Stockout Cost Research Articles

Decision of Lead-Time Compression and Stable Operation of Supply Chain

A cost optimization strategy and a robust control strategy were studied to realize the low-cost robust operation of the supply chain with lead times. Firstly, for the multiple production lead times which existed in the supply chain, a corresponding inventory state model and a supply chain cost model were constructed based on the Takagi-Sugeno fuzzy control system. Then, by considering the actual inventory level, the lead-time compression cost, and the stock-out cost, a cost optimization strategy was proposed. Furthermore, a fuzzy robust control strategy was proposed to realize the flexible switching among the models. Finally, the simulation results show that the total cost of the supply chain could be reduced effectively by the cost optimization strategy, and the stable operation of the supply chain could be realized by the proposed fuzzy robust control strategy.

A multi-attribute decision model for setting production planning parameters

Production and inventory-related decisions, which significantly influence each other and sometimes involve multiple attributes, trade-off assessment and uncertainties, serve a key role in the performance of make-to-stock (mts) manufacturing systems that are controlled by a constant work in process (conwip) order release policy. To benefit from established production planning methods, a crucial task in this context is to define suitable production parameter settings for a given planning horizon. To address this problem, we present a multi-attribute decision model to determine appropriate settings for the planning parameters, namely, cycle time, throughput rate, holding cost and stockout cost. The proposed model uses discrete event simulation to evaluate the performance of a conwip/mts manufacturing system in relation to the work in process and finished goods inventory. Analysis of variance (ANOVA) and a Kruskal-Wallis test are conducted to verify the significant effect on the analyzed parameters. The compromise solution that is recommended for the conwip/mts problem is obtained by considering a multi-attribute expected utility function that is representative of a decision maker’s preferences and risk attitude regarding the probability distribution of the simulation outputs. In contrast with preview studies on planning parameter setting, the result compensates the low performance of one of the attributes as a result of the high performance of another attribute, based on the axiomatic structure of MAUT.Based on the real data of a multi-product assembly line, a numerical application is employed to visualize the steps of this decision model and to demonstrate its usefulness in practical issues.

A Continuous Review Inventory Model with Fuzzy Service Level Constraint and Fuzzy Random Variable Parameters

This paper presents a continuous review inventory system in the presence of fuzziness and randomness in demand and cost parameter. In the realistic situations, determination of the stock-out cost is often difficult. Instead of having stock-out cost term in the objective function, a condition on the service level is established on the model. The model is constructed as a fuzzy stochastic optimization problem, and we developed a methodology to obtain the optimal solutions such that total expected annual cost is minimized in the fuzzy sense. A numerical example is included to illustrate the proposed methodology.

Multi-objective inventory routing with uncertain demand using population-based metaheuristics

This article studies a tri-objective formulation of the inventory routing problem, extending the recently studied bi-objective formulation. As compared to distance cost and inventory cost, which were discussed in previous work, it also considers stockout cost as a third objective. Demand is modeled as a Poisson random variable. State-of-the-art evolutionary multi-objective optimization algorithms and a new method based on swarm intelligence are used to compute approximation of the 3-D Pareto front. A benchmark previously used in bi-objective inventory routing is extended by incorporating a stochastic demand model with an expected value that equals the average demand of the original benchmark. The results provide insights into the shape of the optimal trade-off surface. Based on this the dependences between different objectives are clarified and discussed. Moreover, the performances of the four different algorithmic methods are compared and due to the consistency in the results, it can be concluded that a near optimal approximation to the Pareto front can be found for problems of practically relevant size.

Optimal planning of incentive-based quality in closed-loop supply chains

Electronic firms are being required to collect used products for environmental purposes. In order to meet requirements, these firms carry out collection activities and provide incentive offers to attract product returns. These product returns may then undergo recovery options such as refurbishing, remanufacturing, cannibalizing, and controlled disposal. A mixed integer nonlinear programming model for a closed-loop supply chain that includes decisions for collection activities, incentive offers, and recovery options is formulated and validated. Quantity is modeled as a function of incentive offers between the collection centers and consumers, while quality of product returns follows an arbitrary probability distribution based on the incentive level. Quality of product returns dictates the possible recovery options, which these products can undergo. The model is then subjected to scenario analysis, which identified conditions wherein rebate or discount incentives are preferred, and when low or high incentive levels are favored. High stockout costs to secondary consumers encouraged the model to perform more cash rebate activities to stimulate more product returns. Meanwhile, when both the costs of activities and stockouts are high, the model is induced to carry out discount activities as this would generate sales rather than the cash rebate which simply incentivizes the participation in the take-back program.

Mechanisms for Increasing Sourcing from Capacity‐Constrained Local Suppliers

ABSTRACTThe fresh produce supply chain is characterized by large (mainstream) farms that are located far from consumers, and capacity‐constrained (local) farms that are located close to the consumer. In this setting, we study: (i) how leadtime and capacity asymmetry between mainstream and local farms affect a retail grocer's order policy for fresh produce, and (ii) how various operational mechanisms can increase the amount sourced from local farms. We show that this supply chain structure is disadvantageous for local suppliers (farms) because it induces the retailer to treat the local supply as a de facto responsive supply without paying a premium for the responsiveness. This disadvantage is exacerbated when the retailer's objective is to achieve a high service level. We study three mechanisms that can improve conditions for local farms: working with an intermediary, backhauling, and a retail order policy, purchase guarantee, that explicitly supports local farms. The intermediary and backhauling mechanisms help the local farm by making local supply more attractive to the retailer, inducing her to order more locally sourced produce. The intermediary reduces the retailer's overstock and stockout costs whereas backhauling increases the average margin. The purchase guarantee order policy helps local farms at the expense of retail profit. However, we show that purchase guarantee and backhauling are complementary mechanisms that together can benefit the retailer and local farms.

Extenuating Whip-Lash Effect in Multi-Echelon SCM in Steel Processing Industry Using Optimization Model

AbstractForecasting of inventory is an enigma to various firms at different echelon levels of supply chain. Abnormality in forecasting an inventory may leads to fluctuations in upstream supply chain. On upswing it can be uneconomical in terms of stock-out cost, while on the down trend it can be expensive in terms of stock holding costs. To amend the firms efficiency, in this paper the model developed by (Akhtar Tanveer et al. An Optimization model for Mitigating Bullwhip-Effect in a Two-Echelon Supply chain. Int conf Traf Transp Stud 2014;138:289-97) is successfully applied to steel processing firm to mitigate the Bull-whip or Whip-lash effect in the supply chain. The objective function is to minimize the variation between the actual order quantity and demand forecast of multiple products at various echelons and the simple exponential smoothing is compiled to forecast the demand of products. The model is furthermore validated by an illustration of seven products and it depicts that the model emanates an optimal set of smoothing parameters to attenuate the whip-lash effect.

Optimal ordering and recovery policy in a periodic review integrated inventory model

The present article investigates a single-supplier–single-retailer integrated inventory problem, where the supplier and the retailer establish a long-term strategic partnership and contract to jointly determine the best strategy. We analyse a periodic review integrated inventory model in which the supplier inspects each lot product; if defective items are recoverable then recovered instantaneously for reuse, but otherwise disposed. In addition, we take a service level constraint instead of a stock-out cost term into consideration, which implies that the stock-out level per cycle is bounded, and further treat the ordering cost as one of the decision variables to widen applications for our model. The objective of this paper is to minimise the joint expected annual total cost by simultaneously optimising the review period, the ordering cost, the lead time as well as the number of shipments from the supplier to the retailer in one production run. We present a procedure to find the optimal solution for the model; furthermore, the effects of parameters are studied as well. Finally, numerical examples are given to illustrate the results.

Model Penjadwalan Pengiriman Pasokan pada Strategi Multi-Supplier dengan Variasi Harga dan Lead Time untuk Permintaan Stokastik

Multi-supplier is one of the strategies to minimize holding cost and average stock-out cost as long as to stabilize the supply of raw materials. The common problems that the firms may face when applying the multi-supplier strategy are determining the right schedule and quantity ordered for each supplier. Complexity of the problem increases with the facts that each supplier may have different parameters, demand is uncertain, and the firms’ constraints. Thus, this research is done to answer two main objectives: (1) to determine the optimum safety time (minimum raw material inventory) to prevent the stockout due to the demand uncertainty and (2) to determine the right schedule and quantity ordered for each supplier considering the different suppliers parameters: price, lead time, and supply capacity. The problem is modeled in Mixed Integer Linear Programming with total minimum inventory cost as the objective. With the aim of testing the model, a case of multinational company that apply the multi-supplier strategy is used.

Precautionary and operational costs of freight train delays: a case study of a Swedish grocery company

There is limited up-to-date knowledge about the monetary valuation of improved reliability for freight transports. This means that the benefits of reduced variability in transport time are not properly taken into account in cost-benefit analysis relating to, for example, infrastructure investments. We develop on a conceptual level an estimation approach for the value of reduced transportation time variability (VTTV) related to the cargo component based on precautionary and operative delay costs. This approach is inspired by the safety stock approach but includes more general precautionary measures that firms take to avoid stock-out costs. This paper presents the analysis of a Swedish grocery company’s transports by shuttle train as a case study. First, the distribution of the arrival times of the shuttle train is analyzed in order to estimate the transportation time variability for the firm. Second, precautionary costs for measures undertaken to manage and mitigate the transportation time variability are estimated and the additional operational costs that occur in case of major delays are calculated. It is found that the 10 % worst delays contribute to more than half of the total train delays, showing that actual transportation times exhibit a heavily skewed distribution with fat tails, indicating that the standard deviation might not be a sufficient measure of transport time variability. The calculated VTTV related to the cargo component based on the precautionary costs is around €4 per delay-tonne-hour and around €2.2 per standard deviation of transportation time. We show that by conducting a case study it is possible to get VTTV estimates for the cargo component valid for a specific company. In conclusion, assuming a high degree of transport market concentration with regard to shippers, a limited number of case studies for key companies in the market might be sufficient to get a representative VTTV measure. We therefore advocate further case studies and research aimed at getting more inputs from firms that send and receive goods. More research should also be done on how to incorporate risks for delays and the extremeness of empirical delays in transport models and VTTV definitions.

Modeling the Causes of the Bullwhip Effect and Its Implications on the Theory of Organizational Coordination

This article analyzes the potential cause of bullwhip effect using experimentally simulated data from a beer distribution game. The game represents a simple supply chain that consists of a factory, distributor, wholesaler, and retailer. The study used the bootstrapping estimation technique on ANOVA models, signal processing, and recessive autoregression estimation methods, which resulted in preliminary findings that confirmed that the bullwhip effect has different cost implications to various business partners in the supply chain. The inventory cost is higher in the upstream of the supply chain. Consequently, the stock-out cost is higher in the downstream of the supply chain. The generalization of the preliminary findings suggests that one way or another, the bullwhip effect attacks all business partners in the supply chain. Then, we tried to analyze the causes of the bullwhip effect. The Cochrane-Orcutt autoregression recursive estimation result showed that the bullwhip effect can be caused by intra- and inter-organizational coordination of the business partners in the supply chain. The effect of the intra-organizational coordination on the bullwhip effect showed the drawback of existing theory on organizational coordination. The analysis showed that in order to make the supply chain more efficient and effective a coordination type, named hyper-hybrid coordination, is introduced, which helps us to modify the Kraljic portfolio model. Finally, we have shown some applications of the hyper-hybrid coordination in the supply chain of agricultural products.

How many to produce? The impact of machine flexibility on the performance of a supply chain

This work analyses the impact of machine flexibility on the performance level of a supply chain. A simulation model is developed to reproduce different supply chain configurations, with one player per echelon. Machine flexibility is modelled as the capability of the manufacturer to produce different kinds of products, in response to the customer's requests, and of the supply chain to manage those products. By means of simulation, six supply chain configurations are investigated, resulting from the combination of different numbers of supply chain players (from 3 to 5) and of products managed by the supply chain (1 or 3). For each scenario, we compute some economic outcomes (i.e., cost of holding stocks, stock-out cost, production or order cost and total supply chain cost), as well as the supply chain lead time and the bullwhip effect. Design of experiments and statistical analysis of outcomes are used to assess the significance of the effects measured. Results of the study provide some guidelines about the effect of machine flexibility on the supply chain performance.

Achieving Supply Chain Coordination Through Lot Size Based Discount

A generic mathematical model is developed for a two level supply chain through lot size based discount to achieve supply chain coordination. The model considers a single product with probabilistic demand. It accounts total supply chain cost which entails product cost, inventory holding cost, ordering cost and stock-out cost considering the maximum allowable supply chain inventory. Genetic algorithm is used for determining the optimum levels of ordering quantity, supply chain savings, and discount percentage (i.e. an upper member should offer to a lower member of the supply chain) to enhance supply chain profit. A case situation is elucidated to demonstrate the salient features of the proposed model.

Collaborative Decision-Making Mechanism of Air Material Supply Based on Revenue-Sharing Contract

Due to the complex nature of air material demand and supply process, inventory pooling among a number of airlines is an intuitive way of exploiting the scale economies of availability services, and this paper analyzes the air material supply chain within sharing model and introduces revenue-sharing contract coordination mechanism into air material supply process. On the basis of analyzing stock-out compensation and inventory costs, it establishes the coordination model of air material supply chain whose demand quantity is in accordance with Poisson Distribution and Normal Distribution. The paper presents the collaborative decision-making algorithm for air material supply chain and verifies the coordination model by means of actually analyzing a numerical example for application. Based on a reasonable contract form and contract parameters, the optimization of production and ordering quantity and expected profit of each node on the air material supply chain can be achieved and hence the optimization of collaborative decision-making of air material supply and the performance of supply chain.

The 2Bin system for controlling medical supplies at point-of-use

Abstract The increase in cost of supplies and services is outpacing the increase in revenues at many hospitals. To address this cost increase hospitals are seeking more efficient ways to store and manage vast inventories of medical supplies. A parsimonious and efficient inventory system which we call 2Bin is becoming increasingly popular in North American hospitals. Under the 2Bin system inventory is stored in two equal-sized bins. 2Bin systems are reviewed periodically and empty bins are replenished. In recent years the adoption of RFID technology for 2Bin systems is allowing continuous-time tracking of empty bins, increasing inventory visibility. In this paper we model the 2Bin inventory system under periodic and continuous review. For periodic review we show that the long-run average cost per unit time is quasi-convex, enabling a simple search for the optimal review cycle. For continuous review, we present a semi-Markov decision model, characterize the optimal replenishment policy, and provide a solution approach to obtain the long-run average cost per unit time. Using data obtained from hospitals currently using RFID-enabled 2Bin systems, we estimate the economic benefits of using the best periodic review length (i.e., parameter optimization), and of using a continuous review inventory policy (i.e., policy improvement). We characterize system conditions such as the number of medical supplies used, replenishment costs, stock-out costs, etc. that favor each option, and provide insights to hospital management on system design considerations that favor the use of periodic or continuous review.

Multi-period empty container repositioning with stochastic demand and lost sales

This paper considers repositioning empty containers between multi-ports over multi-periods with stochastic demand and lost sales. The objective is to minimize the total operating cost including container-holding cost, stockout cost, importing cost and exporting cost. First, we formulate the single-port case as an inventory problem over a finite horizon with stochastic import and export of empty containers. The optimal policy for period n is characterized by a pair of critical points (An, Sn), that is, importing empty containers up to An when the number of empty containers in the port is fewer than An; exporting empty containers down to Sn when the number of empty containers in the port is more than Sn; and doing nothing, otherwise. A polynomial-time algorithm is developed to determine the two thresholds, that is, An and Sn, for each period. Next, we formulate the multi-port problem and determine a tight lower bound on the cost function. On the basis of the two-threshold optimal policy for a single port, a polynomial-time algorithm is developed to find an approximate repositioning policy for multi-ports. Simulation results show that the proposed approximate repositioning algorithm performs very effectively and efficiently.

The calculation of extra carrying cost (ECC) and stock out cost (SOC) to determine the raw material's optimal arrival lead time

The calculation of extra carrying cost (ECC) and stock out cost (SOC) to determine the raw material's optimal arrival lead time

Dynamic Re-order Policies for Irregular and Sporadic Demand Profiles

Irregular and sporadic demand profiles frequently occur in different contexts characterised either by a large fragmentation of client requests within a broad product mix or when new products are introduced. Their optimal management often requires the definition of approaches based on Key Performance Indicators (KPI) other than costs, which in the aforementioned situations are characterised by uncertainty. Specifically, holding costs and stock-out costs are difficult to quantify. This paper examines 104 dynamic re-order policies in an environment where demand patterns with very low demand frequency and demand size equal to very few items are required by customers. Furthermore, the cost structure is uncertain. The aforementioned alternative management approaches are experimentally compared in terms of the average inventory level, average and maximum lost demand and average number of stock-out they can ensure.

A decision support system for mean–variance analysis in multi-period inventory control

Traditionally inventory management models have focused on risk-neutral decision making with the objective of maximizing the expected rewards or minimizing costs over a specified time horizon. However, for items marked by high demand volatility such as fashion goods and technology products, this objective needs to be balanced against the risk associated with the decision. Depending on how the product performs vis-à-vis the seller's original forecast, the seller could end up with losses due to either short or surplus supply. Unfortunately, traditional models do not address this issue. Stochastic dynamic programming models have been extensively used for sequential decision making in the context of multi-period inventory management, but in the traditional way where one either minimizes costs or maximizes profits. Risk is implicitly considered by accounting for stock-out costs. Considering risk and reward simultaneously and explicitly in a stochastic dynamic setting is a cumbersome task and often difficult to implement for practical purposes, since dynamic programming is designed to optimize on one variable, not two. In this paper we develop an algorithm, Variance-Retentive Stochastic Dynamic Programming that tracks variance as well as expected reward in a stochastic dynamic programming model for inventory control. We use the mean–variance solutions in a heuristic, RiskTrackr, to construct efficient frontiers which could be an ideal decision support tool for risk-reward analysis.

An inventory model-based spectrum pooling for wireless service provider and unlicensed users

In order to comply with more opportunistic and transient service requirements, many research literatures have paid attention to an approach called spectrum pooling. If we imagine a scenario in which wireless service providers (WSPs) manage the spectrum pool, the radio spectrum itself will be traded as done in a market-based scenario, and moreover, every WSP will likely require new strategies in order to make more profits under such dynamic service requirements. Up to now, most researches have concentrated on investigating the inter-WSP strategies such as market equilibrium under the consideration of noncooperative or cooperative WSPs. In this paper, we direct our attentions to an intra-WSP strategy: a WSP coordinates the spectrum order in order to minimize its cost. To this end, we deploy a probabilistic inventory model that helps WSPs to determine economic order quantity and reorder point yielding minimum total expected cost. The total cost is composed of ordering cost, holding cost, and stockout cost. We believe that this model will be the first investigation of intra-WSP strategy that manages and estimates economic aspects of the spectrum pooling.