Knowledge that Transforms

Problem definition: The asymmetric pull-to-center effect for newsvendors is a robust finding in operations, and understanding why newsvendors make suboptimal decisions is key for identifying ways to improve decision-making quality in this critical task. Although prior studies have indicated that experience does not substantially mitigate the pull-to-center effect, the experience levels achieved in those previous studies are limited in comparison with the experience level that a near-continuous time environment can provide. Methodology/results: We conduct a set of laboratory experiments using a near-continuous time environment to determine the effect that extensive experience has on newsvendor behavior and the extent to which resultant learning is transferable across conditions. Observed behavior clearly demonstrates that the pull-to-center effect is substantially reduced, if not eliminated, with sufficient experience and that this learning can have positive spillovers to more traditional settings. Further, the experiments suggest that it is the repeated feedback regarding a given inventory decision rather than the ability to explore many strategies that drives improved decision making. Managerial implications: Experience can mitigate the pull-to-center effect, and near-continuous time environments can be an effective training tool for gaining such experience.

Problem definition: We consider contract confidentiality in a decentralized supply chain, in which a single principal orders different components from different agents, each of which has private cost information. The principal may commit to publicly observed bilateral contracts or offer secretly observed contracts to each agent simultaneously. We also consider the problem of sequential contracting. Methodology/results: By correcting the main analysis in Hu and Qi [Hu B, Qi A (2018) Optimal procurement mechanisms for assembly. Manufacturing Service Oper. Management 20(4):655–666], we employ passive beliefs to study secret contracting. We show that there is a unique configuration of two-part tariffs under secret offers in both simultaneous and sequential contracting. We also extend the analysis to take into account ex post individual rationality (IR), which is arguably more relevant in this assembly setting. With ex post IR, we show that the traditional two-part tariffs should be augmented with payment adjustments. Managerial implications: We find that the efficiency achieved through public offers can be maintained with secret offers in this assembly setting contrary to conventional wisdom. Our analysis, thus, offers an explanation for why nondisclosure agreements are important and common in practice.

The journal is pleased to publish the abstracts of the six finalists of the 2023 Manufacturing and Service Operations Management Society’s student paper competition. The 2023 prize committee was chaired by Ersin Korpeoglu (UCL), Simone Marinesi (Wharton), and Nur Sunar (UNC). The judges were Adam Elmachtoub, Adem Orsdemir, Agni Orfanoudaki, Alper Nakkas, Amrita Kundu, Antoine Desir, Antoine Feylessoufi, Anton Ovchinnikov, Anyan Qi, Arian Aflaki, Arzum Akkas, Ashish Kabra, Auyon Siddiq, Bilal Gokpinar, Bin Hu, Bob Batt, Bora Keskin, Brent Moritz, Can Zhang, Chloe Glaeser, Cuihong Li, Daniel Freund, Daniel Lin, David Drake, Divya Singhvi, Dongyuan Zhan, Ekaterina Astashkina, Elena Belavina, Elodie Adida, Emre Nadar, Enis Kayis, Fabian Sting, Fanyin Zheng, Fei Gao, Florin Ciocan, Francisco Castro, George Chen, Georgina Hall, Gloria Urrea, Gonzalo Romero, Guihua Wang, Guoming Lai, Heikki Peura, Hessam Bavafa, Hummy Song, Huseyin Gurkan, Ioannis Stamatopoulos, Iris Wang, Jiankun Sun, Jiayi Joey Yu, Jing Wu, Joel Wooten, John Silberholz, Jonas Oddur Jonasson, Jonathan Helm, Jose Guajardo, Junyu Cao, Kaitlin Daniels, Karen Zheng, Ken Moon, Kostas Bimpikis, Lennart Baardman, Lesley Meng, Lina Song, Luyi Yang, Mazhar Arikan, Mehmet Ayvaci, Meng Li, Mengzhenyu Zhang, Miao Bai, Michael Freeman, Mika Sumida, Ming Hu, Morvarid Rahmani, Mostafa Rezaei, Mumin Kurtulus, Nan Yang, Nazli Sonmez, Negin Golrezaei, Nektarios Oraiopoulos, Nikhil Garg, Nikos Trichakis, Nil Karacaoglu, Olga Perdikaki, Onesun Steve Yoo, Ovunc Yilmaz, Ozan Candogan, Panos Markou, Pengyi Shi, Philipp Cornelius, Qiuping Yu, Renyu Zhang, Robert Bray, Ruth Beer, Ruxian Wang, Saed Alizamir, Safak Yucel, Sanjith Gopalakrishnan, Santiago Gallino, Sarah Yini Gao, Scott Rodilitz, Sebastien Martin, Seyed Emadi, Sheng Liu, Shouqiang Wang, Siddharth Singh, Sidika Candogan, Sina Khorasani, So Yeon Chun, Somya Singhvi, Soo-Haeng Cho, Sriram Dasu, Stefanus Jasin, Stephen Leider, Suresh Muthulingam, Sytske Wijnsma, Taghi Khaniyev, Tian Chan, Tim Kraft, Tom Tan, Tugce Martagan, Vasiliki Kostamj, Velibor Misic, Vishal Agrawal, Xiaojia Guo, Xiaoshuai Fan, Xiaoyang Long, Yannis Bellos, Yao Cui, Yehua Wei, Yiangos Papanastasiou, Yi-Chun Chen, Yinghao Zhang, Ying-Ju Chen, Yinghao Zhang, Yuan-Mao Kao, Yuexing Li, Zhaohui (Zoey) Jiang, Zhaowei She, and Zumbul Atan.

The continued success of Manufacturing & Service Operations Management (M&SOM) depends on the volunteer work of many professionals who take their precious time to provide careful and constructive reviews of the manuscripts submitted to the journal in a timely manner. On behalf of M&SOM, editor-in-chief Georgia Perakis expresses her deepest gratitude to all those who served as reviewers for the journal in 2023. Among all reviewers, some individuals have distinguished themselves by reviewing several manuscripts and, with each manuscript, by writing a fair, critical, and constructive review in a timely fashion. In recognition of their outstanding service provided to support the journal’s scholarly mission, M&SOM grants the 2023 Meritorious Service Award to…

Problem definition: The diffusion of technological innovations in low- and middle-income countries (LMICs) has been facilitated by the use of rent-to-own (RTO) business models, which give flexibility to consumers by allowing them to make incremental payments over time. Understanding how to best manage this flexibility is a fundamental problem for firms in LMICs. Motivated by an application of RTO to the distribution of solar lamps, we examine the drivers and impact of payment flexibility in RTO contracts. Methodology/results: We formulate a dynamic programming model that characterizes an important dimension of payment flexibility (i.e., the ability of consumers to make bundled payments (multiple installments paid at once)). We show that consumers may bundle payments because of uncertainty about budget in future periods, which leads to a nonmonotonic impact of income uncertainty on repayment performance. We prove that bundled payments are more likely to occur closer to the end of the ownership cycle. We further show that the firm’s expected profit objective is perfectly aligned with its social mission of increasing consumer access by reducing the consumers’ expected time to ownership. We examine different flexibility levers that the firm can adjust as part of its contract design (repayment frequency and grace period), accounting for the impact of bundled payments. Our results suggest that an intermediate level of flexibility may benefit both the firm and consumers under certain conditions. Our numerical analysis indicates the robustness of our main results to relaxing several modeling assumptions. Managerial implications: Whereas payment flexibility is a fundamental component of RTO contracts in LMICs, our findings indicate that a moderate level of flexibility can go a long way in helping firms and consumers. Hence, RTO firms may not need to offer extreme degrees of flexibility to achieve desirable outcomes. This is an important insight for RTO firms aiming to balance profits and consumer access.

Problem definition: Prototyping and testing are an integral part of almost any new product development process, helping firms navigate the inherent uncertainties of creating new products. Recent developments in rapid prototyping, including technologies that enable cheaper low-fidelity tests, have opened up the possibilities for firms in reconfiguring their product development processes. Firms can, by choosing the level of evaluation fidelity, alter the traditional cost-quality trade-offs inherent in sequential prototyping. Methodology/results: The current article formulates a general model of sequential search where firms can proceed by obtaining noisy low-fidelity evaluations of their prototypes. Our results demonstrate that the imperfect fidelity of evaluations alters the firm’s optimal experimentation, with the starkest difference being that it may make it optimal for the firm to select and launch a prototype that did not yield the best evaluation. In addition, our analysis of optimal measurement technology reveals that the focal firm should demand the most precise measurements when their ex-ante uncertainty is moderate (not too high or low). We also consider extensions analyzing how the optimal choice of evaluation fidelity is affected by the number of available prototypes, by operational flexibility (to dynamically change measurement technology), and by the ability to outsource evaluations to an experimentation platform. Managerial implications: We develop managerial insights for how the optimal choice of fidelity and the optimal length of the evaluation cycle should be planned depending on the evaluation costs and the firm’s ex-ante uncertainty. The resulting framework offers guidance to product and software development firms to successfully leverage imperfect fidelity experiments.

Problem definition: Volunteers, the primary workforce for many charities, represent a complex labor pool; they are unreliable and exhibit substantial heterogeneity in both performance and affinity to the organization. Additionally, many volunteers engage not only to contribute but also to immerse themselves in a volunteering experience that, if rewarding, can inspire them to become future donors. However, practical approaches to volunteer management commonly neglect these traits and the consequential impact that tactical decision-making can have on nurturing potential future donations. Methodology/results: Building on a previous study, we propose a forward-looking volunteer scheduling model that accounts for the heterogeneity among volunteers, mitigates both understaffing and overstaffing costs, and explicitly correlates individual time contribution with their monetary donations. We provide analytical solutions when the charity can reliably estimate distributions (e.g., uniform distribution) from data and suggest a distribution-free method to offer actionable insights where data are limited or uncertain. Managerial implications: At the strategic level, by viewing volunteers as potential donors, the optimal staffing strategy balances meeting the charity’s labor needs and maximizing volunteers’ satisfaction, as this satisfaction influences their likelihood of becoming future donors. We show that charities could avert substantial losses by adopting an integrative approach, thereby challenging conventional organizational structures that compartmentalize volunteer and donor management. Our model suggests that building robust data infrastructures can significantly advance the charity’s core mission. Paradoxically, efforts to increase labor productivity may inadvertently undermine this objective. At the operational level, we provide an Excel-based decision support tool and a decision-tree framework to navigate optimal policies, determining when and how a charity can rely on episodic (less reliable) volunteers. Our results confirm that reducing uncertainty in volunteer turnout benefits charities. However, we also find that when labor value is low, episodic volunteers are preferred, whereas formal (reliable) volunteers are favored when labor value is high.

Problem definition: This paper studies how polarization influences content consumption and production on digital platforms that monetize consumer engagement. Specifically, we consider a content that advocates a particular position on a divisive issue. Consumers with polarized preferences toward the content’s position are sequentially exposed to the content. Initially, consumers are uncertain about the content quality, but they have the opportunity to learn about it using aggregate consumption metrics and other informative signals provided by the platform. Methodology/results: Using a stylized model, we find that under polarization, social learning based on consumption metrics can mislead consumers to perceive low-quality content as higher quality, even in the long run. Consequently, content providers may decrease their effort to improve content quality. These effects are amplified for more polarizing issues, especially when the content’s position is “mainstream” (i.e., aligned with the majority of the population). Our results thus provide a potential explanation for the proliferation of low-quality, polarizing content on platforms. Managerial implications: We offer normative guidance for content platforms seeking to enhance content quality, including offering consumers information to aid the social learning mechanism and appropriately selecting audience to mitigate the echo-chamber effect. Additionally, we propose payment schemes based on content popularity as an effective means to encourage content providers to improve quality.

Problem definition: Most of the display advertising inventory is sold through real-time auctions. The participants of these auctions are typically bidders (Google, Criteo, RTB House, and Trade Desk for instance) that participate on behalf of advertisers. In order to estimate the value of each display opportunity, they usually train advanced machine learning algorithms using historical data. In the labeled training set, the inputs are vectors of features representing each display opportunity, and the labels are the generated rewards. In practice, the rewards are given by the advertiser and are tied to whether a particular user converts. Consequently, the rewards are aggregated at the user level and never observed at the display level. A fundamental task that has, to the best of our knowledge, been overlooked is to account for this mismatch and split, or attribute, the rewards at the right granularity level before training a learning algorithm. We call this the label attribution problem. Methodology/results: In this paper, we develop an approach to the label attribution problem, which is both theoretically justified and practical. In particular, we develop a fixed point algorithm that allows for large-scale implementation and showcase our solution using a large-scale publicly available data set from Criteo, a large demand-side platform. We dub our approach the fixed point label attribution algorithm. Managerial implications: There is often a hidden leap of faith when transforming the advertiser’s signal into display labeling. Demand Side Platforms providers should be careful when building their machine learning pipeline and carefully solve the label attribution step.

Problem definition: We consider a multiportfolio optimization problem in which nonlinear market impact costs result in a strong dependency of one account’s performance on the trading activities of the other accounts. Methodology/results: We develop a novel target-oriented model that jointly optimizes the rebalancing trades and the split of market impact costs. The key advantages of our proposed model include the consideration of clients’ targets on investment returns and the incorporation of distributional uncertainty. The former helps fund managers to circumvent the difficulty in identifying clients’ utility functions or risk parameters, whereas the latter addresses a practical challenge that the probability distribution of risky asset returns cannot be fully observed. Specifically, to evaluate the quality of multiple portfolios’ investment payoffs in achieving targets, we propose a new class of performance measures, called fairness-aware multiparticipant satisficing (FMS) criteria. These criteria can be extended to encompass distributional uncertainty and have the salient feature of addressing the fairness issue with the collective satisficing level as determined by the least satisfied participant. We find that, structurally, the FMS criteria have a dual connection with a set of risk measures. For multiportfolio optimization, we consider the FMS criterion with conditional value-at-risk being the underlying risk measure to further account for the magnitude of shortfalls against targets. The resulting problem, although nonconvex, can be solved efficiently by solving an equivalent converging sequence of tractable subproblems. Managerial implications: For the multiportfolio optimization problem, the numerical study shows that our approach outperforms utility-based models in achieving targets and in out-of-sample performance. More generally, the proposed FMS criteria provide a new decision framework for operational problems in which the decision makers are target-oriented rather than being utility maximizers and issues of fairness and ambiguity should be considered.