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This study develops a plastic cycle toward circular economy practice in Vietnam. First, we analyze inter-relationships between economic sectors and environmental issues concerning plastic waste in 2018. The research method integrates interdisciplinary balance with life cycle inventory, in which input–output (IO) table is both an econometric tool and original database to determine plastic IO between industries. As a result, over 60% of plastics after use was recycled for the production process (called recycled plastics) and nearly 40% of plastics after-use left the process (called disposed plastics). Within the recycled plastics, there was 10–15% of informal recycling collection from trade villages; within the disposed plastics, there was 13–18% unable to be collected and uncontrollably disposed to the environment. Then, we construct the plastic cycle, in which all the imported/domestic flows, single/multiple uses, and recycle/disposal flows are represented in proportional dimensions. This overall yet quantitative picture is an important data-driven basis for proposing plastic waste management solutions toward circular economy practice. As analyzed, the most challenge for waste management in Vietnam is to control single-use products (occupied 15.96% of total plastics) and indiscriminate waste in the environment (occupied 20.36% of total plastics). The case study for polyethylene terephthalate shows the need for expanding producer’s responsibilities to improve plastic recovery efficiency.

The purpose of the paper is to extend the social media analytics frameworks reported in the existing literature to create value from the data captured from heterogenous social media and IoT sources in a global supply-chain. The paper will aid the practitioners to understand the importance of focussed analysis and the key requirements for this analysis by offering a guide to organisational needs. The framework and requirements proposed in the paper will aid managers to capture value from the data (i.e. gain key insights) and augment human decision-making.

Electric distribution companies have a significant role for both households and industries. Benchmarking of the electric distribution companies in the energy sector has become a subject that is studied widely nowadays due to the effect of privatization policies for developing countries. Since there are multiple production stages regarding the generation and supply procedures of electric power, Network DEA technique is used. Directional Distance Function is also integrated into Network DEA technique. Electric distribution companies are organizations that are aiming at maximizing profit while minimizing the expenses. The main problem is how the profit idea can be integrated into the evaluation process. The aim of the proposed model is to evaluate profit efficiency of electric distribution companies while taking into account expansion cost for additional energy supply. This two stage approach is applied to Turkish electric distribution companies. Results are presented based on radial and profit efficiency measures. The proposed model is demonstrates realistic results by considering the expenses and incomes of distribution companies.

While risk management in supply chains is continuing to receive significant attention in the extant literature, research on risk aspects relating to strategic sourcing across various industry sectors from a multinational perspective is scant. Based on 20 case companies in the construction and electronics sectors in Germany and the United Kingdom, this paper undertakes a comparative evaluation of strategic sourcing risk management practices. We find that risk management has not been adopted fully across industries and countries from a strategic sourcing perspective and that differences exist with respect to risk factors considered, methods adopted to analyze risk, and impact on business performance. Our research presents several significant and useful insights for managing risks in strategic sourcing.

The main aim of this paper is to facilitate small and medium sized enterprises (SMEs) to adopt environmental management (EM) and corporate social responsibility (CSR) practices. The study reveals SMEs‟ motivation, pressure, targets and methods for EM and CSR practices. Additionally, the paper investigates how these variables relate to employee number, turnover and geographical locations. The outcomes of the research will add value to SMEs decision-making processes in both strategic and policy levels (e.g. supplier selection) and policymakers‟ initiatives to make SMEs environment and socially friendly. Although there are studies on EM and CSR practices of SMEs, they mainly focus on impact of EM and CSR practices on business performance, and SMEs‟ motivation for adopting EM and CSR practices in specific country. Studies that reveal SMEs‟ motivation, pressure, targets and methods for EM and CSR practices and their relationship with their characteristics (e.g. size, turn over, and geographical location) are scant. This research bridges this gap. Our data originates from 223 carefully selected representative SMEs in the West Midlands, UK (105) and Kolkata, India (118) covering manufacturing and process industries. The relevant data was collected using questionnaires and analysed using Analysis of Variance (ANOVA) and Multivariate Analysis of Variance (MANOVA) methods. The results reveal that perceptions of SMEs‟ motivation, pressure, targets and methods of EM and CSR practices vary considerably with respect to size, turn over and geographical location. The findings are significant to policymakers, client organizations and individual SME for improving EM and CSR practices.

Incineration plants produce heat and power from waste, reduce waste disposal to landfills, and discharge harmful emissions and bottom ash. The objective of the incineration plant is to maximize desirable outputs (heat and power) and minimize undesirable outputs (emissions and bottom ash). Therefore, studying the overall impact of incineration plants in a region so as to maximize the benefits and minimize the environmental impact is significant. Majority of prior works focus on plant specific decision making issues including performance analysis. This study proposes a hybrid Data Envelopment Analysis (DEA), Goal Programming (GP) and Mixed Integer Linear Programming (MILP) model to assess the performance of incineration plants, in a specific region, to enhance overall power production, consumption of waste and reduction of emissions. This model not only helps the plant operators to evaluate the effectiveness of incineration but also facilitates the policy makers to plan for overall waste management of the region through decision-making on adding and closing plants on the basis of their efficiency. Majority of prior studies on incineration plants emphasize on how to improve their performance on heat and power production and neglect the waste management aspects. Additionally, optimizing benefits and minimizing negative outputs through fixing targets in order to make decision on shutting down the suboptimal plants has not been modeled in prior research. This research combines both the aspects and addresses the overall performance enhancement of incineration plants within a region from both policy makers and plant operators’ perspectives. The proposed combined DEA, GP and MILP model enables to optimize incineration plants performance within a region by deriving efficiency of each plant and identifying plants to close down on the basis of their performance. The proposed model has been applied to a group of 22 incineration plants in the UK using secondary data in order to d