Reorder Level Research Articles

Two-commodity queueing-inventory system with two classes of customers

We analyze a two-commodity queueing-inventory system with an individual ordering policy. The maximum storage capacity for the ith commodity is $$S_i$$ $$(i=1,2)$$ . The reorder level for ith commodity is fixed as $$s_i$$ and whenever the inventory level of ith commodity falls on $$s_i$$ an order for $$Q_i\,(=S_i-s_i)$$ items ( $$i=1,2$$ ) is placed for that commodity irrespective of the inventory level of the other commodity. There are two types of customers, which are classified as a priority (Type-1) and ordinary (Type-2) customers. Priority customers demand only commodity-1, whereas ordinary customers demand only commodity-2. Each customer class arrives according to an independent Poisson process with different rates. Service time for each customer class is also independent of each other and follows an exponential distribution. Type-1 customers have non-preemptive priority over Type-2 customers. When the server is idle and there are both types of customers, then the service is offered to Type-1 customers. Type-2 customers have got service when there are no Type-1 customers waiting. It is assumed that waiting space for the priority customers is finite whereas there is no queue capacity for ordinary customers. The system is formulated by a five-dimensional continuous-time Markov chain. The structure of the infinitesimal generator matrix is shown to be of the QBD type. Steady-state distribution is obtained using the matrix-geometric method. The system load is formulated in a closed-form. A comprehensive numerical study is performed on the performance measures. Finally, an optimization study is presented.

On the distribution-free continuous-review production-inventory model with service level constraint

In this article, we study a continuous-review production-inventory model that assembles lost sales and backorders with service level constraint. The study under consideration assumes that the distribution of demand during the lead-time is known partially. The objective of this paper is twofold. Firstly, the distribution-free procedure is applied to obtain a closed-form solution of optimal production quantity, re-order level and lead-time in the random framework. Secondly, considering demand as a fuzzy random variable, the procedure is extended to the fuzzy random framework in which an algorithm is proposed to find the optimal global solution. Two numerical examples are provided to illustrate the methods. Furthermore, sensitivity analysis is performed to present some managerial inferences.

Supply chain management and market responsiveness: a simulation study

PurposeThis study aims to provide a practical solution to the relationship between supply chain (SC) integration and market responsiveness (MR). A method is proposed to integrate SC and MR parameters, namely, product supply and demand in the context of low-value commodities (e.g. cement).Design/methodology/approachSimulation and forecasting approaches are adopted to develop a potential procedure for addressing demand during lead time. To establish inventory measurements (safety stock and reorder level) and increase MR and the satisfaction of customer’s needs, this study considers a downstream SC including manufacturers, depots and central distribution centers that satisfies an unbounded number of customers, which, in turn, transport the cement from the industrialist.FindingsThe demand during lead time is shown to follow a gamma distribution, a rare probability distribution that has not been considered in previous studies. Moreover, inventory measurements, such as the safety stock, depending on the safety factor under a certain service level (SL), which enables the SC to handle different responsiveness levels in accordance with customer requests. In addition, the quantities of the safety stock and reorder point represent an optimal value at each position to avoid over- or understocking. The role of SC characteristics in MR has largely been ignored in existing research.Originality/valueThis study applies SC flexibility analyzes to overcome the obstacles of analytical methods, especially when the production process involves probabilistic variables such as product availability and demand. The use of an efficient method for analyzing the forecasting results is an unprecedented idea that is proven efficacious in investigating non-dominated solutions. This approach provides near-optimal solutions to the trade-off between different levels of demand and the SC responsiveness (SLs) with minimal experimentation times.

An Evaluation of Inventory System via Evidence Theory for Deteriorating Items under Uncertain Condition and Advanced Payment

The inventory model for deteriorating items, which is developed by The Evidential Reasoning Algorithm (ERA) and the imprecise inventory costs, is one of the most important factors in complex systems which plays a vital role in Payment. The ERA is able to strengthen the precision of the model and give the perfect interval-valued utility. In this model, during lead-time and reorder level two different cases can be happened which the mathematical model turns into an imposed nonlinear mixed integer problem with interval objective for each case. Placement of an order, which is overlooked by many researchers till now, is normally connected with the advance payment (AP) in business. Specifying the optimal profit and the optimal number of cycles in the finite time horizon and lot-sizing in each cycle, are our goals so. In order to solve this model, we apply the real-coded genetic algorithm (RCGA) with ranking selection. By the model, we represent some numerical examples and also a sensitivity analysis with the variation of different inventory parameters.

Adaptive Proximate Computing Framework for Mobile Resource Augmentation

Beyond voice and message communication, mobile devices are exploiting to access Internet resources like desktop. Nowadays, MobileApp development and its usage in various domains are also significantly rising. However, resource constraints of mobile devices like limited processing power, low storage, restricted memory and faster dissipation of energy have restricted resource intensive mobile application development and its accessibility. Cloud based mobile resource augmentation needs longer latency time due to larger number of intermediate hubs thereby prolonged execution time and deterioration of energy from mobile devices; hence, we are exploiting proximate computing entities for augmenting resources of the mobile devices by employing soft computing methodologies. The proposed proximate computing framework is intended to augment the resource scarcity of mobile devices by outsourcing their data and processing to an external proximate computing entity like an edge cloud, Raspberry PI controller, Arduino, WiFi Gateway and MNO cloud. An intelligent inventory checker mobile application which is based on the proposed framework, depicts significant mitigates in execution time and energy consumption of mobile devices. Proximate computing entities namely Arduino and Edge Cloud service have provided computation as a service to check the reorder level of every stock thereby providing seamless user experience to the mobile users. This research work provides a feasible solution for the development of resource intensive mobile application and its accessibility by mobile user regardless of the resource scarcity of mobile device.

Design of Stock Control System for Petrol Station

A stock control system is basically a database; it must keep the up-to-date records of all the stock. Adequate stock must be maintained to supply the customers with their needs and minimum delay. In manual inventory system, the information is difficult to share from one person to another and unexpected difficult is human error. To alleviate these difficulties and to ascertain the exact stock level of a company, this paper presents a design of stock control system on a petrol station using stock levels of inventory system such as reorder level, economic order quantity, and maximum level. To demonstrate the presented system, we apply it to a number of sale data in Lucky 7 petrol station in Mandalay. The stock control system is implemented by C# programming language and SQL server database.

Inventory Management and Organisational Efficiency

The study examined inventory management and organisational efficiency. The classical inventory management techniques were applied to an organisation’s inventory system. A door sales company in Ilorin, Nigeria that volunteered information on the basis of anonymity was used and relevant data were collected on six types of doors; panel, flush, sliding, folding and as well as manual and electronic garage doors. The company had no scientific inventory management strategy but the EOQ, inventory cycle time and reorder level were computed for the six doors using the average values of the data obtained for 2011-2017. The results show that the company can minimise its total inventory cost by consciously adopting an inventory management policy of ordering 55 panel doors, 41 flush doors, 41 sliding glass doors, 18 folding doors, 18 manual garage doors and 8 electronic garage doors respectively every cycle time of 17, 14, 17, 30, 35 and 34 days respectively or by placing the above orders each time the inventory level of panel doors, flush doors, sliding glass doors, folding doors, manual garage doors and electronic garage doors fall to 32, 24, 24, 5, 3 and 2 respectively and thus minimise the total cost of inventory.

Exact analysis of (R, s, S) inventory control systems with lost sales and zero lead time

AbstractWe study an (R, s, S) inventory control policy with stochastic demand, lost sales, zero lead‐time and a target service level to be satisfied. The system is modeled as a discrete time Markov chain for which we present a novel approach to derive exact closed‐form solutions for the limiting distribution of the on‐hand inventory level at the end of a review period, given the reorder level (s) and order‐up‐to level (S). We then establish a relationship between the limiting distributions for adjacent values of the reorder point that is used in an efficient recursive algorithm to determine the optimal parameter values of the (R, s, S) replenishment policy. The algorithm is easy to implement and entails less effort than solving the steady‐state equations for the corresponding Markov model. Point‐of‐use hospital inventory systems share the essential characteristics of the inventory system we model, and a case study using real data from such a system shows that with our approach, optimal policies with significant savings in inventory management effort are easily obtained for a large family of items.

On the distribution-free continuous review (Q, r, L) inventory model with lead-time-dependent partial backlogging

ABSTRACTIn this article, we study a distribution-free continuous review inventory system that assembles lost sales and backorders. The order quantity, the reorder point and lead-time are decision variables of the problem. The study under consideration assumes that purchasing cost is order dependent, partial backlogging is lead-time dependent and the distribution of lead-time demand is known partially. The objective of the paper is twofold. First, in the random framework, we provide the optimal order quantity, reorder level and lead-time simultaneously to acquire significant savings in the total costs of the model than Sana and Goyal ((q, r, l) model for stochastic demand with lead-time dependent partial backlogging. Annals of Operations Research. 233(1), 401–410, 2015). Second, we extend the crisp model to the fuzzy random environment employing the uncertain demand rate as a fuzzy random variable. We develop the mathematical methodologies to obtain the optimal solutions such that the total expected cost of the inventory system is minimized in both the cases. An algorithm is proposed with the help of the developed methodologies. We employ the proposed algorithm to solve two numerical examples to demonstrate the effectiveness of the methods.

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.

About New Approaches to Management of Finance Enterprises

Every enterprise in modern society has generation of profit as its goal, and its performance can be determined by how effectively and efficiently its current assets are used. The purpose of this study is to identify and systematize modern approaches to financial management of enterprises. The relevance of this work is conditioned by the fact that in the conditions of market relations the approaches to working capital management of an enterprise have changed significantly. The methodological basis of the study is the achievements of economic theory, theory of finance, financial management, financial policy, and economic analysis, accounting and reporting. This research is based on such a methodology as scientific cognition, and also systemic and dialectical approaches. The problem on financial management of enterprises is always associated with a number of conditions or factors that affect the operation of a company. In this regard, the authors consider the research in the field of approaches to managing current assets in modern conditions, which is the most relevant in the current period. At the present stage, in order to ensure the long-term productive and effective financial activity of a company, a company’s working capital management policy is necessary, which would consist in determining the volume and structure of the current assets of the organization, as well as the sources of their coverage and the relationship between them. In this study, the general trends are defined in that the management of enterprise’s finances is reduced to the implementation of general management functions. This expresses a functional approach to the management of a company and implies the continuity and cyclicity of managerial work. Practical results of the work define that an enterprise cannot achieve the maximum effect by managing only accounts receivable, stocks or cash. Only after a comprehensive analysis reviewing each of the components of working capital in the enterprise, the best results of management could be achieved, because they are all interconnected. In this study, we examined the main approaches to the management of working capital of an enterprise; that has revealed that in market conditions an approach is needed on the application of two types of working capital management. This allows financial managers of modern enterprises to implement more informed management decisions (Forfaiting, Just-in-Time Approach, Re-Order Level System), i.e. the choice of one type is not always the right solution in working capital management at an enterprise.

Optimizing constant pricing and inventory decisions for a periodic review system with batch ordering

In business practice the misalignment between frequent inventory adjustments and a sticky price is common. Motivated by Pipi Milk, a diary manufacturer who commits its customer at a fixed price for a whole year meanwhile weekly replenishes its cheese product stock at a specific batch size, this paper develops an integrated constant pricing and inventory control model in a periodic-review system with batch order consideration. For such a system, our focus is on an (nQ, r)-p policy: the price p is determined and committed at the beginning of the planning horizon; inventories are managed based on an (nQ, r) policy with reorder level r and order quantities at non-negative integer multiples of batch size Q. With a fixed ordering cost included, we develop an efficient algorithm to compute the optimal policy parameters with the purpose to maximize the expected long-run average profit. We also numerically investigate the policy performance and verify its advantages by comparing to a sequential optimization of price and inventory decisions. Finally, managerial insights are generated.

On the $$\varvec{(Q,r)}$$(Q,r) policy for perishables with positive lead times and multiple outstanding orders

We consider an inventory system for perishables with fixed lifetimes, positive replenishment lead times and lost sales in the presence of non-negligible fixed ordering costs. The system is studied under the lotsize reorder level (Q, r) policy. An exact analysis of this system based on the stationary distribution of the remaining lifetime process is provided by Berk and Gurler (Oper Res 56(5):1238–1246, 2008) under the restriction that there is at most one outstanding order at any time ($$r<Q$$). In this work, we generalize their results to allow for more than one outstanding orders $$(r\ge Q)$$. We provide the operating characteristics of the inventory system and construct the exact expected cost rate expression using a renewal theoretic approach. An illustrative numerical study indicates that allowing for multiple outstanding orders $$(r\ge Q)$$ may result in significant savings in the expected cost rate, compared to the case with $$r<Q$$. In particular, when the fixed lifetimes are short and the ordering costs are low, expected costs can be reduced by more than half.

Effect of Packet Loss and Reorder on Quality of Audio Streaming

In this paper, Audio QoE assessment experiments were conducted by using network parameters such as packet loss and reorder. The network model was created by using three computers, which includes sender, receiver, and middle between Linux based router machines was configured. NetEm tool was used to disturb the on-going network traffic to real-time packet loss and reorder in the broadcasting environment. VLC player was used to broadcasting and recording audio from sender to receiver machines and audios were presented to users to listen, and assign ratings according to their level of satisfaction. The results show the user’s satisfaction level is decreased when packet loss and reorder level is increased in audio streams. The user accepted a certain level of network traffic disturbance, however, the increment of disturbance in network traffic damage the audio quality below the then acceptable level. This research work will help to cloud service providers to provide QoE to users according to singed in service level agreements.

Constrained integrated inventory model for multi-item under mixture of distributions

When the demand of different customers are not identical during the lead time, then one cannot use only a single distribution to describe the demand during that lead time. Hence, in this paper we have studied a mixture of normal distributions and a mixture of distribution free for several products under vendor-buyer integrated approach (coordination between both parties). Many integrated inventory models have proved that the integrated total cost is minimum when compared to sum of the total cost of the individuals. The inventory is continuously reviewed by the buyer and next order is placed when the inventory reaches some level called reorder level. The buyer has limited warehouse space capacity and also limited budget to purchase all products. The lead time of receiving all products from the vendor is a variable which is controlled by adding crashing cost. Shortages are allowed for all products and a fraction of shortages will be backordered and the remaining are lost. A mathematical model is developed and a solution procedure is employed in this study to obtain optimum order quantities, lead time and number of shipments in which the integrated total cost function attains its minimum subject to the floor space constraint and budget constraint. The expected integrated cost function is non-linear mixed integer with inequality constraints. Therefore, the proposed model have been solved by using Lagrangian multiplier technique. Finally numerical examples and sensitivity analysis were performed to illustrate the effectiveness of the proposed model.

A Modified Bayesian Network Model to Predict Reorder Level of Printed Circuit Board

Identifying the printed circuit board (PCB) orders with high reorder frequency for batch production can facilitate production capacity balance and reduce cost. In this paper, the repeated orders identification problem is transformed to a reorder level prediction problem. A prediction model based on a modified Bayesian network (BN) with Monte Carlo simulations is presented to identify related variables and evaluate their effects on the reorder level. From the historically accumulated data, different characteristic variables are extracted and specified for the model. Normalization and principal component analysis (PCA) are employed to reduce differences and the redundancy of the datasets, respectively. Entropy minimization based binning is presented to discretize model variables and, therefore, reduce input type and capture better prediction performance. Subsequently, conditional mutual information and link strength percentage are combined for the establishment of BN structure to avoid the defect of tree augmented naïve BN that easily misses strong links between nodes and generates redundant weak links. Monte Carlo simulation is conducted to weaken the influence of uncertainty factors. The model’s performance is compared to three advanced approaches by using the data from a PCB manufacturer and results demonstrate that the proposed method has high prediction accuracy.

CAN-ORDER POLICY WITH ITEM GROUP IN JOINT REPLENISHMENT PROBLEM

In this paper, multi-item inventory management is considered. The associated problem is called the joint replenishment problem (JRP). In this model, warehouse sells multiple items for customer and replenishes items from a supplier. For the replenishing, ordering cost is charged independent on the amount of orders. For this situation, can-order policy is often used. Under a can-order policy, items are ordered when their inventory level drops to or below their re-order level, and other item with an inventory level at or below its can-order level can be included in this order. In this situation, by considering can-order level and grouping the items can reduce ordering cost. In this research, setting of a can-order level and grouping are considered. The main objective in this model is minimizing the number of items lost sales cost, holding cost and ordering cost. In a numerical experiment, inventory movement are simulated.

Perishable Inventory Control in Retrial Service FacilitySemi Markov Decision Process

This article deals the problem of optimally controlling the perishable inventory with exponential perishable rate and exponential lead time in a finite capacity retrial service facility system. Arrival of demands to the system is assumed as Poisson and service times are assumed to follows an exponential distribution. Here, the customers are not allowed to form a queue. A customer who sees the server busy joins the orbit and reattempts the system with exponential distributed time. For the given values of maximum inventory and reorder level, we determine the optimal ordering policy at various instants of time. The system is formulated as a Semi-Markov Decision Process and the optimum inventory control to be employed by using linear programming method so that the long–run expected cost rate is minimized. Numerical examples are provided to illustrate the model.

Influence of delivery quantity on service level in the stock replenishment system based on reorder level for irregular distributions of demand

Influence of delivery quantity on service level in the stock replenishment system based on reorder level for irregular distributions of demand