Traditional Newsvendor Model Research Articles

Pricing strategy based on a stochastic problem with barter exchange under variable promotional effort for a retail channel

Barter exchange platform gains popularity as an effective trade exchange strategy to minimize the vast use of paper money. In retail management, the application of the barter platform is growing because of cashless trade. It allows a retail enterprise to trade unused products in exchange for other assets purchased by reducing the actual purchase of assets. In this context, the study analyzes a stochastic model of a barter exchange problem under a retail channel. It investigates the barter exchange policy in three different models: conventional newsvendor model without barter exchange, with barter exchange, and with barter exchange with promotional effort and emissions level. The market demand for products is considered random and random with deterministic variables for three types of models. Stochastic models are solved in two different ways: with and without probability distribution functions of the random market demand. For maximization of the retailer's profit, the proposed framework obtains optimal decisions on ordered quantity, retail price, promotional effort, and emissions level. Five examples are tested and numerical results show that the barter exchange policy is more profitable than a traditional newsvendor model. A combination of stochastic and deterministic variable demand provides 34.31% more profit than a stochastic demand. Sensitivity analysis, discussions, and managerial insights are provided in detail on optimal decisions.

Behavioral perspective of newsvendor ordering decisions: review, analysis and insights

PurposeThe traditional newsvendor model has focused on deriving the optimal order quantity that minimises the balance between stocking too much or too less number of products. However, the managers make inventory decisions based on intuitions and shortcuts, which may involve human errors and biases. The effect of cognitive biases and heuristics influencing the inventory ordering decisions in newsvendor settings is highlighted. The advancement of research associated to the newsvendor biases is reviewed to appreciate the behavioral aspects of the minds underlying this process.Design/methodology/approachThe use of experimental and non-experimental methods to investigate the ordering behaviour of newsvendors is described and we present a framework of the existing literature and highlight the research gaps to point to future research possibilities and priorities.FindingsThe proposed framework gives a systematic approach to confirm the existence of a substantial scope of research opportunities and points to specific areas for further research. It synthesizes the existing results of behavioral newsvendor research and will act as a key reference paper. In addition, it will help the practitioners and software tool vendors to comprehend the behavioral perspective of newsvendor preferences and design strategies to mitigate this effect. The insights will be helpful for academicians, researchers and practitioners working in the areas of experimental economics, behavioral economics, behavioral operations, bounded rationality theory, newsvendor modelling and supply chain contracts.Originality/valueA summary of literature in this evolving area of research is very scarce. Considering the impact of behavioral economics on managerial decisions in the contemporary world, it is highly important to have an educational summary which can act as a tool for the practitioners and researchers in the area of behavioral operations management.

Self-Adaptive Newsvendor Model with Information Updated

The use of classical newsvendor model is limited by assumptions and prior probability statistics. Self-adaptive newsvendor model is proposed to divide the sales interval and automatically adjust the differential according to the sales information collected by retailer’s information system. Demand interval and its corresponding demand probability value could be adjusted timely to correct the next order prediction quantity. Based on management information system, model calculation tool is designed to improve the feasibility of the model application. Compared to traditional newsvendor model, self-adaptive newsvendor model can effectively meet the needs of short-period, small-quantity, multi-batch products purchasing.

Managing Inventories: The Newsvendor Model

This note uses the traditional newsvendor model to present strategies for inventory management when demand is uncertain. Methods for determining the critical ratio of overage and underage costs and the optimal stock level are discussed. Excerpt UVA-OM-1456 Rev. Oct. 9, 2013 Managing inventories: The Newsvendor model Managing inventories is important in every company and under many circumstances, but it is especially critical for firms facing uncertain demand in only one selling season. This problem is commonly referred to as the newsvendor problem or newsvendor model, derived from the problem faced by a newsvendor who must decide on the number of newspapers to purchase at the beginning of the day before demand can be observed. This model, however, can be applied to any situation where the selling season is short enough that ordering decisions must be made before the season starts, such as for fashion goods, influenza vaccines and other health care products, short-life-cycle technology products (consumer electronics and telecommunications equipment), and perishable goods. Determining the Optimal Inventory Level When determining how much inventory to hold before the selling season starts, a firm needs to balance two possible outcomes. If the firm orders too much, inventory remaining at the end of the selling season will have to be sold at a discount or thrown away. But if the firm doesn't order enough, it may lose potential sales and possibly also cause customers to obtain the product from a competitor. To take those two possible outcomes into account, we define two types of costs: the overstocking cost, Co, which is the loss for the firm for each unit of leftover, and the understocking cost, Cu, which is the loss of margin for the firm for each unit of lost sales. . . .

The Dynamic Newsvendor Model with Correlated Demand

ABSTRACTThe classic newsvendor model was developed under the assumption that period‐to‐period demand is independent over time. In real‐life applications, the notion of independent demand is often challenged. In this article, we examine the newsvendor model in the presence of correlated demands. Specifically under a stationary AR(1) demand, we study the performance of the traditional newsvendor implementation versus a dynamic forecast‐based implementation. We demonstrate theoretically that implementing a minimum mean square error (MSE) forecast model will always have improved performance relative to the traditional implementation in terms of cost savings. In light of the widespread usage of all‐purpose models like the moving‐average method and exponential smoothing method, we compare the performance of these popular alternative forecasting methods against both the MSE‐optimal implementation and the traditional newsvendor implementation. If only alternative forecasting methods are being considered, we find that under certain conditions it is best to ignore the correlation and opt out of forecasting and to simply implement the traditional newsvendor model.

Manufacturing Capacity Decisions with Demand Uncertainty and Tax Cross-Crediting

The U.S. tax law taxes the global income of multinational firms (MNFs) at their home country tax rate. To avoid double taxation, it permits tax cross-crediting. Through this strategy, global firms can use excess foreign tax credits (FTCs), the portion of foreign tax payments that exceed their home country tax liabilities, generated from a subsidiary located in a high-tax country to offset the tax liabilities of their low-tax divisions. This paper studies manufacturing capacity decisions in the subsidiary of an MNF with tax cross-crediting. Casting the problem on a newsvendor model, and assuming the objective of maximizing the global firm’s worldwide after-tax profits, we show that the optimal capacity decision under the effects of tax cross-crediting can behave very differently from that of the traditional newsvendor model. In particular, we show that an improvement in the firm’s after-tax profitability (through tax cross-crediting, an increased profit margin, or a reduced tax rate) might reduce the optimal capacity and that the optimal capacity decision under certain circumstances can be made without the knowledge of the demand distribution. We also discuss the issue of motivating the division manager to use an after-tax performance measure with a managerial tax rate.

A risk-averse newsvendor model with limited capacity and outsourcing under the CVaR criterion

For seasonal products, the retailers usually launch various marketing efforts, like advertising and promotion, to promote them in a selling season. While facing large demand from the customers, one should take the capacity constraint and outsourcing into consideration. Considering the shorten life cycles of most products, in this paper we adopt the traditional newsvendor model to investigate the optimal marketing effort along with optimal order quantity. We address the risk aversion issue and characterize the influence of the sellers’ risk propensity with CVaR criterion, and we develop an effective algorithm to obtain the optimal strategy. The impact of sellers’ risk propensity on the performance of the system is illustrated via numerical examples. The innovation of this paper is threefold. First, the optimal joint strategy of the marketing effort and order quantity is investigated and an efficient algorithm to find the optimal strategy is developed. Second, the capacity constraint option and an outsourcing strategy are studied jointly for excess products. Finally, the risk propensity of the seller and its influence are investigated by using the CVaR criterion, through which we obtain some new managerial insights.

The Dynamic Newsvendor Model with Correlated Demand

The classic newsvendor model was developed under the assumption that period-to-period demand is independent over time. In real-life applications, the notion of independent demand is often challenged. In this paper, we examine the newsvendor model in the presence of correlated demands. Specifically under a stationary AR(1) demand, we study the performance of the traditional newsvendor implementation versus a dynamic forecast-based implementation. We demonstrate theoretically that implementing a minimum mean square error (MSE) forecast model will always have improved performance relative to the traditional implementation in terms of cost savings. In light of the widespread usage of all-purpose models like the moving-average method and exponential smoothing method, we compare the performance of these popular alternative forecasting methods against both the MSE-optimal implementation and the traditional newsvendor implementation. If only alternative forecasting methods are being considered, we find that under certain conditions it is best to ignore the correlation and opt out of forecasting and to simply implement the traditional newsvendor model.

Managing the Newsvendor Modeled Product System with Random Capacity and Capacity-Dependent Price

We consider a newsvendor modeled product system, where the firm provides products to the market. The supply capacity of the product is random, so the firm receives either the amount of order quantity or the realized capacity, whichever is smaller. The market price is capacity dependent. We consider two types of production cost structures: the procurement case and the in-house production case. The firm pays for the received quantity in the former case and for the ordered quantity in the latter case. We obtain the optimal order quantities for both cases. Comparing with the traditional newsvendor model, we find that the optimal order quantity in both the procurement case and the in-house production case are no greater than that in the traditional newsvendor model with a fixed selling price. We also find that the optimal order quantity for the procurement case is greater than that for the in-house production case. Numerical study is conducted to investigate the sensitivity of the optimal solution versus the distribution of the random capacity/demand.

The optimal price discount, order quantity and minimum quantity in newsvendor model with group purchase

Based on the feature of small profits but quick turnover, group-buying, an emerging e-commerce model, benefits both retailers and customers. In this paper, we explore the optimal price discount, order quantity and minimum quantity with a fixed selling price of the product to maximize the sellers' profit. The traditional newsvendor model framework is used in view of the shortened life cycle of most products. The demand of customers is assumed to be in addition form and product form, respectively, and the impacts of demand parameters are examined numerically. It is revealed that in some cases the profit cannot be improved significantly through price discount because of unconspicuous increase in demand. However, when the demand changes obviously with price discount, group-buying can bring more profit through price discount and inspire vendors to order more goods. Through numerical results, it is shown that the influence of demand in the product form is more evident than that in the addition form under the strategy of group-buying. Furthermore, the profit-based minimum quantity and the probability of selling nothing during the group time are also shown in this paper.

Optimal supplier number of a supply chain company under stochastic demand

This paper seeks to provide academic researches with a better understanding about the determination of supplier number to control supply chain risk. By introducing multi-supplier strategy and some assumptions of the suppliers, a supply chain model based on traditional newsvendor model is proposed. This model considers both supply risk and demand risk. A nonlinear integer programming model is built to determine an optimal supplier number of this supply chain, and the risks under this model are analyzed. Simulations are also made to test this model under different market demand distribution. This paper presents a supplier number determination model which can maximize expected profit of a supply chain company under supply risk and demand risk (stochastic demand). Key words: Supply chain, supply chain risk, risk analysis, stochastic demand.

2010 M&SOM Best Paper Award

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeManufacturing & Service Operations ManagementVol. 12, No. 4 2010 M&SOM Best Paper AwardTava Lennon OlsenTava Lennon OlsenPublished Online:1 Oct 2010https://doi.org/10.1287/msom.1100.0313"2010 M&SOM Best Paper Award." Manufacturing & Service Operations Management, 12(4), p. 708 Previous Back to Top FiguresReferencesRelatedInformation Volume 12, Issue 4Fall 2010Pages 547-708 Article Information Metrics Downloaded 50 times in the past 12 months Information Published Online:October 01, 2010 Copyright © 2010, INFORMSCite asTava Lennon Olsen, (2010) 2010 M&SOM Best Paper Award. Manufacturing & Service Operations Management 12(4):708-708. https://doi.org/10.1287/msom.1100.0313 PDF download

Leadtime management in a periodic-review inventory system: A state-dependent base-stock policy

We examine a single-item, periodic-review inventory system with stochastic leadtimes, in which a replenishment order is delivered immediately or one period later, depending probabilistically on costly effort. The objective is to determine a joint inventory policy and effort-choice strategy that minimizes the expected total costs. Our analytical and computational analysis suggests that (i) a state-dependent base-stock policy is optimal, (ii) the optimal effort strategy is such that the marginal cost of effort is equal to the value of immediate delivery, and (iii) the cost impact of leadtime reduction can be very large. We also provide some counter-intuitive results, compared with the traditional multi-period newsvendor model.

Store inventory can affect demand: Empirical evidence from magazine retailing

In retailing, inventory analysis and inventory practice have traditionally been based on the assumption that underlying demand does not vary with inventory levels. A growing body of research supports the contention that the validity of this assumption has significant implications for optimal inventory policies. The concern for such inventory effects motivated a major US magazine publisher to conduct the market study documented in this article. It presents empirical evidence that demand can indeed vary with inventory, and it quantifies the magnitude of these inventory effects which are twofold. An inventory decrease for one brand can, first, result in a decrease of demand for the brand and, second, in an increase of demand for a competing brand. These observations support the expansion of the traditional Newsvendor model to include inventory effects as well as the practice to make inventory decisions for retail categories rather than individual brands.

Implementation of the Newsvendor Model with Clearance Pricing: How to (and How Not to) Estimate a Salvage Value

The newsvendor model is designed to decide how much of a product to order when the product is to be sold over a short selling season with stochastic demand and there are no additional opportunities to replenish inventory. There are many practical situations that reasonably conform to those assumptions, but the traditional newsvendor model also assumes a fixed salvage value: all inventory left over at the end of the season is sold off at a fixed per-unit price. The fixed salvage value assumption is questionable when a clearance price is rationally chosen in response to the events observed during the selling season: a deep discount should be taken if there is plenty of inventory remaining at the end of the season, whereas a shallow discount is appropriate for a product with higher than expected demand. This paper solves for the optimal order quantity in the newsvendor model, assuming rational clearance pricing. We then study the performance of the traditional newsvendor model. The key to effective implementation of the traditional newsvendor model is choosing an appropriate fixed salvage value. (We show that an optimal order quantity cannot be generally achieved by merely enhancing the traditional newsvendor model to include a nonlinear salvage value function.) We demonstrate that several intuitive methods for estimating the salvage value can lead to an excessively large order quantity and a substantial profit loss. Even though the traditional model can result in poor performance, the model seems as if it is working correctly: the order quantity chosen is optimal given the salvage value inputted to the model, and the observed salvage value given the chosen order quantity equals the inputted one. We discuss how to estimate a salvage value that leads the traditional newsvendor model to the optimal or near-optimal order quantity. Our results highlight the importance of understanding how a model can interact with its own inputs: when inputs to a model are influenced by the decisions of the model, care is needed to appreciate how that interaction influences the decisions recommended by the model and how the model’s inputs should be estimated.

Using separable programming to solve the multi-product multiple ex-ante constraint newsvendor problem and extensions

This paper provides an approximating programming technique to solve the multi-product newsvendor model in which product demands are independent and stocking quantities are subject to two or more ex-ante linear contraints, such as budget or volume constraints. Previous research has attempted to solve this problem with Lagrange relaxation techniques or by limiting the distribution of demand. However, by taking advantage of the separable nature of the problem, a close approximation of the optimal solution can be found using convex separable programming for any demand distribution in the traditional newsvendor model and extensions. Sensitivity analysis of the linear program provides managerial insight into the effects of parameters of the problem on the optimal solution and future decisions.