Welding distortion is a critical issue as it leads to severe deterioration of structural integrity of welded work piece and dimensional precision. This study aims at studying the effects of shielded metal arc welding (SMAW) parameters on the evolution of mechanical properties, including tensile strength, impact toughness, and hardness, along with angular distortion on a welded joint from SA 516 grade 70. Such parameters are analyzed and optimized by employing the Taguchi method and Grey relational analysis. SA 516 grade 70 is commercially used for fabrication of storage tanks, boilers and pressure vessels. SMAW is investigated with three levels of root gap, groove angle, electrode diameter, and pre-heat temperature, which were varied on a butt joint in flat (1 G) position to determine their effects on response variables at room temperature. Nine experiments were designed using a Taguchi L9 orthogonal array, welded according to American Society of Mechanical Engineers (ASME) section IX, and samples were prepared and tested as per ASTM A 370. The Taguchi method and Grey relational analysis were employed to observe the most significant parameters and optimal levels that synergically yield improved responses. Results are validated by conducting confirmatory experiments that show good agreement with optimum results.

The agricultural industry is highly underdeveloped and requires transformation in technology for food safety and reliability. A digital world is relying on blockchain technology for the successful implementation of sustainable e-agricultural supply chain management (e-Agri-SCM). In current advancements of blockchain in digital marketing, product website design (web design) is essential to streamline the requirements of the customer and the expectations of supply chain partners. The current research has incorporated the blockchain effect by web design elements into the agricultural supply chain management (Agri-SCM) study. In addition, partners in the digital marketing supply chain (DM-SCM) are also facing issues to identify significant web design elements-based blockchain technology to gain maximum profit. Therefore, a cooperative (Co-op) sustainable e-agricultural SCM model is developed in this study by considering the web design index and variable demand to decide shipments, selling price, cycle time, and advertisement cost for agriculture products. The uncertainties in the model due to intangible web design elements and basic costs are dealt with by the application of the fuzzy system whereas carbon emission is also considered for providing cleaner production. A real-time application of the proposed model is done by undertaking five different cases based on mutual share, demand curve, and advertisement budget among participants. The sensitivity analysis is also performed to identify important factors of the total profit. Findings of this work include significant web design elements (WDEs) i.e., web graphics, search engine optimization, cyber-security, fast loading, and navigation, as essentials for digital marketing to convince customers towards the product in a global SCM. The numerical results and managerial insights are advantageous for managers to get maximum profit by cooperative and digital marketing strategies to attain e-Agri-SCM.

The management of a controllable production in the manufacturing system is essential to achieve viable advantages, particularly during emergency conditions. Disasters, either man-made or natural, affect production and supply chains negatively with perilous effects. On the other hand, flexibility and resilience to manage the perpetuated risks in a manufacturing system are vital for achieving a controllable production rate. Still, these performances are strongly dependent on the multi-criteria decision making in the working environment with the policies launched during the crisis. Undoubtedly, health stability in a society generates ripple effects in the supply chain due to high demand fluctuation, likewise due to the Coronavirus disease-2019 (COVID-19) pandemic. Incorporation of dependent demand factors to manage the risk from uncertainty during this pandemic has been a challenge to achieve a viable profit for the supply chain partners. A non-linear supply chain management model is developed with a controllable production rate to provide an economic benefit to the manufacturing firm in terms of the optimized total cost of production and to deal with the different situations under variable demand. The costs in the model are set as fuzzy to cope up with the uncertain conditions created by lasting pandemic. A numerical experiment is performed by utilizing the data set of the multi-stage manufacturing firm. The optimal results provide support for the industrial managers based on the proactive plan by the optimal utilization of the resources and controllable production rate to cope with the emergencies in a pandemic.

In the realm of supply chain management, the impact of Artificial Intelligence (AI) tools on optimizing commodity distribution is undeniable. This study presents the transformative potential of AI and computer vision in the field of commodity supply chain management. The capability of AI to reduce yield loss and enhance supply chain efficiency is a growing trend and vision-based commodity defect monitoring can be useful in this regard. We explored the employment of real-time computer vision techniques in supply chain flaw management, which include Detection Transformer (DETR), a type of Vision Transformer (ViT), and compared its performance with the You Only Look Once (YOLO) and other AI models. Computational feasibility is assessed, encompassing various computer vision and AI models, by using a dataset comprising images of commodity items used to substantiate our findings. The obtained results have shown the improved performance of DETR with a detection and classification accuracy of 96%, directly correlating with improved supply chain management. On the other hand, the higher computational burden imposed by DETR makes it less feasible for the higher constrained embedded applications. The practicality of AI algorithms for real-time defect identification reveals promising prospects for integration into supply chain systems. This research underscores AI’s potential to revolutionize commodity supply chain management, extending its benefits to various commodity distribution networks.