Computers, Materials & Continua
DOI:10.32604/cmc.2021.014224
images

Article

Ordering Cost Depletion in Inventory Policy with Imperfect Products and Backorder Rebate

Sandeep Kumar1, Teekam Singh1,2, Kamaleldin Abodayeh3 and Wasfi Shatanawi3,4,5,*

1Department of Mathematics, Graphic Era Hill University, Dehradun, 248002, India
2Department of Mathematics, Graphic Era Deemed University, Dehradun, 248002, India
3Department of Mathematics and General Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia
4China Medical University Hospital, China Medical University, Taichung, 40402, Taiwan
5Department of Mathematics, Hashemite University, Zarqa, 13133, Jordan
*Corresponding Author: Wasfi Shatanawi. Email: wshatanawi@psu.edu.sa
Received: 07 September 2020; Accepted: 09 October 2020

Abstract: This study presents an inventory model for imperfect products with depletion in ordering costs and constant lead time where the price discount in the backorder is permitted. The imperfect products are refused or modified or if they reached to the customer, returned and thus some extra costs are experienced. Lately some of the researchers explicitly present on the significant association between size of lot and quality imperfection. In practical situations, the unsatisfied demands increase the period of lead time and decrease the backorders. To control customers' problems and losses, the supplier provides a price discount in backorders during shortages. Also, an order’s policies may result in including some imperfect products in arrival lots. A discount on price may be offered by the supplier on the out-of-stock products to manage the backorder problems. The study aims to develop a model with imperfect products by permitting the price discount in backorders, and the cost of ordering is considered a decision variable. First, it is assumed that the demand for lead time is followed by a normal distribution and then stops it and assumed that the first two moments of demand for lead time are known. Further, the minimax distribution method is used to solve this model, and a separate algorithm is designed. In this study, two models are discussed with and without a normally distributed rate of demand. The current study verified with the help of some numerical examples over various model parameters.

Keywords: Inventory; ordering cost; imperfect product; lead time; backorder

1  Introduction and Literature Review

The occurrence of shortages is an essential factor in the study of inventory control. In markets, several products of profitable brands such as branded shoes and garments may result in a condition in which the customer may like to wait for backorders even during shortages. Asides the branded product, the image of the supplier and showroom attract the customers for backorder. To enhance customer loyalty, the showroom owners upgrade their customer services, provide some gifts to the customers, and maintain the quality of products. But these activities are not free; naturally, some extra costs are there. There should be a policy to lower the cost of shortages of the annual total required cost and lost sales. A vendor must manage an optimal lead time length to find a required rate of back-ordering so that the related inventory cost is minimized. In recent years, researchers have used these characteristics and modified conventional inventory models to include the execution of lead time concepts.

The conventional inventory theory does not consider the time-importance of money for defective products during production. Generally, during production, the maximum products will be imperfect, and due to the cost of opportunity, the time value of money will be there. With this thought, an inventory model for imperfect products must be elaborated to consider into account the value of time of money. Buzacott [1] presented an EOQ model in the case of inflation. Many researchers [2–4] followed the model of Buzacott [1] and introduced models by including the different rates of inflations for all costa, time-value of money, shortages, finite replenishment, etc. Goyal [5] studied complete literature for previous perishable inventory models. These surveys let out that decaying inventory models have attention. Park [6] investigates an economic order quantity as purchasing credit. Vrat et al. [7] presented that the consumption rate of goods is dependent on the size of stock at the initial cycle time. To study the impact of inflation and time value money and inflation for a finite time horizon, Pal [8] added a model with shortages and a linear rate of time-dependent demand. Lio et al. [9] studied a non-deterministic inventory model where the order’s quantity is known where the decision variable is lead time. The study of Lio et al. [9] explored by Ben-Daya et al. [10]. Ouyang et al. [11] have taken a systematic survey of where both the review period and lead time are taken as decision variables. Ouyang et al. [12] elaborated on an integrated policy where the lead time is controllable. Several studies have been carried to present few guidelines in different situations with lead time, such as Hoque et al. [13] and Lee [14]. Gallego et al. [15] presented a newsboy problem, which is distributed free.

It can be observed that due to the unsatisfied demands, some customers may opt for the option of the backorder, and some may refuse it. The customers can be attracted for backorders by offering the discount on backorder price. In general, instead of providing a discount on backorder price on stock-out products, it is better to make the customers more prepared to wait for the wanted products. Pan et al. [16] analyzed a desegregated policy with a discount on the backorder price. Lee et al. [17] elaborated on a joint inventory policy with the cost of ordering cost and varying lead time. Salameh et al. [18] analyzed an inspection and joint policy of lot size. Jaber et al. [19] extended the study of Salameh et al. [18] and provided a straightforward approach to find the lot size quantity. Chang et al. [20] also generalized the analysis of Salameh et al. [18]. They supposed that the non-conforming products could be sold at low cost, rejected, or reworked immediately. Hayek et al. [21] presented a model for imperfect products where the production rate is finite, and shortages are allowed. In the recent study of Annadurai et al. [22] considered the product of imperfect quality and presented a policy with set-up cost and varying lead time. Schwaller [23] developed a policy that expands a model by including the hypothesis that a given percentage of imperfect products in an arrived lot and a cost of inspection are needed to find and destroy the imperfect products. Salameh et al. [24] developed a policy for an EOQ model by including the hypothesis that a known percentage of imperfect products is random. It is also included that after the 100% inspection, the imperfect products could be sold at a lower price in a single batch. Chang [25] analyzed an EOQ fuzzy model with an imperfect rate of demand.

In the modern era, all production firms try to make perfect quality products, but it is not possible to make all products of excellent quality for various reasons. Generally, it is considered that all products are of the best quality, but practically it can be observed that imperfect products being manufactured due to lousy manufacturing methods. The imperfect products must be destroyed, reworked, or refunded by the customers. Paknejad et al. [26] elaborated on a value-compromised stock inventory policy with non-deterministic demand. Sarkar et al. [27] studied an imperfect manufacturing system for imperfect products in an inventory model with a decreased selling price. Chung et al. [28] analyzed that retailers may pay some amount for other stores for business requirements. Many researchers presented inventory models with perfect and imperfect quality separately. Papachristos et al. [29] analyzed EOQ models for imperfect quality products. Eroglu et al. [30] elaborated on an EOQ model for imperfect products and shortages. Wee et al. [31] presented an optimal policy for the products with bad quality and back-ordering. Chang et al. [20] proposed the solutions for an optimal inventory model for imperfect quality products and shortages. Chang [25] analyzed a fuzzy EOQ model for lousy quality items. Khan et al. [32] provided a review of the extended EOQ model for lousy quality items. Hayek et al. [21] presented a lot of size production policy to repair awful quality products. Goyal et al. [33] and Chan et al. [34] also showed their studies for imperfect products. Sarkar et al. [27] analyzed a policy for imperfect products with non-deterministic demand. Lee et al. [35] presented a policy of a model for imperfect products where the quantity of order and lead time are taken as decision variables. Skouri et al. [36] discussed the supply quality effects on costs. In this study, they provided an alternative approach where whole supplied batches may be of low standard and so refused.

In present work, an inventory policy for imperfect products where the discount in backorder price to the customer is allowed and considered as a decision variable. Here two models are discussed first with demand (normally distributed) and second with demand (generally distributed). We proposed a computational algorithm to find the required optimal results. This paper’s blueprint is as follows: In Section 2, notations and assumptions are described, which are used throughout the study. In Section 3, an integrated inventory policy is presented for imperfect products with depletion in ordering cost and constant lead time where the price discount in the backorder is permitted. In Section 3, both the normally distributed model and the non-distributed model are discussed. In Section 4, the numerical verification of the study is provided. Finally, conclusions, suggestions, and future scope of the study are presented in Section 5.

2  Notations and Assumptions

2.1 Notations

The following notations are used in the present study.

B: Demand (annual)

W: Quantity of ordering

images: Actual ordering cost without any investment

C: Per order ordering cost, 0 < C < images

M: Quantity of ordering

images: Length of lead time

images: Per unit discount in backorder price

images: Per unit insignificant profit

images: Per unit cost of inspection

H: Per year per unit non imperfect cost of holding.

images: Per year per unit imperfect cost of holding.

images: Backordered fraction of demand in stock-out period duration, images

images: Upper bound (for the ratio of backorder

images: Imperfect rate (per order lot), a random variable, images

images: PDF (probability density function) for s

images: Opportunity capital cost (fractional annual)

E(.): Mathematics expectation

images: images

Z: The lead time demand, has PDF images with mean Bimages and S.D images

images: The class of the CDF (cumulative density function) images with mean images and S.D images

2.2 Assumptions

The following assumptions are used in the present study.

1.    Replenishment is allowed when inventory level goes to the point of re-order.

2.    If discount in price is larger than the minor gain i.e., images then the provider may not be ready to offer the discount in backorder price.

3.    Inspection is without error.

4.    An arrival lot may have some imperfect products. Suppose that the counting of imperfect products in an arrival lot of size W is considered as a binomial random variables of parameters s (images) and W. All products of an arrival are checked, and imperfect products are returned back.

5.    The lead time (images) contains n components which are not mutually dependent. Assume that images, images and images are minimum duration, normal duration and per unit time crashing cost for images component respectively. Where images.

6.    The point of re-order (R) is given as

R = awaited demand during lead time + stock of safety

i.e., images, here j is the factor of safety.

7.    Assume that images is the lead time length for component images. Then images can be presented as follows

Here the per cycle cost of lead time crashing (U) is as follows

8.    During the period of stock-out, images (ratio of backordering) is a variable and directly proportional to images (per unit discount in backorder price, provided by supplier). So images, where images and images.

9.    If the size of arrival lot is W with an imperfect rate s, then all products are identified and separated the imperfect products from the arrival lot W. So, the actual ordered quantity W is decreased by a quantity W(1 – s).

3  Mathematical Formulation of the Model

It is assumed that the lead time demand, i.e., Z has the probability distribution function images with mean images and S.D images. The per cycle expected counting of backorders is images where images is the shortage against the expected demand after the cycle completion and images is the annual cost of stock-out. The level of net inventory (expected) before the arrival of lot of order is images. The total expected cost (annual) will consist of imperfect holding, non-imperfect holding cost, ordering cost, and inspection cost, the cost of lead time crashing and cost of stock-out. The combined inventory function of cost (TIC) is given as below

Here images is the number of expected orders annually.

In this study the ordering cost C is considered as a decision variable. The sum of capital investment cost and all inventory costs can be minimized by optimizing over W, C, R, images and images with the constraint images, where images is the actual cost of ordering. Now, the capital investment of supplier is images, where images is the per year opportunity capital cost (fractional annual) and obeys the logarithmic investment function defined as images, images, where 1/m is the fraction of decrement in C against the increment in investment (per dollar). Thus from Eq. (1)

Furthermore during the period of stock-out, the ratio of backorder (images) is a variable and directly varies to the price discount in backorder (images) provided by the supplier (per unit). Thus images, where images and images. So the per unit price discount of backorder (images) is considered as a decision inconstant in place of ratio of backorder (images). So Eq. (2) will become

3.1 Normal Distribution Model

It is assumed that the demand of lead time Z abides normal distribution with p.d.f images, mean DL and S.D images. We have images, here j is safety factor and the shortage quantity (expected) at the completion of cycle is given as images, where images. Here images is standard normal p.d.f and images is cumulative distribution function. Hence Eq. (3) can be expressed as

For the solution of Eq. (4) we differentiae images partially with respect to W, C, j, images and images respectively. We have

where images, x is standard normal variable.

By checking the sufficient conditions, for fixed images, images is concave for images as

So, for fixed images, the total expected minimum cost (annual) will exist at the last of the interval images. Now, by putting Eq. (6) equal to zero and then solving for C, we have

Now, putting Eq. (6) equal to zero and then solving for images, we have

Putting the value of images from Eq. (12) in Eq. (5) and then putting it equal to zero, have

where images

Now, again putting the value of images from Eq. (12) in Eq. (7) and then setting it equal to zero, we have

To find the values of images, we solve the Eqs. (11) to (14) for the interval images and denote these values by images respectively. The following postulation claims that, for fixed interval images the point images is the optimal solution for the minimum expected annual cost.

Postulation 1. The Hessian matrix for images in interval images is positive define at the point images.

The effects of C on the total annual cost (expected) can be examined by finding the partial derivative of second order of images with respect to C and obtaining images. Thus images is convex in C for fixed images and images. Since it is not easy to find the solution forimages, so the following algorithm is used to find the solution

Algorithm 1

Step 1. For every images (i = 0,1, 2, 3…n) perform the following sub steps

(i)    Start with images and images

(ii)   Evaluate images by substituting images and images

(iii)  With this value of images find images by Eq. (11), images by Eq. (12) and images by Eq. (14)

(iv)  Determine images and then images

(v)   Repeat sub steps (ii) to (iv) until the values of images and images are unchanged.

Step 2. Compare images with images and images with images, there will be two cases

(i)    If images and images then images and images are feasible. Go to Step 3.

(ii)   If images and images then images and images are infeasible. For a particular images, let images and images and find the values of images by iterative methods with the help of Eqs. (12), (13) and (14). Go to Step 3.

Step 3. For every images determine the corresponding total expected cost (annual) by images using Eq. (4).

Step 4. images and put images = M. Then images is the final solution and the optimal reordering point images can be determined.

3.2 Model without Distribution

In real situations the distribution of probability for the demand of lead time is restricted. A good manager can predict the variance and mean value of the demand of lead time. The actual distribution of probability may not be known. Due to the absence of required things the anticipated shortage images is not known and so images cannot be determined. To defeat this situation, assumption of normal distribution is moderated and assumed that the demand of lead time Z considered with first two moments. For instance the p.d.f images of Z comes from the class images with mean DL and S.D images. Thus the procedure of minmax distribution-free is used to resolve this problem i.e., to determine the best p.d.f images in images for every images and the total expected cost can be minimized.

To find Min-Max images, the following preposition is required which was presented by Gallego et al. [6]

Substituting images in (15), we have

Now using Eq. (3) and inequality (16) and taking the factor of safety j as a decision v factor in place of R, the function of can be presented as

Here images is the t final expected cost for distribution-free and it is the supremum of images. Now we differentiate Eq. (17) with respect to W, C, R, images and images respectively in the interval images, we have

By checking the second order partial derivative, the sufficient conditions, for fixed images, images is concave in the interval images as

So, for fixed images, the total expected minimum cost (annual) will exist at the last of the interval images. Now, by putting Eq. (19) equal to zero and then solving for C, we have

Similarly solving for images by putting Eq. (21) to zero, we have

Again putting Eq. (25) into Eq. (18) and then putting it to zero, we have

where images

Now, putting Eq. (25) into Eq. (20) and then putting it to zero, we have

To find the values of images, we solve the Eqs. (24) to (27) for the interval images and denote these values by respectively (we represent these terms by images). The following postulation claims that, for fixed interval images, the point images is the optimal solution for the minimum expected annual cost.

Postulation 3. The Hessian matrix for images in interval images is positive define at the point images.

The effects of C on the total annual cost (expected) can be examined by finding the partial derivative of second order of images with respect to C and obtaining images.

Thus images is convex in C for fixed images and images. Since it is not easy to find the solution for images, so the Algorithm 2 is executed to determine the solution of images represented by images.

Algorithm 2

Step 1. For every images (i = 0,1, 2, 3, …, n) perform the following sub steps

(i)    Start with images and images

(ii)   Evaluate images by substituting images in Eq. (26)

(iii)  With this value of images find images by Eq. (24), images by Eq. (25) and images by Eq. (27)

(v)   Repeat sub steps (ii) to (iii) until the values of images and images are unchanged.

Step 2. Compare images with images and images with images, there will be two cases

(i)    If images and images then images and images are feasible. Go to Step 3.

(ii)   If images and images then images and images are infeasible. For a particular images, let images and images and find the values of images by iterative methods with the help of Eqs. (26), (27) and (25). Go to Step 3.

Step 3. For every images determine the corresponding total expected cost (annual) by images using Eq. (17).

Step 4. images and put images = M. Then images is the solution and the reorder point images can be obtained.

4  Numerical Verification of the Study

To verify the present study and illustrate the effects of reduction in ordering cost, an inventory product is considered with the same parameter values as in Pan et al. [16]. B = 600 units (every year), H = 20$/unit/year, images = 200$/order, images = 12$/unit/year, images = 7 units/week, images = 150$/unit lost, images = 1.6$/unit. For the reduction in ordering cost, we consider images = 0.1 and m = 5800. There are three components of lead time

Example 1. Suppose a normal distribution follows the demand for lead time. The results are presented in Tab. 1 and Tab. 2 for = 0.2, 0.4, 0.6, 0.8 by using the Algorithm 1. Next, an analysis of optimal solutions is presented in Tab. 3 and to observe the impacts of reduction in the cost of ordering and discount in backorder price. The same Tab. 3 includes the results with the fixed cost of ordering and with no discount on the backorder price. The shavings can be observed by comparing both the cases, which range between 18.52% to 21.45%.

Table 1: Data (Lead time)

Table 2: Solution process for Example 1 (images in weeks)

Table 3: Summarized optimal solution for Example 1 (images in weeks)

Example 2. In this example the data of Example 1 is considered except the condition that distribution of probability of the demand of lead time is not known. Using the Algorithm 2, find the results presented in Tab. 4 and Tab. 5 includes the analyzed optimal values. The comparative results of Tab. 5 reflect that in case of bad distribution of demand of lead time, it is preferable to invest in reduction of ordering cost and permit discount in price of backorder to resolve the problem of ordering during the period of shortages. The shavings can be observed by comparing both the cases, which range between 18.44% to 20%.

Table 4: Solution process for Example 2 (images in weeks)

Table 5: Summarized optimal solution for Example 2 (images in weeks)

With the help of these two examples, it can be observed that the shavings of total expected cost (annual) are examined by the reduction in ordering cost and discount in price of backorder’. Next, we test the importance of distribution-free concept in comparison of normal distribution. If we use the solution in place of images, then images will be the added cost.

This is hugest quantity for the information p.d.f images and the amount is assumed as the additional information of expected value (AIEV) and its précis is shown in Tab. 6. In practice, the model (Reduction in ordering cost) with the discount in backorder price is more like the supply chain of real life.

Table 6: Determination of additional information of expected value (AIEV)

5  Conclusion and Future Scope

The occurrence of unsatisfied demands increases the period of lead time and decreases the backorders in the same ratio. In the case of probabilistic needs, the shortages cannot be ignored. To solve this type of problem and to cover the losses of customers, a discount in backorders may be offered by the supplier. Also, our inventory policies may not be good for the arriving lots, which include some imperfect products. This study aims to check the impact of imperfect products on a unified policy by permitting the discount in the price of backorders and considering the cost of ordering as decision factors. First, it is assumed that the demand for lead time is followed by a normal distribution and then stops it and assumed that the first two moments of demand for lead time are known. Further, the minimax distribution method is used to solve this model, and a separate algorithm is designed. Since the situations of markets are regularly changing, so the policies of corporate should be managed suitably. If it is possible to decrease the cost of ordering correctly and the orders fixed well, the per unit time total concern cost will be upgraded. On the other side, if the seller provides a better discount in the backorder to the purchaser, then the service level will be improved, and the total expected cost (annual) will be reduced. This study covers the above literature gaps and is supported by numerical verification.

By the examples, it is observed that a notable quantity of shavings can be attained. It also reflects the suggestions to invest only when there is a chance of improvement. Computed results tell us the significant effects of weak quality of supply on the related cost and parameter sensitivity. This study provides an application in inventory models involving inspections, ordering quantity, and imperfect products. The presented research further is suitable for more general inventory characteristics in various real-life problems.

Funding Statement: The author(s) received no specific funding for this study. The Graphic Era Hill University Dehradun supported the research of the Sandeep Kumar and Teekam Singh. The corresponding and the third authors thank Prince Sultan University for the financial support.

Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.

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