to determine the feedback and interdependency of those criteria in a network. Finally, a fuzzy theory is applied to resolve the linguistic hedges. An analytic network process (ANP) is adopted to obtain the weights of all the factors and to summarize the result. A real case study is presented for the NPD projects evaluation in a Taiwanese lithium-iron phosphate battery company to validate the model.
The rest of this study is organized as fallows. Section
Since the large amount of demand for the sustainable energy, lithium-iron phosphate battery firms have to carefully evaluate the critical success factors in NPD projects and efficiently make correct decisions. The firms have to utilize a systematic model to solve the problem so as to maintain the competitive edge. In order to arrive at the ultimate business purpose, this study investigates the previous researches before establishing the integrated MCDM model in new product development processes and applying to the Taiwanese LiFePO4 battery company. Hence, this section is going to discuss previous studies pertaining to the new product development (NPD), LiFePO4 battery industry, interpretive structural model (ISM), and fuzzy analytic network process (FANP).
The improvements in technology, equipment, and raw material do not increase the product life circle in this high competitive product design environment. In fact, the product life circle is getting short. Enterprises have to continuously focus on new product innovation in order to enhance customer satisfactions, product value, and opportunities. Existing products can probably keep a firm stable but new products are the motivation that makes a firm thrive. Therefore, enterprises in any industry need to make a right decision in NPD project selection to assure the advantages over others. In other words, NPD is considered as the key for firms to maintain the competitive advantage [
A successful NPD is an important source of survival and competitive advantage for enterprises [
An ever-increasing demand for portable electronic devices drives technological improvements in batteries. In addition, due to oil depletion and climate change, establishing a low carbon environment supported by sustainable energy has been a global topic. As an effective energy storage device for the sustainable energy, LiFePO4 in the lithium-ion batteries has been playing a significant role. An earlier study by Padhi et al. [
Previous researches reviewed the development of LiFePO4 in the past years as well as discussing some issues for LiFePO4 in the future. Ellis et al. [
The interpretive structural modeling (ISM) is an interactive learning process in which a set of different but directly related elements is formed into a comprehensive systematic model [
ISM has been increasing applied to various fields of study to identify the interrelationship among elements regarding the issue. A current research gave an overview about the key concept of ISM approach which was discussed in detail [
Analytic Hierarchy Process (AHP) proposed by Saaty [
Many previous studies have used FANP to solve the complex problems in a decision making model and chose the best alternative or strategy by fuzzy weights. Mikhailov and Singh [
FANP is one of the most popular MCDM models used to select suitable projects, suppliers, locations, and so on. Previous paper presented an integrated model to evaluate different available technologies for NPD by using ISM and FANP methods in a flat panel manufacturer. A hybrid MCDM method based on FANP was adopted for investors to select the green house locating in Iran [
This study employs ISM and FANP to make new product investments more advantageous to the LiFePO4 battery industry. The procedure passes through three phases with those methods to complete the study. In the first phase, factors affecting NPD are collected and analyzed from the related studies and eight experts from different departments of LiFePO4 lithium-ion battery industry. Five criteria and sixteen subcriteria are identified to be conducted in this study. As to the second phase, an ISM is managed to distinguish the relationships among the criteria (outer dependence) and among the subcriteria (inner dependence) for the hierarchical structure after figuring out all the factors by the literature reviews and expert interviews. In the meanwhile, a questionnaire for the expert interviews is undertaken across the LiFePO4 battery divisions. In the third phase, a super-matrix is created by using a FANP, and the weights of all the criteria are computed. A determination of the ranking weights shows the priority of the critical success factors in a new product project.
In this step, an adjacency matrix that shows the contextual relationship among the subcriteria under each criterion is formed. Questionnaires are prepared firstly to identify the contextual relationship between any two criteria and the associated direction of the relation. The number of times of experts’ judgments on the relationship between each pair of criteria is counted. A threshold value of 88% is used to determine if the criteria are dependent. That is, if the number of times is less than 88%, a 0 is set as there is no influence between the criteria; if the frequency of experts’ agreement is greater than or equal to 88%, then a 1 is set. For instance, there are This step develops the reachability matrix and check for transitivity. An initial reachability matrix Based on the reachability matrix Complete the ISM hierarchical structure.
Form the network structure in which the goal, the criteria, and subcriteria are well defined and the relationship of the exterior among the criteria and the relationship of interior among the subcriteria are determined in the last phase. Form pairwise comparison matrices with the 1 to 9 scores received from each of the Obtain the weights and analyze consistency. The priority of the criteria can be compared by the calculation of eigenvectors and eigenvalues:
Create fuzzy positive matrixes. The entries in the pairwise comparison matrixes are transformed into positive triangular fuzzy numbers known as linguistic variables. As suggested by Buckley [ Compute the fuzzy weights of decision factors based on the Lambda-Max method that was proposed by Csutora and Buckley [ Adopt Once the weights of matrixes are obtained, two constants, The upper bound and lower bound weight matrixes are
Combine the judgments of all members of the decision making team. Geometric average is used to integrate the fuzzy weight matrixes of experts:
Process the defuzzification to obtain the final ranking order of the decision factors. Based on the equation proposed by Chen [ Form super-matrices. First, combine each submatrix with priority vectors to be an initial super-matrix. As it may not fit the column stochastic rule, normalize each column matrix to make a weighted super-matrix. Finally, multiply the weighted super-matrix until
The energy company conducted in this study was established in 2005. With the professional team and management, they endeavor to invest in research development, production, and marketing. The major products they develop are lithium-iron phosphate batteries, multifunction display cards, multilevel detection alarm installations, faucet lighting-effects accessory fittings, and lamp structure application of mini night-lights. The products have not only earned the endorsement from domestic manufacturers but also obtained various patents. This company has completed the development of various standardized products such as lithium-iron phosphate battery for vehicle ignition which currently has reached the stage for mass production and sale. In addition, the special design of the protection panel on the product has obtained patens in Taiwan, China, Japan, and Germany and will continue with more patent applications in the United States, Canada, and countries in Europe. Other products such as ultracapacity battery, multifunction portable power supply, and solar-energy charger station are also in hot demand. Today, they continue collaborating with many well-known domestic firms in the development of lithium-iron phosphate battery for vehicles, electric bicycles, electric motorcycles, and UPS. The company is trying to make a revolution in the battery industry.
The company aims at development and proxy of the lithium-iron phosphate battery. The characteristics of lithium-iron phosphate battery such as ecofriendly, quick charge, stability, high safety, long lifespan, energy conservation, and carbon reduction do not exist in conventional lead-acid battery. The company would continue to make their efforts for environment and to reach the goal of green energy development. For those reasons, this company is selected in this case study to evaluate the critical success factors and how deep the factors impact on the process of NPD. To achieve this goal, the following steps are taken.
The purpose of this step is to identify the evaluation criteria of NPD. To do so, related studies are reviewed and an expert committee is established and consulted. The experts from sales and research and development departments in this company are conducted in this study. Since those committee members have more experiences on the LiFePO4 battery marketing, they are able to point out the critical success factors that affect the NPD process and make this study reliable. According to the previous researches and experts’ discussions, the critical success factors consisted of five evaluation criteria and sixteen subcriteria are decided and shown in Table
Critical success factors for the process of NPD.
Criteria | Subcriteria |
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Characteristics of development team (O1) | Senior management commitment (1) |
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Market trends (O2) | Customer’s needs (4) |
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New product features (O3) | Product innovation (7) |
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Information resources and technology (O4) | Compatibility product resources (11) |
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Strategic planning (O5) | Strategic focus (14) |
In this step, an interpretive structural modeling (ISM) method is utilized to form a hierarchical structure based on the relationship among the five criteria and within the six 16 subcriteria. ISM methodology suggests the use of group discussion. For this reason, eight experts from the company are consulted in order to identify the nature of contextual relationship among the factors. ISM also builds a network relationship map. The development of ISM method applied to this study is performed in the following steps.
Network for the criteria and subcriteria.
The result of the ISM shows that the feedback and interdependency exist in the critical success factors of the NPD. Based on the relation structure, this step is to derive the weights of the criteria and subcriteria by using a fuzzy analytic network process (FANP) method. The 9-point scale of relative importance proposed by Satty in 1980 is employed to develop the FANP questionnaire. Five experts from management, sales, and manufacture departments in this and other LiFePO4 battery companies are conducted in the survey. After they get done the pairwise comparison questionnaires, the weights are calculated. The priority of the critical success factors is analyzed for the decision makers to choose right strategies. The holistic process of the FANP is illustrated as follows.
Pairwise comparison matrix (expert 1).
LiFePO4 | (O1) | (O2) | (O3) | (O4) | (O5) |
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(O1) | 1 | 1/3 | 2 | 3 | 3 |
(O2) | 3 | 1 | 4 | 7 | 7 |
(O3) | 1/2 | 1/4 | 1 | 2 | 2 |
(O4) | 1/3 | 1/7 | 1/2 | 1 | 1 |
(O5) | 1/3 | 1/7 | 1/2 | 1 | 1 |
Consistency examination for criteria (expert 1).
LiFePO4 | (O1) | (O2) | (O3) | (O4) | (O5) | Eigenvector |
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(O1) | 1 | 1/3 | 2 | 3 | 3 | 0.21004 |
(O2) | 3 | 1 | 4 | 7 | 7 | 0.52427 |
(O3) | 1/2 | 1/4 | 1 | 2 | 2 | 0.12749 |
(O4) | 1/3 | 1/7 | 1/2 | 1 | 1 | 0.06910 |
(O5) | 1/3 | 1/7 | 1/2 | 1 | 1 | 0.06910 |
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C.I. = 0.00493 | R.I. = 1.12 | C.R.= |
Fuzzy pairwise comparison matrix (expert 1).
LiFePO4 | (O1) (O2) (O3) (O4) (O5) |
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Fuzzy pairwise comparison matrix (5 experts).
(O1) | (O2) | (O3) | (O4) | (O5) | |||||||||||
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(O1) | 1 | 1 | 1 |
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(O2) | 7/8 | 1 1/6 | 1 7/9 | 1 | 1 | 1 |
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(O3) | 4/9 | 5/8 | 1 | 2/9 | 2/7 | 4/9 | 1 | 1 | 1 |
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(O4) | 1/2 | 2/3 | 6/7 | 2/7 | 2/5 | 3/5 | 6/7 | 1 1/5 | 2 | 1 | 1 | 1 |
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(O5) | 3/7 | 3/5 | 5/6 | 1/3 | 2/5 | 2/3 | 5/9 | 3/4 | 1 1/7 | 2/3 | 1 | 1 5/9 | 1 | 1 | 1 |
Result of defuzzification.
LiFePO4 | (O1) | (O2) | (O3) | (O4) | (O5) | Eigenvector |
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(O1) | 1.0000 | 0.8533 | 1.6216 | 1.5919 | 1.7279 | 0.23549 |
(O2) | 1.2764 | 1.0000 | 3.3236 | 2.5024 | 2.5024 | 0.35487 |
(O3) | 0.6751 | 0.3252 | 1.0000 | 0.8335 | 1.3242 | 0.13174 |
(O4) | 0.6610 | 0.4372 | 1.3544 | 1.0000 | 1.0654 | 0.14968 |
(O5) | 0.6228 | 0.4817 | 0.8225 | 1.0654 | 1.0000 | 0.12822 |
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C.I. = 0.01012 | R.I. = 1.12 | C.R. = 0.00904 |
Initial super-matrix.
(O1) | (O2) | (O3) | (O4) | (O5) | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16) | |
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(O1) |
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0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
(O2) |
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0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
(O3) |
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0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
(O4) |
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0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
(O5) |
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0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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(1) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.5994 | 0.5994 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.5994 |
(2) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.2634 | 0.2634 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.2634 |
(3) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.1372 | 0.1372 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.1372 |
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(4) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.6415 | 0.6415 | 0.6415 | 0.6415 | 0.6415 |
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0.6415 |
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0.6415 | 0.0000 | 0.0000 | 0.0000 |
(5) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.2343 | 0.2343 | 0.2343 | 0.2343 | 0.2343 |
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0.2343 |
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0.2343 | 0.0000 | 0.0000 | 0.0000 |
(6) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.1243 | 0.1243 | 0.1243 | 0.1243 | 0.1243 |
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0.1243 |
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0.1243 | 0.0000 | 0.0000 | 0.0000 |
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(7) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.5006 | 0.5006 |
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0.5006 | 0.5006 |
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0.5006 | 0.5006 |
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0.5006 |
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0.5006 |
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0.5006 |
(8) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.1110 | 0.1110 |
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0.1110 | 0.1110 |
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0.1110 | 0.1110 |
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0.1110 |
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0.1110 |
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0.1110 |
(9) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.2470 | 0.2470 |
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0.2470 | 0.2470 |
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0.2470 | 0.2470 |
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0.2470 |
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0.2470 |
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0.2470 |
(10) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.1414 | 0.1414 |
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0.1414 | 0.1414 |
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0.1414 | 0.1414 |
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0.1414 |
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0.1414 |
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0.1414 |
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(11) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.4457 | 0.4457 |
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0.4457 | 0.4457 |
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0.4457 | 0.0000 | 0.0000 | 0.0000 |
(12) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.2322 | 0.2322 |
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0.2322 | 0.2322 |
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0.2322 | 0.0000 | 0.0000 | 0.0000 |
(13) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.3221 | 0.3221 |
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0.3221 | 0.3221 |
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0.3221 | 0.0000 | 0.0000 | 0.0000 |
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(14) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.5945 | 0.5945 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.5945 |
(15) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.2949 | 0.2949 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.2949 |
(16) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.1106 | 0.1106 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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0.1106 |
Weighted super-matrix.
(O1) | (O2) | (O3) | (O4) | (O5) | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16) | |
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(O1) | 0.4089 | 0.2945 | 0.2895 | 0.3157 | 0.2374 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
(O2) | 0.1103 | 0.1269 | 0.1021 | 0.0761 | 0.0962 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
(O3) | 0.1125 | 0.1881 | 0.2458 | 0.2024 | 0.2193 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
(O4) | 0.2003 | 0.1838 | 0.1826 | 0.2559 | 0.2593 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
(O5) | 0.1679 | 0.2066 | 0.1799 | 0.1500 | 0.1877 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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(1) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0875 | 0.1998 | 0.1998 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.1337 | 0.1462 | 0.1998 |
(2) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.1120 | 0.0878 | 0.0878 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0937 | 0.0803 | 0.0878 |
(3) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.1338 | 0.0457 | 0.0457 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.1059 | 0.1069 | 0.0457 |
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(4) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.1909 | 0.2138 | 0.2138 | 0.2138 | 0.2138 | 0.2138 | 0.1545 | 0.2138 | 0.1744 | 0.2138 | 0.0000 | 0.0000 | 0.0000 |
(5) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0496 | 0.0781 | 0.0781 | 0.0781 | 0.0781 | 0.0781 | 0.0826 | 0.0781 | 0.0648 | 0.0781 | 0.0000 | 0.0000 | 0.0000 |
(6) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0929 | 0.0414 | 0.0414 | 0.0414 | 0.0414 | 0.0414 | 0.0962 | 0.0414 | 0.0942 | 0.0414 | 0.0000 | 0.0000 | 0.0000 |
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(7) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.1478 | 0.1669 | 0.1669 | 0.0550 | 0.1669 | 0.1669 | 0.1017 | 0.1669 | 0.1669 | 0.1419 | 0.1669 | 0.1492 | 0.1669 | 0.1044 | 0.1789 | 0.1669 |
(8) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0354 | 0.0370 | 0.0370 | 0.0678 | 0.0370 | 0.0370 | 0.0826 | 0.0370 | 0.0370 | 0.0501 | 0.0370 | 0.0284 | 0.0370 | 0.0674 | 0.0363 | 0.0370 |
(9) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0575 | 0.0823 | 0.0823 | 0.1270 | 0.0823 | 0.0823 | 0.0551 | 0.0823 | 0.0823 | 0.0449 | 0.0823 | 0.0412 | 0.0823 | 0.0758 | 0.0384 | 0.0823 |
(10) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0926 | 0.0471 | 0.0471 | 0.0835 | 0.0471 | 0.0471 | 0.0940 | 0.0471 | 0.0471 | 0.0964 | 0.0471 | 0.1146 | 0.0471 | 0.0858 | 0.0797 | 0.0471 |
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(11) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.2070 | 0.1486 | 0.1486 | 0.2054 | 0.1486 | 0.1486 | 0.1251 | 0.1517 | 0.1670 | 0.1486 | 0.0000 | 0.0000 | 0.0000 |
(12) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0506 | 0.0774 | 0.0774 | 0.0850 | 0.0774 | 0.0774 | 0.0789 | 0.0818 | 0.0601 | 0.0774 | 0.0000 | 0.0000 | 0.0000 |
(13) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0757 | 0.1074 | 0.1074 | 0.0429 | 0.1074 | 0.1074 | 0.1293 | 0.0998 | 0.1062 | 0.1074 | 0.0000 | 0.0000 | 0.0000 |
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(14) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.1192 | 0.1982 | 0.1982 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.1302 | 0.1740 | 0.1982 |
(15) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0883 | 0.0983 | 0.0983 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.1389 | 0.0820 | 0.0983 |
(16) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.1258 | 0.0369 | 0.0369 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0643 | 0.0773 | 0.0369 |
Converged super-matrix.
(O1) | (O2) | (O3) | (O4) | (O5) | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
(O1) | 0.3255 | 0.3255 | 0.3255 | 0.3255 | 0.3255 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
(O2) | 0.1006 | 0.1006 | 0.1006 | 0.1006 | 0.1006 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
(O3) | 0.1826 | 0.1826 | 0.1826 | 0.1826 | 0.1826 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
(O4) | 0.2178 | 0.2178 | 0.2178 | 0.2178 | 0.2178 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
(O5) | 0.1735 | 0.1735 | 0.1735 | 0.1735 | 0.1735 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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(1) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2 |
2 |
2 |
0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2 |
2 |
2 |
(2) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.3 |
1.3 |
1.3 |
0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.3 |
1.3 |
1.3 |
(3) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.2 |
1.2 |
1.2 |
0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.2 |
1.2 |
1.2 |
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(4) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.2022 | 0.2022 | 0.2022 | 0.2022 | 0.2022 | 0.2022 | 0.2022 | 0.2022 | 0.2022 | 0.2022 | 0.2022 | 0.2022 | 0.2022 | 0.2022 | 0.2022 | 0.2022 |
(5) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0717 | 0.0717 | 0.0717 | 0.0717 | 0.0717 | 0.0717 | 0.0717 | 0.0717 | 0.0717 | 0.0717 | 0.0717 | 0.0717 | 0.0717 | 0.0717 | 0.0717 | 0.0717 |
(6) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0594 | 0.0594 | 0.0594 | 0.0594 | 0.0594 | 0.0594 | 0.0594 | 0.0594 | 0.0594 | 0.0594 | 0.0594 | 0.0594 | 0.0594 | 0.0594 | 0.0594 | 0.0594 |
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(7) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.1326 | 0.1326 | 0.1326 | 0.1326 | 0.1326 | 0.1326 | 0.1326 | 0.1326 | 0.1326 | 0.1326 | 0.1326 | 0.1326 | 0.1326 | 0.1326 | 0.1326 | 0.1326 |
(8) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0496 | 0.0496 | 0.0496 | 0.0496 | 0.0496 | 0.0496 | 0.0496 | 0.0496 | 0.0496 | 0.0496 | 0.0496 | 0.0496 | 0.0496 | 0.0496 | 0.0496 | 0.0496 |
(9) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0822 | 0.0822 | 0.0822 | 0.0822 | 0.0822 | 0.0822 | 0.0822 | 0.0822 | 0.0822 | 0.0822 | 0.0822 | 0.0822 | 0.0822 | 0.0822 | 0.0822 | 0.0822 |
(10) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0690 | 0.0690 | 0.0690 | 0.0690 | 0.0690 | 0.0690 | 0.0690 | 0.0690 | 0.0690 | 0.0690 | 0.0690 | 0.0690 | 0.0690 | 0.0690 | 0.0690 | 0.0690 |
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(11) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.1682 | 0.1682 | 0.1682 | 0.1682 | 0.1682 | 0.1682 | 0.1682 | 0.1682 | 0.1682 | 0.1682 | 0.1682 | 0.1682 | 0.1682 | 0.1682 | 0.1682 | 0.1682 |
(12) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0726 | 0.0726 | 0.0726 | 0.0726 | 0.0726 | 0.0726 | 0.0726 | 0.0726 | 0.0726 | 0.0726 | 0.0726 | 0.0726 | 0.0726 | 0.0726 | 0.0726 | 0.0726 |
(13) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0926 | 0.0926 | 0.0926 | 0.0926 | 0.0926 | 0.0926 | 0.0926 | 0.0926 | 0.0926 | 0.0926 | 0.0926 | 0.0926 | 0.0926 | 0.0926 | 0.0926 | 0.0926 |
|
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(14) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.2 |
2.2 |
2.2 |
0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 2.2 |
2.2 |
2.2 |
(15) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.4 |
1.4 |
1.4 |
0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.4 |
1.4 |
1.4 |
(16) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 9.5 |
9.5 |
9.5 |
0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 9.5 |
9.5 |
9.5 |
Weights after normalization.
Criteria | Subcriteria | FANP weight | Ranking |
---|---|---|---|
Characteristics of |
Senior management commitment (1) | 0.0000 | 11 |
Organizational learning (2) | 0.0000 | 11 | |
Cross-functional team (3) | 0.0000 | 11 | |
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|||
Market trends (O2) |
Customer’s needs (4) | 0.6067 | 1 |
Preliminary market assessment (5) | 0.2150 | 7 | |
New product advantages (6) | 0.1783 | 9 | |
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|||
New product features (O3) |
Product innovation (7) | 0.3978 | 3 |
Product specification (8) | 0.1487 | 10 | |
Product identification (9) | 0.2465 | 5 | |
Brand awareness (10) | 0.2070 | 8 | |
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|||
Information resources and technology (O4) 0.2178 | Compatibility product resources (11) | 0.5045 | 2 |
Product design (12) | 0.2177 | 6 | |
Production process and technology (13) | 0.2777 | 4 | |
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|||
Strategic planning (O5) |
Strategic focus (14) | 0.0000 | 11 |
Production scheduling (15) | 0.0000 | 11 | |
Environmental protection (16) | 0.0000 | 11 |
According to the weight listed in Table
Successful new product development projects have been considered the key point for LiFePO4 battery firms to win an advantage over others. However, selecting the right strategies for NPD has been a difficult task since the problems are complicated and often conflicting criteria. Most existing models seem to lack a feasible solution. Even though plenty previous studies propose performance assessment models for many different industries, no one gives a suggestion for the LiFePO4 battery industry. In addition, although continuous efforts have been improving the performance of LiFePO4 batteries that focus on the battery development, a discussion on evaluation of NPD for LiFePO4 batteries is rare.
The LiFePO4 battery industry is currently one of the most valuable industries in the world. However, since more and more Taiwanese firms invest huge amount of money in the LiFePO4 batteries, they are facing an extremely competitive and cost-cutting war. The increasing demand for LiFePO4 batteries has led the firms and academics to stress on new product development (NPD) projects concerning the environment friendly topics to gain and maintain a competitive edge. However, not all of the NPD tactics could reach the business goal and keep the competitive advantage. The strategies of proposed solution must be precisely evaluated to see if they meet the business’s objectives before they move forward.
This study proposes a MCDM model based on appropriate methods including ISM, fuzzy set theory, and ANP. This systemic approach presents a real-world case study and demonstrates the value. This hybrid model helps the LiFePO4 battery firm make right decisions for new product development processes in Taiwan. As the presented model is successful, it can be applied to other firms in the industry. With correct strategies, it is believed that the LiFePO4 battery firms in Taiwan are easier to build sustainable competitive advantage over their competitors in Taiwan or other countries and avoid undesired impact on the firms.
The authors declare that there is no conflict of interests regarding the publication of this paper.