消费者导向类型与电商全渠道决策研究

从现实出发，基于不同消费者导向类型研究了电商的全渠道决策问题。首先建立电商单渠道销售的基础模型，并得出最优产品组合、价格和交付时间。在此基础上，给出了消费者为产品导向型和渠道导向型情况下电商采取全渠道决策的条件，得出双渠道产品组合、价格及交付时间的最优决策；并进行了数值验证。研究发现，同一渠道提供的产品其最优价格相同，最优交付时间只和消费者耐心程度及交付成本函数有关。若消费者为产品导向型，仅当双渠道运营成本之差较小且消费者耐心程度较低时，电商进入线下渠道才有利可图，且在线下渠道提供最受欢迎的产品，线上渠道提供剩余产品。若消费者为渠道导向型，电商进入线下渠道必然有利可图，且在线下渠道提供最受欢迎的产品，线上渠道提供所有产品。     

Bounding Optimal Expected Revenues for Assortment Optimization under Mixtures of Multinomial Logits

We consider assortment problems under a mixture of multinomial logit models. There is a fixed revenue associated with each product. There are multiple customer types. Customers of different types choose according to different multinomial logit models whose parameters depend on the type of the customer. The goal is to find a set of products to offer so as to maximize the expected revenue obtained over all customer types. This assortment problem under the multinomial logit model with multiple customer types is NP‐complete. Although there are heuristics to find good assortments, it is difficult to verify the optimality gap of the heuristics. In this study, motivated by the difficulty of finding optimal solutions and verifying the optimality gap of heuristics, we develop an approach to construct an upper bound on the optimal expected revenue. Our approach can quickly provide upper bounds and these upper bounds can be quite tight. In our computational experiments, over a large set of randomly generated problem instances, the upper bounds provided by our approach deviate from the optimal expected revenues by 0.15% on average and by less than one percent in the worst case. By using our upper bounds, we are able to verify the optimality gaps of a greedy heuristic accurately, even when optimal solutions are not available.     

An integrated production and inventory model for a whole manufacturing supply chain involving reverse logistics with finite horizon period

This paper proposes a model and solution method for coordinating integrated production and inventory cycles in a whole manufacturing supply chain involving reverse logistics for multiple items with finite horizon period. A whole manufacturing supply chain involving reverse logistic consists of tier-2 suppliers supplying raw materials to tier-1 suppliers, tier-1 suppliers producing parts, a manufacturer which manufactures and assembles parts from tier-1 suppliers into finished products, distributors distributing finished products to retailers, retailers selling products to end customers and a third party which collects the used finished products from end customers, dissembles collected products into parts, and feed the parts back to the supply chain. In this system, we consider a finite horizon period. A mathematical model for representing the behaviors of the system is developed. Solution methods based on decentralized and a combination of decentralized and centralized decision making process, referred to as the semi-centralized decision making process, are proposed to solve the model while the centralized decision making process is solved by a mixed integer nonlinear programming method. A numerical example is used to demonstrate the model and the solutions based on the three types of the coordination.     

Simultaneous Determination of Joint Product Cost Allocations and Cost-Plus Prices*

Use of the net realizable value approach for joint manufacturing cost allocations requires knowledge of selling prices of joint products. However, joint product selling prices themselves are functions of the allocated costs under a cost-plus pricing policy. In this case, it is necessary to determine joint cost allocations and joint product prices simultaneously. This paper applies a nonlinear programming (NLP) approach to simultaneously determine the optimal joint production decision, joint product cost-plus prices, and joint cost allocations using the net realizable value method. The NLP solution provides not only the optimal joint production and pricing decisions, but also the necessary conditions for such optimal decisions.     

Integrated retail decisions with multiple selling periods and customer segments: Optimization and insights

Integrating retail decisions on such aspects as assortment, pricing, and inventory greatly improves profitability. We examine a multi-period selling horizon where a retailer jointly optimizes assortment planning, pricing, and inventory decisions for a product line of substitutable products, in a market with multiple customer segments. Focusing on fast-moving retail products, the problem is modeled as a mixed-integer nonlinear program where demand is driven by exogenous consumer reservation prices and endogenous assortment and pricing decisions. A mixed-integer linear reformulation is developed, which enables an exact solution to large problem instances (with up to a hundred products) in manageable times. Empirical evidence is provided in support of a classical deterministic maximum-surplus consumer choice model. Computational results and managerial insights are discussed. We find that the optimal assortment and pricing decisions do not exhibit a simple, intuitive structure that could be analytically characterized, which reflects the usefulness of optimization approaches to numerically identify attractive trade-offs for the decision-maker. We also observe that suboptimal inventory policies significantly decrease profitability, which highlights the importance of integrated decision-making. Finally, we find that the seasonality of consumer preferences and supply costs present an opportunity for boosting the profit via higher inventory levels and wider assortments.     

An improvement on “Integrated production strategy and reuse scenario: A CoFAQ model and case study of mail server system development”

The authors (Tang et al. (2013) [1] developed a CoFAQ model to formulate a solution for the problem of production strategy decision and reuse scenario selection for a software product family. In the previous research, we stated that the CoFAQ model was a 0–1 mixed integer nonlinear program, where only a local optimal solution might be found. In a recent study, we found that the CoFAQ could be transformed into a 0–1 mixed integer linear programming model. By solving the model, a global optimal solution can be obtained. In this paper, we present the improved formulation and the optimal solution for the case study.     

Combined Pricing and Portfolio Option Procurement

In this paper, we study a single‐product periodic‐review inventory system that faces random and price‐dependent demand. The firm can purchase the product either from option contracts or from the spot market. Different option contracts are offered by a set of suppliers with a two‐part fee structure: a unit reservation cost and a unit exercising cost. The spot market price is random and its realization may affect the subsequent option contract prices. The firm decides the reservation quantity from each supplier and the product selling price at the beginning of each period and the number of options to exercise (inventory replenishment) at the end of the period to maximize the total expected profit over its planning horizon. We show that the optimal inventory replenishment policy is order‐up‐to type with a sequence of decreasing thresholds. We also investigate the optimal option‐reservation policy and the optimal pricing strategy. The optimal reservation quantities and selling price are shown to be both decreasing in the starting inventory level when demand function is additive. Building upon the analytical results, we conduct a numerical study to unveil additional managerial insights. Among other things, we quantify the values of the option contracts and dynamic pricing to the firm and show that they are more significant when the market demand becomes more volatile.     

焕新计划下考虑消费者细分的产品定价策略

焕新计划是生产商为提升自身竞争优势而推出的一种促销手段，加入焕新计划的消费者在第一阶段可以享受全方位服务，在第二阶段可以享受以旧换新抵扣优惠。消费者将权衡加入焕新计划的费用、服务水平以及抵扣力度等因素决策是否在第一阶段加入焕新计划。本文假设时尚型消费者每阶段都会购买最新产品，而节俭型消费者第一阶段购买产品后在第二阶段继续使用，针对这两类消费者在实施焕新计划和不实施焕新计划两种情况下，构建两阶段模型以决策产品的最优定价；运用解析方法分析了产品的生产成本等参数对最优定价的影响；运用解析方法和数值算例方法对两模型进行对比。     

Joint Initial Stocking and Transshipment—Asymptotics and Bounds

A common problem faced by many firms in their supply chains can be abstracted as follows. Periodically, or at the beginning of some selling season, the firm needs to distribute finished goods to a set of stocking locations, which, in turn, supply customer demands. Over the selling season, if and when there is a supply‐demand mismatch somewhere, a re‐distribution or transshipment will be needed. Hence, there are two decisions involved: the one‐time stocking decision at the beginning of the season and the supply/transshipment decision throughout the season. Applying a stochastic dynamic programming formulation to a two‐location model with compound Poisson demand processes, we identify the optimal supply/transshipment policy and show that the optimal initial stocking quantities can be obtained via maximizing a concave function whereas the contribution of transshipment is of order square‐root‐of T. Hence, in the context of high‐volume, fast‐moving products, the initial stocking quantity decision is a much more important contributor to the overall profit. The bounds also lead to a heuristic policy, which exhibits excellent performance in our numerical study; and we further prove both the bounds and the heuristic policy are asymptotically optimal when T approaches infinity. Extension to multiple locations is also discussed.     

双边不确定性条件下制造商质量投资与零售商销售努力决策

已有研究较少同时考察供应链中制造商产品质量信号传递和零售商销售努力调整的问题。本文考虑零售商通过努力水平改变市场需求,且制造商分摊一定比例销售努力成本的情形,制造商在决定进行质量投资后,双方对制造商能否成为高质量类型各有不同的信念,零售商只能通过观察制造商提供的契约来修正关于制造商类型的信念,并确定产品销售价格与销售努力水平。构建博弈模型分析了双方的质量投资决策、销售努力决策、定价决策以及成本分摊比例。结果表明,在不考虑质量投资成本的情形下,制造商的质量投资能够达到帕累托最优。为了激励零售商付出最优促销努力水平,销售契约中规定销售成本分摊比例与惩罚措施,由于讨价还价的存在,实际的销售成本分摊比例小于对制造商最有利的成本分摊比例。对零售商而言,制造商承担的销售成本分摊比例并非越高越好,这意味着如果超过一定范围,制造商的成本分摊反而会损害零售商的利益。     

Flexible Capacity Investments and Product Mix: Optimal Decisions and Value of Postponement Options

In this article, we study a firm's interdependent decisions in investing in flexible capacity, capacity allocation to individual products, and eventual production quantities and pricing in meeting uncertain demand. We propose a three‐stage sequential decision model to analyze the firm's decisions, with the firm being a value maximizer owned by risk‐averse investors. At the beginning of the time horizon, the firm sets the flexible capacity level using an aggregate demand forecast on the envelope of products its flexible resources can accommodate. The aggregate demand forecast evolves as a Geometric Brownian Motion process. The potential market share of each product is determined by the Multinomial Logit model. At a later time and before the end of the time horizon, the firm makes a capacity commitment decision on the allocation of the flexible capacity to each product. Finally, at the end of the time horizon, the firm observes the demand and makes the production quantity and pricing decisions for end products. We obtain the optimal solutions at each decision stage and investigate their optimal properties. Our numerical study investigates the value of the postponed capacity commitment option in supplying uncertain operation environments.     

考虑战略顾客行为时的供应链性能分析与协调

动态定价策略的广泛应用使得越来越多的顾客具有了战略性,战略顾客会根据产品在销售期内的价格路径确定最优购买时机,零售商则根据顾客的购买行为确定订货数量和销售价格.研究了双方静态博弈时的理性预期均衡解和零售商进行数量承诺时的情形.研究表明:理性预期均衡时的最优销售价格、最优存货数量和最优期望利润分别小于标准报童模型的情形;数量承诺时的最优存货数量小于理性预期均衡时的最优存货数量;最优期望利润则大于理性预期均衡时的最优期望利润,并且在一定条件下可能会大于标准报童模型的最优期望利润,战略顾客行为的存在对零售商可能有利.最后分析了在分散式供应链中如何利用收入分享契约和数量折扣契约实现供应链协调.     

Allocation of Returned Products among Different Recovery Options through an Opportunity Cost–Based Dynamic Approach*

In a make‐to‐order product recovery environment, we consider the allocation decision for returned products decision under stochastic demand of a firm with three options: refurbishing to resell, parts harvesting, and recycling. We formulate the problem as a multiperiod Markov decision process (MDP) and present a linear programming (LP) approximation that provides an upper bound on the optimal objective function value of the MDP model. We then present two solution approaches to the MDP using the LP solution: a static approach that uses the LP solution directly and a dynamic approach that adopts a revenue management perspective and employs bid‐price controls technique where the LP is resolved after each demand arrival. We calculate the bid prices based on the shadow price interpretation of the dual variables for the inventory constraints and accept a demand if the marginal value is higher than the bid price. Since the need for solving the LP at each demand arrival requires a very efficient solution procedure, we present a transportation problem formulation of the LP via variable redefinitions and develop a one‐pass optimal solution procedure for it. We carry out an extensive numerical analysis to compare the two approaches and find that the dynamic approach provides better performance in all of the tested scenarios. Furthermore, the solutions obtained are within 2% of the upper bound on the optimal objective function value of the MDP model.     

Evaluating new product decision processes

The development of a new product is often a sequential-decision process with many available stages at which the product may take any one of a number of alternate courses of action. This paper identifies an efficient algorithm for solving this sequential-decision process. A computer program, involving the algorithm, has been written to solve problems containing up to one thousand stages. Input into the program includes the subjective probabilities of success for each decision branch, the cost associated with each decision stage and level, the rate for discounting all monetary values to the present plus an indicator for each decision stage and level of the desired decision criterion. The algorithm utilizes the dynamic-programming solution approach. The output of the algorithm contains the minimum, or maximum, expected discounted decision values for each stage and level of the network. The algorithm and computer program have been primarily utilized in the pharmaceutical industry. An illustrative, exemplary use of the algorithm in this industry is presented.     

Optimal Policies for a Two‐Product Inventory System under a Flexible Substitution Scheme

We study a two‐product inventory model that allows substitution. Both products can be used to supply demand over a selling season of N periods, with a one‐time replenishment opportunity at the beginning of the season. A substitution could be offered even when the demanded product is available. The substitution rule is flexible in the sense that the seller can choose whether or not to offer substitution and at what price or discount level, and the customer may or may not accept the offer, with the acceptance probability being a decreasing function of the substitution price. The decisions are the replenishment quantities at the beginning of the season, and the dynamic substitution‐pricing policy in each period of the season. Using a stochastic dynamic programming approach, we present a complete solution to the problem. Furthermore, we show that the objective function is concave and submodular in the inventory levels—structural properties that facilitate the solution procedure and help identify threshold policies for the optimal substitution/pricing decisions. Finally, with a state transformation, we also show that the objective function is ‐concave, which allows us to derive similar structural properties of the optimal policy for multiple‐season problems.     

COORDINATION ALTERNATIVES IN A MANUFACTURER/DEALER INVENTORY SYSTEM UNDER STOCHASTIC DEMAND

This paper introduces a stochastic model of a distribution system where the stocking location is owned by a dealer (or retailer) and the product is supplied by a manufacturer. Inventory is managed by the dealer, and the manufacturer is responsible for delivery of the product through both regular replenishment and expedite shipment modes. The dealer and the manufacturer share the goal of providing a high level of customer service. Demand, moreover, is a function of the service level offered to the market by the dealer. We develop optimal stock control policies for the cases where each decision maker in turn is dominant and acts unilaterally while being constrained by the supply/demand linkages of the system. We also develop an optimum policy for the case where both levels are managed under centralized control (i.e., both levels cooperate). Results indicate that the expected profit for a dominant dealer (or dominant manufacturer) is higher under decentralized control than the optimal solution for either under centralized control. However, the centralized solution is a global-optimal solution and therefore will guarantee longterm stability. Differences between the various solutions are analyzed explicitly to estimate the cost of coordination.     

Assortment Planning for Vertically Differentiated Products

We consider the problem of a retailer managing a category of vertically differentiated products. The retailer has to pay a fixed cost for each product included in the assortment and a variable cost per product sold. Quality levels, fixed, and variable costs are exogenously determined. Customers differ in their valuation of quality and choose the product (if any) that maximizes their utility. First, we consider a setting in which the selling prices are also fixed. We find that the optimal set of products to offer depends on the distribution of customer valuations and might include dominated products, that is, products which are less attractive than at least one other product, on every possible dimension. We develop an efficient algorithm to identify an optimal assortment. Second, we consider a setting in which the retailer also determines the selling prices. We show that in this case the optimal assortment does not include any dominated product and does not vary with the distribution of customer valuations when there is no fixed cost. We develop several efficient algorithms to identify an optimal assortment and optimally price the products. We also test the applicability of our methods with realistic data for two product categories.     

Shelf Loathing: Cross Docking at an Online Retailer

Online customers expect to wait, sometimes for a delay of many days. At the fulfillment center, there might be an opportunity to fill customer orders earlier than the due date through a cross‐docking transaction: rather than picking the item from inventory, the item moves directly from the receiving to the shipping dock, saving shelving and picking transactions. While cross docking reduces shelving and picking costs, it risks changing customer expectations for how soon a product will be delivered. Given customer order arrivals random in quantity and due dates, random replenishment arrivals, and costs (or benefits) for shipping a product early, we characterize the optimal decision as to whether to cross dock a replenishment item to fulfill demand that is not immediately due or to wait to (hopefully) cross dock in later periods. With multiple demands and due dates, the cross‐docking decision depends on the number of unfulfilled demands in each period across the horizon, the number of units that have just arrived (available for cross docking), picking and shelving costs, and the delay cost (or benefit). We formulate the problem as a Markov decision process, determine the structure of the optimal policy, and propose a well‐performing heuristic.     

A Choice‐Based Dynamic Programming Approach for Setting Opaque Prices

Opaque pricing is a form of pricing where certain characteristics of the product or service are hidden from the consumer until after purchase. In essence, opaque selling transforms a differentiated good into a commodity. Opaque pricing has become popular in service pricing as it allows firms to sell their differentiated product at higher prices to regular brand loyal customers while simultaneously selling to non‐brand loyal customers at discounted prices. We use a nested logit model in combination with logistic regression and dynamic programming to illustrate how a service firm can optimally set prices on an opaque sales channel. The choice model allows the characterization of consumer trade‐offs when purchasing opaque products while the dynamic programming approach allows the characterization of the optimal pricing policy as a function of inventory and time remaining. We compare optimal prices and expected revenues when dynamic pricing is restricted to daily price changes. We provide an illustrative example using data from an opaque selling mechanism ( Hotwire.com ) and a Washington DC‐based hotel.     

A Quantal Choice Model for the Detection of Copying on Multiple Choice Examinations

It is difficult to devise a statistical test to detect one student copying from another. Many prior efforts falsely accuse students of cheating. A general methodology based on the quantal choice model of decision theory overcomes these problems. Three steps are involved: (1) for each item estimate the probability and the variance that a given respondent will select each response, (2) for pairs of respondents, these probabilities determine the expected number of matches, and (3) compare the critical value to the number of items matched. Methods differ based on the probability estimation technique. Four methods (simple frequencies, Frary, Tideman, and Watts modification [8], logit, and multinomial probit) are compared on theoretical and empirical grounds. Theory and results show that it is crucial to incorporate the variance of the probability estimates. The probit model has theoretical advantages over the other methods and produces more accurate results.