Revenue Management and Demand Fulfillment: Matching Applications, Models, and Software

Recent years have seen great successes of revenue management, notably in the airline, hotel, and car rental business. Currently, an increasing number of industries, including manufacturers and retailers, are exploring ways to adopt similar concepts. Software companies are taking an active role in promoting the broadening range of applications. Also technological advances, including smart shelves and radio frequency identification (RFID), are removing many of the barriers to extended revenue management. The rapid developments in Supply Chain Planning and Revenue Management software solutions, scientific models, and industry applications have created a complex picture, which appears not yet to be well understood. It is not evident which scientific models fit which industry applications and which aspects are still missing. The relation between available software solutions and applications as well as scientific models appears equally unclear. The goal of this paper is to help overcome this confusion. To this end, we structure and review three dimensions, namely applications, models, and software. Subsequently, we relate these dimensions to each other and highlight commonalities and discrepancies. This comparison also provides a basis for identifying future research needs.Manufacturing;Revenue Management;Software;Advanced Planning Systems;Demand Fulfillment

Quantifying the efficiency of price-only contracts in push supply chains over demand distributions of known supports

In this paper, we quantify the efficiency of price-only contracts in supply chains with demand distributions by imposing prior knowledge only on the support, namely, those distributions with support [a, b] for 0 < a <_ b < +1. By characterizing the price of anarchy (PoA) under various push supply chain configurations, we enrich the application scope of the PoA concept in supply chain contracts along with complementary managerial insights. One of our major findings is that our quantitative analysis can identify scenarios where the price-only contract actually maintains its efficiency, namely, when the demand uncertainty, measured by the relative range b/a, is relatively low, entailing the price-only contract to be more attractive in this regard

Supply Chain Contracting in the Presence of Supply Uncertainty and Store Brand Competition

In today\u27s complex business environment, manufacturers are striving to maintain a competitive advantage over their supply chain partners. Manufacturers\u27 profitability is tightly linked to their strategic interactions with other entities in the supply chain. While numerous studies have been conducted to investigate such interactions in supply chains, certain issues remain unresolved. We apply a game-theoretic framework to analyze two distinct supply chain structures in the presence of supply uncertainty and store brand competition in two essays, respectively. In the first chapter, we study a decentralized assembly supply chain under supply uncertainty. In a decentralized assembly supply chain, one assembler assembles a set of $n$ components, each produced by a different supplier, into a final product to meet an uncertain market demand. Each supplier faces an uncertain production capacity such that only the lesser of the planned production quantity and the realized capacity can be delivered to the assembler. We assume that the suppliers\u27 random capacities and the random demand can follow an arbitrary continuous multivariate distribution. We formulate the problem as a two-stage Stackelberg game. The assembler and the suppliers adopt a so-called Vendor-Managed-Consigned-Inventory (VMCI) contract. We analytically characterize the equilibrium of this game, based on which we obtain several managerial insights. Surprisingly, we show that when a supplier\u27s production cost increases or when his component salvage value decreases, it hurts all other members and the entire supply chain, but it might sometimes benefit this particular supplier. Similarly, when the suppliers do not have supply uncertainty, it benefits the assembler but it does not necessarily benefit the suppliers. Furthermore, we demonstrate that when the suppliers\u27 capacities become more positively correlated, the assembler is always better off, but the suppliers might be better or worse off. Later in the chapter, we also solve the game under the conventional wholesale-price contract. We find that the assembler always prefers the VMCI contract, and the suppliers always prefer the wholesale price contract. In addition, we illustrate that the VMCI contract is more efficient than the wholesale price contract for this decentralized assembly supply chain. In the second chapter, we consider a two-tier decentralized supply chain with a national brand supplier and a retailer. The national brand supplier (she) distributes her products to consumers through the retailer. Meanwhile, the retailer (he) intends to develop and produce his own store brand through a manufacturing source that is different from the national brand supplier. The retailer holds the store brand production unit cost as private information, for which the national brand supplier only has a subjective assessment. Given a supply contract offered by the national brand supplier, the retailer simultaneously decides whether to accept the contract and whether to produce the store brand. The national brand supplier aims to design an optimal menu of contracts to maximize her expected profit as well as extract the retailer\u27s private cost information. We formulate the problem as a two-stage screening game to analyze the strategic interaction between the two players. Despite the inherent computational complexity, we are able to derive the optimal menu of contracts for the national brand supplier, of which the format depends on the national brand supplier\u27s unit production cost. Furthermore, we investigate how the model parameters affect the value of information for each member in the supply chain. We show that the retailer\u27s private cost information becomes less valuable to both the national brand supplier and the retailer when the national brand unit production cost increases. We also illustrate that when the gap between the two possible cost values increases, the private cost information becomes more valuable to the national brand supplier, however the value of information to the retailer himself can either increase or decrease. Finally, we demonstrate that when the perceived quality of the national brand increases, the value of information to the retailer first decreases then increases, but the impact on the value of information to the national brand supplier can be either positive or negative

Real-time Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes

Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real-time. We describe a family of statistical models that address these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These “regime†models are developed using statistical analysis of historical data, and are used in real-time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM), a supply chain environment characterized by competitive procurement and sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and longterm resource allocation decisions. Results show that our method outperforms more traditional shortand long-term predictive modeling approaches.dynamic pricing;trading agent competition;agent-mediated electronic commerce;dynamic markets;economic regimes;enabling technologies;price forecasting;supply-chain

Optimizing inventory levels using financial, lifecycle and forecast variance data

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; in conjunction with the Leaders for Manufacturing Program at MIT, 2007.Includes bibliographical references (leaves 47-48).Significant inventory write-offs have recently plagued ATI Technologies, a world leader in graphics and media processors. ATI's product-centric culture has long deterred attention from supply chain efficiency. Given that manufacturing lead time exceeds customer order lead time for its semiconductors, ATI relies heavily on their demand forecasting team to instigate supply chain activities. The PC business unit forecasting team translates market information into product-line forecast and also sets finished goods inventory levels intended to offset demand uncertainty. Today's inventory decisions are made in response to customer escalations, often ignoring financial implications. To add necessary rigor when setting these inventory levels, this thesis presents a model using wafer and unit cost, profit margin, product lifecycle stage and historical forecast error to categorize products into inventory risk levels. The resultant risk levels become a critical input to monthly demand-supply meetings with marketing, operations and senior executives - the outcome of which are wafer orders and assembly and test plans at the world's largest contract foundries and subcontractors. Finally, the 2006 acquisition of ATI by Advanced Micro Devices (AMD) offers unforeseen flexibility, scale and challenges to the outsourced semiconductor supply chain.by Irene S. Hwang.S.M.M.B.A

Mathematics in the Supply Chain

[no abstract available

Revenue Management and Demand Fulfillment: Matching Applications, Models, and Software

Recent years have seen great successes of revenue management, notably in the airline, hotel, and car rental business. Currently, an increasing number of industries, including manufacturers and retailers, are exploring ways to adopt similar concepts. Software companies are taking an active role in promoting the broadening range of applications. Also technological advances, including smart shelves and radio frequency identification (RFID), are removing many of the barriers to extended revenue management. The rapid developments in Supply Chain Planning and Revenue Management software solutions, scientific models, and industry applications have created a complex picture, which appears not yet to be well understood. It is not evident which scientific models fit which industry applications and which aspects are still missing. The relation between available software solutions and applications as well as scientific models appears equally unclear. The goal of this paper is to help overcome this confusion. To this end, we structure and review three dimensions, namely applications, models, and software. Subsequently, we relate these dimensions to each other and highlight commonalities and discrepancies. This comparison also provides a basis for identifying future research needs