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Sustainable inventory management based on environmental policies for the perishable products under first or last in and first out policy

Abstract Full Text(HTML) Figure(8) / Table(5) Related Papers Cited by
  • Holistic, quality, and sustainability-based inventory policy for perishable items may prove to be a game-changer amid great concern over the carbon footprint and global economic crises. In this paper, first-in-first-out (FIFO) and last-in-first-out (LIFO) dispatching policies have been used to examine the effect of quality on fresh products' sales mind the sustainability concern. The quality of these products worsens with age. Its deterioration rate follows two parameters, Weibull distribution, as it gives better flexibility for various items subjected to deterioration and demonstrates fitness for a range of shape and scale parameters. An adequate preservation effort such as controlled atmosphere (CA) storage and modified atmosphere packaging (MAP) techniques must be applied during storage and handling to maintain such products' quality. Consumers' purchasing behavior and curiosity towards quality and selling price drive these products' demand variability. The demand has been considered a function of quality and unit selling price of the item. The effect of inflation viz. time value of money has also been taken into account, affecting Authoritarian governments' norms on carbon emission control in taxation have also been considered essential for modern sustainable inventory policies. The present models determine the unit price and lot size for the maximum average profit of the system. The behaviors of models have been investigated using a comprehensive sensitivity analysis that offers important operative implications. It has been found that the first-in-first-out model is more profitable as compared to the last-in-first-out model, the price is very rigid, and little flexibility in price drastically decreases the profit. Backlogging has no impact on the selling price of products and order quantity but reduces the average yield because of carbon emission taxation.

    Mathematics Subject Classification: Primary: 90B06, 90B50, 90B60; Secondary: 90C11, 90C31.


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  • Figure 1.  Two warehouse inventory depletion with FIFO distribution strategy

    Figure 2.  Average profit versus price per unit

    Figure 3.  Average profit versus the number of items in system and price per unit

    Figure 4.  Two warehouse inventory depictions with LIFO distribution strategy

    Figure 5.  Number of items in the system versus price per unit

    Figure 6.  Average profit versus the Number of items in the system and Price per unit

    Figure 7.  Average profit versus the Number of items in the system and Price per unit

    Figure 8.  (ⅰ-ⅴ): Variation of average profit (AP) for FIFO and LIFO concerning, (ⅰ) holding costs in RW,(ⅱ) price elasticity(ⅲ) backlogging parameter(ⅳ) quality parameter, and (ⅴ) fraction of carbon emission

    Table 2.  Effect of holding costs in rented and owned warehouse in policy selection

    M N P $ G_F $ $ U_F $ AP (FIFO) P $ G_L $ $ U_L $ AP (LIFO) Policy selected
    1 1 105 200 188.50 1026.4 46.03 182 212.35 553.57 FIFO
    2 105 200 188.50 818.44 46.03 182 212.35 537.66 FIFO
    3 105 182 177.04 631.97 46.03 182 212.35 521.75 FIFO
    2 1 105 200 188.50 988.41 55.34 182 197.15 521.60 FIFO
    2 105 200 188.50 780.44 52.34 182 197.15 498.27 FIFO
    3 105 182 171.04 593.97 52.24 182 201.17 475.64 FIFO
    3 1 105 200 188.50 950.41 64.65 182 188.91 483.89 FIFO
    2 105 200 188.50 742.45 64.65 182 188.91 455.60 FIFO
    3 105 182 171.04 555.97 64.65 182 188.91 427.31 FIFO
     | Show Table
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    Table 3.  Effect different values of demand parameters in policy selection

    Z n p $ G_F $ $ U_F $ AP (FIFO) p $ G_L $ $ U_L $ AP (LIFO) Policy selected
    100000 1.87 55.44 182 205.67 1539.1 52.24 182 251.43 1649.0 LIFO
    2 105 200 188.50 1026.4 46.03 182 212.35 537.66 FIFO
    2.2 105 200 189.10 8892.3 105 200 192.11 184.41 FIFO
    200000 1.8 55.34 182 278.90 3541.7 49.12 182 380.88 3780.9 LIFO
    2 55.34 182 211.69 1343.3 46.03 182 268.11 1478.2 LIFO
    2.2 105 200 188.50 1328.4 46.03 182 208.77 488.11 FIFO
    300000 1.8 52.24 182 379.36 5622.6 49.13 182 506.11 5967.8 LIFO
    2 49.13 182 257.98 2249.0 46.03 182 328.77 2459.0 LIFO
    2.2 105 200 188.65 947.63 42.93 182 246.48 910.45 FIFO
     | Show Table
    DownLoad: CSV

    Table 4.  Effect of different values of the backlogging parameter in policy selection

    $ \sigma $ p $ G_F $ $ U_F $ AP (FIFO) p $ G_L $ $ U_L $ AP (LIFO) Policy selected
    0.7 61.55 182 177.54 552.62 49.13 182 266.14 526.82 FIFO
    0.6 105 182 171.04 561.69 49.13 182 206.14 530.56 FIFO
    0.4 105 200 188.50 766.03 49.13 182 206.14 537.04 FIFO
    0.3 105 200 188.50 863.29 49.13 182 206.14 540.08 FIFO
    0.2 105 200 188.50 949.00 46.03 182 212.35 544.60 FIFO
     | Show Table
    DownLoad: CSV

    Table 5.  Effects quality parameter on policy selection

    $ \beta $ p $ G_F $ $ U_F $ AP (FIFO) p $ G_L $ $ U_L $ AP (LIFO) Policy selected
    5 105 200 188.50 1026.4 46.03 182 212.35 537.66 FIFO
    15 105 200 193.94 640.93 42.93 182 222.22 401.90 FIFO
    25 49.13 182 193.73 265.37 42.93 182 224.12 261.38 FIFO
    35 42.93 182 206.68 132.07 36.72 182 250.22 125.98 FIFO
    45 39.82 182 217.51 3.96 36.72 182 252.62 -1.08 FIFO
     | Show Table
    DownLoad: CSV

    Table 6.  Effect of fractions of carbon emission on policy selection

    $\epsilon$ p $G_F$ $U_F$ AP (FIFO) p $G_L$ $U_L$ AP (LIFO) Policy selected
    0.1 105 200 188.50 1035.2 49.13 182 206.14 477.31 FIFO
    0.2 105 200 188.50 1026.4 46.03 182 212.35 553.57 FIFO
    0.3 105 200 188.50 1017.7 46.03 182 212.35 640.00 FIFO
    0.4 105 200 188.50 1008.50 42.93 182 220.20 743.98 FIFO
     | Show Table
    DownLoad: CSV
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