Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule

In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts'assessments as recorded in historical datasets. Then a data-driven evidential reasoning rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Science Foundation of China is conducted to show the effectiveness of the proposed model. The data-driven evidential reasoning rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historic statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential reasoning rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries.Comment: 20 pages, forthcoming in International Journal of Project Management (2019

An intelligent group decision-support system and its application for project performance evaluation

Purpose: In any organization there are main goals, with lots of projects designed to achieve these goals. It is important for any organization to determine how much these projects affect the achievement of these goals. The purpose of this paper is to develop a fuzzy multiple attribute-based group decision-support system (FMAGDSS) to evaluate projects' performance in promoting the organization's goals utilizing simple additive weighting (SAW) algorithm and technique for order of preference by similarity to ideal solution (TOPSIS) algorithm. The proposed FMAGDSS deals with choosing the most appropriate fuzzy ranking algorithm for solving a given fuzzy multi attribute decision making (FMADM) problem with both qualitative and quantitative criteria (attributes), and uncertain judgments of decision makers. Design/methodology/approach: In this paper, a FMAGDSS model is designed to determine scores and ranks of every project in promoting the organization's goals. In the first step of FMAGDSS model, all projects are assessed by experts based on evaluation criteria and the organization's goals. The proposed FMAGDSS model will then choose the most appropriate fuzzy ranking method to solve the given FMADM problem. Finally, a sensitivity analysis system is developed to assess the reliability of the decision-making process and provide an opportunity to analyze the impacts of "criteria weights" and "projects" performance' on evaluating projects in achieving the organizations' goals, and to assess the reliability of the decision-making process. In addition, a software prototype has been developed on the basis of FMAGDSS model that can be applied to solve every FMADM problem that needs to rank alternatives according to certain attributes. Findings: The result of this study simplifies and accelerates the evaluation process. The proposed system not only helps organizations to choose the most efficient projects for sustainable development, but also helps them to assess the reliability of the decision-making process, and decrease the uncertainty in final decision caused by uncertain judgment of decision makers. Research limitations/implications: Future studies are suggested to expand this system to evaluate and rank the project proposals. To achieve this goal, the efficiency of the projects in line with organization's goals, should be predicted.Originality/value: This study contributes to the relevant literature by proposing a FMAGDSS model to evaluate projects in promoting organization's goals. The proposed FMAGDSS has ability to choose the most appropriate fuzzy ranking algorithm to solve a given FMADM problem based on the type and the number of attributes and alternatives, considering the least computation and time consumption for ranking alternatives. © Emerald Group Publishing Limited

Sustainable R&D portfolio assessment.

Research and development portfolio management is traditionally technologically and financially dominated, with little or no attention to the sustainable focus, which represents the triple bottom line: not only financial (and technical) issues but also human and environmental values. This is mainly due to the lack of quantified and reliable data on the human aspects of product/service development: usability, ecology, ethics, product experience, perceived quality etc. Even if these data are available, then consistent decision support tools are not ready available. Based on the findings from an industry review, we developed a DEA model that permits to support strategic R&D portfolio management. We underscore the usability of this approach with real life examples from two different industries: consumables and materials manufacturing (polymers).R&D portfolio management; Data envelopment analysis; Sustainable R&D;

Analytic Hierarchy Process for New Product Development

The success of a New Product Development (NPD) process strongly depends on the deep comprehension of market needs and Analytic Hierarchy Process (AHP) has been commonly used to find weights for customers’ preferences. AHP best practices suggest that low‐consistency respondents should be considered untrustworthy; however, in some NPD cases – such as the one presented here – this stake can be extremely big. This paper deals with the usage of AHP methodology to define the weights of customer needs connected to the NPD process of a typical impulse buying good, a snack. The aim of the paper is to analyse in a critical way the opportunity to exclude or include non‐consistent respondents in market analysis, addressing the following question: should a non‐consistent potential customer be excluded from the analysis due to his inconsistency or should he be included because, after all, he is still a potential consumer? The chosen methodological approach focuses on evaluating the compatibility of weight vectors among different subsets of respondents, filtered according to their consistency level. Results surprisingly show that weights do not significantly change when non‐consistent respondents are excluded

A FRAMEWORK FOR STRATEGIC PROJECT ANALYSIS AND PRIORITIZATION

Projects that support the long-term strategic intent and alignment are considered strategic projects. Therefore, these projects must consider their alignment with the organization’s current strategy and focus on the risk, organizational capability, resources availability, political influence, and socio-cultural factors. Quantitative and qualitative methods prioritize the projects; however, they are usually suitable for specific industries. Although prioritization models are used in the private sector, the same in the public sector is not widely seen in the literature. The lack of models in the public sector has happened because of the projects’ social implications, the value perception of different projects in the public sector, and potentially differing value perceptions attached to the types of projects in different decision-making environments in the public sector. The thesis proposes a generic framework to develop a priority list of the available basket of projects and decide on projects for the next undertaking. The focus of the thesis is on public projects. The analysis in the framework considers the critical factors for prioritization obtained from the literature clustered through the agglomerative text clustering technique. In the proposed framework, 13 critical clusters are identified and weighted using the Criteria Importance Through Intercriteria Correlation (CRITIC) method to develop their ranking using the Technique for Order of Preference Similarity Ideal Solution (TOPSIS) method. In addition, the proposed framework uses vector weighting to prioritize projects across industries. The applicability of the framework is demonstrated through Qatar’s real estate and transportation projects. The outcome obtained from the framework is compared with those obtained through the experts using the System Usability Scale (SUS). The comparison shows that the framework provides good predictability of the projects for implementation

The Synthetic Efficiency Measures of the Chinese Commercial Bank System with Bad Loans and Reserve Using Two-Stage DEA Model

Recently, the liquidity risk that exit in the b anks has constantly exposed. There was a panic when people heard the massage of money shortage and default. The bank management need to strengthen the safely and liquidity of bank as the same time to pursuit of profit maximization. According to above discover, in this article, efforts are made to analyze the synthetic efficiency of commercial banks combining the safety, liquidity, profitability of commercial banks. In this study, we utilize extend the two-stage centralized and non-cooperative DEA approach to disaggregate, evaluate and test the 16 major Chinese commercial banks in 2012 with the consideration of undesirable/bad output and reserve. The main findings of this study are as follows: i) The non-cooperative model may overestimate the efficiency of ignore the relationship between the traditional stage and financial innovation stage or disagree with the real bank operation. ii) Bad loans has significant negative effect on efficiency indicating that the large and more bad loans lead bank to lower efficiency. iii) The state-owned bank achieved relative lower efficient, it implies that the state-owned commercial banks are necessary to gradually complete their joint-equity reform. Key words: Two-stage DEA model; Game theory; Tobit model; Reserve; Bad loans; Synthetic efficienc

Emergy Analysis and Sustainability Efficiency Analysis of Different Crop-Based Biodiesel in Life Cycle Perspective

Biodiesel as a promising alternative energy resource has been a hot spot in chemical engineering nowadays, but there is also an argument about the sustainability of biodiesel. In order to analyze the sustainability of biodiesel production systems and select the most sustainable scenario, various kinds of crop-based biodiesel including soybean-, rapeseed-, sunflower-, jatropha-and palmbased biodiesel production options are studied by emergy analysis; soybean-based scenario is recognized as the most sustainable scenario that should be chosen for further study in China. DEA method is used to evaluate the sustainability efficiencies of these options, and the biodiesel production systems based on soybean, sunflower, and palm are considered as DEA efficient, whereas rapeseed-based and jatropha-based scenarios are needed to be improved, and the improved methods have also been specified

Occupational Health and Safety (OHS) Issues in Social Marketing

Social marketing has been contributing historically for a better application of public policy, health and safety, environment, education and human rights. Specifically, four major areas that social marketing efforts have focused over the years are health promotion,injurprevention,environmental protection, and community mobilization. Social marketing, at an industrial organization, emphasizes exchange of ideas between the target audience (i.e. the employees) and the marketer (i.e. the employer). This exchange requires that the employees be persuaded to give up the unsafe behaviors that they are accustomed to, to gain an enhanced level of safety with a greater likelihood of preventing injuries in the workplace. In an organizational context, the internal users are treated as customers and marketing inside the organization is an essential part of delivering value to the organization, and ultimately to the end customer. Therefore, effective management strategies are sought to develop the concept of internal marketing with a view to satisfy the employees and in turn, motivate them to do good work and produce a better product or service. The success of any business enterprise largely depends on its manpower with regard to their professional skill level, positive attitude, job satisfaction, and involvement in quality improvement activities. The important aspect of corporate social responsibility (CSR) is the concern for safety and sound health of the workforce, so that employees feel secured and motivated. The concern becomes manifold when the workforce is exposed to menial tasks and occupational risk situations. To make a safe and conducive environment, an organization must build a solid foundation with a clear vision of the future and specific means by which it will achieve the safety mission of the organization. Safety, health and environment systems needs a continual and systematically managed efforts in order to achieve sustainable growth. Presently, many industries are focusing attention on occupational health and safety (OHS) that may help to achieve competitive advantage. This research is concerned with the study of OHS issues in the context of injury prevention social marketing. A detailed study on workplace environment and safety climate makes the implementation of various social marketing principles easier. This may also be useful for the purpose of policy formulation on improving OHS in Indian industries. Three industrial sectors such as construction (Type 1), refractory (Type 2) and steel (Type 3) are considered in this study. These industries are generally viewed as hazardous due to usage of heavy equipment, unsafe and primitive tools, injurious materials and dust produced during operation. The study covers such organizations where size in manpower and investment varies, both organized and unorganized workforce exists, both public and private enterprises exist, and the level of sophistication of tools, methods, and work environment in terms of safety is poor. A study on risk perceptions and understanding of OHS has been conducted in three industrial sectors. Thirty four items are included in the questionnaire through review of related literature and discussion with a focus group. The items are framed to suit the local work practices and culture covering various aspects of OHS. Two hundred eighty eight (or 288) useful responses were tested to examine the validity and reliability of the scale to ensure a quantitative and statistically provenidentification of the responses. The test for quantitative variables was conducted by factor analysis on responses using the principal component method followed by varimax rotation to ensure that the variables are important and suitable for the model using SPSS 16.0. Finally, identified factors were again analyzed using discriminant analysis to highlight statistical difference among practices existing in three sectors. The pattern of influence of input parameters on outputs such as injury level and material damage is difficult to establish, possibly due to existence of some nonlinear relationship among them. Therefore, an artificial neural network (ANN) is adopted to carry out sensitivity analysis and important deficient items have been identified. A comparative evaluation on deficient items among three major types of Indian industries has been made. Quality function deployment (QFD) has been used to develop the system design requirements considering the deficient safety items as voice of customers. The interrelation among the system design requirements is represented in a digraph using Interpretive Structural Modelling (ISM) approach. A predictive methodology for forecasting various types of injuries has been proposed using fuzzy inference system. As fuzzy inference system can be used with little mathematical knowledge and needs only expert knowledge, it can be easily implemented in the field to predict injury types. Further, fuzzy inference system can deal effectively in imprecise and uncertain situations. In order to transfer best practices among various organizations, a benchmarking study has been carried out using data envelopment analysis (DEA). The study finally provides some useful guidelines for the managers for improving safety performance in selected Indian industrial settings