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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.

Under conditions of consumer panic buying, satisfying demand with the available products is a complex problem. In reality, most retailers accept alternative products during panic situations. This study considers the case of firm-driven substitution of products (differing in weight) based on retailer preferences over two time-periods. In the proposed model, panic behavior emerged in the first time-period and interruption in supply occurred in the second time-period. Under this model, retail stores were segmented into high index (valuable) and low index (less valuable) customers. Before meeting the demand of low-index customers, wholesalers attempt to satiate high-index customer's panic buying behavior. To generate maximum wholesaler's total profit, we find the optimal amount of substitution quantity, quantity to order, and leftover units. The model is investigated for both without and with substitution. To gain managerial insights, we also examined the influence of both the degree of interruption in supply and substitution costs on profits and decisions. The results can assist business managers to improve the decision-making process.

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