Optimal Inventory Control Policy Research Articles

Optimal information disclosure in a production-inventory queueing system

This study considers a production-inventory queueing system, where customers decide whether to wait for the item or leave without a purchase if a stockout occurs. The system manager determines the joint pricing and inventory control policy. In addition, the system manager can choose whether or not to provide information about the system state to arriving customers. Our strategic production-inventory queueing system includes the observable, unobservable, and almost unobservable systems, which are obtained by appropriately choosing the information disclosure policy by the system manager. In this study, we find the optimal information disclosure policy, as well as the optimal price and the optimal inventory control policy for the profit maximization and social welfare maximization. Under the optimal policy, the system manager should disclose the system state if and only if the queue length exceeds a certain threshold.

A smart inventory management system with medication demand dependencies in a hospital supply chain: A multi-agent reinforcement learning approach

In light of the intense need for quality health and well-being, the healthcare industry must improve its operations by strengthening its service delivery and reducing overall costs. A smart inventory management system for managing medicines in a hospital supply chain (HSC) is one of the best solutions as its advantages ensure availability of affordable medicines as well as inventory cost reduction. Significant effort is made on inventory management from literature; however, smart inventory systems for HSC considering the various complexities and uniqueness of healthcare systems are limited. This paper attempts to fill this gap by developing a stochastic semi-Markov decision process model which is solved by a multi-agent reinforcement learning method. Data collected from a multispecialty hospital in India’s eastern region serves to validate the proposed model. The results present the optimal order quantities and optimal inventory control policy for the HSC considering the uncertain medication demand at the hospital pharmacy and multiple point-of-care units, and demand dependencies among medicines prescribed to the patients admitted in the hospital. The study also has significant managerial implications for enhancing HSC’s inventory performance, improving the healthcare service delivery, reducing healthcare cost, thereby ensuring the affordable, accessible, and quality healthcare to the society.

Optimal control policies for dynamic inventory systems with service level dependent demand

Empirical studies have shown that stockouts may adversely affect future demand. However, much of the literature on inventory optimization has ignored this effect. In this paper, we investigate the effect of stockouts on a firm’s operational policy and total discounted profit. We show that compared with classic periodic review inventory models with no stockout effect, the optimal inventory control policies for inventory systems with stockout-dependent demand have different but interesting structures. Specifically, the optimal perceived stock-out service levels exhibit a mean-reverting pattern. These results shed lights on operational decisions and customer services to effectively manage inventory systems. Numerical studies show that neglecting stockout-dependent demand can lead to significant drop in the firm’s performance when compared with the optimum. This paper deepens our understanding on inventory systems when customer demand is discouraged by stockouts, and it helps inventory managers develop effective inventory control mechanisms that incorporate stockout effect.

Optimal Supplier Inventory Control Policies when Buyer Purchase Incidence Is Driven by past Service

Optimal Supplier Inventory Control Policies when Buyer Purchase Incidence Is Driven by past Service

Single item periodic review inventory control with sales dependent stochastic return flows

Retailers have to deal with increasing levels of product returns as the shares of e-commerce sales soars. With this increase, it is no longer feasible to dispatch returned products to outlets or landfills, hence retailers must re-evaluate them both to maximize profit and to minimize their environmental impact. Our objective is to study a retailer’s optimal inventory control policy under product returns to maximize expected profit which is the sales revenue minus the procurement, backorder, holding, and salvage costs incurred in a finite horizon. We model a period’s returns to be stochastically dependent on the previous period’s sales quantity. Using dynamic programming formulation, we solve for the optimal periodic review inventory policy and provide structural results on the optimal policy of the final period. Through numerical studies, we show that incorporating detailed sales-dependent returns could increase a retailer’s expected profit by 23%. Ignoring this dependency in determining the optimal inventory policy results with increased order frequency, higher levels of backorders and more leftovers which could eventually end up in a landfill, but above all could lead to a significant overestimation of the resulting profit.

Optimal supplier inventory control policies when buyer purchase incidence is driven by past service

A buyer exposed to a stockout may lose goodwill and be less inclined to select the same supplier in his next procurement. Reversely, an in-stock experience may restore the supplier’s prospect of being selected in the future. What should the supplier’s inventory control policy be in this situation? To address this question, we develop a multiperiod model of a buyer who selects a supplier with a probability that depends on the supplier’s rating. This rating reflects the buyer’s goodwill towards the supplier based on past service, measured in terms of in-stock/out-of-stock incidents, and is updated by the buyer after each service. The supplier’s optimal inventory policy partitions the inventory space in order-up-to and do-not-order intervals for each rating. The optimal decision depends on whether ordering reduces the supplier’s risk of being downgraded enough to offset the increase in her ordering and inventory costs. We derive and evaluate bounds on the optimal policy and expose some of its properties. We obtain conditions for the optimality of basestock policies and show that such policies are optimal if there are only two ratings or if the buyer’s demand is constant. Using our model, we impute the stockout cost in a newsvendor setting. Numerical experiments suggest that (i) the supplier may benefit from holding more inventory in intermediate than in extreme ratings, and from dealing with a buyer who responds less erratically to service, (ii) basestock policies are efficient, and (iii) using an arbitrary stockout cost in the newsvendor setting can significantly hurt profits.

Printing Spare Parts at Remote Locations: Fulfilling the Promise of Additive Manufacturing

We investigate the benefits of on‐site printing at remote geographic locations, where access to spare parts is intermittent and supplies are replenished at fixed intervals. Organizations typically have no solution to spare parts shortages other than expensive expediting orders, or waiting for a part to arrive with the next replenishment. We investigate whether on‐site three‐dimensional (3D) printing of spare parts can bring relief. Our work extends dual‐sourcing literature with fixed order cycles by considering two emergency supply options: expediting regular parts and 3D printing lower quality parts. We model the replenishment and emergency supply decisions as a Markov decision process and find that the optimal inventory control policy consists of two thresholds that control when to expedite, when to print and when to wait for regular parts via the next replenishment. We apply our model to a case study of the Royal Netherlands Army (RNLA) for her United Nations peacekeeping mission in Mali. Our results show that on‐site 3D printing, much more so than expediting, leads to large operational cost savings through on‐site inventory reductions and increased asset availability, thus increasing the ability of the RNLA to operate in remote locations. These results extend to many other organizations that operate in remote locations, for example, those in the mining and offshore industry.

Optimal inventory control policies of a two-stage push–pull production inventory system with lost sales under stochastic production, transportation, and external demand

An analytical model based on Markov processes is proposed for the analysis of a linear, horizontally integrated, two stages, push–pull inventory system. Uncertainty about both supply and demand is taken into consideration. Exponentially distributed lead times, compound Poisson external demand and lost sales are assumed. An algorithm that creates the infinitesimal generator matrix of the system is developed and an exact numerical solution of the system performance measures is also provided. The proposed model can be either used to evaluate what if scenarios exploring the behavior of the system or to optimize performance measures of the considered system. As an example, the model is used to analyze and get insights of the behavior of a supply–demand balanced system.

Optimal inventory control policies for avoiding food waste

We develop and employ a socially responsible inventory model that determines an optimal order quantity and an optimal time instance of donation, taking into account, expectations on the net stock levels at the end of the order cycles. The optimized results of the policy are compared to those derived from the implementation of two currently applied policies namely the non-donation policy and the naive policy which involves the donation of a fixed amount of the company’s produce. A main insight revealed through the implementation of the proposed policies in a numerical experimentation indicates that the employment of the proposed donation policy could potentially lead to significantly higher expected profits compared to the other two policies.

Patient condition-based medicine inventory management in healthcare systems

Existing inventory models for medicines in the healthcare domain presume demand as a random variable independent of any environmental factors. Conversely, various randomly varying factors, such as changing patient condition, uncertain reaction of the patient to treatment, uncertain length of stay and transition from one type of hospital care unit to another at different stages of treatment, may have a significant impact on the demand of medicines. In dealing with such a problem, a Markov Decision Process model is developed for determining the optimal inventory control policy for medicines under stochastic and non-stationary demand scenario. The required data are collected from a multispecialty hospital situated in urban India. Solving the problem by a stochastic dynamic programming approach, the results as obtained demonstrate that the proposed inventory model using the knowledge of patient condition-based medication demand characteristics has a significantly lower total inventory-related cost than those based on historical daily demand without consideration of patient condition characteristics. This modelling may help optimize and support the functioning of any hospital system more effectively.

Enhanced optimal inventory control policies of perishable products in a retail business: a comparative simulation study under different cost structures

This paper experiments different replenishment policies of perishable products using simulation-based optimisation approach. The main contribution of this study is to recommend the best replenishment policy and whether enhancing a typical periodic review policy can lead to a better outcome. It was found that enhancing a typical periodic review policy with a backroom and a reorder level can lead to a higher net profit. In some instances, integrating both of them together can outperform implementing them alone, as they can complement each other. The finding also suggests that enhancing the base policy by screening out bad items before reaching the customers may not be so cost-effective if the removed items cannot be sold to generate extra revenue or the loss of goodwill is not the main concern. These findings can help shop managers to make right decisions on the optimal inventory policy in a certain situation under an uncertain environment.

Enhanced optimal inventory control policies of perishable products in a retail business: a comparative simulation study under different cost structures

This paper experiments different replenishment policies of perishable products using simulation-based optimisation approach. The main contribution of this study is to recommend the best replenishment policy and whether enhancing a typical periodic review policy can lead to a better outcome. It was found that enhancing a typical periodic review policy with a backroom and a reorder level can lead to a higher net profit. In some instances, integrating both of them together can outperform implementing them alone, as they can complement each other. The finding also suggests that enhancing the base policy by screening out bad items before reaching the customers may not be so cost-effective if the removed items cannot be sold to generate extra revenue or the loss of goodwill is not the main concern. These findings can help shop managers to make right decisions on the optimal inventory policy in a certain situation under an uncertain environment.

Multilevel rationing policy for spare parts when demand is state dependent

The multilevel rationing (MR) policy is the optimal inventory control policy for single-item M / M / 1 make-to-stock queues serving different priority classes when demand rate is constant and backlogging is allowed. Make-to-repair queues serving different fleets differ from make-to-stock queues because in the setting of the former, each fleet comprises finitely many machines. This renders the characterization of the optimal control policy of the spare part inventory system difficult. In this paper, we implement the MR policy for such a repair shop/spare part inventory system. The state-dependent arrival rates of broken components at the repair shop necessitate a different queueing-based solution for applying the MR policy from that used for make-to-stock queues. We find the optimal control parameters and the cost of the MR policy; we, then compare its performance to that of the hybrid FCFS and hybrid priority policies described in the literature. We find that the MR policy performs close to the optimal policy and outperforms the hybrid policies.

INVENTORY MANAGEMENT IN A MANUFACTURING-REMANUFACTURING SYSTEM WITH STOCHASTIC RETURNS

The design of a remanufacturing system that recovers sold products is needed to meet environmental objectives. However, to establish closed-loop supply chains for sustainability, it is necessary to consider not only environmental factors but also economic efficiency. By selling remanufactured products with prices lower than new products, market sizes may be expanded and firms may accrue higher profits. However, a cannibalization effect in which consumers who would have purchased new products instead purchase remanufactured products at lower prices can occur. The purpose of this study is to design a manufacturing-remanufacturing system to propose an optimal production planning and inventory control policy in consideration of the cannibalization effect. First, a time series model for the demand is constructed. Next, the economic efficiency and environmental impact of the system are evaluated by simulation experiments.

Approximation Approaches for Inventory Systems with General Production/Ordering Cost Structures

The production/ordering cost structure is fundamental to determining an optimal inventory control policy. For example, it is well known that a base‐stock policy is optimal for inventory systems with linear production costs, whereas an ( s, S) policy is optimal if both linear and fixed costs exist. However, many of the cost structures that have arisen from the practice are quite complex and make the optimal policies too complicated for managers to implement. In this study, we propose several easy‐to‐implement and efficient heuristic policies for inventory systems with general production costs, which include multiple linear pieces and fixed costs, suggesting a wide application to many practical problems that were previously difficult to solve. We establish the worst‐case performance bounds on the proposed heuristic policies by using the concept of K‐approximate convexity. Our extensive numerical studies, which are designed to reflect practical inventory control applications, evaluate the performance of the heuristic policies and show that the best heuristic policy we propose performs extremely well. We also try to provide explanations for the performance of different heuristic policies.

A two-stage capacity reservation supply contract with risky supplier and forecast updating

We propose a single product two-stage supply contract between a retailer and a main supplier. The retailer buys a number of supply options for a non-refundable price from the main supplier at the beginning of the first decision stage (first period), which can be seen as capacity reservation. At the beginning of the second decision stage (second period), these options can be exercised for a given unit cost and transformed, by the retailer, fully or partially, into orders that are delivered by the supplier immediately. Moreover, the retailer faces a stochastic demand, which is concentrated in the second period and is modeled jointly with a correlated exogenous information using a joint probability distribution. In addition, a second supply opportunity from a risky supplier whose availability is random and modeled using a Bernoulli distribution may be available. At the beginning of the second decision stage, the stochastic exogenous information is revealed and the information about the availability or unavailability of the risky supplier becomes known. Therefore, the demand forecast is updated conditionally to the value of the exogenous information. Thus, if the risky supplier is available, another quantity may be ordered from this supplier by the retailer for a given unit cost and delivered immediately. The end-customer demand occurs during the second period and every satisfied demand is charged a given unit price by the retailer. At the end of the selling season, any remaining units are salvaged by the retailer at a salvage value.We model this problem using a dynamic programming approach and we exhibit some characteristics of the structure of the optimal inventory control policy for the retailer. More specifically, we provide the structure of the second decision stage optimal policy and some analytical insights concerning the first stage optimal policy. Furthermore, through a numerical study, we analyze the effect of some of the model parameters on the optimal policy such as the information quality, the probability of the availability of the risky supply option, and the difference between the costs of the two supply options.

Optimal inventory control policy of a hybrid manufacturing – remanufacturing system using a hybrid simulation optimisation algorithm

Remanufacturing is the process of bringing used products back to like-new products. In this study, inventory control policies in remanufacturing with different prioritisations (remanufacturing vs. manufacturing) and coordination (non-coordinating vs. coordinating) are investigated. A proposed hybrid simulation optimisation algorithm, where outputs are exchanging between Mixed-Integer Linear Programming and simulation models, is presented to search for optimality. Obtained results are then compared with the results obtained from the pure analytical model and simulation-based optimisation where the proposed Hybrid Algorithm outperforms other solving methods by obtaining a statistically higher profit, using less number of iterations to find the optimal result. Regarding the inventory control policy, it is found that the returned component ratio (proportion of returned components as compared to the actual customer demand) has an effect on the inventory control policy. The outcome of the study can recommend the best operating solution at each level of the returned component ratios under an uncertain environment.

A two-stage stochastic programming model for inventory management in the blood supply chain

Managing inventories in the blood supply chain is a challenging task, mainly due to the uncertain nature of the demand for blood units, the perishable nature of the blood, and a strong subjective bias towards criteria other than cost minimisation. In this paper, we propose a two-stage stochastic programming model for defining optimal periodic review policies for red blood cells inventory management that focus on minimising operational costs, as well as blood shortage and wastage due to outdating, taking into account perishability and demand uncertainty. The adoption of this framework allows the consideration of more general stochastic processes to model the demand uncertainty than approaches currently available in literature. Moreover, this framework renders a model that can be solved efficiently by general purpose off-the-shelf optimisation software. To illustrate the potential benefits of adopting the proposed model to support the definition of optimal blood inventory control policies, a case study is presented considering realistic data representing the daily demand for blood, which was generated using the average and standard deviation of the demand for eight types of blood. Results suggest meaningful insights concerning practices that could lead to improvement in the blood supply chain performance. In particular, we observed that it would be possible to revise the current inventory control policies by reducing current target levels to diminish wastage and total cost without compromising the service level. Finally, the consideration of blood substitutions showed further improvement in the performance of the blood inventory management system.

Impact of RFID technology on inventory control policy

RFID application can improve operation performance in a supply chain by reducing or eliminating inventory misplacement and shrinkage. In this paper, we present a periodic review inventory model to investigate and characterize the multiperiod inventory control policies in both non-RFID and RFID cases when the firm encounters misplacement and shrinkage. The optimal inventory control policy is proved to be a two-control limit policy. The control limits in both the non-RFID case and the RFID case are analyzed and examined, while considering the impact of shrinkage and misplacement on inventory policies. A critical inventory level is determined to identify the relationship of higher inventory level control limits between the RFID case and the non-RFID case. An intensive numerical study with sensitivity analysis of selling price, misplacement rate, shrinkage rate, inventory recovery rate, and tag price is conducted. We find that when RFID technology is adopted, the inventory control policy in the RFID case is much more stable than that of the non-RFID case, as the misplaced inventory can be recovered perfectly and instantly for sale and the inventory shrinkage can be reduced by RFID technology. In addition, one of our intriguing findings is that when the shrinkage rate is below a threshold value which is independent of parameters, RFID application has no effect on inventory control policy if the misplaced inventory can be recovered in a timely manner by physical audit, which has not been revealed in previous studies.

Inventory Control System for a Healthcare Apparel Service Centre with Stockout Risk: A Case Analysis.

Based on the real-world inventory control problem of a capacitated healthcare apparel service centre in Hong Kong which provides tailor-made apparel-making services for the elderly and disabled people, this paper studies a partial backordered continuous review inventory control problem in which the product demand follows a Poisson process with a constant lead time. The system is controlled by an (Q,r) inventory policy which incorporate the stockout risk, storage capacity, and partial backlog. The healthcare apparel service centre, under the capacity constraint, aims to minimize the inventory cost and achieving a low stockout risk. To address this challenge, an optimization problem is constructed. A real case-based data analysis is conducted, and the result shows that the expected total cost on an order cycle is reduced substantially at around 20% with our proposed optimal inventory control policy. An extensive sensitivity analysis is conducted to generate additional insights.