An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

To stay competitive in a business environment, continuous performance evaluation based on the triple bottom line standard of sustainability is necessary. There is a gap in addressing the computational expense caused by increased decision units due to increasing the performance evaluation indices to more accuracy in the evaluation. We successfully addressed these two gaps through (1) using principal component analysis (PCA) to cut the number of evaluation indices, and (2) since PCA itself has the problem of merely using the data distribution without considering the domain-related knowledge, we utilized Analytic Hierarchy Process (AHP) to rank the indices through the expert’s domain-related knowledge. We propose an integrated approach for sustainability performance assessment in qualitative and quantitative perspectives. Fourteen insurance companies were evaluated using eight economic, three environmental, and four social indices. The indices were ranked by expert judgment though an analytical hierarchy process as subjective weighting, and then principal component analysis as objective weighting was used to reduce the number of indices. The obtained principal components were then used as variables in the data envelopment analysis model. So, subjective and objective evaluations were integrated. Finally, for validating the results, Spearman and Kendall’s Tau correlation tests were used. The results show that Dana, Razi, and Dey had the best sustainability performance.This article belongs to the Special Issue Sustainability Assessmen

An Investigation into Factors Affecting the Chilled Food Industry

With the advent of Industry 4.0, many new approaches towards process monitoring, benchmarking and traceability are becoming available, and these techniques have the potential to radically transform the agri-food sector. In particular, the chilled food supply chain (CFSC) contains a number of unique challenges by virtue of it being thought of as a temperature controlled supply chain. Therefore, once the key issues affecting the CFSC have been identified, algorithms can be proposed, which would allow realistic thresholds to be established for managing these problems on the micro, meso and macro scales. Hence, a study is required into factors affecting the CFSC within the scope of Industry 4.0. The study itself has been broken down into four main topics: identifying the key issues within the CFSC; implementing a philosophy of continuous improvement within the CFSC; identifying uncertainty within the CFSC; improving and measuring the performance of the supply chain. However, as a consequence of this study two further topics were added: a discussion of some of the issues surrounding information sharing between retailers and suppliers; some of the wider issues affecting food losses and wastage (FLW) on the micro, meso and macro scales. A hybrid algorithm is developed, which incorporates the analytic hierarchical process (AHP) for qualitative issues and data envelopment analysis (DEA) for quantitative issues. The hybrid algorithm itself is a development of the internal auditing algorithm proposed by Sueyoshi et al (2009), which in turn was developed following corporate scandals such as Tyco, Enron, and WorldCom, which have led to a decline in public trust. However, the advantage of the proposed solution is that all of the key issues within the CFSC identified can be managed from a single computer terminal, whilst the risk of food contamination such as the 2013 horsemeat scandal can be avoided via improved traceability

Benchmarking with network DEA in a fuzzy environment

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Benchmarking is a powerful and thriving tool to enhance the performance and profitabilities of organizations in business engineering. Though performance benchmarking has practically and theoretically developed in distinct fields such as banking, education, health and so on, supply chain benchmarking across multiple echelons that includes certain characteristics such as intermediate measure differs from other fields. In spite of incremental benchmarking activities in practice, there is the dearth of a unified and effective guideline for benchmarking in organizations. Amongst the benchmarking tools, data envelopment analysis (DEA) as a non-parametric technique has been widely used to measure the relative efficiency of firms. However, the conventional DEA models that are bearing out precise input and output data turn out to be incapable of dealing with uncertainty, particularly when the gathered data encompasses natural language expressions and human judgements. In this paper, we present an imprecise network benchmarking for the purpose of reflecting the human judgments with the fuzzy values rather than precise numbers. In doing so, we propose the fuzzy network DEA models to compute the overall system scale and technical efficiency of those organizations whose internal structure is known. A classification scheme is presented based upon their fuzzy efficiencies with the aim of classifying the organizations. We finally provide a case study of the airport and travel sector to elucidate the details of the proposed method in this study

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

Sustainable Smart Cities and Smart Villages Research

ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [There is ever more research on smart cities and new interdisciplinary approaches proposed on the study of smart cities. At the same time, problems pertinent to communities inhabiting rural areas are being addressed, as part of discussions in contigious fields of research, be it environmental studies, sociology, or agriculture. Even if rural areas and countryside communities have previously been a subject of concern for robust policy frameworks, such as the European Union’s Cohesion Policy and Common Agricultural Policy Arguably, the concept of ‘the village’ has been largely absent in the debate. As a result, when advances in sophisticated information and communication technology (ICT) led to the emergence of a rich body of research on smart cities, the application and usability of ICT in the context of a village has remained underdiscussed in the literature. Against this backdrop, this volume delivers on four objectives. It delineates the conceptual boundaries of the concept of ‘smart village’. It highlights in which ways ‘smart village’ is distinct from ‘smart city’. It examines in which ways smart cities research can enrich smart villages research. It sheds light on the smart village research agenda as it unfolds in European and global contexts.

Classification of the Existing Knowledge Base of OR/MS Research and Practice (1990-2019) using a Proposed Classification Scheme

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordOperations Research/Management Science (OR/MS) has traditionally been defined as the discipline that applies advanced analytical methods to help make better and more informed decisions. The purpose of this paper is to present an analysis of the existing knowledge base of OR/MS research and practice using a proposed keywords-based approach. A conceptual structure is necessary in order to place in context the findings of our keyword analysis. Towards this we first present a classification scheme that relies on keywords that appeared in articles published in important OR/MS journals from 1990-2019 (over 82,000 articles). Our classification scheme applies a methodological approach towards keyword selection and its systematic classification, wherein approximately 1300 most frequently used keywords (in terms of cumulative percentage, these keywords and their derivations account for more than 45% of the approx. 290,000 keyword occurrences used by the authors to represent the content of their articles) were selected and organised in a classification scheme with seven top-level categories and multiple levels of sub-categories. The scheme identified the most commonly used keywords relating to OR/MS problems, modeling techniques and applications. Next, we use this proposed scheme to present an analysis of the last 30 years, in three distinct time periods, to show the changes in OR/MS literature. The contribution of the paper is thus twofold, (a) the development of a proposed discipline-based classification of keywords (like the ACM Computer Classification System and the AMS Mathematics Subject Classification), and (b) an analysis of OR/MS research and practice using the proposed classification

Key performance indicators for sustainable manufacturing evaluation in automotive companies

The automotive industry is regarded as one of the most important and strategic industry in manufacturing sector. It is the largest manufacturing enterprise in the world and one of the most resource intensive industries of all major industrial system. However, its products and processes are a significant source of environmental impact. Thus, there is a need to evaluate sustainable manufacturing performance in this industry. This paper proposes a set of initial key performance indicators (KPIs) for sustainable manufacturing evaluation believed to be appropriate to automotive companies, consisting of three factors divided into nine dimensions and a total of 41 sub-dimensions. A survey will be conducted to confirm the adaptability of the initial KPIs with the industry practices. Future research will focus on developing an evaluation tool to assess sustainable manufacturing performance in automotive companies

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises