<p>Keeping afloat is essential to a company’s long-term wellness and growth in today’s fiercely competitive business environment. Due to technological advancement, a wide range of factors affect today’s market demand. One of the factors is the scarcity effect, defined as a limited quantity of items with a known expiration date due to an event, after which the unsold products have no significance. The present study incorporates a model for managing inventory with the scarcity effect, a psychological effect where people tend to infer that something is limited because of great desire or widespread acquisition. The present research considers a single-period inventory model with a single product for an event in the monopoly market. The model’s primary goal is the maximization of total profit when consumer demand is subject to the principle of scarcity. In addition to describing the model and its key features, a comparative analysis has been done without the scarcity effect. The results obtained support the present model. This validation strengthens the model’s accuracy. Further, the present model includes a numerical illustration and sensitivity analysis. Python 3.12.2 has been used to develop regression estimates of demand coefficients using sales data for event-specific products with a scarcity effect component. The results provide <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13198_2025_2820_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">${R}^{2}$</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mi>R</mi> </mrow> <mn>2</mn> </msup> </math></EquationSource> </InlineEquation> a value of 0.95 assures the goodness of fit, and the <i>p</i>-value confirms the statistical significance of all coefficients. To establish the optimality of the inventory model analytically and graphically, Mathematica 12.3.0 has been used. The results show an optimal order quantity of 16,625 units per cycle and 972 units of lost sales per cycle. The cycle length is 56.06 days, yielding a total profit of ₹13,30,349 per cycle. Based on key findings and managerial insights, the present model assures businesses can increase profitability while minimizing lost sales cost, eventually boosting operational efficiency. Further, the model can be applied to various event-oriented specific products.</p>

Analyzing the effect of scarcity on demand in inventory models with data analytics

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发表日期：

2025年6月19日

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Saurabh Verma,
Mona Verma,
Chandra K. Jaggi

摘要

Keeping afloat is essential to a company’s long-term wellness and growth in today’s fiercely competitive business environment. Due to technological advancement, a wide range of factors affect today’s market demand. One of the factors is the scarcity effect, defined as a limited quantity of items with a known expiration date due to an event, after which the unsold products have no significance. The present study incorporates a model for managing inventory with the scarcity effect, a psychological effect where people tend to infer that something is limited because of great desire or widespread acquisition. The present research considers a single-period inventory model with a single product for an event in the monopoly market. The model’s primary goal is the maximization of total profit when consumer demand is subject to the principle of scarcity. In addition to describing the model and its key features, a comparative analysis has been done without the scarcity effect. The results obtained support the present model. This validation strengthens the model’s accuracy. Further, the present model includes a numerical illustration and sensitivity analysis. Python 3.12.2 has been used to develop regression estimates of demand coefficients using sales data for event-specific products with a scarcity effect component. The results provide ${R}^{2}$ $R^{2}$ a value of 0.95 assures the goodness of fit, and the p-value confirms the statistical significance of all coefficients. To establish the optimality of the inventory model analytically and graphically, Mathematica 12.3.0 has been used. The results show an optimal order quantity of 16,625 units per cycle and 972 units of lost sales per cycle. The cycle length is 56.06 days, yielding a total profit of ₹13,30,349 per cycle. Based on key findings and managerial insights, the present model assures businesses can increase profitability while minimizing lost sales cost, eventually boosting operational efficiency. Further, the model can be applied to various event-oriented specific products.