Search

Spare parts are necessary for ensuring the functioning of the critical equipment of many companies, and as such, they play a central role in these companies’ operations. Inventory control of spare parts is particularly challenging due to the nature of their demand, which is usually slow-moving, erratic and lumpy. As inventory policies rely on the forecasted lead-time demand distribution and this choice impacts the performance of the system, an ill-suited hypothesized distribution may result in high preventable costs. In this study, we contribute to the empirical literature by analyzing what distributions best fit spare parts demand. We use the Kolmogorov Smirnov (K–S) goodness-of-fit test to find the best-fitting distributions to our data and compare our results to those in the literature. Furthermore, we implement a slightly modified K–S test that places greater emphasis on differences in the right tail of the distribution, mirroring real-world inventory applications, and less emphasis on the left tail. Finally, we link the goodness-of-fit of the distributions to their inventory performance. Our first dataset comes from the German renewable energy industry and is composed of the weekly demand for more than 4000 items over the period 2011–2013. The second dataset comes from the Royal Air Force. It is composed of monthly demand for 5000 items over the period 1996–2002.

This paper assesses inventory allocation decisions in light of fuzzy inference systems.It builds upon the scant previous studies in this field of knowledge.Priority indexes are developed based on a comprehensive total cost structure.The proposed approach outperforms specially in terms of total costs.Results are robust for larger retail networks. Inventory allocation decisions in a distribution system concern issues such as how much and where stock should be assigned to orders in a supply chain. When the inventory level of an inventory point is lower than the total number of items ordered by lower echelons in the chain, the decision of how many items to allocate to each ``competing'' order must take into consideration the trade-off between cost and service level. This paper proposes a decision-support system that makes use of fuzzy logic to consider inventory carrying, shortage and ordering costs as well as transportation costs. The proposed system is compared through simulation with three other inventory allocation decision support models in terms of cost and service levels achieved. Conclusions are then drawn.

This paper investigates the dynamic integration and optimization of inventory classification and inventory control decisions to maximize the net present value (NPV) of profit over a planning horizon. A mixed-integer linear programming model is developed by explicitly accounting for various real-world complexities, such as nonstationary demand, arbitrary review period, and limited inventory budget. We apply the model to a 900-SKU experiment and observe an average 7.5% improvement in profit over the traditional ABC approach. A real-world application for a cheese manufacturer provides a nearly 3% improvement in company's profit with a 13% reduction in inventory capital compared to its current eight-class multi-criteria inventory classification scheme. Comprehensive computational experiments are performed to examine the individual and interactive effects of various parameters on the inventory performance. Results show that it is critical for a company to manage its inventory both dynamically in the face of nonstationary demand and holistically by integrating SKU classification and policy setting. Our work provides a practical decision-support tool that simultaneously optimizes multi-period inventory classification and control decisions under nonstationary demand. It contributes to the inventory management literature by bridging two distinct streams of inventory research, inventory classification and inventory optimization, within a practical inventory modeling framework. Our study also offers several managerial implications for manufacturers and distributors managing finished goods inventory.

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 m...

We consider a product sold in multiple variants, each with uncertain demand, produced in a multi-stage process from a standard (i.e., generic) sub-assembly. The fan-out point is defined as the last process stage at which outputs are generic (outputs at every subsequent stage are variant-specific). Insights gained from an analytical study of the system are used to develop heuristics that determine the stage(s) at which safety inventory should be held. We offer a relatively-simple heuristic that approaches globally-optimal results even though it uses only two relatively-local parameters. We call this the VAPT, or value-added/processing time heuristic, because it determines whether a (local) stage should hold inventory based only on the value added at that local stage relative to its downstream stage, along with the processing time at that local stage relative to its downstream stage. Another key insight is that, contrary to possible intuition, safety inventory should not always be held at the fan-out point, although a fan-out point does hold inventory under a wider range of conditions. We also explore when postponement is most valuable and illustrate that postponement may often be less beneficial than suggested by Lee and Tang (1997).

It is vital for make-to-order manufacturers to shorten the lead time to meet the customers’ requirements. Holding work-in-process (WIP) inventory at more stations can reduce the lead time, but it also brings about higher inventory holding cost. Therefore, it is important to seek out the optimal set of stations to hold WIP inventory to minimize the total inventory holding cost, while meeting the required due date for the final product at the same time. Since the problem with deterministic processing times at the stations has been addressed, as a natural extension, in this study, we address the problem with stochastic processing times, which is more realistic in the manufacturing environment. Assuming that the processing times follow normal distributions, we propose a solution procedure using genetic algorithm.

Under the background of the wide application of condition-based maintenance (CBM) in maintenance practice, the joint optimization of maintenance and spare parts inventory is becoming a hot research to take full advantage of CBM and reduce the operational cost. In order to avoid both the high inventory level and the shortage of spare parts, an appointment policy of spare parts is first proposed based on the prediction of remaining useful lifetime, and then a corresponding joint optimization model of preventive maintenance and spare parts inventory is established. Due to the complexity of the model, the combination method of genetic algorithm and Monte Carlo is presented to get the optimal maximum inventory level, safety inventory level, potential failure threshold, and appointment threshold to minimize the cost rate. Finally, the proposed model is studied through a case study and compared with both the separate optimization and the joint optimization without appointment policy, and the results show that the proposed model is more effective. In addition, the sensitivity analysis shows that the proposed model is consistent with the actual situation of maintenance practices and inventory management.

Efficient (condition-based) maintenance planning and inventory control of spares for critical components jointly determine the effectiveness of a maintenance strategy and, thereby, balance system uptime and maintenance costs. Duplicating an optimal policy for a single-component system to a multi-component system is not necessarily optimal, while a separate or sequential optimization of the maintenance and inventory decisions is also not guaranteed to yield the lowest costs. We therefore consider the joint optimization of condition-based maintenance and spares planning for multi-component systems. We formulate our model as a Markov Decision Process, and minimize the long-run average cost per time unit. A key insight from our numerical results is that the ( s, S ) inventory policy, popular in theory as well as practice, can be far from optimal for systems consisting of few components. Significant savings can be obtained by basing both the maintenance decisions and the timing of ordering spare components on the system’s condition.

Re-order point transformed into, a time driven model, real-time inventory model.Developed real-time inventory model eliminates the safety stock.Real-time model is proven to be better by some equations.Real-time model is compared with re-order point model by a simulation experiment. Variance of demand is one of the inevitable problems in the manufacturing environment. Market conditions and competition force companies reducing costs. Different approaches and methods have been developed to remedy these problems. Common points of these approaches are utilizing resources and responding in the shortest time possible. To achieve better inventory management, inventory-holding cost is tried to be decreased under the same service level. Re-order point is arranged dynamically regarding couple of factors in some studies. We try to arrange the re-order point in a time based manner by transforming the re-order point into re-order time, which eliminates the safety stock; and it makes the inventory model real-time.Recent production planning studies focus on real-time planning and dynamic scheduling to increase utilization and robustness. In these studies, real-time data is used for planning, but in most of them manufacturing systems and planning methodology are not transformed into a real-time system approach. The novel aspect of this study is the presentation of a model in which the manufacturing system has been designed as a real-time system that consists of real-time planning activities working with real-time data, and eliminating the safety stock.

We model an inventory management setting in which the decision maker first uses newsvendor model to decide on the amount of ordered perishable inventory for a fixed consumption period, based on the best available forecast of demand at the time of ordering. After a relatively long lead time, the consumption period starts and she has to assign the received inventory to two priority customer classes given the -newly updated- rate of arrival of each class. The assignment of inventory requires two simultaneous decisions: 1) the reservation quantity for the high priority class and 2) the choice of inventory allocation mechanism (Standard Nesting SN or Theft Nesting TN); to minimize the expected units short of the high priority class while minimizing the wasted inventory at the end of the fixed consumption period. We assume that some partial information about the bottom line impact of a shortage in high priority customer class compared to the other can be conjectured. For both inventory allocation mechanisms, we then calculate the monetary benefit for all feasible reserved quantities to identify the optimal reserved quantity. We derive closed form expressions for the expected number of units short in each demand class under SN and TN allocation mechanisms. We showcase the management of electricity smart meter inventory in a multi-year implementation project consisting of multiple fixed consumption periods. Numerical experiments and graphical interpretations feature the optimum allocation policy and the cost minimizing reserved quantity under such policy.

We consider the admission control and inventory management problems of a single-component make-to-order production system. Components are purchased from suppliers in batches of fixed size subject to stochastic lead times and setup costs. A control policy specifies when a batch of components is purchased, and whether the demand for each MTO production is accepted upon arrival. We formulate the problem as a Markov decision process (MDP) model, and characterize the structure of optimal admission control and inventory replenishment policies. We show that a state dependent base-stock policy is optimal for the inventory replenishment, although the MDP value function is not necessarily convex. We also show that the optimal admission control can be identified as a lattice dependent policy. A sensitivity analysis is conducted to show how the optimal policy changes as a function of the system parameters. To effectively coordinate admission and inventory control decisions, we propose simple, implementable, and yet effective heuristic policies. Our extensive numerical results suggest that the proposed heuristics can greatly help firms to effectively coordinate their admission and inventory control activities.

When inventory management, distribution and routing decisions are determined simultaneously, implementing a vendor-managed inventory strategy, a difficult combinatorial optimization problem must be solved to determine which customers to visit, how much to replenish, and how to route the vehicles around them. This is known as the inventory-routing problem. We analyze a distribution system with one depot, one vehicle and many customers under the most commonly used inventory policy, namely the (s,S), for different values of s. In this paper we propose three different customer selection methods: big orders first, lowest storage first, and equal quantity discount. Each of these policies will select a different subset of customers to be replenished in each period. The selected customers must then be visited by a vehicle in order to deliver a commodity to satisfy the customers' demands. The system was analyzed using public benchmark instances of different sizes regarding the number of customers involved. We compare the quality and the robustness of our algorithms and detailed computational experiments show that our methods can significantly improve upon existing solutions from the literature. HighlightsWe propose three new customer selection methods for a dynamic and stochastic inventory-routing problem.We perform a multi-criteria analysis of the solutions, comparing distribution versus inventory management.We perform a single criteria objective experiment on benchmark instances from the literature.Our methods yield improvements over a competing algorithm of 20% on average.

We explore the impact of inventory inaccuracy on the product whose demand is dependent on inventory level based on a general, stochastic demand model. The shrinkage error and misplacement error are considered simultaneously in our paper. To assess the impact of inventory inaccuracy and the benefits of RFID, we consider the following three approaches of inventory decisions: (1) the retailer is not aware of the inventory inaccuracies; (2) the retailer considers the inventory errors when making inventory decisions; (3) RFID technology is adopted to reduce the inventory errors. The comparison between the first two approaches is used to assess the consequence of ignoring inventory inaccuracies, while the comparison between the second and the third approach reflects the benefits of RFID. We derive the optimal ordering quantity and expected profit analytically, and investigate the variation of available for sales quantity, optimal service level and expected optimal profit with the inventory inaccuracies explicitly. A necessary condition that the tag price should be satisfied to guarantee RFID implementation beneficial is derived. By analyzing the variation of the benefits of integrating error information and adopting RFID with the inventory inaccuracies numerically, our results imply the retailers should pay more attention to inventory inaccuracy errors when the product is characterized with high demand sensitivity.

Motivated by innovative rental business models, we study a rental system with random loss of inventory due to use. We utilize a discrete-time model in which the inventory level is chosen before the start of a finite rental season, and customers not immediately served are lost. Demand, rental durations, and rental unit lifetimes are stochastic; sample path coupling allows us to derive structural results that hold under limited distributional assumptions. Considering different “recirculation” rules (i.e., which unit to select to meet a demand), we prove the concavity of the expected profit function and identify the optimal recirculation rule under two different models of a rental unit’s state: the number of times rented out or its condition. We develop two upper bounds on the number of lost rental units and two heuristics for the inventory decision. Numerical study shows the following: (1) accounting for rental unit loss can increase the expected profit by 7% for a single season; (2) the optimal inventory level in response to increasing loss probability is nonmonotonic; (3) both heuristics perform well; and (4) choosing the optimal recirculation rule over a commonly used policy can increase the profit-maximizing service level by up to six percentage points.

[EN] The economic production quantity method is one of the simplest models to control production and inventory. However, the assumptions on which it is based make its application difficult in practical situations. In most companies, set-up and inventory costs are unknown, or may not even exist as such, although other data are known, such as available capacity in machining hours and limits set by the financing subsystem. The present work offers a multi-item inventory model that sets reference values using known values such as inventory investment limits and production capacity. In order to confer the proposed model's larger practical content, a spreadsheet available to be downloaded has been implemented. Finally, the model dynamics is illustrated as a numerical example obtained from a real company.

While sharing storage and transportation units is common for many product types, cold items usually have different temperature requirements and as a result, not all items can share a holding/transportation unit. Consequently, the type of products and their compatibility with other products needs to be considered in the inventory model. In this paper, we propose multi-product inventory models for cold items. The models determine the inventory levels that minimize either the cost or carbon-equivalent emissions. We model the compatibility of items as a constraint in the inventory model. To solve the model, we first find an appropriate “family of products” that can share a storage/transportation unit. Then, for each family of products we develop an approximate solution method to determine the order quantity of each product within a family. Numerical experiments demonstrate the solution procedure, and provide managerial insights into cold item inventory policies.

This study addresses joint inventory control and pricing decisions for a service-inventory system. In such a system both an on-hand inventory item and a positive service time are required to fulfill customer demands. The service-inventory system also captures main features of the classical inventory systems with a positive processing time, e.g., make-to-order systems. In this study the service-inventory system is modeled as an M / M / 1 queue in which the customer arrival rate is price dependent. The inventory of an individual item is continuously reviewed under an ( r , Q ) policy. The replenishment lead times of the inventory are exponentially distributed. Furthermore, customers arriving during stock-out periods are lost. The stochastic customer inter-arrival times, service times, and inventory replenishment lead times cause the high complexity of the problem and the difficulty in solving it. The aim of this study is to formulate the problem and solve it to optimality. We make three main contributions: (1) We integrate inventory control and pricing in the service-inventory system. The problem is formulated and analyzed as a fractional programming problem, and structural properties are explored for the model. (2) Two solution algorithms are proposed. The first one provides optimal solutions, while the second one is more efficient. (3) The impact of the integrated inventory control and pricing decisions on the overall system performance is investigated. We compare the solutions of the models both with and without fill-rate and service-reliability constraints and report the main interesting managerial insights.

LMI developed the PNG inventory control solution to manage inventory items with infrequent demand (i.e., isolated spikes in demand) as well as items with frequent, highly variable demand. Such items account for the majority of hardware stocked at the U.S. Defense Logistics Agency (DLA). The forecasting of demand for these items—no matter how sophisticated the forecasting method—had resulted in years of problems for DLA: excess inventory for some items, backorders for others, and excessive buyer workload. The implementation of PNG, a software package that consists of two inventory solutions, Peak Policy and Next Gen, allowed DLA to shift from trying to forecast each item individually to using a portfolio or risk-management approach to inventory control. Since DLA implemented PNG in January 2013, the agency has achieved its inventory-related goals for better customer service and reduced buyer workload, has experienced no inventory increase, and has saved nearly $400 million per year.

Many enterprises that use expensive capital goods outsource the process of spare parts supply and stocking to a maintenance-, repair-, and overhaul provider (MRO-provider). Because of the high value, these spare parts are stocked in repairable item systems, in which defective parts are repaired after removal from the machine and put back into stock. When setting inventory levels, the providers do not consider all of the spare parts that are stocked in the system because customers hold own stock to bridge the delivery time from the MRO-provider. As these spare parts are not property of the MRO-provider, the provider does not take them into account when planning inventory, incurring overstocking at the central depot. A model, which considers customer stock when determining depot inventory levels, is developed by extending the METRIC model with a heuristics. The heuristics calculates backorder levels according to the depot inventory levels as well as customer stock levels and allows for an inventory reduction. Using the proposed model, savings of depot inventory can be up to 100 %, while keeping the backorder level of part requests below a preset threshold.

Two-warehouse inventory model for deteriorating items with imperfect quality.100% screening is conducted immediately after receiving the order.The screening rate is assumed to be greater than the demand rate (no shortages).Imperfect items are sold as a single batch at a discounted price.The proposed model is a general framework as it includes numerous previous models. The formulation of inventory models is often done with the presumption that all items produced are of perfect quality in nature. However, in real-life scenarios, the lot that arrives in inventory includes a fraction of imperfect quality items due to faulty production or mishandling of goods. It is also a misconception that the goods preserve their characteristics during their presence in the inventory. Thus, these assumptions need to be rectified in the planning/preparation of inventory models. In order to reduce the losses due to deterioration, at times, the retailer is forced to rent other warehouses (RW) with better preserving facilities, owing to the lack of facilities in his own warehouse (OW). The retailer may also rent warehouses voluntarily to store excessive goods that have been obtained at a discounted price or in order to avoid inflation rates. The present research paper is, thus, an attempt to incorporate the aforementioned concepts to develop a new inventory model with two warehouses: for goods that have a fraction of imperfect quality items and those that are deteriorating in nature. The study also states the developed mathematical model along with the solution procedure. Further, the sensitivity analysis of the optimal solution with respect to key parameters of the inventory system has also been performed to develop the managerial insights.

Inventories are idle resources which are maintained by every organisation for efficient and smooth running of its operations. So here we are going to control the inventory cost by minimizing the inventory cost. For maintaining optimum inventory, one must consider the appropriate reorder point as well as the economic order quantity. We propose an efficient genetic algorithm approach to the find economic order quantity at a proper reorder point. Genetic algorithm is an optimization method used for solving multi-variable optimization problems. Job-shops handle a variety of jobs, where each job is different and different inventory levels are to be maintained. A case study of a power plant where different types of raw materials are used. The proposed approach is tested on C-programme.

In this paper, we develop an ( s , S ) inventory model to determine inventory levels of floor stock items (FSIs) in a manufacturing company producing paint products. FSIs are raw materials that are transferred from the main warehouse to be stored at the periphery of production area for a temporary period of time to be used in the manufacturing process. These items are commonly and frequently used materials during production; hence the optimum inventory levels of these materials to be carried in the floor stock storage areas (FSSAs) to prevent costly interruptions in the manufacturing processes should be determined. We model the problem as a multi-item, single-echelon ( s , S ) inventory policy where items can be stored in stocking locations that are supplied by an indoor vendor (i.e., the main warehouse) with infinite capacity. The objective is to minimize the average number of daily replenishments dispatched from the main warehouse subject to the capacity of FSSAs and satisfying a pre-defined fill rate. We consider stochastic demand and lead time, and use simulation for the optimization purpose. Finally, we present and discuss the numerical results of the study along with our conclusions.

Inventory record inaccuracy (IRI) is a major obstacle to realizing operational excellence and lean enterprise principles in the supply chain. This paper proposes an analytical model to study the operational and economic impacts of inventory record inaccuracy stemming from theft-type error in a continuous-review lost sale (s, Q) inventory system. The model builds on a Markovian inventory system and finds optimal safety stock and reorder quantity of product by minimizing the expected costs, which are composed of four components: inventory holding cost, production cost, ordering cost, and lost sales cost. This production-inventory model is analyzed across two scenarios depending on which technology is deployed to optimize replenishment decisions: (1) barcode technology and (2) radio frequency identification (RFID) technology. Our numerical analysis illustrates the solution procedure and the effects of the model parameters on the inventory replenishment decisions and total costs.

The interconnection of maintenance and spare part inventory management often puzzles managers and researchers. The deterioration of the inventory affects decision-making and increases losses. Block replacement and periodic review inventory policies were here used to evaluate a joint optimization problem for multi-unit systems in the presence of inventory deterioration. The deterministic deteriorating inventory (DDI) model was used to describe deteriorating inventory when deteriorating inventory data were available and the stochastic deteriorating inventory (SDI) model was used when they were not. Analytical joint optimization models were established, and the preventive replacement interval and the maximum inventory level served as decision variables to minimize the expected system total cost rate. This work proved the existence of the optimal maximum inventory level and gave the uniqueness condition under the DDI model. Numerical experiments based on the electric locomotives in Slovenian Railways were performed to confirm the effectiveness of the proposed models. Results showed the total cost rate to be sensitive to the maximum inventory level, which indicates that the research of this work is necessary. Further, the optimal preventive replacement interval was reduced and the optimal maximum inventory level was increased to balance the influence of deteriorating inventory. Monte Carlo experiments were used to show that the proposed policy is better than policies that do not take deteriorating inventory into account.

In this paper, the authors propose a reliability based design optimization approach for optimal inventory design in emergency operation, considering reliability target. In this context, the reliability of an inventory system is defined as the probability that the available inventory level meets all population demand during crisis period. Inventory support for emergency operations is complicated by uncertainties of demands and duration of crisis period. The inventory system is considered as a structure that undergoes an external load represented by the demand of population during crisis period. Moreover, the supply chain resists to this load by its strength represented by the quantity of products available at the distribution center. Reliability based design optimization approach is proposed to perform an optimal design of inventory system in emergency operation without making any particular assumption on distribution of population demand and duration crisis period. The simulation software SIMIO is used to validate the proposed method by conducing simulation analysis using several examples with different distributions of population demand and duration crisis period. The results indicate clearly that the proposed method gives an optimal design with reliability much closer to target value.

This research focuses on the method of inventory management to find out the best way in various SCM. There are several comparable methods of inventory management like Min-Max, regular interval, fixed target inventory that have different lead-time and demand for each product. Also, the inventory performances between the case of three- stages and two-stages in SCM are analyzed. 8 scenarios for the simulation are suggested considering order ways and stages. Input data for the simulation is from the result of the previous studies and actual data in the market. The performance measurements are selected as sales revenue, total number of sales, total delivery cost, total inventory shortages, the number of inventory shortage occurrence, the loss for inventory shortages, average inventory, handling cost, net profit. According to the results of the simulation, fixed target inventory management is better than existing inventory management method by net profit and inventory and so on. Also, it was confirmed that the increase in profit is obtained when SCM stages is reduced from three-stages to two-stages in existing SCM. The result of the research provides significant meaning that newly introduced target inventory management system shows good performance and SCM performance can be improved by reducing the stages.

This paper investigates inventory management problems for fast-moving consumer goods (FMCG) with the Physical Internet (interconnected logistic services) where goods are stored and distributed in an interconnected and open network of hubs. Unlike current hierarchical inventory model where the source assignment is predetermined for each replenishment order either from the retailers or from the warehouses, the PI-inventory model enables multiple source selection options to each order and transshipments of inventories among hubs, resulting in complete pooling of inventories within the network. We adopt a continuous review (Q, R) ordering policy for each facility and propose four dynamic source selection strategies related to the supplying nodes' inventory levels, the distance and lead time between the supplying and the ordering nodes. The objective is to determine the optimal replenishment policies for hubs in order to minimize the total logistic costs of the distribution system. A nonlinear optimization model is proposed and a heuristic using simulated annealing is applied. A simulation study based on two categories of FMCG is taken to evaluate the performance of the proposed inventory control policies. Our results suggest that compared to current centralized inventory control policy, the PI-inventory control model with proposed source selection strategies can significantly reduce the total costs as well as the average inventory level of the network of hubs while reaching the same end customer service level.

In a production-inventory system, the manufacturer produces the items at a rate, e.g. R, dispatches the order quantities to the customers in specific intervals and stores the excess inventory for subsequent deliveries. Therefore each inventory cycle of the manufacturer can be divided into two phases, first is the period of production, the second is when the manufacturer does not do any production and utilises the inventory that is in stock. One of the challenges in these models is how to obtain the inventory level of the supplier when there is deterioration. The existing literature that considers multi-echelon systems (including models with single-buyer or multi-buyer), analyses the deterioration/inventory cost of these echelons with the assumption of having huge surplus in production capacity. Then it seems acceptable to drop part of the production period which is for producing the first batch(s) for buyer(s) at the beginning of each production period. In this paper we develop a single-manufacturer, multi-buyer model for a deteriorating item with finite production rate. We also relax the assumption on the production capacity and find the average inventory of the supplier. It is shown that in case the production rate is not high, the existing models may not be sufficiently accurate. It is also illustrated that these models are more applicable to inventory systems (and not production-inventory) as they result in fairly accurate solutions when the manufacturer has much higher production capacity compared to the demand rate. Also a sensitivity analysis is conducted to show how the model reacts to changes in parameters.

This paper studies inventory models for a perishable product with lost sales under the first-in-first-out inventory issuing rule. The lifetime of perishable product is equal to two selling periods. At the beginning of the first period, the manager decides an order quantity, and at the end of the period he has a chance to reorder or return some items. Therefore, we develop two kinds of inventory models, i.e., one is the inventory model with reorder policy and the other is the inventory model with return policy. We find that each inventory model is determined by an inventory threshold. A high inventory threshold is for the return policy, and a low inventory threshold is for the reorder policy. The reorder policy will be used only when the surplus inventory at the end of the first period is less than the low threshold, while the reorder policy will be used only when the surplus inventory at the end of the first period is higher than the high threshold. We also show that each inventory policy has its merit and demerit.

Our work focuses on the quality information aspect of the inventory management domain of the supply chain in the organization. Within the scope of quality information we have concentrated upon the discrepancies known as inventory errors, which are the result of events that can be classified as one of the following three types: Shrinkage, Misplacement and Wrong Scanning [1]. These events lead to differences between inventory reflected in the IT system records and available inventory for sale. These discrepancies and/or their associated negative effects can be minimized should additional information be available. Our work explores four different models that operationally take the discrepancies into consideration in four different scenarios: (1) No Information Scenario, (2) Static Informed Scenario, (3) Informed Scenario, and (4) Full Information Scenario. The objective of our work is to explore and quantify the value of the additional information in Scenarios 2 - 4 compared to the No Information Scenario. We implemented an analytical model for each scenario, where the objective in all four scenarios is to minimize the average expected cost per period. Our findings indicate that additional information greatly influences optimal policies and, if exploited wisely, can save a great deal of money

It addresses some of the literature gaps and overcome curse of dimension problem.The model considers eight blood types with stochastic demand, stochastic supply.It uses news vendor model and the current inventory to control the supply uncertainty.It uses linear programing to solve the single-period inventory model optimally.It uses ADP to solve the multi-period model without any downsizing. This paper introduces a workable model for the establishment of an inventory bank holding perishable blood platelets with a short shelf life. The model considers a blood platelet bank with eight blood types, stochastic demand, stochastic supply, and deterministic lead time. The model is formulated using approximate dynamic programming. The model is evaluated in terms of four measures of effectiveness: blood platelet shortage, outdating, inventory level, and reward gained. Moreover, several alternative inventory control policies are analyzed. The order quantity decision is taken using a news-vendor model. In addition, the variation of the O- percentage is studied. This study confirms that the blood platelet bank reward can be maximized by operating at the optimal inventory level, thereby minimizing the number of outdated units as well as shortages. In addition, the suitable O- percentage within the blood platelet bank inventory was studied. As the O- blood type inventory levels increase to 40%, shortages drop from 3.9% to 1.5%. Outdated units drop from 4.6% to 1.8%. Furthermore, when the order quantity is received twice a day, shortages drop to 1.8% and outdated units drop to 2.1%.

Nowadays, Inventory management has become a major concern for company because it can bring value added. Inventory management focused on maintaining and managing the inventory level in order to fulfill the customer demand with the lowest cost. In retailing and wholesaling, multi items from the same supplier are jointly ordered. This mean, the order interval of each item must be the same. But, there are several manufacturing companies that have not achieving the optimum solution in inventory management for multi items. It leads the company to increase the total inventory cost. Most of them are caused by the improper order interval. Thus, appropriate of inventory management may reduce the total inventory cost. This research tried to identify this problem. The first step that should be done is by identifying the problems, scopes, and objectives of the research which aimed to minimize the inventory control management. Then collecting data and analyzing the supported data in inventory management. By using the total inventory cost analysis as a parameter in comparing the actual system and the proposal system. This research also used forecasting methodology in order to predicting the future demand. In the final section is by implementing the Economic Order Interval in ordering process for determining the optimum order interval. The result in this thesis show that the proposal system can reduce the total inventory cost over 21% from the current condition. Keywords : Inventory control and management, multi-item, Economic Order Interval, total inventory cost, forecasting.

Traditionally, lot sizing decisions in inventory management trade-off the cost of placing orders against the cost of holding inventory. However, when these lot sizes are to be produced in a finite capacity production/inventory system, the lot size has an important impact on the lead times, which in turn determine inventory levels (and costs). In this paper we study the lot sizing decision in a production/inventory setting, where lead times are determined by a queueing model that is linked endogenously to the orders placed by the inventory model. Assuming a continuous review (s, S) inventory policy, we develop a procedure to obtain the distribution of lead times and the distribution of inventory levels, when lead times are endogenously determined by the inventory model. This procedure allows to determine the optimal inventory parameters within the class of (s, S) policies that minimize the expected ordering and inventory related costs over time. We numerically show that ignoring the endogeneity of lead times may lead to inappropriate lot sizing decisions and significantly higher costs. This cost discrepancy is very outspoken if the lot size based on the economic order quantity deviates significantly from desirable production lot sizes. In these cases, the endogenous treatment of lead times is of particular importance. (C) 2014 Elsevier B.V. All rights reserved.

In almost all of the organizations, the cost for acquiring and maintaining the inventory takes a considerable portion of the management budget, and thus a certain constraint is set upon the budget itself. The previous studies on inventory control for each item that aimed to improve the fill rate, backorder, and the expenditure on inventory are fitting for the commercially-operated SCM, but show some discrepancies when they are applied to the spare parts for repairing disabled systems. Therefore, many studies on systematic approach concept considering spare parts of various kinds simultaneously have been conducted to achieve effective performance for the inventory control at a lower cost, and primarily, METRIC series models can be named. However, the past studies were limited when dealing with the probability distributions for representing the situation on demand and transportation of the parts, with the (S-1, S) inventory control policy, and so on. To address these shortcomings, the Continuous Time Markov Chain (CTMC) model, which considers the phase-type distributions and the (s, Q) inventory control policies to best describe the real-world situations inclusively, is presented in this study. Additionally, by considering the cost versus the system availability, the optimization of the inventory level, based on this model, is also covered.

Various authors have suggested that the cost of purchasing and holding inventory can account for as much as 60%–80% of the total cost of a product or service. Usually, only some of the inventory (about 10%) contributes to 70%–80% of locked up inventory or has 70%–80% consumption value or their availability is vital for maintenance of the manufacturing process. Hence, the need for an effective and efficient inventory management is germane to any manufacturing industry. This paper presents the software, ‘manInvent’, an interactive system developed using visual basic 6.0, with a view to reduce manual interaction with the inventory management of a Nigerian manufacturing industry. Working with 5 years’ production data for the industry, the objective of the software ‘manInvent’ was validated with respect to the manual inventory system of the industry, and the result is shown.

Firstly, the inventory models were analyzed, and the symbol and assumptions were given out. Then the models of the distribution centre and supplier were studied. Finally, a numeric example was given out by applying the models. The results show that the models are easily to be carried out in engineering field, and it is useful to control the inventory and raise the store efficiency.

Inventory is an important link in supply chain, according to characteristics and objectives of inventory control, the reasonable safety inventory, order point, the highest inventory and inventory strategy were determined based on historical sales data. Using Witness simulation software, the optimal strategy was selected from nine inventory strategies by running simulation model for one year. If goods shortage was allowing, the seventh schemes (72000,158100) was better ,the minimum total cost was 888158 yuan ; considering allowing a small amount of shortage, the cost of eighth schemes ( 82000,158100) was least of 1095167; the third scenarios (92000,138100) was selected if without shortage, and the minimum total cost was 1254909 yuan. This shows, the application of computer simulation to assist in the management of inventory is scientific and feasible, it can help enterprises to save the order and operation cost.

Inventory managers often group inventory items into classes to manage and control them more efficiently. The well-known ABC inventory classification approach categorizes inventory items into A, B and C classes according to their sales and usage volume. In this paper, we present an optimization model to enhance the quality of inventory grouping. Our model simultaneously optimizes the number of inventory groups, their corresponding service levels and assignment of SKUs to groups, under limited inventory spending budget. Our methodology provides inventory and purchasing managers with a decision-support tool to optimally exploit the tradeoff among service level, inventory cost and net profit. The model and solution are applied for an inventory classification project of a real-life company, and outperform the traditional ABC method. Computational experiments are performed to obtain managerial insights on optimal inventory grouping decisions.

The success parameters for any company are on time completion, within specific budget and with requisite performance. In particular an efficient and effective inventory management helps a firm maintaining competitive advantage, especially in a time of accelerating globalization. From this point of view several organizations employ the ABC analysis to have an efficient control on a large number of inventory items. With the increasing levels of integration in manufacturing and service systems conventional ABC analysis is limited because it accounts for only one criterion, mostly “annual dollar usage”, for classifying inventory items. To alleviate this shortcoming, this paper proposes a modified version of ABC analysis and Cross Analysis based on Analytic Network Process, a multicriteria approach, that allows to consider several criteria all at once for the optimal choice of materials management.

The inventory problem consists of two parts; one is the modeling which can provide insight to solve the inventory problem and the other is the solution because the accuracy of the solution procedure can help and simplify the implementation of the inventory. In other words, the modeling and the solution procedure to the inventory problem are equally important. Musa and Sani (2012) explore an EOQ model for non-instantaneous deteriorating items with permissible delay in payments. However, some of their inventory models are incorrect which will result in some of their solution procedures are invalid. In addition, the solution procedure in Musa and Sani (2012) seems easy to cause misunderstandings. Consequently, the main purpose of this paper is twofold:This paper reestablishes valid inventory models for Musa and Sani (2012). From the practical view, we assumed that the demand rate during the period before the deterioration sets in is bigger than the demand rate after the deterioration sets in.This pap...

The inventory model in this paper is targeted to production systems with constant production rates but underlying possibilities for undesirable circumstances to threaten the production schedule. The inventory policy proposed explicitly considers energy cost when determining optimal size for order quantity, safety stock and inventory cycle length such that the total expected cost per unit time is minimised. The results are compared to a traditional inventory policy that does not consider the direct impact of energy cost. An analysis of the model reveals three production environment characteristics in which inventory policies are most significantly affected by changes in energy cost: heavy product weight, high regular product demand or high emergency product demand. If any one of the three key factors increases, then changes of the inventory decisions or related logistics costs become more significant. The cost effectiveness of implementing the proposed inventory policy also becomes more significant as any ...

The major purpose of this paper is to present a concept for reliable planning of the sales of small firms, where the large number of product variants complicates the implementation of this kind of system considerably. First, a methodology is presented to set up sales forecasting so that it can be integrated into the inventory management process. This inventory management software interprets forecasting information and provides users with a decision support system to minimize stocks in stores while at the same time avoiding missed sales. It is best applied in company types requiring high precision inventories, notably those in the textile industry; a large range of patterns are produced with many small variations (colors, size, customizations, etc.) and these products have a limited lifetime. Inventory management is difficult due to the multitude of products to account for and the necessity to sell them quickly. The methodology is intended for inventory management at the end of the supply chain. In store, ...

Firstly, the inventory control models were studied, considering of the cost of store and lack of stock. Secondly, both the models of the distribution centre and supplier were presented. Finally, a numeric example was analyzed by applying the models. The results show that the models are easily to be carried out in engineering field, and it is useful to control the inventory and raise the store efficiency.

Impact of display stock has been extensively discussed in literature in terms of stock dependent demand rate. In some recent papers a three-component inventory level dependent demand (as displayed stock level (DSL)) has been proposed. It has been assumed that demand is independent of instantaneous inventory level if inventory is above a certain level in a shelf. As the demand is independent of instantaneous inventory level, a retailer is always in dilemma whether inventory additional to that level will be kept in the shelf or to rent a new storage. In this paper we focus on this by developing three mathematical models. We show that the problem in choosing rented storage or shelf to keep additional inventory can always be eliminated (a) without the knowledge of optimal stocking points or (b) with the knowledge of any one optimal stocking point. Also, if there is enough opportunity to have an extra storage and a mild limit on inventory holding cost in the shelf is satisfied, then there is no need to display inventory in the shelf above the certain level. The results are illustrated by numerical examples and sensitivity analysis is carried out.

The International Financial Reporting Standards (IFRS) No. 2 has been the worldwide accounting principle for the reduction of inventory to market allowance since January 1, 2005. Using make-to-stock manufacturing strategies and inventory accounting for only approximately 14% of the total costs, integrated device manufacturers have found maintaining robust records for financial statements increasingly difficult. For example, one company in the case study conducted in this study must write-down losses of 2-100% of the total inventory costs for products with inventory ages of 18months-3years. However, the average cycle time for producing flash memory is approximately 3months. In other words, when the system variation and safety stock policy are considered, the company must write-down the reduction of inventory to market allowance for most of work-in-process inventory. However, little research has been done to addressing the practical management of operations according to inventory aging processes. This study develops a polynomial-time-based model to obtain significant features, including inventory ages, accounting principles, and product structures (bill of material), for the accurate prediction of inventory write-downs to reduce the impact of the carrying value fluctuation of inventory. An empirical study was conducted on a Taiwanese semiconductor manufacturer. The results show that predicting 3-month inventory write-downs of a complete flash memory production line comprising approximately 8500 product types can be conducted in less than 10s, with the mean absolute percentage error (MAPE) less than 3.5%. Discussions regarding the sensitivity analysis and cost tornado diagrams suggest the priority of affecting factors. The results show the viability of implementing the proposed model to predict inventory write-downs in the semiconductor manufacturing industry.

Reliability and inventory levels of spare parts are major factors that determine the service level for the maintenance of machines provided by original equipment manufacturers (OEMs). In general, decisions on reliability and stock levels are made separately in practice, and academic literature offers little guidance on how to jointly make these two decisions. In order to fill in the gap in the literature and provide guidance to OEMs, we jointly model reliability and inventory problems. We consider three different service measures: aggregate fill rate, average downtime per system per year and expected total number of long downs in a year. Our models minimize the sum of holding and emergency shipment costs subject to a limited reliability improvement budget and a target service level. We develop an algorithm that considers reliability and inventory decisions simultaneously, test our solution approach on real-life and randomly generated data sets and compare the results with an approach that considers reliability and inventory decisions sequentially. Numerical results show substantial benefits of integrating reliability and inventory decisions.

This paper studies an integrated scheduling problem for a single-item, make-to-order supply chain system consisting of one supplier, one capacitated transporter and one customer. Specifically, we assume the existence in the production stage of an intermediate inventory that works as a buffer to balance the production rate and the transportation speed. Jobs are first processed on a single machine in the production stage, and then delivered to the pre-specified customer by a capacitated vehicle in the delivery stage. Each job has a due date specified by the customer, and must be delivered to the customer before its due date. Moreover, it is assumed that a job that is finished before its departure date or arrives at the customer before its due date will incur a stage-dependent corresponding inventory cost (WIP inventory, finished-good inventory or customer inventory cost). The objective is to find a coordinated production and delivery schedule such that the sum of setup, delivery and inventory costs is minim...

Service Parts Logistics (SPL) problems induce strong interaction between network design and inventory stocking due to high costs and low demands of parts and response time based service requirements. These pressures motivate the inventory sharing practice among stocking facilities. We incorporate inventory sharing effects within a simplified version of the integrated SPL problem, capturing the sharing fill rates in 2-facility inventory sharing pools. The problem decides which facilities in which pools should be stocked and how the demand should be allocated to stocked facilities, given full inventory sharing between the facilities within each pool so as to minimize the total facility, inventory and transportation costs subject to a time-based service level constraint. Our analysis for the single pool problem leads us to model this otherwise non-linear integer optimization problem as a modified version of the binary knapsack problem. Our numerical results show that a greedy heuristic for a network of 100 facilities is on average within 0.12% of the optimal solution. Furthermore, we observe that a greater degree of sharing occurs when a large amount of customer demands are located in the area overlapping the time windows of both facilities in 2-facility pools.