Knowledge that Transforms

Available clinical evidence is inconclusive on whether radiologists should use the patient risk profile information when interpreting mammograms. On the one hand, risk profile information is informative and can improve radiologists’ performance, but on the other hand, it may impair their judgment by introducing biases in mammography interpretation. Therefore, it is important to assess whether and when profile information use translates into improved outcomes. We model the use of profile information in mammography, using a decision theoretic approach and explore the value of profile information using three process design choices: mammography only, unbiased, and biased reading. We estimate the parameters of our model using clinical data and find that using profile information along with the mammography information can achieve a better performance than not using the profile information. However, the better performance is contingent on the weight assigned to the profile information as well as the extent of bias due to profile information. Translating our findings into clinical practice would require properly designed experiments aiming to quantify the effect of the timing and the use of profile information on performance while accounting for radiologist and patient characteristics. When conducting an experiment is not feasible, a uniform operational sequence for interpreting mammograms and related guidelines may be a useful starting point to improve the quality of mammography operations.

We consider supplier‐facilitated transshipments for achieving supply chain coordination in a single supplier, multi‐retailer distribution system with non‐cooperative retailers. The previous transshipment literature has focused on coordination through retailer‐negotiated transshipments and thus does not consider the supplier's decision‐making. In contrast, in this study, we assume the supplier is an active participant in the system and we seek to understand how the supplier can facilitate the implementation of coordinating transshipments. We study a two‐period model with wholesale orders at the start of the first period and preventive transshipments performed at the start of the second period. Inspired by a supplier‐facilitated transshipment scheme observed in practice, we assume the supplier implements transshipments through a bi‐directional adjustment contract. Under this contract, each retailer can either buy additional inventory from, or sell back excess inventory to, the supplier. We show that coordination can be achieved through carefully designed contracts with state‐dependent adjustment prices and a wholesale price menu. We demonstrate that the supplier's role in facilitating coordinating transshipments is critical. In addition, we use our understanding of the coordinating contract form to derive some simpler and easier‐to‐implement heuristic contracts. We use a numerical study to demonstrate the value, to the supplier, of using the coordinating adjustment and wholesale prices, and to evaluate the heuristics’ performance.

We model a supply chain consisting of a supplier and multiple retailers facing deterministic demand. We denote some retailers as strategic in the sense that given the supplier inventory information, they will implement the optimal stocking policy by incorporating such information. On the other hand, some retailers are denoted as naïve in the sense that they ignore supply information and resort to a simplistic ordering policy. Naïve retailers learn the optimal policy over time and adjust their orders accordingly. We study the dynamics of this game and investigate the impact of such strategic and naïve retailers on the cost, ordering pattern and stocking policies of all parties. We analyze the supply chain under two scenarios: the centralized supply chain where the objective is to minimize the total supply chain cost, and the decentralized supply chain where each self‐interested player minimizes its own cost in a Stackelberg game setting. We fully characterize the optimal policies under both centralized and decentralized scenarios and show that, surprisingly, the supply chain might be better off by virtue of naïve retailers. The result is driven by the fact that strategic and naïve players’ decisions shift the positioning of inventory in the supply chain with its final impact being determined by the relative costs of different retailer‐types. Our results also offer managerial insights into how access to supply information can improve supply chain performance.

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.

Many manufacturers ensure supply capacity by using more than one supplier and sharing their capacity investment costs via supplier development programs. Their suppliers face competitive pressure from peers despite the reduced capacity investment cost. Although standard game theory makes clear prediction that cost sharing increases the suppliers' capacity choice and supply chain profit, the complex decision environment of capacity competition makes it interesting to test whether the theory predictions are robust and, if not, whether systematic deviations occur. We present a laboratory experiment study. The experiment data show that supplier subjects invested in higher capacities than what our theoretical analysis predicted, resulting in profit loss for the supply chain. Our econometric analysis indicates that the subjects are bounded rational and their concern for relative standing may be the potential driver of capacity over‐investment. Based on the experimental findings, we study a modified cost‐sharing mechanism that adapts to the behavioral biases. Its performance is validated in a second experiment.

Even though more and more transactions and payments are conducted electronically, physical currency (banknotes and coins) still plays an essential role in commerce and trade, and it is expected to maintain its dominance in the near future. Over the past decade, several studies have analyzed various currency supply chains across the world. This study provides an overview of this research. The structure of a general banknote supply chain is given before the discussion of the problems from three different perspectives: (i) the supply side, that is, the parties who are in charge of supplying currency in the supply chain; (ii) the demand side, that is, the parties who request the currency; and (iii) the secure third‐party logistics providers. We also provide a framework for analyzing the US coin supply chain and descriptions of the coin supply chains in other countries. Future research directions are proposed based on the research work carried out so far and for the field in general.

The organization of charitable distribution channels to ensure donor contributions reach beneficiaries in an efficient manner and the use of accounting metrics of such efficiency (whether provided directly or by charity rating groups) are oft‐discussed issues in the nonprofit sector. The two issues are inextricably linked since reported efficiency measures influence subsequent donor giving. This study develops a parsimonious model of a charity that must decide how best to employ its resources, either by acting as a direct service provider or as a grant provider to organizations that provide services to beneficiaries. We show that the desire to boost perceptions of efficiency vis‐à‐vis accounting reports leads an organization to rely more on others to provide services rather than being a direct service provider. This temptation to expand either the scope or length of the charity supply line is muted by a desire to avoid redundant costs and improve service delivery. The model's results have implications both for the role of nonprofit accounting and observed distribution strategies of nonprofits.

This paper studies a stochastic model of optimal stopping processes, which arise frequently in operational problems (e.g., when a manager needs to determine an optimal epoch to stop a process). For such problems, we propose an effective method of characterizing the structure of the optimal stopping policy for the class of discrete‐time optimal stopping problems. Using this method, we also derive a set of metatheorems that can help identify when a threshold or control‐band type stopping policy is optimal. We show that our proposed method can determine the structure of the optimal policy for some stopping problems that conventional methods fail to do so. In some cases, our method also simplifies the analysis of some existing results. Moreover, the metatheorems we propose help identify sufficient conditions that yield simple optimal policies when such policies are not generally optimal. We demonstrate these benefits by applying our method to several optimal stopping problems frequently encountered in, for example, the operations, marketing, finance, and economics literatures. We note that with structural results, optimal‐stopping policies are easier to follow, describe, and compute and hence implement. They also help determine how a stopping policy should be adjusted in response to changes in the operational environment. In addition, as structural results are critical for the development of efficient algorithms to solve optimal stopping problems numerically, we hope that the method and results provided in the study will contribute to that effort.

This study examines the effects of a relatively new channel structure on prices and sales in a large department store, which in recent years has switched the management of many of its product categories from a traditional retailer‐managed system to a manufacturer‐managed system. We find that the change caused overall retail prices to decrease. However, there was significant heterogeneity in the response across brands. In the cell phone category, brands with high market shares and inelastic demand did not change prices. In the watch category, the retail prices of relatively low‐end brands decreased while the prices of premium brands increased substantially after the switch. In addition to sales increases due to lower prices, we find that the channel structure change further caused sales to increase by 9–10% in the cell phone category and by 11–17% in the watch category. These results are consistent with previous theoretical predictions. We believe that our results provide important academic and managerial implications due to the increasing prevalence of manufacturer‐managed systems in the retail industry.

We study a joint capacity leasing and demand acceptance problem in intermodal transportation. The model features multiple sources of evolving supply and demand, and endogenizes the interplay of three levers—forecasting, leasing, and demand acceptance. We characterize the optimal policy, and show how dynamic forecasting coordinates leasing and acceptance. We find (i) the value of dynamic forecasting depends critically on scarcity, stochasticity, and volatility; (ii) traditional mean‐value equivalence approach performs poorly in volatile intermodal context; (iii) mean‐value‐based forecast may outperform stationary distribution‐based forecast. Our work enriches revenue management models and applications. It advances our understanding on when and how to use dynamic forecasting in intermodal revenue management.