Problem definition: We consider the incentive design problem of a retailer that delegates stocking decisions to its store managers who are privately informed about local demand. Academic/practical relevance: Shortages are highly costly in retail, but are less of a concern for store managers, as their exact amounts are usually not recorded. In order to align incentives and attain desired service levels, retailers need to design mechanisms in the absence of information on shortage quantities. Methodology: The headquarters knows that the underlying demand process at a store is one of J possible Wiener processes, whereas the store manager knows the specific process. The store manager creates a single order before each period. The headquarters uses an incentive scheme that is based on the end-of-period leftover inventory and on a stock-out occasion at a prespecified inspection time before the end of a period. The problem for the headquarters is to determine the inspection time and the significance of a stock-out relative to leftover inventory in evaluating the performance of the store manager. We formulate the problem as a constrained nonlinear optimization problem in the single period setting and a dynamic program in the multiperiod setting. Results: We show that the proposed "early inspection" scheme leads to perfect alignment when J equals two under mild conditions. In more general cases, we show that the scheme performs strictly better than inspecting stock-outs at the end and achieves near-perfect alignment. Our numerical experiments, using both synthetic and real data, reveal that this scheme can lead to considerable cost reductions. Managerial implications: Stock-out-related measures are typically not included in store managers' performance scorecards in retail. We propose a novel, easy, and practical performance measurement scheme that does not depend on the actual amount of shortages. This new scheme incentivizes the store managers to use their private information in the retailer's best interest and clearly outperforms centralized ordering systems that are common practice.
论文原文：
Alp, O. and A. Sen (2021). "Delegation of Stocking Decisions Under Asymmetric Demand Information." M&Som-Manufacturing & Service Operations Management 23(1): 55-69.
Problem definition: The recent surge in demand for cloud services has posed a significant capacity-expansion problem for cloud infrastructure providers. Although the growth of demand for capacity attributes-for example, CPU and RAM-is disproportionate, these attributes are often provided in preconfigured packages (cluster-types), and the fixed ratio of attributes in a package does not match with the time-varying ratio of demand. We analyze a class of expansion policies to determine the timing andmagnitude of expansions, using preconfigured cluster-types, and we examine the optimal configurations of the cluster-types. Academic/practical relevance: Cloud computing is a major technological advance that is influencing businesses significantly, giving rise to an emerging industry but also posing the above-noted capacity-expansion problem. To our knowledge, this is a new issue that has not been studied in the literature. Methodology: We consider growing demand for two attributes and analyze a class of policies that consist of capacity expansion cycles (CECs), whereby capacities are added through sequential or simultaneous replenishments of two configured cluster-types. Results: We first derive the optimal timing and magnitude of expansions for every CEC, and then we devise two algorithms, the dynamic-programming-based (DP) algorithm and the forward-looking (FL) heuristic, to determine the optimal cycle lengths. We also propose a cluster-selection heuristic for choosing the optimal configurations of the cluster-types. Managerial implications: The FL-heuristic is effective, easy to communicate, and can be used as an excellent starting point for the search of the DP-algorithm. Moreover, because there is a desire in practice to reduce the variety of cluster-types, we find conditions under which the employment of only two cluster-types is as efficient as the employment of many cluster-types. Finally, we provide useful guidelines for the optimal configurations of these two cluster-types.
论文原文：
Arbabian, M. E., et al. (2021). "Capacity Expansions with Bundled Supplies of Attributes: An Application to Server Procurement in Cloud Computing." M&Som-Manufacturing & Service Operations Management 23(1): 191-209.
Problem definition: Deceased-donor kidney transplant candidates in the United States are ranked according to characteristics of both the donor and the recipient. We seek the ranking policy that optimizes the efficiency-equity tradeoff among all such policies, taking into account patients' strategic choices. Academic/practical relevance: Our approach considers a broad class of ranking policies, which provides approximations to the previously and currently used policies in practice. It also subsumes other policies proposed in the literature previously. As such, it facilitates a unified way of characterizing good policies. Methodology: We use a fluid model to approximate the transplant waitlist. Modeling patients as rational decision makers, we compute the resulting equilibria under a broad class of ranking policies, namely the achievable region. We then develop an algorithm that optimizes the system performance over the achievable region. Results: We show analytically that it suffices to restrict attention to priority scores that are affine in the patient's waiting time. We also show through a numerical study that the total quality-adjusted life-years can be increased substantially by allowing patient rankings to depend on the kidney quality. Last, we observe that there is almost no improvement if only the healthier patients are prioritized for certain kidney types. Managerial implications: Our results verify that ranking patients differently for kidneys of different quality can reduce the survival mismatch and the kidney wastage significantly. Consequently, the policy change in 2014, that implemented prioritizing the healthiest patients when allocating the highest 20% quality organs, is a step in the right direction. For further improvement, one may consider revising the new policy by also prioritizing the least healthy patients on the waitlist for the lowest-quality organs.
论文原文：
Ata, B., et al. (2021). "An Achievable-Region-Based Approach for Kidney Allocation Policy Design with Endogenous Patient Choice." M&Som-Manufacturing & Service Operations Management 23(1): 36-54.
We study the impact of grocery-store density on the food waste generated at stores and by households. Food waste is a major contributor to carbon emissions (as big as road transport). Identifying and influencing market conditions that can decrease food waste is thus important to combat global warming. We build and calibrate a stylized two-echelon perishable-inventory model to capture grocery purchases and expiration at competing stores and households in a market. We examine how the equilibrium waste in this model changes with store density. An increase in store density decreases consumer waste due to improved access to groceries, whereas increasing retail waste due to decentralization of inventory increased variability propagation in the supply chain (cycle truncation) and diminished demand by customers. Higher density also induces more competition which further increases (decreases) waste when stores compete on prices (service levels). Overall, consumer waste reductions compete with store waste increases and the effects of increased competition. Our analysis shows that higher density reduces food waste up to a threshold density; it leads to higher food waste beyond this threshold. Put differently, in so far as food waste is concerned, there exists an optimal store density. Calibration using grocery industry, economic, and demographic data reveals that actual store density in most American cities is well below this threshold/optimal level, and modest increases in store density substantially reduce waste; for example, in Chicago, just 3-4 more stores (per 10 sq km) can lead to a 6%-9% waste reduction, and a 1%-4% decrease in grocery expenses. These results arise from the principal role of consumer waste, suggesting that activists and policy makers' focus on retail waste may be misguided. Store operators, urban planners, and decision makers should aim to increase store densities to make grocery shopping more affordable and sustainable.
Belavina, E. (2021). "Grocery Store Density and Food Waste." M&Som-Manufacturing & Service Operations Management 23(1): 1-18.
Problem definition: We consider an entrepreneur designing a fixed funding rewards-based crowdfunding campaign for an innovative product. Product quality is known to the entrepreneur but unknown to some backers. We study how the entrepreneur can signal quality to backers via the design of the crowdfunding campaign, including the price of the reward and the funding target. Academic/practical relevance: Crowdfunding is a new and popular way of funding innovative products. Despite numerous advantages, there are challenges to this model, one of the most significant being credibly signaling information about product quality to a pool of small, uninformed investors. We explore how an entrepreneur might accomplish this and overcome a key obstacle to crowdfunding. Methodology: We employ a game theoretic model of signaling between an entrepreneur and campaign backers. Results: We find that the entrepreneur should signal high quality by setting a high target that is distorted above the full information optimal level. While a separating equilibrium always exists, a pooling equilibrium can only occur under very specific circumstances. We show that the high target affects the quality choice of entrepreneurs and may deter unique, high-quality projects. In addition, we discuss how the entrepreneur should modify the signaling strategy when a high target potentially deters backers from pledging because of the cost of participating in a failed campaign. Managerial implications: We show how entrepreneurs can effectively design their crowdfunding campaign to signal high quality, thus providing guidance to creators listing products on crowdfunding websites. We also show information asymmetry and signaling affect product quality decisions by creators, which in turn is of interest to platform designers seeking to solicit high-quality products for their platforms.
论文原文：
Chakraborty, S. and R. Swinney (2021). "Signaling to the Crowd: Private Quality Information and Rewards-Based Crowdfunding." M&Som-Manufacturing & Service Operations Management 23(1): 155-169.
Problem definition: We study a service setting where the provider has information about some customers' future service needs and may initiate service for such customers proactively, if they agree to be flexible with respect to the timing of service delivery. Academic/practical relevance: Information about future customer-service needs is becoming increasingly available through remote monitoring systems and data analytics. However, the literature has not systematically examined proactive service as a tool that can be used to better match demand to service supply when customers are strategic. Methodology: We combine (i) queueing theory, and in particular a diffusion approximation developed specifically for this problem that allows us to derive analytic approximations for customer waiting times, with (ii) game theory, which captures customer incentives to adopt proactive service. Results: We show that proactive service can reduce customer waiting times, even if only a relatively small proportion of customers agree to be flexible, the information lead time is limited, and the system makes occasional errors in providing proactive service-in fact, we show that the system's ability to tolerate errors increases with (nominal) utilization. Nevertheless, we showthat these benefits may fail to materialize in equilibrium because of economic frictions: Customers will underadopt proactive service (due to free-riding) and overjoin the system (due to negative congestion-based externalities). We also show that the service provider can incentivize optimal customer behavior through appropriate pricing. Managerial implications: Our results suggest that proactive service may offer substantial operational benefits, but caution that it may fail to fulfill its potential due to customer self-interested behavior.
论文原文：
Delana, K., et al. (2021). "Proactive Customer Service: Operational Benefits and Economic Frictions." M&Som-Manufacturing & Service Operations Management 23(1): 70-87.
Problem definition: This study extends the literature on consumer returns by assessing the impact of return period policy change on a multichannel retailer performance. Academic/practical relevance: Many retailers have recently sought to tighten their return policy by decreasing the return period window. We investigate the impact of such a policy change on sales, returns, and profitability for a multichannel retailer. Methodology: We conduct a multimethodology research in which we (1) develop theoretical predictions using an analytical model, (2) empirically test analytical predictions using data from a jewelry retailer that changed its return policy from 100 days to 60 days, and (3) extend the empirical analysis to estimate the impact of the policy change on profitability. Results: We find that the return period policy change does not have any statistically significant effect on sales and return rates for online stores, whereas it decreases sales by 8%, return rate by 2.7 percentage points, and profit by 7.3% per brick-and-mortar store, corresponding to a 2.7% decrease in annual sales for the retailer. The two likely explanations for the insignificant effect for online stores are (1) the low proportion of online customers affected by the policy change and (2) the return displacement effect-in response to the policy change, some affected customers likely accelerate their product evaluation to return within the restrictive policy period. Our analysis also demonstrates that a return period policy change may increase retail profitability if a majority of retail stores operates under high sales volume and high return rates. Managerial implications: Our study suggests that managers of multichannel retailers that consider a change in their return period policies should carefully assess their operational structures and evaluate consumer return behavior at each channel, particularly over the time window between the proposed policy period and the existing policy period.
论文原文：
Ertekin, N. and A. Agrawal (2021). "How Does a Return Period Policy Change Affect Multichannel Retailer Profitability?" M&Som-Manufacturing & Service Operations Management 23(1): 210-229.
Problem definition: Nanostores are traditional, small and independent retailers that are present in large numbers in the megacities of the developing world. Consumer packaged goods (CPG) manufacturers can choose to deliver to nanostores either directly-visiting thousands of stores per day-or via wholesalers-saving on distribution cost but forfeiting the direct access to the store owners to develop demand. We study a manufacturer's channel strategy within a finite time horizon. Academic/practical relevance: The channel strategy in emerging markets has both marketing and operational elements, which lead to a newly formulated problem with novel characteristics. High costs are involved in the nanostore distribution, and the difference in wholesale price, logistics cost, product availability, and market growth leads to a multidimensional problem that is not trivial to analyze. Methodology: We develop an analytical model to derive the optimal channel policy. We conduct a numerical study with parameters tuned by field data. We develop managerial insights based on our formal results and our numerical analysis. Results: The optimal channel policy structure depends mainly on two channel metrics: the gross profitability, which is the gross margin at a particular moment in time; and the growth-adjusted profitability, which includes the growth potential of a particular channel strategy to develop the market and realize future profits. With demand growth over time, we show that, in the optimal policy, there is at most one switch between the wholesale and direct-channel strategies within the time horizon. Managerial implications: Depending on the two metrics, it may be optimal to first expand the market by using the direct channel and then switch to the wholesale channel to exploit the expanded market. In other cases, it may be optimal to first expand the market slowly by using the wholesale channel and then switch to the direct channel to benefit from high demand growth. The optimal channel strategy is also dependent on the time horizon, with a longer time horizon leading to relatively longer use of the direct channel.
论文原文：
Ge, J. W., et al. (2021). "Supplying to Mom and Pop: Traditional Retail Channel Selection in Megacities." M&Som-Manufacturing & Service Operations Management 23(1): 19-35.
Problem definition: The shortage of inpatient beds is a major cause of delays and cancellations in many hospitals. It may also lead to patients being admitted to inappropriate wards, resulting in a lower quality of care and a longer length of stay. Academic/practical relevance: Investment in additional beds is not always feasible. Instead, new and creative solutions for a more efficient use of existing resources must be sought. Methodology: We propose a new configuration of inpatient beds, which we call the clustered overflow configuration. In this configuration, patients who are denied admission to their primary wards as a result of beds being fully occupied are admitted to overflow wards, with each designated to serve overflows from a certain subset of specialties and providing the same quality of care as in primary wards. We propose two different formulations for partitioning and bed allocation in the proposed configuration: one minimizing the sum of average daily costs of turning patients away and nursing teams, and another minimizing the numbers turned away subject to nursing cost falling below a given threshold. We heuristically solve instances from both formulations. Results: Applying the models to real data shows that the configurations obtained from our models compare very well with the other configurations proposed in the literature, provided that patients' willingness to wait is relatively short. Managerial implications: The proposed configuration provides the combined advantages of the dedicated configuration, wherein patients are only admitted to their primary wards, and the flexible configuration, in which all specialties share a single ward. On the other hand, it restricts the adverse impacts of pooling and minimizes cross-training costs through appropriate partitioning and bed allocation. As such, it serves as a viable alternative to existing inpatient configurations.
论文原文：
Izady, N. and I. Mohamed (2021). "A Clustered Overflow Configuration of Inpatient Beds in Hospitals." M&Som-Manufacturing & Service Operations Management 23(1): 139-154.
Problem definition: We study a fundamental online resource allocation problem in service operations in which a heterogeneous stream of arrivals that varies in service times and rewards makes service requests from a finite number of servers/providers. This is an online adversarial setting in which nothing more is known about the arrival process of customers. Each server has a finite regular capacity but can be expanded at the expense of overtime cost. Upon arrival of each customer, the system chooses both a server and a time for service over a scheduling horizon subject to capacity constraints. The system seeks easy-to-implement online policies that admit a competitive ratio (CR), guaranteeing the worst-case relative performance. Academic/practical relevance: On the academic side, we propose online algorithms with theoretical CRs for the problem described above. On the practical side, we investigate the real-world applicability of our methods and models on appointment-scheduling data from a partner health system. Methodology: We develop new online primal-dual approaches for making not only a server-date allocation decision for each arriving customer, but also an overtime decision for each server on each day within a horizon. We also derive a competitive analysis to prove a theoretical performance guarantee. Results: Our online policies are (i) robust to future information, (ii) easy-to-implement and extremely efficient to compute, and (iii) admitting a theoretical CR. Comparing our online policy with the optimal offline policy, we obtain a CR that guarantees the worst-case performance of our online policy. Managerial implications: We evaluate the performance of our online algorithms by using real appointment scheduling data from a partner health system. Our results show that the proposed online policies perform much better than their theoretical CR, and outperform the pervasive First-Come-First-Served (FCFS) and nested threshold policies (NTPO) by a large margin.
论文原文：
Keyvanshokooh, E., et al. (2021). "Online Advance Scheduling with Overtime: A Primal-Dual Approach." M&Som-Manufacturing & Service Operations Management 23(1): 246-266.
Problem definition: With the development of the internet and e-commerce, retailers often offer preorders for new, to-be-released products. To encourage preorders, retailers such as Amazon offer preorder price guarantee (PG). That is, if the product price drops before or on the release date, preorder consumers automatically receive a refund for the difference between the preorder price and the new price. Should retailers offer PG for preorders? If so, how should they decide the prices and inventory level? Academic/practical relevance: Both advance selling and price matching have been observed in practice and studied in literature. However, the combination of these two is a new phenomenon in practice and receives very limited attention in academic. So our study is closely related to both academic and practical communities. Methodology: Dynamic pricing and game theory. Results: We find that, if preorder demand uncertainty is high, a firm should adopt PG in advance selling. If preorder demand uncertainty is low, then a firm should adopt PG if and only if the percentage of high-valuation consumers is high. Furthermore, we find that a firm's optimal profit under PG monotonically increases in preorder demand uncertainty while the firm's optimal profit without PG stays unchanged. That is, PG enables a firm to profit from preorder demand uncertainty. In addition, we show that price commitment is dominated by dynamic pricing when the retailer can optimally decide whether to offer PG under dynamic pricing. We also demonstrate that a retailer should sell in advance if a product's marginal cost is less than a certain threshold, which is higher than the traditional threshold in the advance selling literature without consideration of PG. Managerial implications: Our results provide guidance for retailers on whether or not they should offer PG and how to design the prices under PG.
论文原文：
Pang, Z., et al. (2021). "Preorder Price Guarantee in e-Commerce." M&Som-Manufacturing & Service Operations Management 23(1): 123-138.
Problem definition: We examine how the presence of capital market frictions influences the decision to invest in production cost reduction and the resultant production volume. This investment can increase the firm's cash flow by increasing the profit margin, but it can also decrease the firm's risk-free cash reserves and thus affect its exposure to capital market frictions. Academic/practical relevance: Process improvement aimed at production cost reduction has generated myriad of theoretical questions about efficient investment options and capacity choices. From a managerial perspective, process improvement is a fundamental concern in operations strategy. Nevertheless, its analysis typically excludes financial constraints by assuming a perfect capital market. Methodology: We formulate a two-stage profit maximization model in which a capital-constrained firm commits to a cost-reduction investment in the first stage in anticipation of its production decision in the second stage of this two-stage decision process. The firm considers capital market frictions when making decisions at each stage, while considering uncertainty in demand for its offering and in reducing its unit production cost. Results: When a firm faces small initial capital and low preinvestment unit production costs, it can benefit from investing in production cost reduction in the presence of capital market frictions more so than in their absence. Moreover, uncertainty in the production cost reduction mitigates the impact of market frictions on the net benefit (i.e., additional profit), whereas demand uncertainty decreases the feasible parameter space, where investing in production cost reduction is optimal. Managerial implications: A firm's decision to invest in production cost reduction affects its operational and financial capabilities. Managers should thus consider this investment as an operational hedge not only against the uncertainty of matching supply and demand but also against exposure to capital market frictions and the resultant financial risk.
