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Optimal inventory model for non-instantaneous deteriorating items: a strategy blend of learning effects, advance sales, freshness-driven demand, online payments, and discounts policies

Komal Sharma et al.

International Journal of Procurement Management2026https://doi.org/10.1504/ijpm.2026.152264article
AJG 1
Weight
0.50

What the paper says

In today's fast-paced e-commerce landscape, managing perishable products requires innovative strategies to enhance profitability. Retailers often use discount policies to boost sales, but their success depends on strategic implementation. This study examines the impact of learning on the optimal replenishment inventory policy for non-instantaneous deteriorating items, focusing on pre-order discounts and online payments. A novel EOQ model is proposed to maximise total profit by balancing pricing strategies, advertising frequency, and the effects of online payments on demand. Advanced inventory systems, when paired with strategic advertising and favorable banking conditions, are shown to significantly enhance profitability. The research highlights the importance of incorporating learning effects into cost parameters, amplifying profit. Numerical examples and sensitivity analyses validate the model, providing actionable insights. Figure 1 presents a graphical abstract of the study, illustrating its framework and findings.

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https://doi.org/https://doi.org/10.1504/ijpm.2026.152264

Or copy a formatted citation

@article{komal2026,
  title        = {{Optimal inventory model for non-instantaneous deteriorating items: a strategy blend of learning effects, advance sales, freshness-driven demand, online payments, and discounts policies}},
  author       = {Komal Sharma et al.},
  journal      = {International Journal of Procurement Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijpm.2026.152264},
}

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Optimal inventory model for non-instantaneous deteriorating items: a strategy blend of learning effects, advance sales, freshness-driven demand, online payments, and discounts policies

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.