http://nopr.niscpr.res.in/handle/123456789/66562 2026-10-09T19:44:38Z http://nopr.niscpr.res.in/handle/123456789/66571 Title: A State-of-the-art Review and Meta-analysis of Quantitative Sustainable Supply Chain Models: Research Implications and Future Perspectives Authors: Paul, Arpan; Sankar Mahapatra, Siba Abstract: In the era of industrialization, implementing successful Sustainable Supply Chain Management (SSCM) in industries has become essential due to limited natural resources and increased pressure from the government and customers. This study reviewed fifty-one peer-reviewed journal articles on sustainability implementation in Indian manufacturing industries following a data-driven approach using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The paper considered quantitative models of the forward supply chain to determine and analyze the critical sustainability implementation drivers and barriers. The first part of the paper elaborates on a state-of-the-art review of the selected topic from 2011 to 2022. Subsequently, a metadata analysis is conducted based on various aspects such as publication year, alignment with Sustainable Development Goals (SDGs), methodological approach, industrial context, and research focus. Thirdly, a step-by-step guideline has been provided to establish a sustainability framework with a practical case implementation. Finally, several future directions have been suggested for improving and extending existing research through methodological advancements and broader industrial applications. The literature study revealed a growing trend in publications, with increasing interest in organizational factors, a dominance of Multi-Criteria Decision-Making (MCDM) tools as research methods, and a comprehensive consideration of all three dimensions of sustainability: economic, social, and environmental. Page(s): 931-944 2025-09-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/66570 Title: A Computational Intelligence Framework for Industry 4.0-based Intelligent Motion Control using AI-Integrated PLCs Authors: Lazarus Mayaluri, Zefree; Kumar Naik, Asit; Samantaray, Rajat; Rath, Adyasha; Panda, Ganapati Abstract: Industry 4.0 has revolutionized industrial automation by introducing smart, interconnected, and autonomous systems. However, traditional PLC-based motion control systems suffer from rigid programming, lack of adaptability, and the absence of predictive maintenance capabilities. This paper proposes a computational intelligence-based framework that integrates AI, IoT, and PLCs for intelligent motion control. The system leverages Neural Networks for self-learning control, Fuzzy Logic for real-time adaptive decision-making, and Machine Learning for predictive maintenance. A cloud-based MySQL database supports real-time monitoring and data-driven decision-making. Experimental validation demonstrates that the AI-enhanced PLC system achieves 30% faster response times, reduces motion errors by 40%, and improves predictive maintenance accuracy to 95%. These findings confirm that the proposed AI-based control framework significantly enhances industrial motion control, ensuring efficiency, scalability, and Industry 4.0 readiness. Page(s): 945-956 2025-09-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/66569 Title: Intelligent Approach for Analysing and Forecasting Land Changes using Multispectral Images Authors: Femi Sherley S, Eliza; Elizabeth Darmanayagam, Shiloah; Retmin Raj Cyril Raj, Sunil Abstract: Monitoring the changes in land over time is useful to understand the impacts of human activities in the environment. Urban change detection using satellite images plays a major role in research on global environmental change identification and management of natural resources. With availability of multi-temporal satellite data, the proposed work aims to analyse the change on land in Indian regions. This work groups the regions of land with a Firefly Algorithm based clustering approach, which optimizes the cluster center identification process when compared to a conventional clustering approach, as a process of analysing changes with available multi-temporal data. Based on the vegetation, water, and built-up index values, clusters are labelled into appropriate land regions. The extent of change is then assessed using spatio-temporal information available and this helps to identify the pattern of change. Moreover, a deep learning technique, namely Regression-Long Short Term Memory (LSTM) network, is used to forecast future changes, which may be useful in managing urban resources. Understanding how the land has changed in the past, present, and predicted changes in the future may help in making right decisions. Experiments are carried out on Landsat 8 data from the Indian region to identify changes in land using unsupervised learning techniques. A Silhouette index of 0.88 was obtained on average by K-means clustering with three clusters, while 0.93 was obtained using Firefly integrated K-means clustering. Future land image forecasts generated by LSTM are compared to the actual image using the Structural Similarity Index (SSIM) and Root Mean Square Error (RMSE), resulting in SSIM of 0.85 and RMSE of 0.41 when the forecasted spectral band images are stacked into a multispectral image, indicating the effectiveness of the forecasting approach. Page(s): 957-970 2025-09-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/66568 Title: Agrotourism Business Innovation: By Modelling and Ranking Success Factors Using ISM-MICMAC Analysis Authors: Joshi, Mangesh; Khandekar, Priya Abstract: The Indian tourism industry has witnessed substantial growth, presenting economic opportunities, especially in rural areas. Agrotourism has emerged as a promising strategy to diversify agriculture and enhance income. However, the sector encounters several challenges. This study aims to identify and analyze these barriers to support sustainable agrotourism development. Using a literature review and expert input, twenty key barriers were identified. The research applied Interpretive Structural Modeling (ISM) to understand the relationships among these barriers and MICMAC analysis to categorize them into clusters based on their influence and dependence. This integrated approach offers a comprehensive perspective on the critical success factors for agrotourism ventures. The analysis reveals that government policies and support are the most influential in determining agrotourism success. Other important factors include capital availability, location, season, and transport access. Meanwhile, event space, quality accommodation, varied activities, and market demand emerged as highly dependent factors. This research contributes unique insights by systematically addressing agrotourism barriers in India—an area previously underexplored. The findings can guide entrepreneurs, policymakers, and stakeholders in strategic planning. Emphasizing collaboration between government and local businesses, the study highlights the need for targeted efforts to foster innovation and growth in this emerging sector Page(s): 971-981 2025-09-01T00:00:00Z