A Study on Identification of Evaluative Dimensions and Development of Decision-Making Tool(s) for Project Evaluation and Selection of New Product Portfolio Management
The rapidly evolving global market scenario raised multiple challenges for an organization such as: change in customer needs and lifestyle, increased competition, compulsion to enter into new markets, pursue to innovate and so on, which raises an additional challenge for organization to sustain and succeed. In order to meet these multiple challenges, continuous New Product Development (NPD) turns out to be one of the essential tasks for any organization to improve market share, profitability and to succeed. In this scenario, a new product portfolio with best mix of new projects that ensures strategic alignment, balance of portfolio and improves organizations’ potential gain is compulsion. However, From the literature, it is observed that, ‘As nearly half of initial NPD ideas occur informally or without a specific goal, even a best performing organization requires a major improvement in the decision making process of Project Evaluation and Selection (PES)’. This emphasizes the significance of decision on Project Evaluation and Selection (PES) of NPD. Additionally, huge investments and resources need to be employed based on decision that is taken at PES phase of NPD. Thus PES turns out to be a crucial and essential phase of New Product Process (NPP). All these stated aspects of this challenging and crucial strategic decision of PES provoke for the requirement of an efficient management system and decision making model. In the literature the management system and decision making processes for formulation of portfolio is termed as “New Product Portfolio Management (NPPM)”. Though various researchers have been focusing on this particular issue of improving NPPM Performance, from the analysis of literature, to the best of our knowledge, it is observed that no one has identified or considered an exhaustive list of possible evaluative dimensions while taking the decision on PES of NPPM (PES-NPPM). This thesis makes an attempt to address this research gap, and the scope of this study is pertained to three sectors of manufacturing industry, namely, Automotive, Electronics and Machine Tools. Accordingly, the main objective of this thesis is “ In order to achieve this particular objective the following sub-objectives, methodologies, and analysis are carried out. For this purpose, first and foremost analysis of literature on PES is carried out. Accordingly, five evaluative dimensions are identified for PES-NPPM and they are: (i) Strategic Fit; (ii) Portfolio-Innovation Balance; (iii) Risk-Uncertainty Estimation; (iv) Cost-Revenue Estimation and (v) Optimized Resource Allocation. Furthermore, it is observed that, there is no study considering all the five evaluative dimensions simultaneously for PES-NPPM either to analyze their impact on performance of NPPM or to develop a decision making model. Thus, we are addressing a new problem configuration in the area of PES-NPPM. Additionally, though the requirements of multi-criteria models for PES-NPPM is discussed both in academic and practioners points of view, the real demonstration of the applicability of multi-criteria models are given a scant treatment in the literature. . By the end of the achieving this objective, we identified five distinct evaluative dimensions which are used in different combinations for PES-NPPM. Further, for measuring each of these five evaluative dimensions, we identified 23, 11, 15, 10, and 18 measurement variables respectively. Based on the evaluative dimensions considered in this study, a framework work is proposed for PES-NPPM. Due to the limitation of empirical evidences on considering the identified evaluative dimensions and respective measurement variables towards the proposed initial framework for PES-NPPM, another exploratory study: a case study method is carried out. In addition to the process of triangulation, the case study approach is carried out to understand (a) significance and nature of the identified measurement variables of all the five evaluative dimensions for PES-NPPM, and (b) real-life practices in decision making process of PES-NPPM and to identify the requirements of decision making tools. Accordingly, 12 case studies (4 each) from three manufacturing sectors, considered in this study, are conducted. Further, 12 case study reports are prepared and inferences are drawn. The inferences drawn are verified by conducting an individual brain-storming session with 3 academicians and 4 practitioners. The detailed analysis of the 12 case study reports endorsed the necessity of considering all the five identified evaluative dimensions in the proposed framework for PES-NPPM. In addition, the case study analysis revealed some of the variables originally considered for measuring the evaluative dimensions are not really the measurement variables, whereas those variables are expected to impact the decision making environment of PES-NPPM (or) NPPM Performance. Further those non-measurement variables are classified into (a) Characteristic Variables of PES-NPPM, and (b) Moderating variables for NPPM. Based on this, case study analysis identified 8 characteristic variables and 8 moderating variables. This specific observation resulted to analyze further the existing literature in order to identify if there exist any additional variables which impact decision making environment of PES-NPPM (or) NPPM Performance. Thus, from the analysis of literature and case study analysis 17 characteristic variable and 13 moderating variables are identified. Additionally, For this purpose, Partial Least Square – Path Modeling (PLS-PM) (or) regression analysis is conducted depending upon type of variables with 104 observations (representing 34, 39, and 31 observations of the three sectors respectively) to analyze the relationships between characteristic and moderating variables on decision-making environment of PES-NPPM and NPPM Performance respectively. From case study analysis, it is observed that the decision making tool required should provide: (a) ability to incorporate judgmental scores along with financial and other quantitative metrics, (b) ability to attain a balance of portfolio and consider interactions among project, and (c) ability to provide alternatives and rank the alternatives. In addition to the observation drawn from the case study analysis on the need of MCDM based tool(s), analysis of the literature is carried out to verify the same. As this problem scenario considers both quantitative and qualitative data for the development of a decision making tool, an appropriate technique/methodology needs to be employed. Based on analysis of literature and the case study reports, this study proposes an integrated Data Envelopment Analysis and Balanced Scorecard (DEA-BSC) model for individual PES. Further, the proposed DEA-BSC model is extended for evaluation of new product portfolio. In the process of formulation of new product portfolio, first, every new product project is evaluated with the proposed integrated DEA-BSC model. Second, an algorithm is designed to generate alternate portfolios with the selected set of efficient new product projects. Then, DEA-BSC model is employed to evaluate the generated portfolios. At this step, an accumulation functions are proposed which considers interactions among projects. These accumulation functions determine the overall input and output of the portfolio along with interactions involved. Accordingly, the proposed integrated DEA-BSC model for portfolio evaluation is expected to result in a balanced portfolio with profitable new product projects. In addition, the workability of the proposed integrated DEA-BSC model is demonstrated by developing a suitable numerical example. Finally, a sensitivity analysis is carried out on proposed DEA-BSC model to analyze the robustness of the results. In summary, this thesis examined a problem of decision making of NPPM. Further, this problem was retained with main focus on PES phase. Accordingly, the major contributions of this thesis are as follows: Identified an exhaustive lists of evaluative dimensions: (i) Strategic Fit; (ii) Portfolio-Innovation Balance; (iii) Risk-Uncertainty Estimation; (iv) Cost-Revenue Estimation and (v) Optimized Resource Allocation. Also identified the significance of these five dimensions in case of PES-NPPM. In addition, all the five evaluative dimensions are considered simultaneously for development of a multi criteria decision making tool for PES-NPPM. Identified the required measurement variables for each of the evaluative dimensions, considered in this study, that are essential for PES, and analyzed their influence on performance of NPPM. Identified and analyzed characteristic and moderating variables that influence decision making environment of PES-NPPM and performance of NPPM respectively. Identified the requirements of a decision making tool for PES-NPPM and developed an integrated DEA-BSC model for PES. To the best of our knowledge, the proposed integrated DEA-BSC model is considered to be the first hybrid model applied to PES-NPPM. Furthermore for implementing the proposed DEA-BSC model, an algorithm is proposed in this study and this is expected to assist decision maker for selecting the right set of projects for new product portfolio with higher development potential, profitability and minimize the associated risk. Identified possible project interactions caused due to external or internal factors and accordingly proposed an accumulation function to capture these interactions. Proposed an algorithm for formulation of new product portfolio and accordingly proposed a detail step-by-step procedure for implementation of the proposed integrated DEA-BSC model. Though this study analyzes the impact of characteristic variables on decision-making environment of PES-NPPM, we limit to link this impact to DEA-BSC Model. In this study, an attempt is made to capture the moderating effect on NPPM Performance, but this study limits to link this moderating effect in proposed DEA-BSC model. Finally, the validation of the workability of proposed DEA-BSC model is limited to the numerical example considered in the study and not to the real-life problems scenarios.
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