N. Agrawal, Sami Najafi-Asadolahi, Stephen A. Smith
Problem definition: Managers in ad agencies are responsible for delivering digital ads to viewers on behalf of advertisers, subject to the terms specified in the ad campaigns. They need to develop bidding policies to obtain viewers on an ad exchange and allocate them to the campaigns to maximize the agency’s profits, subject to the goals of the ad campaigns. Academic/practical relevance: Determining a rigorous solution methodology is complicated by uncertainties in the arrival rates of viewers and campaigns, as well as uncertainty in the outcomes of bids on the ad exchange. In practice, ad hoc strategies are often deployed. Our methodology jointly determines optimal bidding and viewer-allocation strategies and obtains insights about the characteristics of the optimal policies. Methodology: New ad campaigns and viewers are treated as Poisson arrivals, and the resulting model is a Markov decision process, where the state of the system is the number of undelivered impressions in queue for each campaign type in each period. We develop solution methods for bid optimization and viewer allocation and perform a sensitivity analysis with respect to the key problem parameters. Results: We solve for the optimal dynamic, state-dependent bidding and allocation policies as a function of the number of ad impressions in queue, for both the finite horizon and steady-state cases. We show that the resulting optimization problems are strictly concave in the decision variables and develop and evaluate a heuristic method that can be applied to large problems. Managerial implications: Numerical analysis of our heuristic solution shows that its errors are generally small and that the optimal dynamic, state-dependent bidding policies obtained by our model are significantly better than optimal static policies. Our proposed approach is managerially attractive because it is easy to implement in practice. We identify the capacity of the impression queue as an important managerial control lever and show that it can be more effective than using higher bids to reduce delay penalties. We quantify potential operational benefits from the consolidation of ad campaigns, as well as merging ad exchanges. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1142 .
{"title":"A Markov Decision Model for Managing Display-Advertising Campaigns","authors":"N. Agrawal, Sami Najafi-Asadolahi, Stephen A. Smith","doi":"10.1287/msom.2022.1142","DOIUrl":"https://doi.org/10.1287/msom.2022.1142","url":null,"abstract":"Problem definition: Managers in ad agencies are responsible for delivering digital ads to viewers on behalf of advertisers, subject to the terms specified in the ad campaigns. They need to develop bidding policies to obtain viewers on an ad exchange and allocate them to the campaigns to maximize the agency’s profits, subject to the goals of the ad campaigns. Academic/practical relevance: Determining a rigorous solution methodology is complicated by uncertainties in the arrival rates of viewers and campaigns, as well as uncertainty in the outcomes of bids on the ad exchange. In practice, ad hoc strategies are often deployed. Our methodology jointly determines optimal bidding and viewer-allocation strategies and obtains insights about the characteristics of the optimal policies. Methodology: New ad campaigns and viewers are treated as Poisson arrivals, and the resulting model is a Markov decision process, where the state of the system is the number of undelivered impressions in queue for each campaign type in each period. We develop solution methods for bid optimization and viewer allocation and perform a sensitivity analysis with respect to the key problem parameters. Results: We solve for the optimal dynamic, state-dependent bidding and allocation policies as a function of the number of ad impressions in queue, for both the finite horizon and steady-state cases. We show that the resulting optimization problems are strictly concave in the decision variables and develop and evaluate a heuristic method that can be applied to large problems. Managerial implications: Numerical analysis of our heuristic solution shows that its errors are generally small and that the optimal dynamic, state-dependent bidding policies obtained by our model are significantly better than optimal static policies. Our proposed approach is managerially attractive because it is easy to implement in practice. We identify the capacity of the impression queue as an important managerial control lever and show that it can be more effective than using higher bids to reduce delay penalties. We quantify potential operational benefits from the consolidation of ad campaigns, as well as merging ad exchanges. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1142 .","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"30 1","pages":"489-507"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80874054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: In this paper, we explore how a firm’s concern about profit distribution and the size of downstream firms in supply chains affect corporate social responsibility (CSR) investment strategy. Methodology/results: In a supply chain consisting of one supplier and one manufacturer, both players decide whether to invest to reduce CSR violations, and they negotiate over a wholesale price. Distributive comparison behavior makes the manufacturer compare the profit with his equitable payoff, which is determined by the supplier’s profit. Advantageous (resp. disadvantageous) inequality occurs when the manufacturer’s profit is higher (resp. lower) than the manufacturer’s equitable payoff. We compare this supply chain to the one without distributive comparison behavior. We find that when advantageous inequality occurs, or when neither inequality occurs and the manufacturer’s sensitivity to the supplier’s profit is low, the manufacturer’s distributive comparison behavior makes the manufacturer less (resp. supplier more) likely to invest in CSR, which we call negative (resp. positive) impacts of distributive comparison behavior; otherwise, it makes the manufacturer more (resp. supplier less) likely to invest. In most cases, the weak bargaining power of the small manufacturer leads to larger positive or smaller negative impacts of distributive comparison behavior. Also, the low efficiency of the small manufacturer to reduce CSR violations leads to smaller negative impacts of distributive comparison behavior. Managerial implications: Our results show that governments and nongovernmental organizations (NGOs) should investigate firms’ distributive comparison behavior in supply chains. When downstream firms show the aversion to lower (resp. higher) profits than ones from upstream firms, the measures to monitor and support upstream (resp. downstream) firms’ CSR investments should be taken to avoid CSR violations. In the supply chains with small downstream firms, extra efforts should be made to induce firms’ distributive comparison behavior. Funding: M. Wang was supported partially by the National Natural Science Foundation of China [Grants 71931009 and 71671023]; X. Fang is grateful for the support under a Lee Kong Chian Fellowship and Retail Centre of Excellence Research Grant; Z. Wang was supported partially by the National Natural Science Foundation of China [Grants 72010107002, 71671023, and 72171212]; and Y. Chen was supported partially by the Research Grants Council of the Hong Kong Special Administrative Region, China [HKUST C6020-21GF]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1172 .
{"title":"Impacts of Distributive Comparison Behavior on Corporate Social Responsibility in Supply Chains: The Role of Small Firms","authors":"Mingzheng Wang, X. Fang, Zizhuo Wang, Ying‐ju Chen","doi":"10.1287/msom.2022.1172","DOIUrl":"https://doi.org/10.1287/msom.2022.1172","url":null,"abstract":"Problem definition: In this paper, we explore how a firm’s concern about profit distribution and the size of downstream firms in supply chains affect corporate social responsibility (CSR) investment strategy. Methodology/results: In a supply chain consisting of one supplier and one manufacturer, both players decide whether to invest to reduce CSR violations, and they negotiate over a wholesale price. Distributive comparison behavior makes the manufacturer compare the profit with his equitable payoff, which is determined by the supplier’s profit. Advantageous (resp. disadvantageous) inequality occurs when the manufacturer’s profit is higher (resp. lower) than the manufacturer’s equitable payoff. We compare this supply chain to the one without distributive comparison behavior. We find that when advantageous inequality occurs, or when neither inequality occurs and the manufacturer’s sensitivity to the supplier’s profit is low, the manufacturer’s distributive comparison behavior makes the manufacturer less (resp. supplier more) likely to invest in CSR, which we call negative (resp. positive) impacts of distributive comparison behavior; otherwise, it makes the manufacturer more (resp. supplier less) likely to invest. In most cases, the weak bargaining power of the small manufacturer leads to larger positive or smaller negative impacts of distributive comparison behavior. Also, the low efficiency of the small manufacturer to reduce CSR violations leads to smaller negative impacts of distributive comparison behavior. Managerial implications: Our results show that governments and nongovernmental organizations (NGOs) should investigate firms’ distributive comparison behavior in supply chains. When downstream firms show the aversion to lower (resp. higher) profits than ones from upstream firms, the measures to monitor and support upstream (resp. downstream) firms’ CSR investments should be taken to avoid CSR violations. In the supply chains with small downstream firms, extra efforts should be made to induce firms’ distributive comparison behavior. Funding: M. Wang was supported partially by the National Natural Science Foundation of China [Grants 71931009 and 71671023]; X. Fang is grateful for the support under a Lee Kong Chian Fellowship and Retail Centre of Excellence Research Grant; Z. Wang was supported partially by the National Natural Science Foundation of China [Grants 72010107002, 71671023, and 72171212]; and Y. Chen was supported partially by the Research Grants Council of the Hong Kong Special Administrative Region, China [HKUST C6020-21GF]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1172 .","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"292 1","pages":"686-703"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77170710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We consider the setting where a retailer with many physical stores and an online presence seeks to fulfill online orders using an omnichannel fulfillment program, such as buy-online ship-from-store. These fulfillment strategies try to minimize cost while fulfilling orders within acceptable service times. We focus on single-item orders. Typically, all online orders for the item are sent to a favorable set of locations to be filled. Failed trials are sent back for further stages of trial fulfillment until the process times out. The multistage order fulfillment problem is thus an interplay of the pick-failure probabilities at the stores where they may be shipped from and the picking, shipping, and cancellation costs from these locations. Methodology: We model the problem as one of sequencing the stores from which an order is attempted to be picked and shipped in the most cost-effective way over multiple stages. We solve the fulfillment problem optimally by taking into account the changing pick-failure probabilities as a result of other online order fulfillment trials by casting it as a network flow problem with convex costs. We incorporate this as the second stage of a two-stage online order acceptance problem and generalize earlier results to the case with pick failures at stores. Results: We investigate the real-world performance of our methods and models on real order data of several of the top U.S. retailers that use our collaborating e-commerce solutions provider to optimize their fulfillment strategies. Academic/Practical Relevance: Our work enables retailers to incorporate pick failure in their order management systems for ship-from-store programs. Our new online order-acceptance policies that take into account pick failures can thus create significant savings for omnichannel retailers. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1164 .
{"title":"Order Fulfillment Under Pick Failure in Omnichannel Ship-From-Store Programs","authors":"Sagnik Das, R. Ravi, S. Sridhar","doi":"10.1287/msom.2022.1164","DOIUrl":"https://doi.org/10.1287/msom.2022.1164","url":null,"abstract":"Problem definition: We consider the setting where a retailer with many physical stores and an online presence seeks to fulfill online orders using an omnichannel fulfillment program, such as buy-online ship-from-store. These fulfillment strategies try to minimize cost while fulfilling orders within acceptable service times. We focus on single-item orders. Typically, all online orders for the item are sent to a favorable set of locations to be filled. Failed trials are sent back for further stages of trial fulfillment until the process times out. The multistage order fulfillment problem is thus an interplay of the pick-failure probabilities at the stores where they may be shipped from and the picking, shipping, and cancellation costs from these locations. Methodology: We model the problem as one of sequencing the stores from which an order is attempted to be picked and shipped in the most cost-effective way over multiple stages. We solve the fulfillment problem optimally by taking into account the changing pick-failure probabilities as a result of other online order fulfillment trials by casting it as a network flow problem with convex costs. We incorporate this as the second stage of a two-stage online order acceptance problem and generalize earlier results to the case with pick failures at stores. Results: We investigate the real-world performance of our methods and models on real order data of several of the top U.S. retailers that use our collaborating e-commerce solutions provider to optimize their fulfillment strategies. Academic/Practical Relevance: Our work enables retailers to incorporate pick failure in their order management systems for ship-from-store programs. Our new online order-acceptance policies that take into account pick failures can thus create significant savings for omnichannel retailers. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1164 .","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"75 1","pages":"508-523"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86144964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem Definition: This paper considers a setting in which an airline company sells seats periodically, and each period consists of two selling phases, an early-bird discount phase and a regular-price phase. In each period, when the early-bird discount seat is stocked out, an early-bird customer who comes for the discounted seat either purchases the regular-price seat as a substitute (called buy-up substitution) or simply leaves. Methodology/Results: The optimal inventory level of the discounted seats reserved for the early-bird sale is a critical decision for the airline company to maximize its revenue. The airline company learns about the demands for both discounted and regular-price seats and the buy-up substitution probability from historical sales data, which, in turn, are affected by past inventory allocation decisions. In this paper, we investigate two information scenarios based on whether lost sales are observable, and we provide the corresponding Bayesian updating mechanism for learning about demand parameters and substitution probability. We then construct a dynamic programming model to derive the Bayesian optimal inventory level decisions in a multiperiod setting. The literature finds that the unobservability of lost sales drives the inventory manager to stock more (i.e., the Bayesian optimal inventory level should be kept higher than the myopic inventory level) to observe and learn more about demand distributions. Here, we show that when the buy-up substitution probability is known, one may stock less, because one can infer some information about the primary demand for the discounted seat from the customer substitution behavior. We also find that to learn about the unknown buy-up substitution probability drives the inventory manager to stock less so as to induce more substitution trials. Finally, we develop a SoftMax algorithm to solve our dynamic programming problem. We show that the obtained stock more (less) result can be utilized to speed up the convergence of the algorithm to the optimal solution. Managerial Implications: Our results shed light on the airline seat protection level decision with learning about demand parameters and buy-up substitution probability. Compared with myopic optimization, Bayesian inventory decisions that consider the exploration-exploitation tradeoff can avoid getting stuck in local optima and improve the revenue. We also identify new driving forces behind the stock more (less) result that complement the Bayesian inventory management literature. Funding: Z. Luo acknowledges the financial support by the Internal Start-up Fund of The Hong Kong Polytechnic University [Grant P0039035]. P. Guo acknowledges the financial support from the Research Grants Council of Hong Kong [Grant 15508518]. Y. Wang’s work was supported by the Research Grants Council of Hong Kong [Grant 15505318] and the National Natural Science Foundation of China [Grant 71971184]. Supplemental Material: The e-companion is available at htt
{"title":"Manage Inventories with Learning on Demands and Buy-up Substitution Probability","authors":"Zhenwei Luo, Pengfei Guo, Yulan Wang","doi":"10.1287/msom.2022.1169","DOIUrl":"https://doi.org/10.1287/msom.2022.1169","url":null,"abstract":"Problem Definition: This paper considers a setting in which an airline company sells seats periodically, and each period consists of two selling phases, an early-bird discount phase and a regular-price phase. In each period, when the early-bird discount seat is stocked out, an early-bird customer who comes for the discounted seat either purchases the regular-price seat as a substitute (called buy-up substitution) or simply leaves. Methodology/Results: The optimal inventory level of the discounted seats reserved for the early-bird sale is a critical decision for the airline company to maximize its revenue. The airline company learns about the demands for both discounted and regular-price seats and the buy-up substitution probability from historical sales data, which, in turn, are affected by past inventory allocation decisions. In this paper, we investigate two information scenarios based on whether lost sales are observable, and we provide the corresponding Bayesian updating mechanism for learning about demand parameters and substitution probability. We then construct a dynamic programming model to derive the Bayesian optimal inventory level decisions in a multiperiod setting. The literature finds that the unobservability of lost sales drives the inventory manager to stock more (i.e., the Bayesian optimal inventory level should be kept higher than the myopic inventory level) to observe and learn more about demand distributions. Here, we show that when the buy-up substitution probability is known, one may stock less, because one can infer some information about the primary demand for the discounted seat from the customer substitution behavior. We also find that to learn about the unknown buy-up substitution probability drives the inventory manager to stock less so as to induce more substitution trials. Finally, we develop a SoftMax algorithm to solve our dynamic programming problem. We show that the obtained stock more (less) result can be utilized to speed up the convergence of the algorithm to the optimal solution. Managerial Implications: Our results shed light on the airline seat protection level decision with learning about demand parameters and buy-up substitution probability. Compared with myopic optimization, Bayesian inventory decisions that consider the exploration-exploitation tradeoff can avoid getting stuck in local optima and improve the revenue. We also identify new driving forces behind the stock more (less) result that complement the Bayesian inventory management literature. Funding: Z. Luo acknowledges the financial support by the Internal Start-up Fund of The Hong Kong Polytechnic University [Grant P0039035]. P. Guo acknowledges the financial support from the Research Grants Council of Hong Kong [Grant 15508518]. Y. Wang’s work was supported by the Research Grants Council of Hong Kong [Grant 15505318] and the National Natural Science Foundation of China [Grant 71971184]. Supplemental Material: The e-companion is available at htt","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"24 1","pages":"563-580"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89145526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: The cost for developing a new drug ranged from $1 billion to more than $2 billion between 2010 and 2019. In addition to high development costs, the efficacy of the candidate drug, patient enrollment, the market exclusivity period (MEP), and the planning horizon are uncertain. Moreover, slow enrollment leads to increased costs, canceled clinical trials, and lost potential revenue. Many firms, hoping to detect efficacy versus futility of the candidate drug early to save development costs, plan interim analyses of patient-response data in their clinical trials. Academic/practical relevance: The problem for optimizing patient-enrollment rates has an uncertain planning horizon. We developed a continuous-time dynamic programming (DP) model with learning of a drug’s efficacy and MEP to assist firms in developing optimal enrollment policies in their clinical trials. We also established the optimality equation for this DP model. Through a clinical trial for testing a cancer drug developed by a leading pharmaceutical firm, we demonstrate that our DP model can help firms effectively manage their trials with a sizable profit gain (as large as $270 million per drug). Firms can also use our model in simulation to select their trial design parameters (e.g., the sample sizes of interim analyses). Methodology: We update a drug’s efficacy by Bayes’ rules. Using the stochastic order and the likelihood-ratio order of distribution functions, we prove the monotonic properties of the value function and an optimal policy. Results: We established that the value of the drug-development project increases as the average response from patients using the candidate drug increases. For drugs having low annual revenue or a strong market brand or treating rare diseases, we also established that the optimal enrollment policy is monotonic in the average patient response. Moreover, the optimal enrollment rate increases as the variance of the MEP decreases. Managerial implications: Firms can use the properties of the value function to select late-stage clinical trials for their drug-development project portfolios. Firms can also use our optimal policy to guide patient recruitment in their clinical trials considering competition from other drugs in the marketplace. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1162 .
{"title":"Optimal Enrollment in Late-Stage New Drug Development with Learning of Drug's Efficacy for Group-Sequential Clinical Trials","authors":"Zhili Tian, Gordon B. Hazen, Hong Li","doi":"10.1287/msom.2022.1162","DOIUrl":"https://doi.org/10.1287/msom.2022.1162","url":null,"abstract":"Problem definition: The cost for developing a new drug ranged from $1 billion to more than $2 billion between 2010 and 2019. In addition to high development costs, the efficacy of the candidate drug, patient enrollment, the market exclusivity period (MEP), and the planning horizon are uncertain. Moreover, slow enrollment leads to increased costs, canceled clinical trials, and lost potential revenue. Many firms, hoping to detect efficacy versus futility of the candidate drug early to save development costs, plan interim analyses of patient-response data in their clinical trials. Academic/practical relevance: The problem for optimizing patient-enrollment rates has an uncertain planning horizon. We developed a continuous-time dynamic programming (DP) model with learning of a drug’s efficacy and MEP to assist firms in developing optimal enrollment policies in their clinical trials. We also established the optimality equation for this DP model. Through a clinical trial for testing a cancer drug developed by a leading pharmaceutical firm, we demonstrate that our DP model can help firms effectively manage their trials with a sizable profit gain (as large as $270 million per drug). Firms can also use our model in simulation to select their trial design parameters (e.g., the sample sizes of interim analyses). Methodology: We update a drug’s efficacy by Bayes’ rules. Using the stochastic order and the likelihood-ratio order of distribution functions, we prove the monotonic properties of the value function and an optimal policy. Results: We established that the value of the drug-development project increases as the average response from patients using the candidate drug increases. For drugs having low annual revenue or a strong market brand or treating rare diseases, we also established that the optimal enrollment policy is monotonic in the average patient response. Moreover, the optimal enrollment rate increases as the variance of the MEP decreases. Managerial implications: Firms can use the properties of the value function to select late-stage clinical trials for their drug-development project portfolios. Firms can also use our optimal policy to guide patient recruitment in their clinical trials considering competition from other drugs in the marketplace. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1162 .","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"21 1","pages":"88-107"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81898518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Benjaafar, David Chen, Rowan Wang, Zhenzhen Yan
Problem definition: This paper studies an appointment system where a finite number of customers are scheduled to arrive in such a way that (1) the expected waiting time of each individual customer cannot exceed a given threshold; and (2) the appointment times are set as early as possible (without breaking the waiting time constraint). Methodology/results: First, we show that, under the service-level constraint, a prospective schedule can be obtained from a sequential scheduling approach. In particular, we can schedule the appointment time of the next customer based on the scheduled appointment times of the previous customers. Then, we use a transient queueing-analysis approach and apply the theory of majorization to analytically characterize the structure of the optimal appointment schedule. We prove that, to keep the expected waiting time of each customer below a certain threshold, the minimum inter-appointment time required increases with the arrival sequence. We further identify additional properties of the optimal schedule. For example, a later arrival has a higher chance of finding an empty system and is more likely to wait less than the duration of his expected service time. We show the convergence of the service-level-constrained system to the D/M/1 queueing system as the number of arrivals approaches infinity and propose a simple, yet practical, heuristic schedule that is asymptotically optimal. We also develop algorithms that can help system managers determine the number of customers that can be scheduled in a fixed time window. We compare the service-level-constrained appointment system with other widely studied systems (including the equal-space and cost-minimization systems). We show that the service-level-constrained system leads to a lower upper bound on each customer’s waiting time; ensures a fair waiting experience among customers; and performs quite well in terms of system overtime. Finally, we investigate various extended settings of our analysis, including customer no-shows; mixed Erlang service times; multiple servers; and probability-based service-level constraints. Managerial implications: Our results provide guidelines on how to design appointment schedules with individual service-level constraints. Such a design ensures fairness and incorporates the threshold-type waiting perception of customers. It is also free from cost estimation and can be easily applied in practice. In addition, under the service-level-constrained appointment system, customers with later appointment times can have better waiting experiences, in contrast to the situation under other commonly studied systems. Funding: Z. Yan was partly supported by a Nanyang Technological University startup grant; the Ministry of Education Academic Research Fund Tier 1 [Grant RG17/21] and Tier 2 [Grant MOE2019-T2-1-045]; and Neptune Orient Lines [Fellowship Grant NOL21RP04]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.1
问题定义:本文研究了一个预约系统，其中有限数量的顾客被安排到达，并且:(1)每个顾客的期望等待时间不能超过给定的阈值;(2)尽早设置预约时间(不打破等待时间限制)。方法/结果:首先，我们证明了在服务水平约束下，可以通过顺序调度方法获得预期调度。特别是，我们可以根据前一个客户的预约时间来安排下一个客户的预约时间。然后，利用暂态排队分析方法，运用多数化理论对最优预约调度的结构进行了解析表征。我们证明，为了使每个顾客的期望等待时间低于某一阈值，所需的最小预约间隔时间随着到达顺序的增加而增加。我们进一步确定了最优调度的附加性质。例如，较晚到达的人更有可能找到空系统，并且等待的时间更有可能少于他预期的服务时间。我们展示了服务水平约束系统对D/M/1排队系统趋近于无穷时的收敛性，并提出了一个简单而实用的启发式渐近最优调度。我们还开发了算法，可以帮助系统管理人员确定在固定时间窗口内可以安排的客户数量。我们将服务水平约束的预约系统与其他广泛研究的系统(包括等空间和成本最小化系统)进行了比较。我们证明了服务水平约束的系统会导致每个顾客等待时间的下上界;确保顾客有公平的等待体验;在系统加班方面表现得很好。最后，我们调查了我们分析的各种扩展设置，包括客户缺席;混合Erlang服务时间;多个服务器;以及基于概率的服务水平约束。管理意义:我们的结果为如何设计具有个人服务水平约束的预约安排提供了指导。这样的设计既保证了公平性，又融入了顾客阈值式的等待感知。它也不需要成本估算，可以很容易地在实践中应用。此外，在服务水平约束的预约制度下，预约时间较晚的客户可以获得更好的等待体验，而不是在其他常见的研究制度下。项目资助:Yan获得了南洋理工大学创业基金的部分资助;教育部学术研究基金一级[资助RG17/21]和二级[资助MOE2019-T2-1-045];和Neptune Orient Lines [Fellowship Grant NOL21RP04]。补充材料:在线补充材料可在https://doi.org/10.1287/msom.2022.1159上获得。
{"title":"Appointment Scheduling Under a Service-Level Constraint","authors":"S. Benjaafar, David Chen, Rowan Wang, Zhenzhen Yan","doi":"10.2139/ssrn.3548348","DOIUrl":"https://doi.org/10.2139/ssrn.3548348","url":null,"abstract":"Problem definition: This paper studies an appointment system where a finite number of customers are scheduled to arrive in such a way that (1) the expected waiting time of each individual customer cannot exceed a given threshold; and (2) the appointment times are set as early as possible (without breaking the waiting time constraint). Methodology/results: First, we show that, under the service-level constraint, a prospective schedule can be obtained from a sequential scheduling approach. In particular, we can schedule the appointment time of the next customer based on the scheduled appointment times of the previous customers. Then, we use a transient queueing-analysis approach and apply the theory of majorization to analytically characterize the structure of the optimal appointment schedule. We prove that, to keep the expected waiting time of each customer below a certain threshold, the minimum inter-appointment time required increases with the arrival sequence. We further identify additional properties of the optimal schedule. For example, a later arrival has a higher chance of finding an empty system and is more likely to wait less than the duration of his expected service time. We show the convergence of the service-level-constrained system to the D/M/1 queueing system as the number of arrivals approaches infinity and propose a simple, yet practical, heuristic schedule that is asymptotically optimal. We also develop algorithms that can help system managers determine the number of customers that can be scheduled in a fixed time window. We compare the service-level-constrained appointment system with other widely studied systems (including the equal-space and cost-minimization systems). We show that the service-level-constrained system leads to a lower upper bound on each customer’s waiting time; ensures a fair waiting experience among customers; and performs quite well in terms of system overtime. Finally, we investigate various extended settings of our analysis, including customer no-shows; mixed Erlang service times; multiple servers; and probability-based service-level constraints. Managerial implications: Our results provide guidelines on how to design appointment schedules with individual service-level constraints. Such a design ensures fairness and incorporates the threshold-type waiting perception of customers. It is also free from cost estimation and can be easily applied in practice. In addition, under the service-level-constrained appointment system, customers with later appointment times can have better waiting experiences, in contrast to the situation under other commonly studied systems. Funding: Z. Yan was partly supported by a Nanyang Technological University startup grant; the Ministry of Education Academic Research Fund Tier 1 [Grant RG17/21] and Tier 2 [Grant MOE2019-T2-1-045]; and Neptune Orient Lines [Fellowship Grant NOL21RP04]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.1","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"22 1","pages":"70-87"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84944156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: The main goal of many nonprofit or nongovernmental organizations is to increase the number of customers who receive service (i.e., service coverage) and social welfare. However, the limited number of employees, volunteers, and service locations results in long service wait. In addition, getting customers living in remote areas to receive services by traveling long distances is difficult. We propose an innovative distance-based service priority policy that would reduce the service waiting time for customers who must travel farther for the service by giving them higher service priority, thereby providing them with a new incentive to seek service. Methodology/results: Using a game-theoretic queueing model, we show that the proposed policy can significantly attract more customers to a service. The increase can be up to 50% compared with the ordinary first-come-first-served service discipline. The policy can also achieve higher social welfare, however, that may come at the cost of reduced customer welfare. We therefore propose a possible remedy for a social planner to coordinate welfare under such circumstance. It ensures all stakeholders, including the service provider, customers, and society, can benefit from the policy at the same time. Finally, we compare our distance-based service priority policy with two existing strategies from the literature—namely, the price discrimination strategy and the probabilistic priority strategy. Managerial implications: Our proposed policy can play a pivotal role in a nonprofit service provider’s mission to increase service coverage and social welfare, especially when customers’ travel costs to obtain service are significant. Furthermore, our policy may create fewer implementation and fairness concerns compared with related strategies.
{"title":"Distance-Based Service Priority: An Innovative Mechanism to Increase System Throughput and Social Welfare","authors":"Zhongbin Wang, Shiliang Cui, Lei Fang","doi":"10.1287/msom.2022.1157","DOIUrl":"https://doi.org/10.1287/msom.2022.1157","url":null,"abstract":"Problem definition: The main goal of many nonprofit or nongovernmental organizations is to increase the number of customers who receive service (i.e., service coverage) and social welfare. However, the limited number of employees, volunteers, and service locations results in long service wait. In addition, getting customers living in remote areas to receive services by traveling long distances is difficult. We propose an innovative distance-based service priority policy that would reduce the service waiting time for customers who must travel farther for the service by giving them higher service priority, thereby providing them with a new incentive to seek service. Methodology/results: Using a game-theoretic queueing model, we show that the proposed policy can significantly attract more customers to a service. The increase can be up to 50% compared with the ordinary first-come-first-served service discipline. The policy can also achieve higher social welfare, however, that may come at the cost of reduced customer welfare. We therefore propose a possible remedy for a social planner to coordinate welfare under such circumstance. It ensures all stakeholders, including the service provider, customers, and society, can benefit from the policy at the same time. Finally, we compare our distance-based service priority policy with two existing strategies from the literature—namely, the price discrimination strategy and the probabilistic priority strategy. Managerial implications: Our proposed policy can play a pivotal role in a nonprofit service provider’s mission to increase service coverage and social welfare, especially when customers’ travel costs to obtain service are significant. Furthermore, our policy may create fewer implementation and fairness concerns compared with related strategies.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"12 4 1","pages":"353-369"},"PeriodicalIF":0.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75688561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Customer joining behavior is of major concern for service systems where the service capacity is uncertain. Academic/practical relevance: It remains unclear whether customer inference of uncertain service capacity can lead to follow the crowd (FTC) behavior. Management can release capacity information, but how it affects system performance needs to be understood. Methodology: We use a single-server queue to analyze the joining behavior of customers who infer the actual service capacity based on the queue length upon arrival. We also characterize the impact of capacity information disclosure both analytically and numerically. Results: We find that when other customers’ tendency to join the service increases, a tagged customer can make more accurate inferences of service capacity based on queue length. This inference effect arises together with the congestion effect and can lead to FTC when it outweighs the latter. When multiple equilibria exist, we characterize the conditions under which the inference effect is significant at the aggregate customer level so that the joining equilibrium with a larger joining threshold is Pareto-optimal. Managerial implications: Management needs to be careful in setting the information disclosure policy, as the key problem parameters may affect its impact on system throughput and social welfare in opposite directions.
{"title":"Follow the Crowd with Uncertain Service Capacity","authors":"Liu Yang, Weixin Shang, Liming Liu","doi":"10.1287/msom.2022.1139","DOIUrl":"https://doi.org/10.1287/msom.2022.1139","url":null,"abstract":"Problem definition: Customer joining behavior is of major concern for service systems where the service capacity is uncertain. Academic/practical relevance: It remains unclear whether customer inference of uncertain service capacity can lead to follow the crowd (FTC) behavior. Management can release capacity information, but how it affects system performance needs to be understood. Methodology: We use a single-server queue to analyze the joining behavior of customers who infer the actual service capacity based on the queue length upon arrival. We also characterize the impact of capacity information disclosure both analytically and numerically. Results: We find that when other customers’ tendency to join the service increases, a tagged customer can make more accurate inferences of service capacity based on queue length. This inference effect arises together with the congestion effect and can lead to FTC when it outweighs the latter. When multiple equilibria exist, we characterize the conditions under which the inference effect is significant at the aggregate customer level so that the joining equilibrium with a larger joining threshold is Pareto-optimal. Managerial implications: Management needs to be careful in setting the information disclosure policy, as the key problem parameters may affect its impact on system throughput and social welfare in opposite directions.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"42 1","pages":"341-352"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86925449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Firms heavily invest in big data technologies to collect consumer data and infer consumer preferences for price discrimination. However, consumers can use technological devices to manipulate their data and fool firms to obtain better deals. We examine how a firm invests in collecting consumer data and makes pricing decisions and whether it should disclose its scope of data collection to consumers who can manipulate their data. Methodology/results: We develop a game-theoretic model to consider a market in which a firm caters to consumers with heterogeneous preferences for a product. The firm collects consumer data to identify their types and issue an individualized price, whereas consumers can incur a cost to manipulate data and mimic the other type. We find that when the firm does not disclose its scope of data collection to consumers, it collects more consumer data. When the firm discloses its scope of data collection, it reduces data collection even when collecting more data is costless. The optimal scope of data collection increases when it is more costly for consumers to manipulate data but decreases when consumer demand becomes more heterogeneous. Moreover, a lower cost for consumers to manipulate data can be detrimental to both the firm and consumers. Lastly, disclosure of data collection scope increases firm profit, consumer surplus, and social welfare. Managerial implications: Our findings suggest that a firm should adjust its scope of data collection and prices based on whether the firm discloses the data collection scope, consumers’ manipulation cost, and demand heterogeneity. Public policies should require firms to disclose their data collection scope to increase consumer surplus and social welfare. Even without such a mandatory disclosure policy, firms should voluntarily disclose their data collection scope to increase profit. Moreover, public educational programs that train consumers to manipulate their data or raise their awareness of manipulation tools can ultimately hurt consumers and firms.
{"title":"Beating the Algorithm: Consumer Manipulation, Personalized Pricing, and Big Data Management","authors":"Xi Li, Krista J. Li","doi":"10.1287/msom.2022.1153","DOIUrl":"https://doi.org/10.1287/msom.2022.1153","url":null,"abstract":"Problem definition: Firms heavily invest in big data technologies to collect consumer data and infer consumer preferences for price discrimination. However, consumers can use technological devices to manipulate their data and fool firms to obtain better deals. We examine how a firm invests in collecting consumer data and makes pricing decisions and whether it should disclose its scope of data collection to consumers who can manipulate their data. Methodology/results: We develop a game-theoretic model to consider a market in which a firm caters to consumers with heterogeneous preferences for a product. The firm collects consumer data to identify their types and issue an individualized price, whereas consumers can incur a cost to manipulate data and mimic the other type. We find that when the firm does not disclose its scope of data collection to consumers, it collects more consumer data. When the firm discloses its scope of data collection, it reduces data collection even when collecting more data is costless. The optimal scope of data collection increases when it is more costly for consumers to manipulate data but decreases when consumer demand becomes more heterogeneous. Moreover, a lower cost for consumers to manipulate data can be detrimental to both the firm and consumers. Lastly, disclosure of data collection scope increases firm profit, consumer surplus, and social welfare. Managerial implications: Our findings suggest that a firm should adjust its scope of data collection and prices based on whether the firm discloses the data collection scope, consumers’ manipulation cost, and demand heterogeneity. Public policies should require firms to disclose their data collection scope to increase consumer surplus and social welfare. Even without such a mandatory disclosure policy, firms should voluntarily disclose their data collection scope to increase profit. Moreover, public educational programs that train consumers to manipulate their data or raise their awareness of manipulation tools can ultimately hurt consumers and firms.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"90 1","pages":"36-49"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80401963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: In today’s highly dynamic and competitive app markets, a significant portion of development takes place after the initial product launch via the addition of new features and the enhancement of existing products. In managing the sequential innovation process in mobile app development, two key operational questions arise. (i) What features and attributes should be added to existing products in successive versions? (ii) How should these features and attributes be implemented for greater market success? We investigate the implications of three different types of mobile app development activities on market performance. Academic/practical relevance: Our study contributes to the operations management literature by providing an empirically based understanding of sequential innovation and its market performance implications in mobile app development, an important industry in terms of size, scope and potential. Methodology: Using a novel data set of mobile apps in the Productivity category, we leverage text-mining and information retrieval techniques to study the rich information in the release notes of apps. We then characterize product development activities at each version release and link these activities with app performance in a dynamic estimation model. We also incorporate an instrumental variables analysis to substantiate our findings. Results: We find that greater update dissimilarity (i.e., dissimilarity of the features and attributes of a new update from those of previous updates) is associated with higher performance, especially in mature apps. We also find that the greater the product update market orientation (i.e., the greater the similarity of the focal firm’s new features and attributes with respect to the recent additions of its competitors), the higher is the market performance. This finding suggests that the market rewards those developers who have a responsive policy to their competitors’ product innovation efforts. Our results also suggest that a rapid introduction of updates dampens the potential market benefits that the mobile app developers might gain from market orientation. We find no evidence of a beneficial effect of product update scope (i.e., incorporating features and attributes from other product subcategories) on market performance. Managerial implications: Our study offers managerial insights into mobile app development by exploring the sequential innovation characteristics that are associated with greater market success in pursuing and implementing new features and attributes.
{"title":"Sequential Innovation in Mobile App Development","authors":"Nilam Kaushik, Bilal Gokpinar","doi":"10.1287/msom.2022.1154","DOIUrl":"https://doi.org/10.1287/msom.2022.1154","url":null,"abstract":"Problem definition: In today’s highly dynamic and competitive app markets, a significant portion of development takes place after the initial product launch via the addition of new features and the enhancement of existing products. In managing the sequential innovation process in mobile app development, two key operational questions arise. (i) What features and attributes should be added to existing products in successive versions? (ii) How should these features and attributes be implemented for greater market success? We investigate the implications of three different types of mobile app development activities on market performance. Academic/practical relevance: Our study contributes to the operations management literature by providing an empirically based understanding of sequential innovation and its market performance implications in mobile app development, an important industry in terms of size, scope and potential. Methodology: Using a novel data set of mobile apps in the Productivity category, we leverage text-mining and information retrieval techniques to study the rich information in the release notes of apps. We then characterize product development activities at each version release and link these activities with app performance in a dynamic estimation model. We also incorporate an instrumental variables analysis to substantiate our findings. Results: We find that greater update dissimilarity (i.e., dissimilarity of the features and attributes of a new update from those of previous updates) is associated with higher performance, especially in mature apps. We also find that the greater the product update market orientation (i.e., the greater the similarity of the focal firm’s new features and attributes with respect to the recent additions of its competitors), the higher is the market performance. This finding suggests that the market rewards those developers who have a responsive policy to their competitors’ product innovation efforts. Our results also suggest that a rapid introduction of updates dampens the potential market benefits that the mobile app developers might gain from market orientation. We find no evidence of a beneficial effect of product update scope (i.e., incorporating features and attributes from other product subcategories) on market performance. Managerial implications: Our study offers managerial insights into mobile app development by exploring the sequential innovation characteristics that are associated with greater market success in pursuing and implementing new features and attributes.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"22 1","pages":"182-199"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82631757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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