Pareto‐Improving Contracts for Express Package Delivery Services

We address the problem of an express package delivery company in structuring a long‐term customer contract whose terms may include prices that differ by day‐of‐week and by speed‐of‐service. The company traditionally offered speed‐of‐service pricing to its customers, but without day‐of‐week differentiation, resulting in customer demands with considerable day‐of‐week seasonality. The package delivery company hoped that using day‐of‐week and speed‐of‐service price differentiation for contract customers would induce these customers to adjust their demands to become counter‐cyclical to the non‐contract demand. Although this usually cannot be achieved by pricing alone, we devise an approach that utilizes day‐of‐week and speed‐of‐service pricing as an element of a Pareto‐improving contract. The contract provides the lowest‐cost arrangement for the package delivery company while ensuring that the customer is at least as well off as he would have been under the existing pricing structure. The contract pricing smoothes the package delivery company's demand and reduces peak requirements for transport capacity. The latter helps to decrease capital costs, which may allow a further price reduction for the customer. We formulate the pricing problem as a biconvex optimization model, and present a methodology for designing the contract and numerical examples that illustrate the achievable savings.     

Revenue‐Sharing vs. Wholesale‐Price Contracts in Assembly Systems with Random Demand

Assembly and kitting operations, as well as jointly sold products, are rather basic yet intriguing A decentralized supply chains, where achieving coordination through appropriate incentives is very important, especially when demand is uncertain. We investigate two very distinct types of arrangements between an assembler/retailer and its suppliers. One scheme is a vendor‐managed inventory with revenue sharing, and the other a wholesale‐price driven contract. In the VMI case, each supplier faces strategic uncertainty as to the amounts of components, which need to be mated with its own, that other suppliers will deliver. We explore the resulting components' delivery quantities equilibrium in this decentralized supply chain and its implications for participants' and system's expected profits. We derive the revenue shares the assembler should select in order to maximize its own profits. We then explore a revenue‐plus‐surplus‐subsidy incentive scheme, where, in addition to a share of revenue, the assembler also provides a subsidy to component suppliers for their unsold components. We show that, by using this two‐parameter contract, the assembler can achieve channel coordination and increase the profits of all parties involved. We then explore a wholesale‐price‐driven scheme, both as a single lever and in combination with buybacks. The channel performance of a wholesale‐price‐only scheme is shown to degrade with the number of suppliers, which is not the case with a revenue‐share‐only contract.     

Controversial Role of GPOs in Healthcare‐Product Supply Chains

This paper examines the controversial role that Group Purchasing Organizations (GPOs) play in the supply chains for healthcare products. Among the controversies, perhaps the most fundamental one is whether or not GPOs reduce purchasing costs for their members. However, the fiercest controversy is around the “contract administration fees (CAFs)” that GPOs charge to manufacturers. We examine these and other controversies using a Hotelling duopoly model. Among our conclusions: GPOs increase competition between manufacturers and lower prices for healthcare providers. However, GPOs reduce manufacturers' incentives to introduce innovations to existing products. We also demonstrate that the existence of lower off‐contract prices is not, per se, evidence of anticompetitive behavior on the part of GPOs. Indeed, we demonstrate that, under certain circumstances, the presence of a GPO lowers off‐contract prices. We also examine the consequences of eliminating the “safe harbor” provisions that permit healthcare GPOs to charge CAFs to manufacturers, and conclude that it would not affect any party's profits or costs.     

服务水平约束下基于顾客策略性退货的供应链契约协调研究

研究由一个制造商和一个零售商及一组顾客群组成的单周期供应链的协调决策问题。建立了顾客策略性退货模型,并确定了服务水平约束下,集中式和分散式决策模式下,商品的最优价格、最优订货量和最优退货价格。为了解决分散式决策的双重边际化效应,设计了一般的回购契约、基于差别定价的回购契约和销售回扣契约对供应链进行协调,并探讨了三种契约的协调机制。结果表明,一般的回购契约及销售回扣契约不能实现包含顾客退货的供应链的协调,而基于差别定价的回购契约可以实现供应链协调。最后,通过数值算例,分析了服务水平约束对供应链总体利润的影响以及差别定价回购契约的协调效果。     

Coordinating a Semi‐Centralized Global Production Network Through Different Levels of Headquarters Involvement

Motivated by our experience with a global company, we propose and study the concept of a semi‐centralized supply chain and analyze its coordination issues. We focus on a supply chain consisting of a home plant and a foreign branch, both of which are under the same parent company but have considerable autonomy. The role of the home plant is to provide a key component to the foreign branch with guaranteed service (required by the headquarters). Because of a high fixed order cost, the branch orders the component rather infrequently, causing high expediting costs at the home plant. Our purpose is to help the headquarters to improve the supply chain efficiency. We show that under certain conditions, the headquarters can coordinate the supply chain by setting an upper bound on the expediting frequency. If these conditions fail to hold, a simple fixed cost‐sharing contract coordinates the supply chain. When centralized control is too costly or infeasible, the headquarters may delegate the contract design rights to the subunits. If the home plant receives the rights, the supply chain performance can be significantly improved (sometimes to near optimality). These results provide guidance to the headquarters on whether, when, and to whom to delegate the coordination initiatives.     

Coordinated Contract Decisions in a Make‐to‐Order Manufacturing Supply Chain: A Stochastic Programming Approach

One of the important objectives of supply chain S&OP (Sales and Operations Planning) is the profitable alignment of customer demand with supply chain capabilities through the coordinated planning of sales, production, distribution, and procurement. In the make‐to‐order manufacturing context considered in this paper, sales plans cover both contract and spot sales, and procurement plans require the selection of supplier contracts. S&OP decisions also involve the allocation of capacity to support sales plans. This article studies the coordinated contract selection and capacity allocation problem, in a three‐tier manufacturing supply chain, with the objective to maximize the manufacturer's profitability. Using a modeling approach based on stochastic programming with recourse, we show how these S&OP decisions can be made taking into account economic, market, supply, and system uncertainties. The research is based on a real business case in the Oriented Strand Board (OSB) industry. The computational results show that the proposed approach provides realistic and robust solutions. For the case considered, the planning method elaborated yields significant performance improvements over the solutions obtained from the mixed integer programming model previously suggested for S&OP.     

Performance Improvement Paths in the U.S. Airline Industry:Linking Trade‐offs to Asset Frontiers

Several articles have been written during the past few years examining performance improvement paths and various forms of efficiency frontiers in operations strategy. These articles focus primarily on defining and describing these frontiers and raise questions concerning how to improve operations. In this paper, we provide one of the first empirical studies aimed at validating these earlier studies. Using a database on the 10 largest U.S. airlines for a period of 11 years, we test and validate some of the models presented in the operations literature. The 10 major airlines are separated into 2 groups for analysis: geographic specialists and geographic generalists. Our analysis shows that better performing airlines (in terms of cost‐quality position) in both groups confirm the predictions of the sand cone model when operating further away from their asset frontiers, although trade‐offs do occur when operating close to asset frontiers.     

Managing Safety‐Related Disruptions: Evidence from the U.S. Nuclear Power Industry

Low‐probability, high‐impact events are difficult to manage. Firms may underinvest in risk assessments for low‐probability, high‐impact events because it is not easy to link the direct and indirect benefits of doing so. Scholarly research on the effectiveness of programs aimed at reducing such events faces the same challenge. In this article, we draw on comprehensive industry‐wide data from the U.S. nuclear power industry to explore the impact of conducting probabilistic risk assessment (PRA) on preventing safety‐related disruptions. We examine this using data from over 25,000 monthly event reports across 101 U.S. nuclear reactors from 1985 to 1998. Using Poisson fixed effects models with time trends, we find that the number of safety‐related disruptions reduced between 8% and 27% per month in periods after operators submitted their PRA in response to the Nuclear Regulatory Commission's Generic Letter 88‐20, which required all operators to conduct a PRA. One possible mechanism for this is that the adoption of PRA may have increased learning rates, lowering the rate of recurring events by 42%. We find that operators that completed their PRA before Generic Letter 88‐20 continued to experience safety improvements during 1990–1995. This suggests that revisiting PRA or conducting it again can be beneficial. Our results suggest that even in a highly safety‐conscious industry as nuclear utilities, a more formal approach to quantifying risk has its benefits.     

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.     

Disease Spread Models to Estimate Highly Uncertain Emerging Diseases Losses for Animal Agriculture Insurance Policies: An Application to the U.S. Farm‐Raised Catfish Industry

Emerging diseases (ED) can have devastating effects on agriculture. Consequently, agricultural insurance for ED can develop if basic insurability criteria are met, including the capability to estimate the severity of ED outbreaks with associated uncertainty. The U.S. farm‐raised channel catfish (Ictalurus punctatus) industry was used to evaluate the feasibility of using a disease spread simulation modeling framework to estimate the potential losses from new ED for agricultural insurance purposes. Two stochastic models were used to simulate the spread of ED between and within channel catfish ponds in Mississippi (MS) under high, medium, and low disease impact scenarios. The mean (95% prediction interval (PI)) proportion of ponds infected within disease‐impacted farms was 7.6% (3.8%, 22.8%), 24.5% (3.8%, 72.0%), and 45.6% (4.0%, 92.3%), and the mean (95% PI) proportion of fish mortalities in ponds affected by the disease was 9.8% (1.4%, 26.7%), 49.2% (4.7%, 60.7%), and 88.3% (85.9%, 90.5%) for the low, medium, and high impact scenarios, respectively. The farm‐level mortality losses from an ED were up to 40.3% of the total farm inventory and can be used for insurance premium rate development. Disease spread modeling provides a systematic way to organize the current knowledge on the ED perils and, ultimately, use this information to help develop actuarially sound agricultural insurance policies and premiums. However, the estimates obtained will include a large amount of uncertainty driven by the stochastic nature of disease outbreaks, by the uncertainty in the frequency of future ED occurrences, and by the often sparse data available from past outbreaks.