Best-worst multi-criteria decision-making method

In this paper, a new method, called best-worst method (BWM) is proposed to solve multi-criteria decision-making (MCDM) problems. In an MCDM problem, a number of alternatives are evaluated with respect to a number of criteria in order to select the best alternative(s). According to BWM, the best (e.g. most desirable, most important) and the worst (e.g. least desirable, least important) criteria are identified first by the decision-maker. Pairwise comparisons are then conducted between each of these two criteria (best and worst) and the other criteria. A maximin problem is then formulated and solved to determine the weights of different criteria. The weights of the alternatives with respect to different criteria are obtained using the same process. The final scores of the alternatives are derived by aggregating the weights from different sets of criteria and alternatives, based on which the best alternative is selected. A consistency ratio is proposed for the BWM to check the reliability of the comparisons. To illustrate the proposed method and evaluate its performance, we used some numerical examples and a real-word decision-making problem (mobile phone selection). For the purpose of comparison, we chose AHP (analytic hierarchy process), which is also a pairwise comparison-based method. Statistical results show that BWM performs significantly better than AHP with respect to the consistency ratio, and the other evaluation criteria: minimum violation, total deviation, and conformity. The salient features of the proposed method, compared to the existing MCDM methods, are: (1) it requires less comparison data; (2) it leads to more consistent comparisons, which means that it produces more reliable results.     

Data envelopment analysis: Prior to choosing a model

In this paper, we address several issues related to the use of data envelopment analysis (DEA). These issues include model orientation, input and output selection/definition, the use of mixed and raw data, and the number of inputs and outputs to use versus the number of decision making units (DMUs). We believe that within the DEA community, researchers, practitioners, and reviewers may have concerns and, in many cases, incorrect views about these issues. Some of the concerns stem from what is perceived as being the purpose of the DEA exercise. While the DEA frontier can rightly be viewed as a production frontier, it must be remembered that ultimately DEA is a method for performance evaluation and benchmarking against best-practice. DEA can be viewed as a tool for multiple-criteria evaluation problems where DMUs are alternatives and each DMU is represented by its performance in multiple criteria which are coined/classified as DEA inputs and outputs. The purpose of this paper is to offer some clarification and direction on these matters.     

Motivation for ISO 14000 certification: development of a predictive model

Environmental issues have become critical concerns of businesses in recent years. The Singapore Environment Ministry is urging organizations to consider adopting the ISO 14000 Environmental Management Standards. The main purpose of this study was to investigate and identify a number of variables which would be able to predict the motivation of organizations in adopting the ISO 14000 Standards. Through extensive literature search eight possible predictive variables/factors (cost savings, top management concern, employee welfare, meeting environmental regulations, meeting customer expectations, concern over trade barriers, following head office environmental practices, and gaining competitive advantages) were identified. In total, 300 pre-tested survey questionnaires were mailed out to companies from the Electronic and Chemical industries in Singapore. A response rate of about 20% was obtained. The survey instrument was tested for reliability and validity. Using stepwise discriminant analysis, a predictive discriminant function was developed. Only four out of the originally identified eight variables were included in the model. Possible benefits of such a model for Singapore and other industrializing countries are highlighted.     

Planning training programmes to support business initiatives: a model for business and training partnership

The ‘Model for Business and Training Partnership’ (MBTP) draws upon insights of HRD and adult education programme-planning scholars, integrating training and development processes with a typical business-planning model. It is a practical, client-focused programme-planning model designed to promote strategic alliances between the HRD practitioner and business manager at all levels and development phases. The model may be utilized on a cross-functional group project for implementation of a customer relationship management (CRM) system or other large system change initiative within a large organization. This model has not been tested empirically.     

DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment

Using an additive super-efficiency data envelopment analysis (DEA) model, this paper develops a new assessment index based on two frontiers for predicting corporate failure and success. The proposed approach is applied to a random sample of 1001 firms, which is composed of 50 large US bankrupt firms randomly selected from Altman's bankruptcy database and 901 healthy matching firms. This sample represents the largest firms that went bankrupt over the period 1991–2004 and represents a full spectrum of industries. Our findings demonstrate that the DEA model is relatively weak in predicting corporate failures compared to healthy firm predictions, and the assessment index improves this weakness by giving the decision maker various options to achieve different precision levels of bankrupt, non-bankrupt, and total predictions.     

Barriers in Green Lean implementation: a combined systematic literature review and interpretive structural modelling approach

Green Lean has recently emerged as an alternative strategy for organisations to pursue both operational and sustainability excellence. The interest on this approach has rapidly risen in both academic and industry circles. However, despite this interest, very limited research has focused on its implementation, and no research has investigated the barriers that hinder the success of such activity. This study investigates the Green Lean implementation barriers and their contextual relationships and effects on the integration and deployment of this approach. A Systematic Literature Review (SLR), Interpretative Structural Modelling and fuzzy Matriced’ Impacts Croise’s Multiplication Appliqée a UN Classement (MICMAC) analyses were carried out. Fifteen barriers were extracted from the SLR and then validated in consultation with industry and academic experts. The Interpretive Structural Modelling (ISM) method was used to understand the relationship between the fifteen barriers and to develop a hierarchical model of these. The different barriers were classified into ‘linkage’ and ‘dependent’ barriers by using MICMAC analysis. The results suggested that all the identified barriers play an important role, and hence can equally act as a significant hurdle to the implementation of Green Lean projects. This study can help managers and policy-makers in better understanding these barriers. Thus, they can be assisted in managing and prioritising barriers towards the successful implementation of Green Lean initiatives for better financial and environmental performance.