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The primary objective of this article is to develop a mathematical model and determine the optimal policies of an inventory system involving power demand and controlled deterioration through preservation technology. This model comes in handy in a power demand-oriented inventory system with demand high at the end of the period. The model incorporates backlogged shortages and linear holding cost. The triangular neutrosophic numbers (TNN's) are used for a nuanced representation of uncertain and imprecise inventory-related expenses. An efficient algorithm is constructed to minimize the total cost, and obtain optimal positive inventory time, optimum cycle time and minimum preservation technology investment. Few numerical examples are used to illustrate and validate the model. The comparative study conducted between models with and without preservation technology investment reveals a significant reduction in total inventory costs facilitated by the preservation facility. Also, the numerical results obtained in crisp and neutrosophic environment are compared. Specific previously obtained results are discussed to illustrate the theoretical findings. Sensitivity analysis of the model provides managerial insights replicating reality. [ABSTRACT FROM AUTHOR]

Obsolescence of critical manufacturing inputs is a major supply chain risk and represents a growing problem for procurement professionals that must be handled appropriately to profitably meet customers' needs. Supply side obsolescence occurs when suppliers end production of components and has been defined as "the loss or impending loss of original manufacturers of items or suppliers of items or raw materials." Supply side obsolescence may radically increase costs to an organization, including high inventory holding costs, paying high prices for components on the gray market, and lost sales or lost market share due to an inability to complete products. This research utilized a grounded theory approach and discovered: (1) the risks of supply side obsolescence, (2) the impacts of supply side obsolescence on focal firms, and (3) how leading firms utilize obsolescence procurement teams to combat this potentially disastrous problem. Of specific interest to managers, obsolescence procurement teams are identified as formal teams that focus on dealing with obsolescence proactively and work to prevent the negative effects of supply side obsolescence. Finally, the study provides propositions to guide future research and concludes with suggestions for managers regarding mitigating supply side obsolescence, which will only become more urgent as the rate of technological change continues to increase. [ABSTRACT FROM AUTHOR]

The potential for profit-seeking suppliers in emergency supply chains (ESCs) to default and how to penalize defaulting suppliers has not been adequately discussed. In this paper, we construct an ESC consisting of a supplier and a government, in which the supplier pre-stocks supplies for the government for a pre-disaster contract period, and the government orders supplies from the supplier after the disaster based on actual demand. Our research question is how to choose an appropriate penalty measure (PM) in the face of default by suppliers who stock different types of supplies. The results show that the higher storage cost for supplies not be ordered after the contract period can reduce suppliers' incentives to pre-stock supplies and even lead to supplier default, and that a reduction in the quantity ordered by the government from suppliers can reduce the likelihood of supplier default. The government should take merciful PMs to penalize the defaulting supplier who pre-stocks supplies with high marketability or high market price, and strict PMs to penalize the defaulting supplier who pre-stocks supplies with low marketability or low market price. In addition, although it is difficult to accurately measure the losses incurred by default, we can still penalize defaulting suppliers based on the default quantity. Finally, we also find that constructing compromise PMs allows the government to find a balance between strictness and mercy. By adjusting the degree of compromise, the government can choose the penalty strength appropriate to the situation. • Even in humanitarian logistics, suppliers still have thoughts of default. • Government initiatives to lower supplier expectations can reduce default risks. • Strict or merciful punishment is related to the supplies type. • When default loss is imprecise, penalties can be based on default quantity. [ABSTRACT FROM AUTHOR]

Facing demand uncertainty and supply uncertainty, the retailer needs to make the order decision before the selling period. Therefore, the retailer can pay in advance to reduce supply risk. In order to protect his capital, the retailer will also require his customers to pay for the goods in advance. Customers may receive a corresponding price discount as well. In this paper, a two tier, advance payment supply chain is modeled. Four scenarios are drawn and the optimal ordering strategy for each scenario is discussed. It was discovered that, in order for the retailer to optimize his inventory cost, increasing the unit sales price and shortening the order cycle simultaneously can help reduce the annual average cost. It was also found that, in order to reduce the retailer's costs, they should negotiate payment terms with his supplier, particularly requesting for lower portions of advance payments. [ABSTRACT FROM AUTHOR]

A multiperiodic structure always underlies the procurement problem in practice. Option contracts are widely used to make a firm capable of responding flexibly to high demand uncertainty. However, so far, the impact of different option contracts on the multiperiod decisions and performance of a firm has been not discussed. Motivated by this problem, in this paper we analyze the multiperiod procurement problem within the framework of wholesale price, call, put, and bidirectional option contracts. Based on the dynamic programming theory, we characterize the structure of the firm's optimal replenishment policy for each period in four cases. We then develop a heuristic to approximate the corresponding policy parameters. Through system comparisons of four cases, we examine the value of option contracts for the firm and discuss the best option contract type for the firm. The results show that the firm will gain higher profits after the option contracts are introduced. Moreover, if the unit purchase and exercise costs of the bidirectional option are equal to those of the unilateral option, then bidirectional option contracts are preferred over unilateral option contracts. If the unit purchase cost of the bidirectional option is higher than that of the unilateral option, unilateral option contracts may be more profitable than bidirectional option contracts. Furthermore, if the unit backlogging cost is high and the unit inventory holding cost is low, call option contracts are better than put option contracts; if the unit backlogging cost is low and the unit inventory holding cost is high, then put option contracts work better than call option contracts. [ABSTRACT FROM AUTHOR]

Inventories represent stocks of goods necessary for operations of sales or manufacturing in a company. These allow to the companies meet their sales levels, while representing an opportunity to the cost control and the decision-making. This paper presents an optimization approach to minimize the inventory costs in probabilistic inventory models of independent demand. The approach has been validated for set the policy optimal of inventories with probabilistic demand within a company that markets disposable products. The established policy aims to minimize the inventory costs by using the standard deviation of the historical data, the mean deviation of forecast errors and the mean deviation of the historical data. For the determination of the economic order quantities, three types of products were selected, taking historical sales data. Likewise, different forecasting methods were used, selecting the one that minimizes the mean squared error for the forecasted demand. The proposed methodology is practical and easy to use in companies where inventories have probabilistic and independent demand. Also, the proposed approach allowed optimize the costs related to holding costs, ordering costs and safety stock costs. [ABSTRACT FROM AUTHOR]

Managing the inventory of blood is a crucial operation in hospitals owing to its significance in medical treatment. At the same time, blood is characterized by unique attributes, such as perishability and the unpredictable nature of its supply and demand. While models have been developed to optimize the said process, gaps in literature exist in terms of considering the possibility of variable pricing and extensively accounting for uncertainties in the supply chain. In this light, the present study proposes a stochastic multi-period mixed integer linear programming cost minimization model that determines the optimal inventory plan for a hospital purchasing platelets, assuming that prices fluctuate along with the blood center's supply. To implement uncertain supply and demand, the model considers a discrete set of scenarios for each parameter. A study was performed, and the results indicate a promising direction as inventory costs decreased relative to models without the new considerations. [ABSTRACT FROM AUTHOR]

This paper applies the credibility distribution of fuzzy variables in uncertainty theory to formulate a fuzzy optimization model, so as to study the coordination mechanism of a distribution supply chain system with fuzzy demand. This practical system, focusing on the demand side of the supply chain, comprises multi-period, multi-manufacturer, multi-retailer, and multi-consumer. Next, using a novel modified sequence quadratic programming algorithm, the optimal order quantity, sales volume of the retailers, and the maximum expected return of the distribution supply chain are obtained. The effects of the wholesale price, retail price, and inventory cost on the expected return are also analyzed. Finally, the usfulness of the proposed modified sequence quadratic programming algorithm is compared against the conventional MATLAB optimal toolbox and genetic algorithm. Our computational results validate in favour of the proposed algorithm in terms of the computational time, number of iterations, and convergence rate. [ABSTRACT FROM AUTHOR]

This paper proposes multi-echelon inventory models with three types of demand. The stakeholders consist of a retailer, a plant and a number of middlemen. Because the modes of demand for stakeholders are different, the stakeholders have different inventory modes. Based on these different inventory modes, three models and the procedures to solve these models are developed. In computational experiments, the applicability of the proposed models and procedures is verified. Management insights are summarized through analyzing the composition of average total inventory cost. Finally, the relationship between the parameters and the optimal solutions is investigated by two sensitivity analyses. [ABSTRACT FROM AUTHOR]

In spite of significant advancements in medicine, there is still a shortage of human blood in the world. At present, there is no alternative chemical process or product that can produce blood, and only humans are capable of doing so. It is for this reason that blood is such an important component of our healthcare system. Due to the perishability of blood, managing blood inventories can be challenging. The challenge is to maintain a high level of supply while minimizing loss due to expiration. The purpose of this study is to present a mathematical model that reduces inventory costs, determines the optimal ordering policy in hospitals, and prevents the loss of blood units. To determine the optimal inventory level and order volume, a mixed integer programming model is presented in both deterministic and non-deterministic conditions. In order to address the uncertainty in the problem, a robust optimization approach is used. This model minimizes the transfer of blood groups and transmission between hospitals by considering compatibility and priority. A sensitivity analysis has also been conducted on the model. Based on a case study, it is demonstrated that the costs of buying, storing, ordering, and wasting two important RBCs and platelets can be reduced. [ABSTRACT FROM AUTHOR]

We consider a variant of the economic order quantity (EOQ) model. Mainly, we assume that demand occurs at random, one unit at a time, and is characterized by independent and identically distributed times between two demand epochs. We also assume that the ordering policy is characterized by ordering the same amount whenever the inventory level drops to zero, and a demand occurs. Surprisingly, we show that the optimal order quantity that minimizes the expected inventory cost follows the familiar EOQ formula. [ABSTRACT FROM AUTHOR]

In this paper, we explore the value of considering simultaneous pickups and deliveries in multi-product inventory-routing problems both with deterministic and uncertain demand. We propose a multi-commodity, develop an exact branch-and-cut algorithm with patching heuristics to efficiently tackle this problem, and provide insightful analyses based on optimal plans. The simplicity of the proposed approach is an important aspect, as it facilitates its usage in practice, opposed to complicated stochastic or probabilistic methods. The computational experiments suggest that in the deterministic demand setting, pickups are mainly used to balance initial inventories, achieving an average total cost reduction of 1.1%, while transshipping 2.4% of total demand. Under uncertain demand, pickups are used extensively, achieving cost savings of up to 6.5% in specific settings. Overall, our sensitivity analysis shows that high inventory costs and high degrees of demand uncertainty drive the usage of pickups, which, counter-intuitively, are not desirable in every case. • We propose a mathematical model considering pickups and deliveries in multi-product inventory-routing problems. • We propose an exact branch-and-cut algorithm with a patching heuristic which repairs routing-infeasible solutions and performs local search. • Our deterministic model is tested on a stochastic demand setting through a rolling horizon heuristic resembling the daily routine of a planner. • In the deterministic demand setting, pickups and deliveries are useful for capacity reasons and to cope with initial inventory imbalances. • In rolling horizon planning with stochastic demand, pickups provide further savings in specific settings. [ABSTRACT FROM AUTHOR]

Spare parts are one of the important components of the equipment comprehensive support system. Spare parts management plays a decisive role in achieving the desired availability with the minimum cost. With the equipment complexity increasing, the price of spare parts has risen sharply. The traditional spare parts management makes the contradiction between fund shortage and spare parts shortage increasingly prominent. Based on the analysis of the multi-echelon and multi-indenture spare parts support model VARI-METRIC (vary multi-echelon technology for recoverable item control, VARI-METRIC), which is widely used by troops and enterprises in various countries, the model is mainly used in high system availability scenarios. However, in the case of low equipment system availability, the accuracy and cost of model inventory prediction are not ideal. This paper proposed the multi-level spare parts optimization model, which is based on the demand-supply steady-state process. It is an analytical model, which is used to solve the low accuracy problem of the VARI-METRIC model in the low equipment system availability. The analytical model is based on the multi-level spare parts support process. The article deduces methods for solving demand rate, demand–supply rate, equipment system availability, and support system availability. The marginal analysis method is used in the model to analyze the spare parts inventory allocation strategy's current based cost and availability optimal value. Finally, a simulation model is established to evaluate and verify the model. Then, the simulation results show that, when the low availability of equipment systems are 0.4, 0.6, the relative errors of the analytical model are 3.54%, 3.86%, and its costs are 0.52, 1.795 million ¥ RMB. The experiment proves that the inventory prediction accuracy of the analytical model is significantly higher than that of the VARI-METRIC model in low equipment system availability. Finally, the conclusion and future research directions are discussed. [ABSTRACT FROM AUTHOR]

Abstract: This study analyzes how an airline operates differentiated cargo services to meet shipping demand for different goods so as to enhance its service level, air freight demand, and total profit. The shipping alternative choice model assumes shippers with different demand characteristics choose the alternative with the lowest transportation and inventory costs between express and standard shipping services offered by different airlines. The study then formulates airline cost functions with respect to providing standard and express services. Furthermore, the study explores demand–supply interactions and constructs a mathematical programming model to determine flight frequencies and shipping charges for standard and express services by maximizing the object airline''s total profit. The results show that those shipping high-value freights tend to select express shipping services and, for the object airline, operating differentiated cargo services rather than the current standard services can raise its market share and total profit by 19.4% and 8.6%, respectively. Moreover, for perishable goods stored in lower temperature-control devices, the charges should be higher. The results also show that not only airlines, but also shippers, can markedly reduce their costs and enhance their competitive advantages with express shipping services. [Copyright &y& Elsevier]

Abstract: This paper analyses an infinite horizon two-echelon supply chain inventory problem and shows that a sequence of the optimum ordering policies does not yield globally optimal solutions for the overall supply chain. First-order autoregressive demand pattern is assumed and each participant adopts the order-up-to (OUT) policy with a minimum mean square error forecasting scheme to generate replenishment orders. To control the dynamics of the supply chain, a proportional controller is incorporated into the OUT policy, which we call a generalised OUT policy. A two-echelon supply chain with this generalised OUT policy achieves over 10% inventory related cost reduction. To enjoy this cost saving, the attitude of first echelon player to cost increases is an essential factor. This attitude also reduces the bullwhip effect. An important insight revealed herein is that a significant amount of benefit comes from the player doing what is the best for the overall supply chain, rather than what is the best for local cost minimisation. [Copyright &y& Elsevier]

A production scheduling model based on the minimisation of inventory costs is proposed for small scale organisations . This is an interactive model characterised by its ability to cope with continuous changes in production and demand conditions. The model is built on the foundation of the Wagner-Whitin algorithm, and has incorporated factors of safety stock, economic batch quantity, production capacity and past forecast accuracy in inventory and production scheduling. It has been implemented in a computer programme and tested within a company. Results show that by using this model substantial savings can be achieved in inventory costs. [ABSTRACT FROM AUTHOR]

In today's global economy, supply chain (SC) entities have become increasingly interconnected with demand and supply relationships due to the need for strategic outsourcing. Such interdependence among firms not only increases efficiency but also creates more vulnerabilities in the system. Natural and human-made disasters such as floods and transport accidents may halt operations and lead to economic losses. Due to the interdependence among firms, the adverse effects of any disruption can be amplified and spread throughout the systems. This paper aims at studying the robustness of SC networks against cascading failures. Considering the upper and lower bound load constraints, i.e., inventory and cost, we examine the fraction of failed entities under load decrease and load fluctuation scenarios. The simulation results obtained from synthetic networks and a European supply chain network (Cardoso et al., 2015) both confirm that the recovery strategies of surplus inventory and backup suppliers often adopted in actual SCs can enhance the system robustness, compared with the system without the recovery process. In addition, the system is relatively robust against load fluctuations but is more fragile to demand shocks. For the underload-driven model without the recovery process, we found an occurrence of a discontinuous phase transition. Differently from other systems studied under overload cascading failures, this system is more robust for power-law distributions than uniform distributions of the lower bound parameter for the studied scenarios. • Constructs an underload cascading failure model for supply chain networks. • Evaluates the robustness of systems with and without recovery measures. • The system is robust against load fluctuations but is more fragile to load decrease. • The system robustness is enhanced by recovery measures. • A discontinuous phase transition is found for the system without recovery measures. [ABSTRACT FROM AUTHOR]