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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]

This paper analyses the performance of non-banking finance companies (NBFC) in the Indian context using data envelopment analysis (DEA). The underlying objective of this study is to fill the void in the domain of NBFC, although a lot of research has been done on the banking industry in the context of the application of DEA, but none on NBFCs. The paper takes the panel data of the last 5 years (2014–2018) to calculate super-efficiencies in the first stage and then regresses the same on exogenous factors in stage-2. Descriptive statistics are used to estimate the efficiency by carrying out the calculations using both the traditional models (OTE, PTE and SE) and super-efficiency model. A comparison is made by categorizing NBFC's based on the size of total assets and using non-parametric statistic tests to find whether the efficiency scores are significantly different across different categories. The second stage DEA analysis uses Tobit regression to find the exogenous factors which affect the model significantly. Based on traditional models, the total number of efficient DMUs are 8 out of 43 while there are 15 after considering the super-efficiency algorithm. Malmquist Indices are used to study the productivity indices of NBFCs over the last 5 years, and it gives us a maximum productivity growth of 8.53%. It was noticed that there is a significant difference in the mean efficiency values of different sized NBFC's which can be explained by the lack of standardization in the NBFC domain and the few companies which are listed on the stock market. The managers should not consider ROE as a significant indicator of efficiency and should instead focus on aspects such as ROA and income diversity. Through the Malmquist analysis, the managers can break down the productivity change into technical and efficiency shifts for further investigation. [ABSTRACT FROM AUTHOR]

In today's world, industries are facing massive pressure to integrate sustainability issues for efficient and successful supply chain management (SCM). Hence, worldwide it has become critically important to make economic operational balance satisfying environment protection norms and social welfare perspectives. Consequently, the industries are investigating their SCM structures in association with a third party logistics (3PL) service provider adopting the triple bottom line framework for improving the overall supply chain performance. Therefore, selection of the right 3PL provider for the sustainable alliance is supremely important for broader perspective of greater business value. Thus, the main objective of this research work is the selection of most appropriate 3PL provider for a food manufacturing company (FMC) after systematic evaluation of six different feasible logistic providers serving over a decade in India. Selection of optimal alternative 3PL provider is very complex and challenging because of the qualitative description of service provider performances and the inherent uncertainty due to subjectivity. The concept of interval-valued fuzzy-rough number (IVFRN) offers perfect treatment of such uncertainty. In this paper, we develop a multi criteria decision making (MCDM) model combining the factor relationship (FARE) and multi-attributive border approximation area comparison (MABAC) models based on IVFRN. The proposed model is tested and validated on a case study where the optimal selection of 3PL providers is performed for an Indian FMC. Based on the results obtained in sensitivity analysis, it was shown that the proposed IVFRN based FARE-MABAC model produces stable/consistent solutions. Through the research presented in this paper, it is shown that the new hybrid MCDM method is a useful and reliable tool for rational decision-making. [ABSTRACT FROM AUTHOR]

The global food insecurity, malnourishment and rising world hunger are the major hindrances in accomplishing the zero hunger sustainable development goal by 2030. Due to the continuous increment of wheat production in the past few decades, India received the second rank in the global wheat production after China. However, storage capacity has not been expanded with similar extent. The administrative bodies in India are constructing several capacitated silos in major geographically widespread producing and consuming states to curtail this gap. This paper presents a multi-period single objective mathematical model to support their decision-making process. The model minimizes the silo establishment, transportation, food grain loss, inventory holding, carbon emission, and risk penalty costs. The proposed model is solved using the variant of the particle swarm optimization combined with global, local and near neighbor social structures along with traditional PSO. The solutions obtained through two metaheuristic algorithms are compared with the optimal solutions. The impact of supply, demand and capacity of silos on the model solution is investigated through sensitivity analysis. Finally, some actionable theoretical and managerial implications are discussed after analysing the obtained results. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic ship berth allocation problem for a container handling port, focusing on vessel waiting time at the anchorage due to the unavailability of the berth and quay cranes. A mixed integer linear programming model considering the fuel cost associated with waiting time and operational time of the docked vessel is developed. The hiring of the quay cranes to load/unload the containers from the ship and arrangement of the vessels in different berths is taken into account. Fuel consumed by the vessels while performing their respective port operations is incorporated in the model for addressing the sustainability aspects in berth allocation problem. A chemical reaction optimization algorithm is proposed to solve the problem in a large-scale realistic environment and compared with the results with block-based genetic algorithm, genetic algorithm and particle swarm optimization. The computational experiment illustrates and validates the proposed model on a real case scenario of the port located in India. The case shows that the developed model achieves better utilization of port resources and available berths. [ABSTRACT FROM AUTHOR]

The humanitarian aid delivery problem associated with the early recovery phase of a disaster often incorporates multiple attributes. In this paper, the relative importance of various humanitarian aid attributes was measured using a discrete choice multi-attribute value method. This approach identifies all possible non-dominated pairs explicitly ranked by experts and provides an overall complete ranking of attributes. The performance score of each aid delivery plan was then calculated using the attributes' ranking by solving a corresponding linear programming model. As an application study, the issues pertaining to the early recovery phase of 2017 flood in Assam, India, were analyzed. It was concluded that the 'delivery amount' is the most preferred attribute selected by humanitarian experts. [ABSTRACT FROM AUTHOR]

In this paper, we apply project management concepts and frameworks to the context of disaster resilience and examine how groups can increase the disaster resilience of a community. Based on our literature review and case study methodology, we develop a model that draws upon the relevant literatures in project management, operations management, disaster management, and organizational behaviour; we then compare that model with 12 disaster-related cases supported by four Non-Governmental Organizations (NGOs) in India. Our model measures disaster resilience using both an encompassing measure we refer to as Total Cost to Community (TCC) that captures the interrelatedness of level of recovery (deliverables), speed of recovery (time), and loss minimization (cost) at a community group level, as well as through learning (single domain or alternate domain). The model indicates that the external elements of the disaster management process (scale, goal complexity, immediacy, and stakeholder variance) influence the internal characteristics of disaster project management (information demands and uncertainty), which in turn influence disaster resilience. The level of community group processes (group strength, group continuity, and group capacity) also influences learning, both directly and indirectly, through internal characteristics of project management. In addition, the relationship between the external elements of disaster recovery and the internal characteristics of disaster project management is moderated by resources available. This model provides interesting new avenues for future theory and research, such as creating operations research models to identify the trigger points for groups becoming effective and exploring the quantification of TCC, a new construct developed in this research. Ultimately, this model can provide a roadmap for NGOs and government entities interested in building disaster resilience among micro-enterprises in vulnerable communities. [ABSTRACT FROM AUTHOR]

Coordination among actors in a humanitarian relief supply chain decides whether a relief operation can be or successful or not. In humanitarian supply chains, due to the urgency and importance of the situation combined with scarce resources, actors have to coordinate and trust each other in order to achieve joint goals. This paper investigated empirically the role of swift trust as mediating variable for achieving supply chain coordination. Based on commitment-trust theory we explore enablers of swift-trust and how swift trust translates into coordination through commitment. Based on a path analytic model we test data from the National Disaster Management Authority of India. Our study is the first testing commitment-trust theory in the humanitarian context, highlighting the importance of swift trust and commitment for much thought after coordination. Furthermore, the study shows that information sharing and behavioral uncertainty reduction act as enablers for swift trust. The study findings offer practical guidance and suggest that swift trust is a missing link for the success of humanitarian supply chains. [ABSTRACT FROM AUTHOR]

Conventional Data Envelopment Analysis (DEA) models focus only on initial inputs and final outputs for efficiency evaluation. Thus, these models treat the production process as a ‘black box’, i.e., they do not take into account how exactly inputs are related to outputs. Various models that came later take care of internal processes of DMU. The existing models of internal processes for parallel sub-units consist of three stages: the first stage calculates the relative weights of sub-units, the second stage calculates the efficiencies of sub-units, and in the third stage efficiencies of sub-units are aggregated as the efficiency of DMU. It is observed that when existing models of internal processes are applied to non-homogeneous parallel sub-units, in the first stage, the weight assigned to the maximum efficient sub-unit is one and to other sub-units is zero. This implies that the efficiency of a DMU is equal to the maximum of efficiencies of its sub-units indicating that the efficiency of a DMU is not sensitive to the efficiencies of sub-units other than the sub-unit with maximum efficiency. This paper proposes a single stage DEA approach where the efficiency of a DMU and its sub-units can be measured simultaneously. The advantage of the proposed approach is that the efficiency of a DMU is sensitive to the changes in the efficiency of its sub-units, and weights of sub-units can be assigned a priori by the decision maker. The development of the proposed approach is inspired from the growing interest in evaluating efficiency of higher education system in India. In the proposed application, states are considered as DMUs and universities, colleges and stand-alone institutions are taken as three non-homogeneous parallel sub-units of DMUs. [ABSTRACT FROM AUTHOR]

Owing to the importance of internal structure of decision making units (DMUs) and data uncertainties in real situations, the present paper focuses on multi-component data envelopment analysis (MC-DEA) approach with imprecise data. The undesirable outputs and shared resources are also incorporated in the production process of multi-component DMUs to validate real problems. The interval efficiencies of DMUs and their components in MC-DEA are often challenging with imprecise data. In many practical situations, different set of weights may be resulted into valid efficiency intervals for DMUs but invalid interval efficiencies for their components. Therefore, the present study proposes a new common set of weights methodology, based on interval arithmetic and unified production frontier, to determine unique weights for measuring these interval efficiencies. It is a two-level mathematical programming approach that preserves linearity of DEA and exhibits stronger discrimination power among the DMUs as compared to some existing approaches. Theoretically, the aggregate efficiency interval of each DMU lies between the components' interval efficiencies. Further, the proposed approach is also applied to banks in India for proving its acceptability in practical applications. The performance of each bank is investigated in terms of two components: general business and bancassurance business for the years 2011-2013. The present study emphasized expanding pattern of bancassurance business in current market scenario with more percentage increase as contrasted to general business. [ABSTRACT FROM AUTHOR]