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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026
Influence of Selling Price, Freshness, Inventory Levels, Advertising
Frequency on Demand and Cost Structures for Perishable Products
in India
Mamta
1
, Dr. Mahender Poonia
2
1
Ph.D. Scholar, Department of Mathematics, Om Sterling Global University, Hisar (Haryana), India.
2
Assistant Professor, Department of Mathematics, Om Sterling Global University, Hisar
(Haryana),India
DOI: https://doi.org/10.51583/IJLTEMAS.2026.15020000115
Received: 08 February 2026; Accepted: 13 March 2026; Published: 21 March 2026
ABSTRACT
In this study we developed an integrated inventory model to analyse how Indian perishable product influence
Selling Price, Freshness, Inventory Levels, Advertising Frequency on Demand and Cost Structures of Perishable
Products in India. We take a price dependent demand function that is influenced by selling price, freshness,
inventory levels, and advertising frequency. The main objective of this study is to check how these key
parameters affect the demand and cost structure of perishable products in Indian markets. We perform a
numerical and sensitive analysis based on Indian conditions to understand how these key variables affect the
demand and cost of perishable products in India. A numerical example is performed with the help of MATLAB.
Keywords: Price dependent demand, Freshness, Advertising frequence, EQO Model, Perishable Inventory.
INTRODUCTION
Perishable fresh products in India have a limited shelf life, quality deterioration, and high spoilage costs;
therefore, management of these requires considerable attention. Traditional Inventory models are primarily
focused on constant and time-dependent deterioration demand, but demand for perishable products in India is
influenced by multiple interacting variables like selling price, product freshness, advertising frequency,
inventory level, etc. that are recognized in recent studies. Early contributions were established by [1, 2]. They
study the inventory model, multivariate demand, which is price sensitive, and jointly optimize the price and
maximize the profit. After that, other inventory models show that product quality decay directly affects consumer
purchasing willingness over time, and these models include freshness-dependent demand [3,4]. Stock-dependent
demand inventory also developed, which shows a higher display inventory level influences the demand of
perishable products. They highlight the effect of inventory by improving the stock availability and display
through the holding costs [5,6]. They build an inventory for perishable products in which a shortage is allowed
to find the optimal solution of ordering quantity and total cost [7]. An EOQ model is developed to maximize the
profit and optimize the cycle time and green effort and combine the freshness, stock, and price-dependent
demand of perishable products [8]. Further study extends these inventory models where demand jointly depends
on freshness, price, and stock and adds the nonlinear holding cost to better understand the real-world scenario
[10].
Many recent inventory models include market-related variables such as advertising and promotional effects
because these variables directly affect the customer demand of perishable products. In the above paper, they
study an inventory model with price- and promotion-dependent demand to optimize the cost and maximize the
profit of perishable products. The highlight is the importance of advertising and marketing effects [11]. In this
study, an EOQ inventory model is developed to determine the optimal solution for an existing inventory level,
unit price, and cycle time where demand is directly dependent on selling price, display stock, and freshness with
non-zero inventory sold at market markdown price[12].Model integrate promotional policies ,trade credit and
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privational technology to show the realistic behaviour of supply chain by analyse the inventory by optimizing
the ordering policies for reduce the costs [15,14]. Markovian EOQ model to capture how demand in the next
period depends on market effect. It optimizes the advertising and marketing effect to improve the inventory
decision [13]. Recent work developed stochastic inventory models for price-sensitive demand that jointly
optimize promotional replenishment, preservation technology investment, pricing, and shortage for maximizing
the profit under quantity discounts and partial backlogging [16]. The EOQ model for fresh agricultural perishable
products focuses on carbon emissions and freshness-dependent demand. It focuses on optimizing the inventory
control strategy [17]. This work develops an inventory model for perishable products under carbon tax policy
and inspection, and demand depends on advertising and stock [18].
Previous research studies mainly focus on perishable products in international countries, but there's limited
research on Indian perishable products. The Indian perishable product market has some unique challenges like
high prices, freshness, advertising, and inventory management. Prior research shows how all key parameters
affect the demand and cost structure of perishable products.
The main aim of this research is to analyze how key variables like selling price, freshness, inventory, and
advertising frequency affect the demand and cost structure of perishable products in India. This research mainly
focuses on Indian perishable products; we can use secondary research data to analyze the combined effect of key
variables on Indian perishable products.
Assumptions:
This inventory model focuses on one perishable item from India, which has a fixed shelf life.
There are two different types of degradation of the product in inventory with time: one is constant degradation
that is manifested physically, and the second is gradual loss of freshness that is observed.
The shelf-life of the item is determined and finite, and once the item reaches that shelf-life, it will not be able to
be sold in the market.
The demand is function of selling price, the amount of on-hand stock, the freshness of the product, and also
advertising frequency. The demand factor that is price-dependent is assumed to take a linear function.
It is assumed that in each cycle end, the inventory level becomes zero.
Holding costs are time-dependent and nonlinear and have the quadratic nature of time.
The deterioration cost and the salvage value are considered for units that deteriorate during the inventory time.
It assumed that horizon planning is infinite. The lead time is still of concern, and it is assumed to be insignificant;
therefore, replenishment is set to occur at the very beginning of every cycle.
In the beginning of the cycle (t=0), item are absolutely fresh; therefore, no depreciation of demand on freshness
takes place. With time, the items become stale, and thus, it reduces the demand rate.
The cycle length T cannot exceed the shelf life of item N because the item cannot be sold if it is expired.
Table 1: Notation
Category
Symbol / Function
Description
Parameter
p
Selling price in INR
Parameter
A
Advertising frequency
Parameter
Cₐ
Expenditure per advertising in INR
Parameter
T
Replenishment cycle length
Parameter
θ
Rate at which inventory deteriorates
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Parameter
ᴤ
Product shelf life
Parameter
x, y
Price-demand parameters
Parameter
ω
Inventory-dependent demand coefficient
Parameter
W
Maximum shelf space
Parameter
s
Salvage cost of deteriorated items
Parameter
c
Purchase cost in INR
Parameter
c_d
Deterioration cost in INR
Parameter
h
Holding cost in time
Parameter
h₁
Holding cost in time2
Parameter
h₂
Holding cost in time3
Parameter
Q
Size of order
Parameter
O
Ordering cost per order in INR
Parameter
γ
Advertising elasticity
Parameter
µ
Salvage coefficient (0 ≤ µ ≤ 1)
Function
d(p)
Linear form price-dependent demand
Function
I(t)
At time t, quantity of inventory available
Function
D(p, I(t), A, t)
Demand at time t depending on price, inventory,
advertisement, and age (freshness)
Function
H(t)
Holding cost function
Function
Π(p, T, A)
Total profit
Decision Variable
p, T, A
Selling price, replenishment cycle length, advertising
frequency
Dependent Decision Variable
Q
Size of order
Mathematical Model formulation:
The demand function is an extension of [1] with advertising dependent demand where the product has a set shelf
life, and each replenishment cycle should be limited within a specific period, which shelf life not more that a
specific period. The demand function is depend on selling price, Inventory level, freshness and advertising
frequence of the product. The inventory model is formulated in a way that the level of the stock becomes zero at
the end of the inventory. In cycle, some part of the inventory is wearing out and deterioration costs, as well as
the value of the salvage, are part of the analysis.
Demand Function:
At Time t the demand rate is defined as:
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where d(p) represents the price dependent demand, is taken as
linear demand where p is the selling price, At time t the inventory level is I(t), A represents advertising frequency,
is product shelf life. By considering assumptions, Differential equations is:
With boundary conditions
By solving differential equation I(t) is expressed as
The ordering quantity occurs when I(0) and ordering quantity represented Q
The profit is calculated by difference between total revenue and total costs during a replenishment cycle divided
by the cycle length . All the cost components of the profit function are calculated in detail and are
mathematically shown as below:
Sales revenue generated per cycle
Deteriorated items per cycle Salvage value is
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Ordering cost generated per cycle
The Holding cost generated per cycle
=
[
Purchase Cost generated per cycle
The Deterioration cost generated per cycle
The Advertising cost per cycle
The total profit generated per cycle
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Algorithm:
Step 1 Give values to all key parameters of inventory model
Step 2 For Optimal solution put
,
,
and solve together for p, T, A.
Step 3 Calculate optimal Solution.
Numerical Applications:
We consider a numerical example for a fresh perishable product, namely tomatoes, in an urban Indian retail
market. The parameter values are obtained from secondary sources such as government retail price reports and
post-harvest loss studies. The average retail price of tomatoes in India is approximately 52 per kg. Retail prices
typically fluctuate between 20 and 85 per kg depending on season and supply conditions. Post-harvest losses for
perishable crops in India can reach 30–40%, indicating a high deterioration rate in the supply chain. The
following parameter values are used
By solving we find optimal solution p*=41.8/kg, T*=0.12week, Q*=61.4 units and total profit π*=752.0 for per
cycle. Concavity of profit function shown figure below.
Sensitivity analysis:
We perform sensitivity analysis with the help of all key parameters and changing values of one parameter at a
time. By using that method, we check the effect of all key parameters of inventory model. All parameters are
varied ±20% while keeping others constant.
Sensitivity analysis Table
Parameter
% Change of parameter
p*
T*
Π*
%Change in profit
x
-20%
39.0
0.104
601.0
-20.1%
x
+20%
44.3
0.137
904.0
+20.2%
y
-20%
46.2
0.124
841.0
+11.8%
y
+20%
37.1
0.112
667.0
-11.3%
Ɵ
-20%
42.6
0.131
826.0
+9.8%
Ɵ
+20%
40.9
0.109
678.0
-9.8%
h
-20%_
42.1
0.125
783.0
+4.1%
h
+20%
41.5
0.113
720.0
-4.3%
O
-20%
41.9
0.116
779.0
+3.6%
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O
+20%
41.7
0.122
721.0
-4.1%
A
-20%
40.2
0.111
659.0
-12.4%
A
+20%
43.4
0.130
866.O
+15.2%
The results indicate that the demand scale parameter has the most significant influence on profit may be presented
in Diagrammatical representation. An increase in demand substantially improves profitability, while higher price
sensitivity reduces profit margins not equity. Less deterioration rate has a strong negative impact, highlighting
the importance of freshness preservation. Advertising frequency positively influences demand and profit,
demonstrating the effectiveness of promotional strategies in perishable product markets. Impact solution must
compare with exact optimal values.
CONCLUSIONS
This study develops inventory model shows influence of Selling Price, Freshness, Inventory Levels, Advertising
Frequency on Demand and Cost Structures for Perishable Products in India. This shows when demand function
is extended to advertising frequency it higher the marketing cost but it also increases the profit. Using Indian
parameters, the base-case optimum, in terms of the parameters, is about 41.8/kg, a cycle time of about 0.12 week,
and a profit of about 7,520/cycle. Sensitivity analysis shows that market demand and freshness have the strongest
impact keeping freshness and price display the same in parallel with relatively low advertisement activity give
the highest amount of profit increases. The research integrates pricing, freshness control, inventory, and
advertising to achieve maximum profit with minimum waste. The inventory model mainly focuses on the Indian
context. The result shows that by combine the advertising frequency increase with the demand of perishable
product reduce the wastage of perishable product in India.
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