PRODUCT OFFERING,PRICING, AND MAKE‐TO‐STOCK/MAKE‐TO‐ORDER DECISIONS WITH SHARED CAPACITY

In an era of mass customization, many firms continue to expand their product lines to remain competitive. These broader product lines may help to increase market share and may allow higher prices to be charged, but they also cause challenges associated with diseconomies of scope. To investigate this tradeoff, we considered a monopolist who faces demand curves, which for each of its potential products, decline with both price and response time (time to deliver the product). The firm must decide which products to offer, how to price them, whether each should be make‐to‐stock (mts) or make‐to‐order (mto), and how often to produce them. The offered products share a single manufacturing facility. Setup times introduce disceonomies of scope and setup costs introduce economies of scale. We provide motivating problem scenarios, model the monopolist's problem as a non‐linear, integer programming problem, characterize of the optimal policy, develop near‐optimal procedures, and discuss managerial insights.     

Revenue Management with End‐of‐Period Discounts in the Presence of Customer Learning

Consider a firm that sells identical products over a series of selling periods (e.g., weekly all‐inclusive vacations at the same resort). To stimulate demand and enhance revenue, in some periods, the firm may choose to offer a part of its available inventory at a discount. As customers learn to expect such discounts, a fraction may wait rather than purchase at a regular price. A problem the firm faces is how to incorporate this waiting and learning into its revenue management decisions. To address this problem we summarize two types of learning behaviors and propose a general model that allows for both stochastic consumer demand and stochastic waiting. For the case with two customer classes, we develop a novel solution approach to the resulting dynamic program. We then examine two simplified models, where either the demand or the waiting behavior are deterministic, and present the solution in a closed form. We extend the model to incorporate three customer classes and discuss the effects of overselling the capacity and bumping customers. Through numerical simulations we study the value of offering end‐of‐period deals optimally and analyze how this value changes under different consumer behavior and demand scenarios.     

Pay‐Back‐Revenue‐Sharing Contract in Coordinating Supply Chains with Random Yield

We consider coordination issues in supply chains where supplier's production process is subject to random yield losses. For a simple supply chain with a single supplier and retailer facing deterministic demand, a pay back contract which has the retailer paying a discount price for the supplier's excess units can provide the right incentive for the supplier to increase his production size and achieve coordination. Building upon this result, we consider coordination issues for two other supply chains: one with competing retailers, the other with stochastic demand. When retailers compete for both demand and supply, they tend to over‐order. We show that a combination of a pay back and revenue sharing mechanism can coordinate the supply chain, with the pay back mechanism correcting the supplier's under‐producing problem and the revenue sharing mechanism correcting the retailers' over‐ordering problem. When demand is stochastic, we consider a modified pay‐back‐revenue‐sharing contract under which the retailer agrees to not only purchase the supplier's excess output (beyond the retailer's order), but also share with the supplier a portion of the revenue made from the sales of the excess output. We show that this contract, by giving the supplier additional incentives in the form of revenue share, can achieve coordination.     

基于预期网络规模的双寡头质量竞争

考虑一种具有网络效应特征的产品,本文研究当产品预期网络规模主要受质量影响时,双寡头企业如何进行质量竞争。本文建立了一个两阶段博弈模型的方法分析了这种问题。结果表明,市场领导者获得大于其预期网络规模的市场份额,而市场跟随者获得小于其预期网络规模的市场份额。对于弱网络效应产品,随着网络效应的增强, 两企业的产品质量分化加剧,价格及利润差异随之扩大。当网络效应的强度足够低时,市场跟随者利用后发优势得到较大利润。而对于强网络效应产品,市场领导者利用先发优势得到更大利润。     

Quality of Service Based Pricing Schemes for Content Sharing in Peer‐to‐Peer Networks

In this paper, we study quality‐of‐service (QoS) based pricing schemes that serve as incentive mechanisms to induce sharing behaviors in Peer‐to‐Peer (P2P) networks. We incorporate operational QoS metrics into users’ utility functions and demonstrate how they affect individual users’ content sharing decisions. Using a game‐theoretic model, our study reveals how organizations respond to the changes of operational QoS metrics in their design of pricing schemes for various business objectives at different stages of network evolution. Our results show that a higher upload capacity can foster rational sharing to start when the network is small; however, it also discourages sharing behaviors when the network becomes large. In order to induce a socially optimal behavior, a pricing scheme will not charge users for requesting content while compensating them for sharing content. Such compensation is found to increase faster with the network size when the network is large. In order to maximize the profit of a monopolistic provider, however, a pricing scheme will charge content requests with a positive price while providing less compensation to sharing users compared to the socially optimal scheme. When the network size is small, such compensation can be even negative, which implies that a monopolistic provider discourages content sharing when the network is small, but encourages it when the network becomes larger. In addition, we find that more information about peer upload capacity discourages peers to share.     

Joint Life‐Cycle Dynamics of New and Remanufactured Products

Many products considered for remanufacturing are durables that exhibit a well‐pronounced product life cycle—they diffuse gradually through the market. The remanufactured product, which is a cheaper substitute for the new product, is often put on the market during the life cycle of the new product and affects its sales dynamics. In this paper, we study the integrated dynamic management of a portfolio of new and remanufactured products that progressively penetrate a potential market over the product life cycle. To this end, we extend the Bass diffusion model in a way that maintains the two essential features of remanufacturing settings: (a) substitution between new and remanufactured products, and (b) a constraint on the diffusion of remanufactured products due to the limited supply of used products that can be remanufactured. We identify characteristics of the diffusion paths of new and remanufactured products. Finally, we analyze the impact of levers such as remanufacturability level, capacity profile and reverse channel speed on profitability.     

Branch‐and‐Price Methods for Prescribing Profitable Upgrades of High‐Technology Products with Stochastic Demands*

This paper develops a model that can be used as a decision support aid, helping manufacturers make profitable decisions in upgrading the features of a family of high‐technology products over its life cycle. The model integrates various organizations in the enterprise: product design, marketing, manufacturing, production planning, and supply chain management. Customer demand is assumed random and this uncertainty is addressed using scenario analysis. A branch‐and‐price (B&P) solution approach is devised to optimize the stochastic problem effectively. Sets of random instances are generated to evaluate the effectiveness of our solution approach in comparison with that of commercial software on the basis of run time. Computational results indicate that our approach outperforms commercial software on all of our test problems and is capable of solving practical problems in reasonable run time. We present several examples to demonstrate how managers can use our models to answer “what if” questions.     

Product Positioning Using a Self‐Organizing Map and the Rings of Influence

In this article, we propose a new product positioning method based on the neural network methodology of a self‐organizing map. The method incorporates the concept of rings of influence, where a firm evaluates individual consumers and decides on the intensity to pursue a consumer, based on the probability that this consumer will purchase a competing product. The method has several advantages over earlier work. First, no limitations are imposed on the number of competing products and second, the method can position multiple products in multiple market segments. Using simulations, we compare the new product positioning method with a quasi‐Newton method and find that the new method always approaches the best solution obtained by the quasi‐Newton method. The quasi‐Newton method, however, is dependent on the initial positions of the new products, with the majority of cases ending in a local optimum. Furthermore, the computational time required by the quasi‐Newton method increases exponentially, while the time required by the new method is small and remains almost unchanged, when the number of new products positioned increases. We also compute the expected utility that a firm will provide consumers by offering its products. We show that as the intensity with which a firm pursues consumers increases, the new method results in near‐optimal solutions in terms of market share, but with higher expected utility provided to consumers when compared to that obtained by a quasi‐Newton method. Thus, the new method can serve as a managerial decision‐making tool to compare the short‐term market share objective with the long‐term expected utility that a firm will provide to consumers, when it positions its products and intensifies its effort to attract consumers away from competition.     

Estimating Social Influences from Social Networking Sites—Articulated Friendships versus Communication Interactions

Despite the ubiquity of social networking sites, the online social networking industry is in search of effective marketing strategies to better profit from their established user base. Social media marketing strategies build on the premise that the social network of online users can be predicted and social influences among online users can be estimated. However, the existence of various heterogeneous social interactions on social networking sites presents a challenge for social network prediction and social influence estimation. In this article we draw upon the literatures on self‐presentation on social networking sites and signaling in online social networking to categorize six heterogeneous online social interactions on social networking sites into two types—articulated friendships and communication interactions. This article provides empirical evidence for the differences between articulated friendships and communication interactions and the corresponding articulated and communication networks. In order to compare the impacts of the social influences based on these two networks, we utilize support vector machines to build a classifier to predict virtual community membership and we further estimate the marginal effects of these social influences using a two‐stage probit least squares method. We find significant explanatory power of social influences in predicting virtual community membership. Although the communication network is much sparser than the articulated network, social influences based on the communication network achieve similar performance as the articulated network. These findings provide important implications for social media marketing as well as the management of virtual communities.     

Impeding the Juggernaut of Innovation Diffusion: A Production‐Constrained Model

Models of innovation diffusion typically depict an inexorable momentum once the process begins to roll. Limited production capacity, however, can place a cap on this process, leading to waiting lines of potential customers, thus diminishing overall service quality and the speed of diffusion. Identifying the minimum production capacity needed for unimpeded and unimpaired diffusion can ensure that there are no customers waiting to adopt the product. We propose a production‐capacity‐constrained diffusion model that considers an exogenous industry production capacity and accounts for word‐of‐mouth effects from adopters as well as waiting customers. We derive analytical expressions for minimum capacity needed under multiple production scenarios. We present a dual‐objective non‐linear least squares procedure with large‐scale grid search for estimating the parameters. We apply our model to several new product innovation data sets, ranging from vacuum cleaners to sports utility vehicles in the United States to iPhones globally. Our estimates show that product shortages exist, ranging from mild to severe, in all of these product markets. We are able to corroborate some of our findings with independent external sources of evidence. We find that information on industry capacity can be recovered with as few as 5 years of sales data. Our model has practical implications for policy makers and can help equity analysts triangulate industry capacity better, particularly when such information is closely held.     

In‐Store Experiments to Determine the Impact of Price on Sales

This paper describes an experimentation methodology to measure how demand varies with price and the results of its application at a toy retailer. The same product is assigned different price‐points in different store panels and the resulting sales are used to estimate a demand curve. We use a variant of the k‐median problem to form store panels that control for differences between stores and produce results that are representative of the entire chain. We use the estimated demand curve to find a price that maximizes profit. Our experiment yielded the unexpected result that demand increases with price in some cases. We present likely reasons for this finding from our discussions with retail managers. Our methodology can be used to analyze the effect of several marketing and promotional levers employed in a retail store besides pricing.     

The Impact of Mega‐Catastrophes on Insurers: An Exposure‐Based Analysis of the U.S. Homeowners’ Insurance Market

Insurance is a key risk‐sharing mechanism that protects citizens and governments from the losses caused by natural catastrophes. Given the increase in the frequency and intensity of natural catastrophes over recent years, this article analyzes the performance effects of mega‐catastrophes for U.S. insurance firms using a measure of market expectations. Specifically, we analyze the share price losses of insurance firms in response to catastrophe events to ascertain whether mega‐catastrophes significantly damage the performance of insurers and whether different types of mega‐catastrophes have different impacts. The main message from our analysis is that the impact of mega‐catastrophes on insurers has not been too damaging. While the exact impact of catastrophes depends on the nature of the event and the degree of competition within the relevant insurance market (less competition allows insurers to recoup catastrophe losses through adjustments to premiums), our overall results suggest that U.S. insurance firms can adequately manage the risks and costs of mega‐catastrophes. From a public policy perspective, our results show that insurance provides a robust means of sharing catastrophe losses to help reduce the financial consequences of a catastrophe event.     

Investment in Facility Changeover Flexibility for Early Entry into High‐Tech Markets

A model is introduced to analyze the manufacturing‐marketing interface for a firm in a high‐tech industry that produces a series of high‐volume products with short product life cycles on a single facility. The one‐time strategic decision regarding the firm's investment in changeover flexibility establishes the link between market opportunities and manufacturing capabilities. Specifically, the optimal changeover flexibility decision is determined in the context of the firm's market entry strategy for successive product generations, the changeover cost between generations, and the production efficiency of the facility. Moreover, the dynamic pricing policy for each product generation is obtained as a function of the firm's market entry strategy and manufacturing efficiency. Our findings provide insights linking internal manufacturing capabilities with external market forces for the high‐tech and high‐volume manufacturer of products with short life cycles. We show the impact of manufacturing efficiency and a firm's ability to benefit from volume‐based learning on the dynamic pricing policy for each product generation. The results demonstrate the benefits realized by a firm that works with its manufacturing equipment suppliers to develop more efficient and flexible technology. In addition, we explore how opportunities afforded by pioneer advantage enable a firm operating a less efficient facility to realize long term competitive advantage by deploying an earlier market entry strategy.     

Life‐Cycle Channel Coordination Issues in Launching an Innovative Durable Product

We analyze the dynamic strategic interactions between a manufacturer and a retailer in a decentralized distribution channel used to launch an innovative durable product (IDP). The underlying retail demand for the IDP is influenced by word‐of‐mouth from past adopters and follows a Bass‐type diffusion process. The word‐of‐mouth influence creates a trade‐off between immediate and future sales and profits, resulting in a multi‐period dynamic supply chain coordination problem. Our analysis shows that while in some environments, the manufacturer is better off with a far‐sighted retailer, there are also environments in which the manufacturer is better off with a myopic retailer. We characterize equilibrium dynamic pricing strategies and the resulting sales and profit trajectories. We demonstrate that revenue‐sharing contracts can coordinate the IDP's supply chain with both far‐sighted and myopic retailers throughout the entire planning horizon and arbitrarily allocate the channel profit.     

Single‐Period Two‐Product Assemble‐to‐Order Systems with a Common Component and Uncertain Demand Patterns

We consider a single‐period assemble‐to‐order system that produces two types of end products to satisfy two independent and stochastic customer orders. Each type of product is used to fulfill a particular customer order and these two products share a common component. Furthermore, one customer may confirm her order before the other one, and the manufacturer needs to make a commitment immediately upon the receipt of each customer order on how many products to be delivered. We propose a model for optimizing the inventory and production decisions under the above ATO environment. We also extend our model to the situation where the manufacturer can fulfill the unsatisfied low‐priority demand using the left‐over inventories after fulfilling the high‐priority demand, in case the low‐priority customer arrives first. Numerical experiments are conducted, which provide some interesting insights on the impact of uncertain demand pattern.     

Multi‐Product Quality Competition: Impact of Resource Constraints

We study a multi‐product firm with limited capacity where the products are vertically (quality) differentiated and the customer base is heterogeneous in their valuation of quality. While the demand structure creates opportunities through proliferation, the firm should avoid cannibalization between its own products. Moreover, the oligopolistic market structure puts competitive pressure and limits the firm's market share. On the other hand, the firm has limited resources that cause a supply‐side fight for adequate and profitable production. We explicitly characterize the conditions where each force dominates. Our focus is on understanding how capacity constraints and competition affect a firm's product‐mix decisions. We find that considering capacity constraints could significantly change traditional insights (that ignore capacity) related to product‐line design and the role of competition therein. In particular, we show that when the resources are limited, the firm should offer only the product that has the highest margin per unit capacity. We find that this product could be the diametrically opposite product suggested by the existing literature. In addition, we show that for intermediate capacity levels, whereas the margin per unit capacity effect dominates in a less competitive market, proliferation and cannibalization effects dominate in a more competitive market.     

整体环境和个体关联对群体性事件产生与演化的影响

对群体性事件产生与演化规律的研究已经成为社会管理的重要主题,而发展迅速的计算社会学方法为深入研究这一问题提供了新的途径。在已有研究的基础上,通过考察群体性事件的部分案例,可以发现个体对政府的信任程度、个体利益诉求渠道和社会普遍情绪这些内生性因素构成了群体性事件发生的整体环境要素,这些内生性因素的存在和组合达到一定水平,就构成了群体性事件发生的重要条件,而在这些宏观条件下,个体之间通过社会网络产生的关联和相互作用对群体事件的产生与演化也有重要影响。通过基于社会网络的计算社会学模型的模拟检验,本文对群体性事件的产生与演化进行了计算分析,讨论了整体环境和个体关联的各要素的作用,获得了部分内生性因素在其他条件既定时引发群体性事件的临界值。研究工作为进一步研究群体性事件的演化与形成机制提供了必要的模型基础。     

FROM BUYER TO INTEGRATOR: THE TRANSFORMATION OF THE SUPPLY‐CHAIN MANAGER IN THE VERTICALLY DISINTEGRATING FIRM

Using case study data, we describe how a large personal computer manufacturer changed its supply‐chain management strategy after outsourcing the majority of its design and manufacturing activities to a network of focused suppliers. To cope with this new structure, the firm created highly skilled generalists, “supply‐chain integrators,” who coordinate product development, marketing, production, and logistics from product concept to delivery across firm boundaries. We particularly focus on the skill‐set that characterizes these integrators. Finally, we use the case evidence, combined with previous theory, to suggest a specific program of research into coordinating product development across disaggregated supply chains.     

Production and Sales Planning in Capacitated New Product Introductions

How should a firm with limited capacity introduce a new product? Should it introduce the product as soon as possible or delay introduction to build up inventory? How do the product and market characteristics affect the firm's decisions? To answer such questions, we analyze new product introductions under capacity restrictions using a two‐period model with diffusion‐type demand. Combining marketing and operations management decisions in a stylized model, we optimize the production and sales plans of the firm for a single product. We identify four different introduction policies and show that when the holding cost is low and the capacity is low to moderate, a (partial) build‐up policy is indeed optimal if consumers are sensitive to delay. Under such a policy, the firm (partially) delays the introduction of its product and incurs short‐term backlog costs to manage its future demand and total costs more effectively. However, as either the holding cost or the capacity increases, or consumer sensitivity to delay decreases, the build‐up policy starts to lose its appeal, and instead, the firm prefers an immediate product introduction. We extend our analysis by studying the optimal capacity decision of the firm and show that capacity shortages may be intentional.     

The Economics of Public Beta Testing

A growing number of software firms now rely on public beta testing to improve the quality of their products before commercial release. While the benefits resulting from improved software reliability are well recognized, some important market‐related benefits have not been studied. Through word‐of‐mouth, public beta testers can accelerate the diffusion of a software product after its release. Additionally, because of network effects, public beta testers can increase users’ valuation of a product. In this study, we consider both reliability‐related and market‐related benefits, and develop models to determine the optimal number of public beta testers and the optimal duration of testing. Our analyses show that public beta testing can be profitable even if word‐of‐mouth and network effects are the only benefits. Furthermore, when both benefits are considered, there is significant “economies of scope”—the net profit increases at a faster rate when both word‐of‐mouth and network effects are significant than when only one benefit is present. Finally, our sensitivity analyses demonstrate that public beta testing remains highly valuable to software firms over a wide range of testing and market conditions. In particular, firms will realize greater profits when recruiting public beta testers who are interested in the software but unable to afford it.