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The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore the enablers for the implementation of electronic traceability in agri-food supply chain in India. In several agri-food supply chains, the lack of any form of traceability or the presence of paper-based traceability impacts the trade of the concerned food product. Electronic traceability (e-traceability) will assist agri-food firms in improving their performance, minimize food fraud activities, ensure efficient recall of the products and contribute in overall agri-food supply chain management. With the help of literature review and expert opinions, enablers of e-traceability are modelled and analyzed using Fuzzy ISM and FUZZY MICMAC. The combination of both these techniques helps in identifying the essential drivers in the implementation of e-traceability in agri-food supply chains. The proposed approach found that that electronic form of traceability is better than paper-based traceability in agri-food supply chains. The significant drivers in e-traceability implementation, particularly in agri-food supply chain are appropriate technology for e-traceability, competitive advantage, coordination and transparency and management support. The identified enablers would guide the managers or decision-makers in the adoption of e-traceability in their existing supply chains in the agri-food sector. [ABSTRACT FROM AUTHOR]

Six Sigma is a widely practiced, systematic, and structured methodology embedded with statistical methods and managerial philosophies for quality improvement by large firms along with small firms in the industrialized economies. Application of Six Sigma in Micro Small Medium Enterprises (MSMEs) in developing economies is limited due to several barriers these firms face. In academic literature, there is unavailability of sufficient evidence of successful implementation of the practice in these firms to encourage and provide a roadmap for its implementation. Thus, there is a need for studies that presents frameworks with illustrative case studies demonstrating the implementation of Six Sigma in MSMEs. In the present times, MSME firms in developing economies are facing competition from world leaders, and thus it has become essential for them to focus on the quality of their manufacturing products and at the same time being productive and efficient. Moreover, in the literature, it is observed that the potential of operations research (OR) methods is not sufficiently explored in Six Sigma studies. OR-based methods and tools should be integrated into Six Sigma implementation frameworks as these methods expand the application portfolio and tackle some of the barriers that limit the application of Six Sigma in small-scale firms. In this paper, a Six Sigma implementation framework for an MSME organization is developed incorporating Multi Criteria Decision Making (MCDM) tool Decision-Making Trial and Evaluation Laboratory, apart from other conventional tools like cause and effect analysis, benefit effort analysis, Pareto analysis, control charts, etc. The proposed framework is illustrated in detail with the help of a real-life case study of an MSME in India. The results obtained after the implementation of this framework signify that the incorporation of MCDM in Six Sigma lead to a significant improvement in the sigma level of the firm despite of unavailability of sufficient resources. [ABSTRACT FROM AUTHOR]

This paper provides the humanitarian community with an automated tool that can detect a disaster using tweets posted on Twitter, alongside a portal to identify local and regional Non-Governmental Organisations (NGOs) that are best-positioned to provide support to people adversely affected by a disaster. The proposed disaster detection tool uses a linear Support Vector Classifier (SVC) to detect man-made and natural disasters, and a density-based spatial clustering of applications with noise (DBSCAN) algorithm to accurately estimate a disaster's geographic location. This paper provides two original contributions. The first is combining the automated disaster detection tool with the prototype portal for NGO identification. This unique combination could help reduce the time taken to raise awareness of the disaster detected, improve the coordination of aid, increase the amount of aid delivered as a percentage of initial donations and improve aid effectiveness. The second contribution is a general framework that categorises the different approaches that can be adopted for disaster detection. Furthermore, this paper uses responses obtained from an on-the-ground survey with NGOs in the disaster-hit region of Uttar Pradesh, India, to provide actionable insights into how the portal can be developed further. [ABSTRACT FROM AUTHOR]

This paper aims to study the determinants of the Lean Service System (LSS) on the Operational Performance (OP) of India's mail service in the National Sorting Hub (NSH), Mangaluru, Karnataka, the southern part of India. Measuring the OP in mail service is a big challenge in the postal service industry. Hence, we have conducted a survey, and 150 usable data has measured the impact of Lean Service Practices (LSP), Lean Workplace Environment Practices (LWEP), and Lean Social Practices (LSoP) on the OP. The results are analyzed from the partial least square based structural equation modelling (PLS-SEM) with the support of R programming. The analysis shows that there is positive and significant impact of LSP (β = 0.380, p <.05), followed by LWEP (β = 0.281, p <.05), and LSoP (β = 0.266, p <.05) on OP. The practical effect of the findings of LSS are effectively implemented for enhancing the OP of the business. This research addresses the appropriate empirical model to test LSS in India's postal service industry, which is scant in the existing literature. Moreover, this study helps India Post to review its policy so as to sustain the effectiveness of Lean Service (LS) implementation. [ABSTRACT FROM AUTHOR]

Farmers submit claims to insurance providers when affected by sowing/planting risk, standing crop risk, post-harvest risk, and localized calamities risk. Decision making for settlement of claims submitted by farmers has been observed to comprise of type-I and type-II errors. The existence of these errors reduces confidence on agri-insurance providers and government in general as it fails to serve the needy farmers (type-I error) and sometimes serve the ineligible farmers (type-II error). The gaps in currently used underlying data, methods and timelines including anomalies in locational data used in crop sampling, inclusion of invalid data points in computation, estimation of crop yield, and determination of the total sown area create barriers in executing the indemnity payments for small and marginal farmers in India. In this paper, we present a satellite big data analytics based case study in a region in India and explain how the anomalies in the legacy processes were addressed to minimize type-I and type-II errors and thereby make ethical decisions while approving farmer claims. Our study demonstrates what big data analytics can offer to increase the ethicality of the decisions and the confidence at which the decision is made, especially when the beneficiaries of the decision are poor and powerless. [ABSTRACT FROM AUTHOR]