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In this paper, we consider a periodic-review stochastic inventory system with both minimum and maximum order quantity (MinOQ and MaxOQ, respectively) requirements. In each period, if an order is placed, the order quantity is bounded, at least at the MinOQ and at most the MaxOQ. The optimal policy of such a system is unknown, and even if it exists, it must be quite complicated. We propose a heuristic policy, called the modified (s, S) policy, under which whenever the inventory position drops to the reorder point s or below, an order is placed to raise the inventory position as close as possible to the order-up-to level S. Applying a discrete-time Markov chain approach, we are able to compute the system-wide long-run average cost. We provide bounds for the optimal values of s and S and design an efficient algorithm to optimize our proposed policy. In addition, the proposed heuristic policy has excellent performance in our numerical studies. We also measure the impact of some inventory parameters.

The situation where serviceable products are sold together with a proportion of deteriorating products to consumers is rarely discussed in the literature. This article proposes an inventory model with disparate inventory ordering policies under a situation where a portion of serviceable products and a portion of deteriorating products are sold together to consumers (i.e. mixed sales). The ordering policies consider a hybrid payment strategy with multiple prepayment and partial trade credit schemes linked to order quantity under situations where no inventory shortage is allowed and inventory shortage is allowed with full backorder. The hybrid payment policy offered by a supplier is introduced into the classical economic ordering quantity model to investigate the optimal inventory cycle and the fraction of demand that is filled from the deteriorating products under inspection policy. Further, a new solution method is proposed that identifies optimal annual total profit with mixed sales assuming no inventory shortage and inventory shortage with full backorder. The impact of an inspection policy is investigated on the optimality of the solution under hybrid payment strategies for the deteriorating products. The validation of the proposed model and its solution method is demonstrated through several numerical examples. The results indicate that the inventory model along with the solution method provide a powerful tool to the retail managers under real-world situations. Results demonstrate that it is essential for the managers to consider inclusion of an inspection policy in the mixed sales of products, as the inspection policy significantly increases the net annual profit.

In this paper, we are concerned with the optimal order quantities for a risk-averse newsvendor with backlogging, where it is assumed that all or part of the unsatisfied demands can be fullfilled by backordering. The optimal order quantities are derived under the famous conditional value-at-risk (CVaR) criterion and mean-CVaR criterion on controlling the profit loss due to the uncertainty of market demands. With the optimal order quantities to the proposed models, several important monotone properties are obtained and their relationships with the existing results are discussed. Moreover, it is shown in this paper that low risk means low expected profit while high expected profit comes with high risk. Finally, we present some managerial insights for the risk-averse newsvendor model under backlogging.

The classic economic order quantity model assumes that purchasing cost should be paid immediately after the delivery time. In practice, sometimes the vendors ask the buyers to prepay the entire or a percentage of the purchasing cost before delivery time. In this paper the buyer’s inventory control system for a decaying item under full prepayment scheme based on various conditions consisting of (1) no shortage, (2) full backordering shortage is allowed and (3) partial lost sale is permitted, are developed. Numerical analysis is provided to show the performance of the model and some managerial insights are presented based on the proposed solution technique and sensitivity analysis.

Improper planning of inventory will affect the factory operating costs, building costs, the cost of loss, and the cost of product defects due to being stored for too long which will eventually become a loss. This research discusses the processing industry which is experiencing lumpy demand. In carrying out the production process, the company has never made plans for future demand, resulting in a waste of message costs due to repeated orders of raw materials ordered to suppliers. This paper contributes to overcoming this issue by simulating future demand by using the Material Requirement Planning (MRP) method with a probabilistic Economic Order Quantity (EOQ) and Periodic Order Quantity (POQ) model. The demand in the coming period is determined using the Autoregressive Integrated Moving Average (ARIMA) method, and an aggregate plan is carried out to determine the regular cost of raw material production and optimal subcontracting. The final analysis states that the calculation of MRP on the selected items using POQ produces the lowest cost for planning S45C-F, SGT-R, and SKD11-R, while SLD-R uses the probabilistic EOQ method.

One of the assumptions in the classic economic order quantity model is that the buyer pays the purchasing cost for an order immediately after receiving the goods. In the real world, the vendors sometimes offer the buyers to pay all or a proportion of the purchasing cost after receiving the items to encourage them to increase their orders (i.e., delayed payment). Furthermore, in some cases, the powerful vendors may ask the buyers to prepay the entire, or a fraction of the purchasing cost before the delivery to mitigate the risk of cancellation or procuring the primary parts (i.e., advance payment). Advance payment is widely used by firms, but its effects on customer’s inventory decisions are seldom discussed. In this paper, we investigate the customer’s inventory policy by considering two different conditions: (a) full prepayment with shortage, (b) partial prepayment–partial delayed payment with shortage. We discuss the effect of parameters such as price discount linked to prepayment and length of prepayment on optimal periods of inventory and shortage quantity. The conclusions shows that the length of the advance payment period does not influence the inventory cycle, but that parameters such as discount factor linked to advance payment and delayed payment period affect the optimal inventory cycle in all cases. Numerical examples and a sensitivity analysis are presented to demonstrate the performance of the model and the results.

The amount of global ammonia (NH3) emissions is growing continuously, similar to the damage to the environment, particularly humans and animals, caused by those emissions. Various problems derived from pollution by ammonia emissions have attracted increasing attention in recent years. In particular, accumulation of ammonia in poultry farms is a concern for the poultry industry as it can lead to possible damage due to reduced bird performance, damage to the respiratory tract and skin of birds, and thus loss of customers. As birds age, ammonia production increases due to factors such as feeding and mobility, requiring the application of solutions to reduce it such as the use of fans, feed supplements, and temperature adjustments to improve bird health. These solutions impose additional costs on poultry farms to combat ammonia emissions. This study presents a general economic growing quantity (EGQ) model that includes the cost of inhibition of ammonia production during the growing period. In addition, the model is formulated under an all-units discount policy, where the price of newborn items is related to the size of the order purchased from the supplier. Furthermore, the model assumed that some newborn items are dead when the lot is received because of stress experiences and incidents during the catching, loading, transportation, and unloading. Finally, two versions of the proposed general EGQ model are presented: EGQ with no discount and EGQ with known slaughter age.

The classical economic growing quantity (EGQ) model is a key concept in the inventory control problems research literature. The EGQ model is commonly employed for the purpose of inventory control in the management of growing items, such as fish and farm animals, within industries such as livestock, seafood, and aviculture. The economic order quantity (EOQ) model assumes that customer demand is satisfied without interruption in each cycle; however, this assumption is not always true for some companies as they do not have continuous operations, except for item storage, during non-working times such as weekends, natural idle periods, or spare time. In this study, we extend the traditional EGQ model by incorporating the concept of working and non-working periods, resulting in the development of a new model called discontinuous economic growing quantity (DEGQ). Unlike the conventional EGQ model, the DEGQ model considers the presence of intermittent operational periods, in which the firm is actively engaged in its activities, and non-working periods, during which only storage-related operations occur. By incorporating this discontinuity, the DEGQ model provides a more accurate representation of real-world scenarios where businesses operate in a non-continuous manner, thus enhancing the effectiveness of inventory control and management strategies. The study aims to obtain the optimal number of periods in each cycle and the optimal slaughter age for the breeding items, and, subsequently, to find the optimal order size to minimize the total cost. Finally, we propose an optimal analytical procedure to determine the optimal solutions. This procedure entails finding the optimal number of periods using a closed-form equation and determining the optimal slaughter age by exhaustively searching the entire range of possible growth times.

In the current global system; supply chains are at risk due to increasing procurement shortages, supply disruptions, and the reliability of on-time deliveries with the original order quantities. As a result, an anticipated management model is of vital importance to provide companies with the productive flexibility necessary to adapt quickly to supply changes, in order to ensure the quality and delivery time through efficient management of stocks and supply costs. In this context, this research aims to develop a system to complement classical procurement planning based on inventory management methods and MRP (material requirements planning) systems by considering suppliers’ behavior regarding procurement risks. For this purpose, a system is developed that seeks to simulate the impacts of procurement shortages of different natures. Moreover, the research investigates the development of a system that performs procurement planning of a component manufacturer to determine the supply orders necessary to meet the master production schedule. The system is analyzed based on a set of indicators in the event that the supplier of a material needed for production does not supply on time or has short-term problems. Several scenarios are simulated, and the results are quantified by changing the procurement order quantities, which may or may not follow the economic order quantity (EOQ) model, and the potential procurement disruptions or shortages. The results show how the simulation and anticipation of potential suppliers’ procurement behavior concerning potential shortages and their probability are key for successful procurement within a joint strategy with classical procurement methods.

Certain inventory items are living organisms, for example livestock, and are therefore capable of growing during the replenishment cycle. These items often serve as various saleable food items downstream in supply chains. The purpose of this paper is to develop a lot sizing model for growing items if the supplier of the items offers incremental quantity discounts. A mathematical model is derived to determine the optimal inventory policy which minimises the total inventory cost in both the owned and rented facilities. A solution procedure for solving the model is developed and illustrated through a numerical example. Sensitivity analysis is performed to demonstrate the response of the order quantity and total costs to some key input parameters. Incremental quantity discounts result in reduced purchasing costs; however, ordering very large quantities has downsides as well. The biggest downsides include the increased holding costs, the risks of running out of storage capacity and item deterioration since the cycle time increases if larger quantities are purchased. Owing to the importance of growing items in the food supply chains, the model presented in this article can be used by procurement and inventory mangers when making purchasing decisions.