Supply Chain and Revenue Management for Online Retailing

This dissertation focuses on optimizing inventory and pricing decisions in the online retail industry. Motivated by the importance of great customer service quality in the online retail marketplace, we investigate service-level-constrained inventory control problems in both static and dynamic settings. The first essay studies multi-period production planning problems (with or without pricing options) under stochastic demand. A joint service-level constraint is enforced to restrict the joint probability of having backorders in any period. We use the Sample Average Approximation (SAA) approach to reformulate both chance-constrained models as mixed-integer linear programs (MILPs). Via computations of diverse instances, we demonstrate the effectiveness of the SAA approach, analyze the solution feasibility and objective bounds, and conduct sensitivity analysis. The approaches can be generalized to a wide variety of production planning problems. The second essay investigates the dynamic versions of the service-level-constrained inventory control problems, in which retailers have the flexibility to adjust their inventory policies in each period. We formulate two periodic-review stochastic inventory models (backlogging model and remanufacturing model) via Dynamic Programs (DP), and establish the optimality of generalized base-stock policies. We also propose 2-approximation algorithms for both models, which is computationally more efficient than the brute-force DP. The core concept developed in our algorithms is called the delayed marginal cost, which is proven effective in dealing with service-level-constrained inventory systems. The third essay is motivated by the exploding use of sales rank information in today's internet-based e-commerce marketplace. The sales rank affects consumers' shopping preference and therefore, is critical for retailers to utilize when making pricing decisions. We study periodic-review dynamic pricing problems in presence of sales rank, in which customers' demand is a function of both prices and sales rank. We propose rank-based pricing models and characterize the structure and monotonicity of optimal pricing policies. Our numerical experiments illustrate the potential of revenue increases when strategic cyclic policy is used.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144159/1/ycjiang_1.pd

Revenue Management In Manufacturing: A Research Landscape

Revenue management is the science of using past history and current levels of order activity to forecast demand as accurately as possible in order to set and update pricing and product availability decisions across various sales channels to maximize profitability. In much the same way that revenue management has transformed the airline industry in selling tickets for the same flight at markedly different rates based upon product restrictions, time to departure, and the number of unsold seats, many manufacturing companies have started exploring innovative revenue management strategies in an effort to improve their operations and profitability. These strategies employ sophisticated demand forecasting and optimization models that are based on research from many areas, including management science and economics, and that can take advantage of the vast amount of data available through customer relationship management systems in order to calibrate the models. In this paper, we present an overview of revenue management systems and provide an extensive survey of published research along a landscape delineated by three fundamental dimensions of capacity management, pricing, and market segmentation

Dynamic pricing models for electronic business

Dynamic pricing is the dynamic adjustment of prices to consumers depending upon the value these customers attribute to a product or service. Today’s digital economy is ready for dynamic pricing; however recent research has shown that the prices will have to be adjusted in fairly sophisticated ways, based on sound mathematical models, to derive the benefits of dynamic pricing. This article attempts to survey different models that have been used in dynamic pricing. We first motivate dynamic pricing and present underlying concepts, with several examples, and explain conditions under which dynamic pricing is likely to succeed. We then bring out the role of models in computing dynamic prices. The models surveyed include inventory-based models, data-driven models, auctions, and machine learning. We present a detailed example of an e-business market to show the use of reinforcement learning in dynamic pricing

On the Benefit of Inventory-Based Dynamic Pricing Strategies

We study the optimal pricing and replenishment decisions in an inventory system with a price-sensitive demand, focusing on the benefit of the inventory-based dynamic pricing strategy. We find that demand variability impacts the benefit of dynamic pricing not only through the magnitude of the variability but also through its functional form (e.g., whether it is additive, multiplicative, or others). We provide an approach to quantify the profit improvement of dynamic pricing over static pricing without having to solve the dynamic pricing problem. We also demonstrate that dynamic pricing is most effective when it is jointly optimized with inventory replenishment decisions, and that its advantage can be mostly realized by using one or two price changes over a replenishment cycle.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78685/1/j.1937-5956.2009.01099.x.pd

Spare Parts Management of Aging Capital Products

Spare parts are critical for operations of capital products such as aircraft, refineries, trucks, etc/, which require maintenance regularly. Original Equipment Manufacturers (OEMs) bear the responsibility of undisrupted maintenance service and spare parts flow for their capital products. Due to various factors OEMs lose their spare parts suppliers occasionally and these losses threaten the reliability of their maintenance service and capital products. In this thesis, we consider supply risk in management of spare parts inventory. The thesis consists of two parts: First we develop advance indicators for future supply problems of spare parts and suggest a model utilizing those indicators for inventory control of spare parts. Our results indicate that OEMs can save significantly by utilizing those indicators together with our model in their daily business. Second, we consider secondary markets and their effects on spare parts supply chains of OEMs. Secondary markets are chap supply sources for spare parts needs of OEMs. Therefore effective usage of them yield significant cost savings and boost service rate of OEMs. Furthermore, secondary markets are sources of competition since low prices on those markets attract some customers of OEMs. These two factors are considered from the perspective of spare parts inventory control. In the second part, we conclude that for OEMs it is beneficial to use secondary markets as a supply source as long as they adjust their selling prices accordingly

Combined Pricing and Portfolio Option Procurement

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90569/1/poms1255.pd