Service and replenishment policy design based on improving-forward process in quick response

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.

This paper analyzes the use of a constant critical level policy for fast-moving items, where rationing is used to provide differentiated service levels to two demand classes (high priority and low priority). The previous work on critical level models, with either a continuous or periodic review policy, has only considered slow-moving items with Poisson demand. In this work, we consider a continuous review (Q, r, C) policy with two demand classes that are modeled through continuous distributions, and the service levels are measured by the probability of satisfying the entire demand of each class during the lead time. We formulate a service level problem as an non-linear problem with chance constraints for which we optimally solve a relaxation obtaining a closed-form solution that can be computed easily. For instances, we tested, computational results show that our solution approach provides good-quality solutions that are on average $$0.3~\%$$0.3% from the optimal solution.

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.

Quick response under strategic consumers with high-price and stockout regrets

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.

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.

Cheap SiCl 4 feedstock could boost China's fumed silica output

In this paper, the optimal (r, Q) replenishment policies are developed for stochastic mixture inventory models for both the without order crossover and the order crossover cases. The shortage costs are considered on a per stockout basis. Expressions of total cost are developed for both cases, and their convexities are proved. A normal approximation method is used to determine the distribution of the lead-time demand. The effect of the changing proportions of the mixture of backorders and lost sales (q) on various inventory performance measures, such as total inventory cost, service levels and turnover ratio, is studied. Further, a simulation study is performed to validate the order crossover model. The results obtained are compared with the factorial experiments, which are conducted for the expressions developed. Regression equations are developed for determining the optimal order quantity, inventory cost and safety stock factor. It is determined that there is a remarkable reduction in inventory cost by considering ordering crossover in inventory models. Finally, a sensitivity analysis is done to study the effect of the reorder point (r) on the total inventory cost. The results of a robustness study show that the order crossover model is robust and applicable to many real-world inventory problems.

This article develops a game theoretic model of a supply chain with one supplier and two risk-averse retailers competing in price, service and lot size, where the Economic Order Quantity (EOQ) production-inventory policy is adopted. After studying the existence of EOQ equilibrium, we investigate how to adjust their decisions when the economic environment changes. We find that one retailer will decrease its optimal lot size, retail price and service level when it becomes more risk averse or its service investment efficiency decreases. When the supplier's capacity increases, the retailers will reduce retail price and increase service level, which in turn results in a higher lot size. The effect of the risk sensitivity of one retailer on the rival retailer's decisions depends on the relative service cost efficiency of the retailer. When the absolute risk aversion and service investment efficiency of one retailer increase, the supplier's profitability will increase due to a higher total expected demand. We further give the managerial insights for practitioners, which helps managers to make right decisions in an uncertain environment.

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%.

We determine replenishment and sales decisions jointly for an inventory system with random demand, lost sales and random yield. Demands in consecutive periods are independent random variables and their distributions are known. We incorporate discretionary sales, when inventory may be set aside to satisfy future demand even if some present demand may be lost. Our objective is to minimize the total discounted cost over the problem horizon by choosing an optimal replenishment and discretionary sales policy. We obtain the structure of the optimal replenishment and discretionary sales policy and show that the optimal policy for finite horizon problem converges to that of the infinite horizon problem. Moreover, we compare the optimal policy under random yield with that under certain yield, and show that the optimal order quantity (sales quantity) under random yield is more (less) than that under certain yield.

The Design of Experiential Services with Acclimation and Memory Decay: Optimal Sequence and Duration

Robotic Mobile Fulfillment System (RMFS) is a well-known automated parts-to-picker system that is highly suitable in a fast-moving warehouse for handling critical challenges in the e-commerce industry. Implementing this system in the e-commerce industry has been shown to boost the throughput compared with the traditional picker-to-parts picking system. Nevertheless, there are still several ways to increase the efficiency of the warehouse. Therefore, this study proposes product-to-Pod or SKU-to-Pod assignment and replenishment policies that can increase warehouse efficiency using a simulation approach. There are three SKU-to-pod assignment policies evaluated in this study. They are Random Assignments, One Pod One Class, and Mix Class One Pod assignments. In addition, four replenishment policies, including the Emptiest Pod, Pod Inventory Level, Warehouse Inventory-SKU in Pod, and Stockout Probability, are also simulated. The simulation results show that the Mix Class One Pod assignments combined with Warehouse Inventory-SKU in Pod is the best policy. The SKU-to-pod policy can improve pod utilization by increasing pick units in each visit. Pod with more SKU types is likely to fulfill more orders. The replenishment policy has the role of maintaining the inventory of the warehouse and keeping the pod at a high service level. Other than that, replenishment triggers reduced visits to the picking station. A pod with insufficient capacity could not be assigned with the new order, although it already has the most order assigned.

We consider a two-echelon inventory system with a capacitated centralized production facility and several distribution centers (DCs). Both production and transportation times are stochastic with general distributions. Demand arrives at each DC according to an independent Poisson process and is backlogged if the DC is out of stock. We allow different holding and backlog costs at the different DCs. We assume that inventory at DCs is managed using the one-for-one replenishment policy. The main objective of this paper is to investigate the control of the multiechelon M/G/1 setting with general transportation times. To achieve this objective, we analyze several decentralized allocation policies including the first-come, first-served (FCFS), strict priority (SP), and multilevel rationing (MR) policies. For our analytic results, we assume no order crossing. We derive the cost function for a capacitated two-echelon inventory system with general transportation times under these policies. Our numerical examples show that the FCFS policy may outperform the MR policy, even though the latter has been shown to be better in the centralized setting. This suggests that in decentralized settings there is a need to focus on policies that prioritize customers when there is backlog. This focus is in contrast to the centralized settings, where inventory rationing policies that focus on prioritization when there is available inventory are effective. We therefore introduce and analyze the generalized multilevel rationing (GMR) priority policy. We compare the GMR policy with other policies and show that the GMR policy outperforms the three policies used in the centralized setting. We also compare the GMR policy with the myopic (T), longest queue first (LQF), and the optimal (when order crossing is allowed during the transportation time) policies. Our results show that when the uncertainty of the transportation times is low, the GMR policy outperforms the myopic (T) and LQF policies and that the gap between the optimal policy and the GMR policy is not high.

In today’s fast-paced and competitive market place, organizations need every edge available to them to ensure success in planning and managing inventory of items with demand uncertainty. In such an effort, cost effective methods in determining optimal replenishment policies are paramount. In this paper, a mathematical model is proposed that optimize inventory replenishment policies of a periodic review inventory system under stochastic demand; with particular focus on malaria drugs in Ugandan pharmacies. Adopting a Markov decision process approach, the states of a Markov chain represent possible states of demand for drugs that treat malaria. Using weekly equal intervals, the decisions of whether or not to replenish additional units of drugs were made using discrete time Markov chains and dynamic programming over a finite period planning horizon. Empirical data was collected from two pharmacies in Uganda. The customer transactions of drugs were taken on a weekly basis; where data collected was analyzed and tested to establish the optimal replenishment policy and inventory costs of drugs. Results from the study indicated the existence of an optimal state-dependent replenishment policy and inventory costs of drugs at the respective pharmacies.