Critical level rationing in inventory systems with continuously distributed demand

We consider an inventory system under continuous review with two demand classes that are different in terms of service level required (or penalty cost incurred for backordering of demand). Prior literature has proposed the critical level rationing (CLR) policy under which the demand from the lower priority class is backordered once inventory falls below the critical level. While this reduces the penalty cost for the higher demand class, the fill rate achieved for the lower priority demand class gets compromised. In this study, we propose a new class of two‐bin (2B) policy for the problem. The proposed 2B policy assigns separate bins of inventory for the two demand classes. The demand for each class is fulfilled from its assigned bin. However, when the bin intended for the higher demand class is empty, the demand from the higher class can still be fulfilled with the inventory from the other bin. The advantage of the 2B policy is that better fill rates are achieved, especially for the lower demand class. Computational results show that the proposed policy is able to provide a much higher service level for the lower priority class demand without increasing the total cost too much and without affecting the service level for the higher priority class. When a service level constrained optimization problem is considered, the 2B policy dominates the CLR policy when the service level difference for the two classes is not too high or the service levels required for both the classes are relatively lower.

In a multi-stage production/inventory system, customer service level is a function of the service levels at individual stages. The objective of a multi-stage system can be defined in terms of determining the stage-specific service levels, which minimize the total cost of the system subject to achieving a desired customer service level. The literature available so far discusses the service level perspective of multi-stage systems under the continuous review and periodic review policy. In this paper, we consider systems operating under the base stock policy, and derive service level relationships in multi-stage serial and assembly systems. First, we develop models for a two-stage serial system, and illustrate the models with the help of a numerical example. Finally, we propose an extension of the models for multi-stage serial and assembly systems.

Blood bank operation is a facility which involves the process of collecting, testing, processing and storing blood for later use in blood transfusion. Blood bank management faces a crucial problem due to shortage of blood supply that sometimes does not meet the demand. Blood inventory is difficult to organize and control especially in festive seasons when demand is high due to road accidents and casualties. Blood bank management needs to balance the demand and supply to avoid shortage, and since blood is perishable, the management also needs to avoid wastage. To solve this issue, an effective inventory method should be implemented. This study compares two policies: the continuous review policy and the periodic review policy. The continuous review policy observes the monitory level more accurately and on time, while the periodic policy applies only periodically inventory control. This study looks at the relationships between the service level, safety stock and inventory cost that are based on three case studies, which are variable demand and constant lead time, variable lead time and constant demand and variable demand and variable lead time. The result shows that the continuous review policy should be implemented in blood bank management due to lower inventory cost and being more systematic with higher efficiency.

Joint optimization of inventory replenishment and rationing policies for an omnichannel store with both in-store and online demands

The coal shortage in the stockpile of the coal-fired power plant will possibly stop the power plant operation, which leads to negative impact to the company’s operational performance as well as the interconnected electrical power system. Coal inventory is usually stored in the stockpile with an excess amounts to maintain service level. But the excessive level of coal would make a financial burden in terms of storage and maintenance costs. The objective of this study is to plan the optimum coal safety stock level in XYZ coal-fired power plant so it could serve the system with the low cost of inventory without affecting the operation reliability. The results showed that continuous review policy was more suitable to calculate coal inventory than periodic review policy because it stored less coal and limited stockpile capacity in the power plant. This scenario could reduce the cost of coal inventory planning up to ~ 14%.

RFID provides real-time tracking, resulting in two additional benefits as an information technology. When inventory is inaccurate and real-time tracking is not available organizations have to use a periodic review policy. When inventory is accurate and realtime tracking is available, they may switch to a continuous review policy. Based on a case study in a radiology practice, we compare the operational and economic differences between a system that uses barcode technology and periodic review, and one that uses RFID technology and continuous review. While the first switch from barcode to RFID is a technology improvement providing automatic counting, the second switch from periodic to continuous review is a process innovation. We measure the value of automatic counting, process innovation, and the total of the two, (the value of RFID). We also explain how these benefits change with service level, lead time, demand, etc.

This paper aims at comparing and verifying the effectiveness of existing (r, Q) inventory systems for perishables with fixed lead time and lifetime. The total inventory operating costs are compared to measure the performance for each policy in order to choose the best one that leads to the lowest cost. We show that, under backordered demand, the (r, Q) policy obtained from [8] deviates on average by less than one percent from the optimal (r, Q) policy and performs better than a previous model developed by Chiu [2] when Q ≥ r. When excess demand are lost, Chiu's model [2] performs better than the model of [8]. But both models are unsuitable when the ordering cost K increases. Keywords: Perishable products, Inventory control, Continuous review policy.

Traditional inventory systems treat all demands of a given item equally. This approach is optimal if the penalty costs of all customers are the same, but it is not optimal if the penalty costs are different for different customer classes. Then, demands of customers with high penalty costs must be filled before demands of customers with low penalty costs. A commonly used inventory policy for dealing with demands with different penalty costs is the critical level inventory policy. Under this policy demands with low penalty costs are filled as long as inventory is above a certain critical level. If the inventory reaches the critical level, only demands with high penalty costs are filled and demands with low penalty costs are backordered. In this article, we consider a critical level policy for a periodic review inventory system with two demand classes. Because traditional approaches cannot be used to find the optimal parameters of the policy, we use a multidimensional Markov chain to model the inventory system. We use a sample path approach to prove several properties of this inventory system. Although the cost function is not convex, we can build on these properties to develop an optimization approach that finds the optimal solution. We also present some numerical results. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008

This paper is dedicated to modeling and performances analysis of inventory systems using BDSPN, a stochastic Petri net model capable of describing essential characteristics of logistical systems (batch behaviors, randomness, operational policies, synchronization of various flows). In this paper, an analytic method based on the Markov regenerative theory introduced by Choi for a particular class of stochastic Petri nets is presented and applied to our model. An inventory system with (batch) stochastic demand; constant replenishment lead time and continuous review (s, S) policy is modeled and its performances are evaluated using the analytic method

A multi-period inventory management model for perishable products is developed with simulation approach. A single-product scenario is modelled under continuous and periodic review policies, considering Weibull demand, non-null deterministic lead time, a combination of lost sales and backorders, fixed product shelf life, and service constraints. Eight inputs, including managerial aspects and product characteristics, and six outputs, i.e. the minimum cost and the optimal operating leverages of the two policies, are evaluated using Design of Experiments. Discrete-time simulation is used to determine, for each configuration, the optimal operating leverages, namely target stock level, reorder period, and base stock, which minimise the total cost under service constraints. The outcomes were then analyzed through Analysis of Variance and Response Surface Methodology, to assess the impact of each input parameter on the responses. Results show that the optimal operating leverages mainly depend on demand features, some unitary costs (order issuing in particular), shelf life, and lost sales fraction. The shelf life turns out to be significant only if lower than a threshold value, which varies with the shape factor of the demand distribution: for shelf life values greater than the threshold, the products could be managed as non-perishable from an inventory point of view.

PT. XYZ is a company operating in the electromedical sector that has supported many domestic medical devices in recent years. The demand for electromedical equipment seen from sales data is very volatile and difficult to predict, apart from that 70% of the requests received come from government institutions. The problem is compounded by the absence of a forecasting policy so that production activity planning is not optimal. Product inventory that is not accompanied by careful calculations will result in overstock and stockout which can increase total costs and reduce service levels. In carrying out its business PT. XYZ implements an existing inventory management policy which is carried out by ordering product components in large quantities but there is no clear reorder point. So this research will observe the product components CMS-600 PLUS, ECG-1200G, TENSIONE, ECG-300G, SON-C, SON-B, SON-PRO, USG PROMAX, PROMIST-3, MED-01, ECG- 100G, and ULTRAMIST then proposed the Croston and Syntetos-Boylan Approximation (SBA) forecasting method to estimate possible demand in the future and then continuous review inventory management policies (s, S) and (s, Q) to determine the maximum reorder point value. stock and order quantity. After that, a Monte Carlo simulation was carried out to see and compare existing policies with improvement policies based on total cost and service level variables. Next, a sensitivity analysis was carried out to determine the robustness of the simulation model to changes in uncertainty. The results obtained are recommendations for a continuous review (s, Q) policy with total cost savings of IDR 22,206,322,981 and an increase in service level of 1.36% to 99.98% compared to the existing policy.

Healthcare systems in the US and Canada are under significant pressure to cut costs. While the origins of wasteful spending are diverse, until recently, the industry has exclusively focused on reducing product purchase costs. Belatedly, management echelons started addressing logistics and supply chain costs. Inventory management is emerging as the next target. Our ultimate objective is to develop an inventory optimization system for the healthcare industry. Given the magnitude of this challenge, this paper starts with a single product single echelon simulation model that identifies and addresses some of the critical waste factors in healthcare inventory systems with [s, S] continuous review and replenishment policy and stochastic demand and lead times. The simulation is performed over [s, S] space and identifies policies that minimize inventory costs by balancing stockouts costs which drive higher inventory with other costs that drive lower inventory.

Research on inventory models has been conducted intensively, including the model for stochastic demand. However, inventory models for stochastic demand are not easy to solve using an exact algorithm. In this paper, we develop a Monte Carlo simulation method to solve inventory problems with stochastic and intermittent demand. Simulation is conducted to evaluate continuous and periodic review policies. The simulation models are optimized using the evolutionary algorithm. The models are applied to data from one bicycle shop in Indonesia for five different items. The result shows that the economic order quantity ( R,Q ) policy is better than the ( s,S ) policy for two items and it is better than the ( S,T ) policy for three items.

Analytical model of the (R, Q) inventory policy with limited storage capacity, backlog, and lost sales for continuous and periodic review policies

Service and replenishment policy design based on a joint decision of cost sharing, and a method of improvingforward process is studied. Optimization of cycle service level and replenishment policy in improving-forward process is proposed for a cycle review policy of quick response. A service design in quick response for a continuous review policy is also proposed. Two replenishment policies for improving-forward service design in quick response are designed for both cycle review and continuous review policy. The conclusion is that, the quick response incurred by the improving-forward process, will increase service level and profit of the supply chain. The relatively higher and more stable service level based on improving-forward service design, achieves higher level of quick response, and product availability. Four managerial levers are also proposed to highlight the importance of improving-forward service design with forecast accuracy increasing in the real performance.