A Theoretical Framework for Project Evaluation and

International Conference on Sustainable Innovation and Successful Product Development for a Turbulent Global Market,

December 16-18, 2013, Chennai, India

A Theoretical Framework for Project Evaluation and Selection in New Product Management KIRANMAYI P1 and MATHIRAJAN M1 1

Department of Management Studies, Indian Institute of Science, Bangalore-560012, India. E-mail: [email protected]/ [email protected]

New Product Development has become compulsion for every organization because of tremendous change in market situations and technologies, shortening product life cycles, increasing rate of change in customer needs/preferences and increasing global competition. For which every organization has to be unique and challenging and needs to develop new products continuously, faster with more value added into it. For this purpose successful identification of right set of projects for developing associated new product at right time is a major activity of Project Evaluation and Selection (PES) in New Product Management (NPM). Of all the decisions taken in case of New Product, PES is pivotal because of complexity raised due constraints like economical resources, financial and technical. From literature it is identified that decision making of PES phase would be involved with multiple evaluative dimensions such as Strategic fit, PortfolioInnovation Balance, Cost-Revenue analysis, Risk-Uncertainty analysis and Resource Allocation. Through the review of literature, it is observed there are few studies concentrating on three or four dimensions simultaneously, but to the best of our knowledge there is no study carried out considering all the five evaluative dimensions. In this paper we develop a theoretical framework considering all the evaluative dimensions simultaneously along with respective variables to measure these dimensions. This framework is expected to demonstrate the influence of dimensions in decision making process of PES. Keywords: New Product Management, Project Selection and Evaluation, Decision Making Framework.

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Introduction

“A successful new product does more good for an organization than anything else that can happen”. - Crawford. According to Crawford (2011), “Product is a multi-dimensional concept, so change in one or more dimensions or as a whole is considered to be a new product, depending upon the extent of innovation into it”. New Product Management (NPM) is defined as the transformation of a market opportunity and a set of assumptions about product technology into a product available for sale. NPM is interdisciplinary and challenging activity where it includes contribution from marketing, operations, engineering design and finance functions, it might be an up gradation to existing product or newto-firm or new-to market product. Be it a physical good or a service, it must be better than competitors. Competitors do most damage when (1) there is so little product differentiation that price-cutting takes everyone’s margins away or (2) they have a desirable new item. To survive the competition and restrain the position in the market every organization has to be unique in the way possible, where the scenario of New Product Development (NPD) arises. The continuous development and market introduction of new products is an important determinant of sustained organization performance (Capon et al. 1990, Chaney and Devinney 1992, Urban and Hauser 1993, Blundell et al.1999, and Brockhoff 1999b).The ability to target the right customers in the right way is a challenge. Added, the complex nature of the process is one of the reasons why so many organizations lose touch with the reality of what consumers want and misspend huge amount of investments on products that fail. According to a profitability analysis of top 20% companies conducted by Product Development and Management Association (PDMA), new product sales will contribute as high as 42% of the profit. This survey also shows that a New Product Project failure ratio reaches 41%, justifying the importance of New Product Management (NPM) and its non-negligible high risk (Chiang et al. 2010). 1.1 Characteristics of NPP: New Product Process (NPP) generally can be divided into five Phases of Development/ Management, they are: Opportunity Identification and Selection » Concept Generation » Concept/ Project Evaluation and Selection » Development » Launch. Throughout the NPP, each and every phase encloses number of decisions to be taken, of all the decisions, Project Evaluation and Selection (PES) decisions play a major key role in success of New Product and are pivotal for effective and efficient risk reduction in NPM (Cooper 1994, Schmidt and Calantone 1998, Büyüközkan and Feyzioglu 2006). In this research study the main focus is given to the decisions taken at PES phase. The main purpose of the phase: PES can (1) help an organization decide whether it should go forward with that particular project or not, (2) help to manage the process in an optimized way as well as better portfolio management by sorting the concepts and identifying the best ones, and (3) even encourages cross functional communication for New Product Success.

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Sustainable Innovation and Successful Product Development for a Turbulent Global Market

In case of PES decision, in practice, it appears that practitioners make two types of errors: First, to consider a nonsuccessful project to be successful and risk the huge investments; Second, to consider a successful project to be nonsuccessful and miss the opportunity of earning huge profits. In case of traditional products, first error is considered to be more serious compared to second. However, in the case of new product both the errors would do equal damage to an organization, because the second error lead to the chances of wiping out the organization from the market if competitor comes out with that particular project idea. Knowing that uncertainty highly disturbs the accuracy of decisions in NPP, it is important to balance decision of developing right projects by allocating right amount of resources while executing the projects by minimizing risk and developing at right time. For this selecting a right project is a key to profitability. Thus in NPP, of all, the Project Evaluation and Selection (PES) is considered as an important activity as it significantly influences upstream and downstream activities (Ayag, 2005). Added to it large amount of investments are made at development phase, which implies the decision of PES is crucial for right amount of investments to be allotted to right project. For this purpose, there is necessity for an organized and efficient decision making process. In this process, a set of evaluative dimensions have to be considered to make an accurate and efficient PES decision. Among the different evaluative dimensions considered in the literature, Cost-Revenue Analysis and Resource Allocation are the major evaluative dimensions (Lockett and Stratford 1987, Regan and Holtzman 1995, Loch and Kavadias 2002, Mc Nally 2009, Oh et al. 2012) which are focused by industries from a very long time, for PES. In addition to these evaluative dimensions there are several other dimensions like Strategic Fit, Risk-Uncertainty Analysis and Portfolio-Innovation Balance to be considered in PES for success of project. In the following section, literature on significance of these evaluative dimensions is presented along with different PES studies which employ these evaluative dimensions. 2

Literature Review

There is a significant remark drawn from literature that the development of new product is an essential element associated with economic growth and prosperity for any organization to sustain in present day competition (Cooper and Kleinschmidt 1986, Calantone et al. 1997, Langerak and Hultink 2006). Among the vast literature on NPD one of the earliest studies of New Product Success was Myers and Marquis’s (1969). As stated by many researches, the three main activities or objectives of decision making perspective of NPP research studies are: information search, performance criteria judgment and new product decisions (Krishnan and Ulrich, 2001; McCarthy et al., 2006; Cooper, 2008; Jespersen, 2008). Adopting the perspective that product development is a deliberate business process involving score of such generic decisions is what describes as decision perspective according to Krishnan and Ulrich (2001). The decision perspective helps us get a glimpse inside the black box of product development without being concerned about how these decisions are made and thereby offer an opportunity to generalize. However, studies focusing on decision-making aspect of NPM had not been as widespread (Yahaya et al. 2007). Importance of upfront evaluation as stated by Crawford (2011), “In recent years there has been much more activity at this phase of PES, prior to development. There still is not nearly enough research carried out, but the practice is spreading, for four reasons: Quality, Time, Cost and Marketing.” Project Selection is often the Rubicon and the most critical stage of NPP. It is often mentioned in literature that nearly 60-80% of the cost is committed after PES phase (Duffy et al. 1993). Product/ Project Managers use different evaluation criteria and dimensions for Project Evaluation and Selection. In addition, the ability of decision models to accurately select the best projects varies with both criteria used and weights applied to the criteria (Baker and Albaum, 1986). Managers rate PES as the weakest NPM area and report that formal portfolio discussion and explicit decision criteria are lacking (Cooper et al., 2001). In this study, we make an attempt to understand the PES phase and capture all evaluative dimensions that are necessary to be considered in making PES decision. We also made an attempt to study the interactions between these dimensions and how these dimensions influence new product success. 2.1 Evaluative Dimensions for PES: Cooper et al., (1997a) reports that managers use three broad dimensions to evaluate the organizations portfolio of New Product Projects: Value Maximization, Portfolio Optimization and Strategic Fit. Zhang and Doll (2001) developed a causal model for front-end fuzziness in terms of environmental uncertainties. A study by Büyüközkan et al., (2004) concentrated on Opportunity Identification, Product Evaluation and Selection Phases, for this they developed a fuzzy logic based decision making approach where major concentration is given to minimize the risk and uncertainty in NPP. Ozer (2005) presented an integrated framework for understanding various factors which affect decision making in NPP. Ayag (2005) proposed a fuzzy AHP- based simulation approach for concept design evaluation for the phase of Concept

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generation. Kaharman et al., (2007) developed a two phase multi-attribute decision making approach for new product introduction. Jespersen (2009) examined how the exploration/exploitation continuum is applied by decision makers in new product project evaluation decisions. With the above background, we proceed further and elaborate each and every evaluative dimension contribution towards decision making of PES in the following sub subsections. 2.1.1

Strategic Fit :

As stated by Whitney (1988), NPM is a strategic activity by intent or by default and it is the foundation for new products management and serves as tool for the integration of all the people and resources used in generating new products. Cooper (1984), implied that, the strategy an organization elects for NPM is closely linked to the organization performance. Organizations that fail to manage their NPP activities strategically are not only running their business from a position of disadvantage but are risking their future (Fitzsimmons et al. 1991). Thus critical role of NPM in the survival and success of organization and the need for managing it strategically is being increasingly recognized in both academic (Finger and Dixon, 1989a, 1989b; Brown and Eisenhardt, 1995; Griffin and Hauser, 1996; Krishnan and Ulrich, 2001) and practitioner (Gates, 1999; Chesbrough and Teece, 2002; Welch and Kerwin, 2003) literature. Managers usually perceive that their New Product Projects’ generally are aligned with their organizations strategic objectives and goals (Cooper et al., 2004), which is important because this dimension correlates strongly with New Product’s Performance (Adams-Bigelow et al., 2006). Empirical research conducted with Spanish firms developing highly innovative products supports Ronkainen’s (1985) results, adding strategic fit as important evaluative dimension. Accordingly, in this study the “Strategic fit” is considered as one of the evaluative dimension for PES. 2.1.2 Portfolio-Innovation Balance: NPM presents a difficult challenge at this phase because resources must be allocated between different innovation projects ranging from radical innovation to basic incremental innovation and each project poses conflicting directions in terms of organization strategy. Projects may be product innovation or process innovation or both, there might be common product platforms among projects, along with different technical and operational feasibility constraints. In this case Balance, is a critical NPM criteria, as it is second most strongly correlated practice after value maximization with superior New Product Development performance (Cooper et al., 2004). The study carried out by Martinsuo (2007), reports that systematic decision making of single project, as a part of development process is positively related to portfolio management efficiency, which implies increase in organization profitability. Adding to this many researches, (Cooper et al. 1997a, 1997b, Graves 2000) have focused on the dimension of Project Portfolio balance that concerns processes relating to selection of projects to be included in the portfolio. But in these studies the balance with already existing projects along with balance of innovation is not concentrated. Though the importance of balance of innovation and portfolio for new product success is studied in the literature, there is no significant study were “Portfolio-Innovation Balance” is considered as evaluative dimension for PES. Whereas we strongly believe that Portfolio-Innovation Balance should be considered for PES. 2.1.3 Resource Allocation: Interdependencies increases the benefits, organizations prefer to develop more than one product in order to sustain the present competitive situation. Resource Allocation becomes complex, when number of projects and inter dependency between them increases. Thus Project Complexity and Newness has to be defined or known clearly to allocate resources. Loch and Kavadias (2002) focus on the optimal resource allocation across NPD programs. They do not consider how the types of the NPD investment or the investment horizon impact the allocation decision. Many researchers have developed different frameworks to increase effectiveness of resource allocation across NPD initiatives. McNally (2009) in his study uses Resource Availability and Project Newness as variables in developing the model. Buyukozkan et al., (2004) in his study fuzzy based model to minimize the risk connected with resource allocation list down Available Equipment Facility; Available Production Process; Available Distribution Channels; Product Quality as important measures to evaluate resource allocation. 2.1.4 Cost-Revenue Analysis: Through the literature review carried, it is observed that Cost-Revenue , Development Cost Evaluation/ Benefit- Cost Analysis is the evaluative dimension which is focused from past two to three decades and received major attention by academicians as well as practitioners. A number of models address the issue of return on investment from new product programs. Ali et al. (1993) consider a competitive setting where firms decide to invest in a single incremental or radical product idea. They focus on a single project and consider project completion to be an exogenous random variable. Chui and Chan (1994) used the expected NPV to evaluate the conditions for an R&D Project’s Success or Failure. Mahmoodzadeh et al. (2007) proposed a new method for project selection using fuzzy AHP and TOPSIS technique by reviewing four common methods of comparing alternative investments (net present value, rate of return, benefit cost

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analysis and payback period) and many other research studies e.g., Lockett and Stratford(1987), Regan and Holtzman(1995), and Ghasemzadeh and Archer(2000) were carried out mainly focusing investments and other evaluative dimensions based on the main dimension of Cost Revenue Evaluation. The limitations of these research studies are they didn’t consider multiple projects and over emphasized only on one particular evaluative dimension of cost- Revenue / Cost benefit analysis. 2.1.5 Risk- Uncertainty Analysis: Uncertainty management is an integral part of NPP, different approaches exist in literature to define and analyze uncertainty. As stated by Büyüközkan et al. (2006), NPP decisions, especially necessary at early stages of the development, contain considerable amount of uncertainty causing elements, which confuses decision maker to reach the target performance. At this phase of PES, decision maker have to make lot of decisions based on inadequate information about the project, vagueness in challenging issues, no dependency on previous data leads to uncertainty and increase the risk involved in development of product. Thus “Risk-uncertainty” is also considered as another important evaluative dimension for PES. Recently, researchers have concentrated on minimizing risk and uncertainties involved in NPP (e.g., Büyüközkan et al. 2004, 2007, Mahmoodzadeh et al. 2007, Chiang et al. 2010). Focus on PES phase of New Product Process, enables identification of best new product project to be developed together with suited development strategy while trying to minimize the associated risk and uncertainty (Büyüközkan et al. 2004, 2007). Büyüközkan et al. (2007) used a Heuristic multi-attribute utility function approach for project evaluation and he considered two types of risks, namely Systematic Risks (Financial and Technical) and Unsystematic Risks (Managerial and Personnel) in his hierarchical structure of decision criteria. Chaing et al. (2010) in their study concentrated on Risk and Uncertainty evaluative dimension i.e., Expected Revenue Risk, Manufacturability Risk and Time- to- Market Risk are calculated using Bayesian Belief Network. Using this risk evaluation model Fuzzy DEA is used to evaluate and rank the projects. Finally, though PES models were developed in the past few decades, recent focus is drawn to develop multi criteria decision making model (Chen et al., 2010; Chiang et al., 2010). Osawa and Murakami (2002) proposed a methodology to evaluate R&D Projects in terms of Strategic Fit and Financial Credibility. Bhattacharya et al., (2011) developed a Fuzzy R&D Portfolio Selection Model for interdependent projects by considering three objectives_ Minimization of Risk, Minimization of Project Cost and Maximization of Project Outcome. The study carried out by Mohanthy et al., (2005) considered four evaluative dimensions: Strategic Fit, Cost-Revenue, Resource Allocation and Risk and uncertainty for R&D project Selection. Oh et al., (2012) considered portfolio balance, but the limitation is he considers balance between the set of projects which are yet to be selected for the portfolio and did not consider existing projects and the amount of innovation or type of innovation involved, whereas in this study it is proposed to consider existing projects along with the set of new projects in order to achieve PortfolioInnovation Balance. From the analysis on the closely related review on decision making perspective of PES (Table 1), to the best of our knowledge, no one has considered all the five evaluative dimensions: Cost-Revenue; Resource Allocation; Strategic Fit; Portfolio-Innovation Balance and Risk-Uncertainty simultaneously for PES decision. In this study we are making one such attempt by developing a suitable theoretical framework by considering all the five dimensions simultaneously for PES decision. The details on the proposed theoretical framework are discussed in the next section. 3. Theoretical Framework In order to understand the influence of evaluative dimensions on PES decision and interactions between them, we will first develop a theoretical framework based on literature review and a review of relevant past theoretical studies. We then study different variables related to marketing, operational, management and financial which are used to measure the respective evaluative dimensions. As stated by Cooper et al., (1999) an organization which emphasizes only on a particular evaluative dimension for making a PES decision is linked with poorer performance. Moreover, the success of these mathematical models depends upon accuracy of deterministic cash flow values and the life of the project as projected by the organization; where evaluation of these cash flows involves lot of uncertainty and risk. So along with cost- revenue evaluation, Risk Uncertainty evaluation should also be carried out to increases the efficiency of decision. The objective of Project Portfolio decision is to allocate limited set of resources to projects in a way to balance innovation, minimize risk and increase the benefit at the same time alignment to organization strategy. Chao and Kavadias (2008) uses concept of strategic buckets in order to allocate limited resources throughout project portfolio.

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Table 1: Closely Related Literature and Evaluative Dimensions for PES Closely Related Study

Evaluative Dimensions of Study

Author

RiskStrategic Resource Uncertainty Fit Allocation R & D Project Portfolio Selection Battacharya et al., 2011 + + Wang and Hwang, 2007 + R & D Project Selection Osawa and Murakami, 2002 + Mohanthy et al., 2005 + + + NPD Project Portfolio Selection Oh et al., 2012 + + NPD Project Selection Thieme et al. 2000 Buyukozkan, 2006 + + Mahmoodzadeh et al., 2007 Chiang and Che, 2010 +

PortfolioInnovation Cost-Revenue Balance + + + + +

+

+ + + +

Cost-Revenue Analysis Strategic Fit

Resource Allocation

PES Decision

Portfolio- Innovation Balance Risk-Uncertainty Analysis

Figure 1: Proposed Theoretical Framework

In order to achieve maximum benefits over a project, project with common platforms, the one which is more aligned to organizations strategy has to be selected. Thus implying a inter relationship between evaluative dimensions of portfolio balance, resource allocation and strategic fit. Summing up different project selection and evaluation studies the framework shown in figure 1 has been developed. It clearly posturizes dimensions which have to be considered while making a PES decisions and inter-relationships between them. Rather than looking at different evaluative dimensions impact on PES decision in isolation, the proposed theoretical framework provide a comprehensive way to develop a holistic understanding of each and every dimension impact on PES decision by examining the interactions among the aforementioned dimensions. This interaction among evaluative dimensions and influence on PES Decision is drawn from literature review. In order to evaluate these dimensions a certain set of variables are required. These variables are related to different attributes like Operational, Financial, Market and Management. These variables are identified from a thorough literature review of closely related studies which are mentioned in Literature Review section and other studies which specifically concentrated on a particular dimension. In table 2 a summary of variables used to evaluate these dimensions are presented along with their definitions.

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Strategic planning requires coming from the heads of the functions in the firm- marketing, technical, manufacturing and finance and from planning of suppliers, customers and others. So organization structure for cross-functionality, amount of involvement of suppliers and customers, amount of risk to be taken, degree of innovation, amount of profitability Table 2. Definitions of variables of PES evaluative dimensions. Variable

Definition

References

Strategic Fit Type of project such as Fundamental research, process improvement or maintenance project or new product line and so on Knowledge / Information Amount of Information that has to be shared for the development of Sharing project, No. of people that are to be involved for this Cross-Functionality Degree of functional heterogeneity in an NPD team The degree of cooperation among multiple functions and Internal Integrity interaction among team members in an NPD initiative The involvement of external partners like suppliers and customers External Integrity in a new product initiative Top Management The degree of support received from top management in order to Support proceed through the project Goal of organization such as defending current base of products Strategic Goal and Needs versus extending the base The extent to which an NPD project’s vision, mission, goals, and Goal Clarity definition are clearly identified and communicated Amount of risk the organization is willing to accept for the Acceptance of Risk development of new product Promotion Strategy The strategy involved in promoting the product into the market Measure of profitability like, Short term versus long term; high Degree of Profitability profits versus low profits Portfolio-Innovation Balance The percentage of an industry or market's total sales that is earned Market Share by a particular company over a specified time period Market Growth The rate of change of demand for the particular new product Estimating the competitor’s product design and features, time-toCompetitor Assessment market, product price and other elated details about the similar product Time-to-Market Expected market launch time Project Type

Market Newness Human Resources Potential Value of Innovation Innovation Type Degree of technical Interaction Degree of design Integrity Process Technology Process Quality Ongoing Projects Process Newness and Complexity Degree of Process Integrity Resource Allocation Product Quality Product Technology Degree of Product Uniqueness Development Time Suppliers Availability Resources Availability Cost-Revenue Analysis Product Cost

The degree of newness of target market from previous existing market of organization Amount of human resources that are estimated to involve in development of new product The economic value derived to organization from the innovation involved with the product. Type of innovation such as radical, new-to-firm, extension of product line, revision of existing product Degree of commonness in the technology involved in product with the existing products Degree of commonness in the design of the product to the existing products Type of technology involved in the development of the product Amount of quality and precision expected to develop a product Number of ongoing projects in the organization Degree of newness and complexity involved on the development of the product Degree of commonness in the process of developing the product with the existing products

[27] [*] [7] [14] [27] [14] [14] [7] [14] [14] [27] [14] [27] [14] [27] [27]

[28] [35] [42] [50] [65] [28] [35] [42] [50] [*] [49] [52] [65] [27] [27] [27] [27] [55] [65] [55] [65] [27] [65] [55] [14] [52] [55]

Degree of quality and precision required for the product Type of technology involved in the product

[7] [27] [27] [65]

Degree of newness in the product compared to the existing products

[14] [27]

Amount of time required for the physical development of the product Availability of suppliers to provide raw material and sub assembly products and so on Availability of resources i.e., machinery, labor, finance and so on to develop the product The cost of Materials and labor for manufacturing the product

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[27] [65] [27] [65] [7] [27] [52] [16] [27] [42] [45] [57]


IP Cost Resources Cost Inventory Cost Product Development Cost Marketing Cost Training Cost Promotion Cost Sales Volume

The cost of licensing the required patents for product development plus cost of patenting the developed product The cost of production facility and maintenance The cost of holding and maintaining the inventory

[*] [16] [16]

The cost of developing product from concept to manufacturing

[16] [27] [42] [45] [57]

The cost for market survey and market test The cost for training labor about new technology / Process involved in manufacturing new product The cost for promoting product Estimated no of sales of a product

[16] [*] [*] [16] [27] [42] [45] [57]

Uncertainty and Risk Analysis Customer Uncertainty Competitor Uncertainty Time-to-Market Risk Product Complexity Risk Technology Transfer and Adaptability Risk Manufacturing Ability Risk Supplier Uncertainty Supplier Availability Risk Development Cost Uncertainty Product Price Uncertainty Demand Volume Uncertainty

Uncertainty in identification of appropriate product characteristics and need and Uncertainty in expressing those requirements Uncertainty in identification of competitors’ product specification and technology, design and product integrity. Risk involved when delivery of product within specific period is not met Risk involved in meeting the product specification standards, quality and customer requirement appropriately Uncertainty in transferring technology into product and market Risk involved when manufacturing ability of production facility is not met Uncertainty of suppliers design and manufacturing capability Risk involved in availability of material and products from suppliers

[7] [56] [65] [7] [33] [65] [16] [33] [65] [33] [*] [16] [33] [56] [65] [33] [33]

Uncertainty in estimation of development cost

[16] [7]

Uncertainty in estimation of exact Product Price

[16] [7]

Uncertainty involved in estimation of demand for the product

[16] [7]

[*] variables introduced by authors

etc., are the variables through which a strategic fit is evaluated. So an organization should be having certain goal, and some objectives and constraints while preparing a strategy, with the help of which a projects’ strategic fit is verified. Mohanty et al., (2005) in his study considers project attributes- Degree of technical interaction with existing products; Degree of Market interaction with existing product; Type of Innovation and Value of innovation in developing a fuzzy ANP based approach for R&D project portfolio suggestion. Fox et al. (1998) combine three dimensions of uncertainty as technical, market and process. Mullins and Sutherland (1998) identify three levels of uncertainty- first, customer uncertainty, where a customer cannot articulate needs that a new technology or product may fulfill; second, NPD managers are uncertain about how to turn new technologies into new products; third, uncertainty faced by senior management in allocation of capital in pursuit of rapidly changing markets. Miller and Lessard (2001) identify three categories of risks: first, Completion Risk where technical, operational and constructional risks are involved; second, Market related risk where demand, financial and supply risks are summed together and third, Institutional risk grouped by social acceptability and sovereign risks. Finally, all these variables along with evaluative dimensions are presented in the detailed framework shown in figure 2. These variables are categorized into Market, Operational, Financial and Management attributes for a better understanding. From the figure, we can observe that variables related to all the attributes are not necessary to measure a particular evaluative dimension. This framework can be used as a first step to understand the impact of PES decision on project success. We can study how to measure each evaluative dimension with respective to other and how to optimize the costs and increase the profitability from each project. At the same time, it can be used as a guideline or roadmap to the development of multi criteria decision making model. 4. Conclusion In this paper, we have combined five evaluative dimension perspectives, namely Strategic Fit, Portfolio-Innovation Balance, Resource Allocation, Cost-Revenue Evaluation and Risk-Uncertainty Evaluation, to develop a theoretical framework for understanding the dimensions affecting the PES decision and to get a clear picture of inter relationships between these dimensions. Our proposed framework helps to understand the process better, reduce the complexities involved in decision making, reduce the risk and uncertainty and optimize the NPP. It increases the effectiveness and accuracy of the PES decisions, which in turn increase the rate of product success. This proposed framework helps to understand the role of organization and top management and complementary investments in enhancing the profits

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derived from a new product. The proposed theoretical framework could be used to inform and develop a multi criteria decision model of PES in NPM. We hope to empirically test the framework to understand the inter relationship between the dimensions and impact on PES decisions in different sectors of industries and enhance the proposed framework. Then develop a multi-criteria decision making model. This forms scope for future work.

Strategic Fit: - Operational Attributes: Type of Project; Familiarity of Project Technology; Familiarity of Development Process; Adaptability to Technology. - Management Attributes: Knowledge / Information Sharing; Cross-Functionality; Internal Integrity; External Integrity; Top Management Support; Strategic Goal and Needs; Goal Clarity.

Portfolio-Innovation Balance: - Operational Attributes: Type of Project; Type of Innovation; Process Concurrency; Process Formulization; Time Frame of Projects; Number of Projects; Potential Value of Innovation; Resources Flexibility.

Resource Allocation: - Operational Attributes: Project Newness; Project Complexity; Available Equipment Facility; Available Production Process; Product Quality; Development time; - Management Attributes: Opportunistic learning required; - Market Attributes: Available Distribution Channels; Market Size; Market Growth; Value to customer.

Cost-Revenue Analysis: - Financial attributes: Product Cost ; IP Cost; Resources Cost; Inventory Cost; Product Development Cost; - Management attributes: Training Cost; Promotion Cost. - Market attributes: Marketing Cost; Market Share; Sales Volume.

Risk-Uncertainty Analysis: - Market Attributes: Customer Uncertainty; Competitor Uncertainty; Time-to-Market Risk. - Operational Attributes: Product Complexity; Technology Transfer and Adaptability Risk; Manufacturability Risk; Uncertainty of process functions or input characteristics; Supplier Uncertainty. - Financial Attributes: Development Cost Uncertainty; Sales Volume Uncertainty.

Figure 1. Proposed theoretical framework of PES evaluative dimensions

References

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PES DECISION


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