Editors-in-Chief
Prof. Jian Li
School of Economics and Management，Beijing University of Technology,China
Industrial Management Advances
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3029-1674 (Online) Journal Abbreviation:
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ISSN: 3029-1674(Online) Industrial Management Advances (IMA) is a peer-reviewed, open-access journal dedicated to the field of industrial management. The journal invites submissions from researchers and practitioners worldwide, providing a platform for sharing innovative ideas and findings. IMA welcomes various types of articles, including original research articles, review articles, editorials, case reports, and commentaries, fostering a comprehensive dialogue on advancements in industrial management practices and methodologies. The research topics of IMA include but are not limited to:
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ISSN: Table of Contents
Open Accessarticle
by Renuka Sheshrao Namwad, Nitin Kumar Mishra, Prerna Jain, Ranu .
2025,3(1);
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Abstract
In today’s highly dynamic and price-sensitive market environment, inventory management faces increasing challenges due to fluctuating demand and the need for efficient coordination between suppliers and retailers. Based on these premises, this paper develops an exponential complete backordering model of the inventory system that considers the shortages within a finite planning horizon and price-sensitive demand in the presence of linear trends. Included in considerations are variability of demand, backordering costs, and a demand-plus-price relationship modeled through exponential backordering functions taken into consideration. Supportive of MATHEMATICA 12, iterative calculations were performed and Python-based tools created graphs and heatmaps to clearly delineate the result. This would decrease the total variable cost for determining the optimal period of replenishment and shortage. A numerical example is then used to show the ability of the model in optimizing the inventory management, which in turn leads to reduced stockouts, good coordination among suppliers and retailers, and lower costs. The sensitivity analysis presented here is applied to represent the significance of parameters. This research now provides a strong structure for improvement in supply chain management involving dynamic demand environments, especially when price sensitivity and backordering largely influence the processes.
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