Problem definition: Customers are likely to initiate retrial calls when their previous contact with a call center fails to deliver a satisfactory resolution. According to industry reports, retrials are listed as a top annoying issue for customers and hurt call centers’ profits. Though recognizing this problem, call centers find it challenging to reduce retrials without overshooting their operating expenses. Our research aims to empirically understand the mechanism of customer retrials and then provide economically feasible solutions to reduce retrials. Academic/practical relevance: Little empirical research has been done to understand customers’ strategic retrials, and theoretical research studies retrials by assuming the degree to which pickup speed and service quality impact retrials. Our research empirically investigates the mechanism of customer retrials by studying whether speed and quality truly matter and, if so, how strong the impact is from each of them and whether the impacts are different across various customer segments. The quantified mechanism can then guide service providers to reduce retrials cost-effectively. Methodology: We use a random-coefficient dynamic structural model to characterize customer decisions in pursuing a satisfactory resolution and estimate the parameters from call-by-call records of a uniquely designed call center. Our model tracks customer decisions in the online waiting stage, in which customers are waiting for an agent but weighing whether to abandon, and in the off-line waiting stage, in which customers are not directly connected but are actively debating whether to retry. Utilizing the hybrid system that sequentially places customers into queues for three distinct quality service groups, we disentangle the effects of pickup speed and service quality on customers’ abandonment and retrial decisions. Results: Our estimations confirm that high service quality and quick pickup speed reduce retrials. Moreover, we discover that private customers are more sensitive to quality but less sensitive to speed compared with business customers. We suggest two service designs to reduce retrials cost-effectively by tailoring services to customer preferences. One reallocates the service groups for different customer segments without expanding the system, and the other adjusts the staffing ratios by hiring low-cost, ordinary-quality agents. Under the two tailoring designs, business customer surplus increases by up to 14.4% and private customer surplus by up to 14.9%. Managerial implications: First, our research highlights the importance of recognizing customers’ off-line decisions, which are impacted by online service offerings and, in turn, affect future online service operations. Neglecting customer retrials leads to suboptimal service designs. Second, by understanding the mechanism of customer retrials empirically, our research guides call centers to reduce retrials cost-effectively with speed–quality balance. Third, our r
{"title":"Understanding Customer Retrials in Call Centers: Preferences for Service Quality and Service Speed","authors":"K. Hu, Gad Allon, Achal Bassamboo","doi":"10.1287/MSOM.2021.0976","DOIUrl":"https://doi.org/10.1287/MSOM.2021.0976","url":null,"abstract":"Problem definition: Customers are likely to initiate retrial calls when their previous contact with a call center fails to deliver a satisfactory resolution. According to industry reports, retrials are listed as a top annoying issue for customers and hurt call centers’ profits. Though recognizing this problem, call centers find it challenging to reduce retrials without overshooting their operating expenses. Our research aims to empirically understand the mechanism of customer retrials and then provide economically feasible solutions to reduce retrials. Academic/practical relevance: Little empirical research has been done to understand customers’ strategic retrials, and theoretical research studies retrials by assuming the degree to which pickup speed and service quality impact retrials. Our research empirically investigates the mechanism of customer retrials by studying whether speed and quality truly matter and, if so, how strong the impact is from each of them and whether the impacts are different across various customer segments. The quantified mechanism can then guide service providers to reduce retrials cost-effectively. Methodology: We use a random-coefficient dynamic structural model to characterize customer decisions in pursuing a satisfactory resolution and estimate the parameters from call-by-call records of a uniquely designed call center. Our model tracks customer decisions in the online waiting stage, in which customers are waiting for an agent but weighing whether to abandon, and in the off-line waiting stage, in which customers are not directly connected but are actively debating whether to retry. Utilizing the hybrid system that sequentially places customers into queues for three distinct quality service groups, we disentangle the effects of pickup speed and service quality on customers’ abandonment and retrial decisions. Results: Our estimations confirm that high service quality and quick pickup speed reduce retrials. Moreover, we discover that private customers are more sensitive to quality but less sensitive to speed compared with business customers. We suggest two service designs to reduce retrials cost-effectively by tailoring services to customer preferences. One reallocates the service groups for different customer segments without expanding the system, and the other adjusts the staffing ratios by hiring low-cost, ordinary-quality agents. Under the two tailoring designs, business customer surplus increases by up to 14.4% and private customer surplus by up to 14.9%. Managerial implications: First, our research highlights the importance of recognizing customers’ off-line decisions, which are impacted by online service offerings and, in turn, affect future online service operations. Neglecting customer retrials leads to suboptimal service designs. Second, by understanding the mechanism of customer retrials empirically, our research guides call centers to reduce retrials cost-effectively with speed–quality balance. Third, our r","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"15 1","pages":"1002-1020"},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83802588","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}
As the nature of work has become more service oriented, knowledge intensive, and rapidly changing, people—be they workers or customers—have become more central to operational processes and have impacted operational outcomes in novel and perhaps more fundamental ways. Research in people-centric operations (PCO) studies how people affect the performance of operational processes. In this OM Forum, we define PCO as an area of study, offer a categorization scheme to take stock of where the field has allocated its attention to date, and offer our thoughts on promising directions for future research. The future of PCO is bright: Thanks to today’s availability of granular data, PCO researchers have numerous and growing opportunities to study, from both descriptive and prescriptive angles, the link between people’s behavior and operational performance.
{"title":"OM Forum - People-Centric Operations: Achievements and Future Research Directions","authors":"G. Roels, B. Staats","doi":"10.1287/MSOM.2021.0977","DOIUrl":"https://doi.org/10.1287/MSOM.2021.0977","url":null,"abstract":"As the nature of work has become more service oriented, knowledge intensive, and rapidly changing, people—be they workers or customers—have become more central to operational processes and have impacted operational outcomes in novel and perhaps more fundamental ways. Research in people-centric operations (PCO) studies how people affect the performance of operational processes. In this OM Forum, we define PCO as an area of study, offer a categorization scheme to take stock of where the field has allocated its attention to date, and offer our thoughts on promising directions for future research. The future of PCO is bright: Thanks to today’s availability of granular data, PCO researchers have numerous and growing opportunities to study, from both descriptive and prescriptive angles, the link between people’s behavior and operational performance.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"18 1","pages":"745-757"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78772839","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 work focuses on the hotel housekeeping process. In a field study, a possible channel of disease transmission between consecutive guests in hotel rooms is revealed. In order to prevent the transmission, an innovative assembly-line housekeeping method is developed. Academic/practical relevance: The transmission of infectious diseases during hotel stays (e.g., by touching unclean towels or bed linens) has been reported globally. Under the current COVID-19 pandemic, having contact with saliva or mucus left by an infected person could cause infection. The standard housekeeping process used by the majority of hotels leaves a channel for new towels and bed linens in refreshed rooms to be contaminated by bacteria or viruses from used towels and bed linens. Eliminating the contamination channel and preventing disease transmission are crucial for protecting the health and safety of hotel guests, especially under a disease outbreak such as the current COVID-19 pandemic. Methodology: The research was conducted during a field study at a hotel. To design the assembly-line process, the service time distribution of each housekeeping operational step is characterized using data collected from the practice at hundreds of hotel rooms. An optimization model is proposed to optimize the operation. Through a pilot test, the performance of the assembly-line and the traditional housekeeping methods is compared. Results: The pilot test results show that the assembly-line housekeeping method has the potential to improve not only hygienic standards but also, labor efficiency and service quality (error rate). Managerial implications: The outbreak of the COVID-19 pandemic draws tremendous public attention on disease transmission and public hygiene. The principle of the assembly-line method (i.e., eliminating contamination channels through teamwork operational design) can be applied to not only hotel housekeeping practices but also, many other service settings. It leads to hygienic, efficient, and reliable operations, at no additional cost.
{"title":"Divide and Conquer: A Hygienic, Efficient, and Reliable Assembly Line for Housekeeping","authors":"Xiao Chen, Rowan Wang, Jianghua Zhang","doi":"10.1287/MSOM.2021.0984","DOIUrl":"https://doi.org/10.1287/MSOM.2021.0984","url":null,"abstract":"Problem definition: This work focuses on the hotel housekeeping process. In a field study, a possible channel of disease transmission between consecutive guests in hotel rooms is revealed. In order to prevent the transmission, an innovative assembly-line housekeeping method is developed. Academic/practical relevance: The transmission of infectious diseases during hotel stays (e.g., by touching unclean towels or bed linens) has been reported globally. Under the current COVID-19 pandemic, having contact with saliva or mucus left by an infected person could cause infection. The standard housekeeping process used by the majority of hotels leaves a channel for new towels and bed linens in refreshed rooms to be contaminated by bacteria or viruses from used towels and bed linens. Eliminating the contamination channel and preventing disease transmission are crucial for protecting the health and safety of hotel guests, especially under a disease outbreak such as the current COVID-19 pandemic. Methodology: The research was conducted during a field study at a hotel. To design the assembly-line process, the service time distribution of each housekeeping operational step is characterized using data collected from the practice at hundreds of hotel rooms. An optimization model is proposed to optimize the operation. Through a pilot test, the performance of the assembly-line and the traditional housekeeping methods is compared. Results: The pilot test results show that the assembly-line housekeeping method has the potential to improve not only hygienic standards but also, labor efficiency and service quality (error rate). Managerial implications: The outbreak of the COVID-19 pandemic draws tremendous public attention on disease transmission and public hygiene. The principle of the assembly-line method (i.e., eliminating contamination channels through teamwork operational design) can be applied to not only hotel housekeeping practices but also, many other service settings. It leads to hygienic, efficient, and reliable operations, at no additional cost.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"65 1","pages":"938-955"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85003979","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}
The last decade has seen rapid growth in business models built around digital platforms that bring together buyers and sellers to interact and trade in new and innovative ways. This growth has been fueled by pervasive internet and increased access to smart, connected, and mobile devices. Some of these platforms have been successful in overcoming the inefficiencies of peer-to-peer interactions by reducing transaction and search costs, facilitating payments, reducing moral hazard, and enabling trust among strangers. Others have been successful in reducing the costs of providing services on demand by harnessing economies of scale, tapping into idle assets, or leveraging the crowd. This has also led to the adoption of business models of products as services built around selling a product’s functionality instead of the product itself. These business models have ushered in new forms of economic interactions that have been alternatively referred to as the “sharing economy,” “on-demand economy,” and “platform economy.” In all cases, these new forms of economic interactions are moving economic activity away from traditional firms to innovative marketplaces where the buyers and sellers of products and services aremany and engage inmany small transactions. This special issue features emerging research in operations management (OM) that is beginning to study these new forms of economic activity, the associated business models, and the underlying operational processes. For the OM community to be at the forefront of the study of this new economy is perhaps natural, given that the central challenge for these innovative marketplaces is one involving the efficient matching of supply and demand. It has been exciting to see the rapid growth in research in this area over the last five years and the growing consensus that this area will be core to the development of OM research in the future. The call for papers for this special issue was announced in August 2017. We received 73 submissions. Ten of those papers were accepted in time to be included in this special issue, with four more still in the review process and may be published in a future regular issue. The papers included in this issue cover a lot of ground and illustrate the wide range of applications, research questions, and research methodologies being deployed, including papers grounded in analytical modeling, empirical evidence, and laboratory experiments. Below, we briefly comment on each of the papers.
{"title":"Introduction to the Special Issue on Sharing Economy and Innovative Marketplaces","authors":"S. Benjaafar, Ming Hu","doi":"10.1287/msom.2021.0998","DOIUrl":"https://doi.org/10.1287/msom.2021.0998","url":null,"abstract":"The last decade has seen rapid growth in business models built around digital platforms that bring together buyers and sellers to interact and trade in new and innovative ways. This growth has been fueled by pervasive internet and increased access to smart, connected, and mobile devices. Some of these platforms have been successful in overcoming the inefficiencies of peer-to-peer interactions by reducing transaction and search costs, facilitating payments, reducing moral hazard, and enabling trust among strangers. Others have been successful in reducing the costs of providing services on demand by harnessing economies of scale, tapping into idle assets, or leveraging the crowd. This has also led to the adoption of business models of products as services built around selling a product’s functionality instead of the product itself. These business models have ushered in new forms of economic interactions that have been alternatively referred to as the “sharing economy,” “on-demand economy,” and “platform economy.” In all cases, these new forms of economic interactions are moving economic activity away from traditional firms to innovative marketplaces where the buyers and sellers of products and services aremany and engage inmany small transactions. This special issue features emerging research in operations management (OM) that is beginning to study these new forms of economic activity, the associated business models, and the underlying operational processes. For the OM community to be at the forefront of the study of this new economy is perhaps natural, given that the central challenge for these innovative marketplaces is one involving the efficient matching of supply and demand. It has been exciting to see the rapid growth in research in this area over the last five years and the growing consensus that this area will be core to the development of OM research in the future. The call for papers for this special issue was announced in August 2017. We received 73 submissions. Ten of those papers were accepted in time to be included in this special issue, with four more still in the review process and may be published in a future regular issue. The papers included in this issue cover a lot of ground and illustrate the wide range of applications, research questions, and research methodologies being deployed, including papers grounded in analytical modeling, empirical evidence, and laboratory experiments. Below, we briefly comment on each of the papers.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"48 1","pages":"549-552"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79728934","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 data set contains 187 consecutive weeks of Intel microprocessor demand information for all five distribution centers in one of its five sales geographies. For every stock keeping unit (SKU) at every location, the weekly forecasted demand and actual customer orders are provided as well as the SKU’s average selling price category. These data are provided by week and by distribution center, producing 26,114 records in total. Academic/practical relevance: The 86 SKUs in the data set span five product generations. It provides years of product evolution across generations and price points. Methodology: As a data set paper, its purpose is to provide interesting and rich real-world data for researchers developing forecasting, inventory, pricing, and product assortment models. Results: The data set demonstrates the presence of significant forecast bias, heterogeneity of forecast errors between distribution centers, generational differences, product life cycles, and pricing dynamics. Managerial implications: This data set provides access to a rich pricing and sales setting from a major corporation that has not been made available before.
{"title":"Data Set: 187 Weeks of Customer Forecasts and Orders for Microprocessors from Intel Corporation","authors":"Matthew P. Manary, S. Willems","doi":"10.1287/MSOM.2020.0933","DOIUrl":"https://doi.org/10.1287/MSOM.2020.0933","url":null,"abstract":"Problem definition: This data set contains 187 consecutive weeks of Intel microprocessor demand information for all five distribution centers in one of its five sales geographies. For every stock keeping unit (SKU) at every location, the weekly forecasted demand and actual customer orders are provided as well as the SKU’s average selling price category. These data are provided by week and by distribution center, producing 26,114 records in total. Academic/practical relevance: The 86 SKUs in the data set span five product generations. It provides years of product evolution across generations and price points. Methodology: As a data set paper, its purpose is to provide interesting and rich real-world data for researchers developing forecasting, inventory, pricing, and product assortment models. Results: The data set demonstrates the presence of significant forecast bias, heterogeneity of forecast errors between distribution centers, generational differences, product life cycles, and pricing dynamics. Managerial implications: This data set provides access to a rich pricing and sales setting from a major corporation that has not been made available before.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"14 1","pages":"682-689"},"PeriodicalIF":0.0,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73508141","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}
{"title":"Statement of the Manufacturing & Service Operations Management Journal","authors":"G. Perakis","doi":"10.1287/MSOM.2021.0990","DOIUrl":"https://doi.org/10.1287/MSOM.2021.0990","url":null,"abstract":"","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"69 1","pages":"547-548"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77896371","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: Clinical trials are crucial to new drug development. This study investigates optimal patient enrollment in clinical trials with interim analyses, which are analyses of treatment responses from patients at intermediate points. Our model considers uncertainties in patient enrollment and drug treatment effectiveness. We consider the benefits of completing a trial early and the cost of accelerating a trial by maximizing the net present value of drug cumulative profit. Academic/practical relevance: Clinical trials frequently account for the largest cost in drug development, and patient enrollment is an important problem in trial management. Our study develops a dynamic program, accurately capturing the dynamics of the problem, to optimize patient enrollment while learning the treatment effectiveness of an investigated drug. Methodology: The model explicitly captures both the physical state (enrolled patients) and belief states about the effectiveness of the investigated drug and a standard treatment drug. Using Bayesian updates and dynamic programming, we establish monotonicity of the value function in state variables and characterize an optimal enrollment policy. We also introduce, for the first time, the use of backward approximate dynamic programming (ADP) for this problem class. We illustrate the findings using a clinical trial program from a leading firm. Our study performs sensitivity analyses of the input parameters on the optimal enrollment policy. Results: The value function is monotonic in cumulative patient enrollment and the average responses of treatment for the investigated drug and standard treatment drug. The optimal enrollment policy is nondecreasing in the average response from patients using the investigated drug and is nonincreasing in cumulative patient enrollment in periods between two successive interim analyses. The forward ADP algorithm (or backward ADP algorithm) exploiting the monotonicity of the value function reduced the run time from 1.5 months using the exact method to a day (or 20 minutes) within 4% of the exact method. Through an application to a leading firm’s clinical trial program, the study demonstrates that the firm can have a sizable gain of drug profit following the optimal policy that our model provides. Managerial implications: We developed a new model for improving the management of clinical trials. Our study provides insights of an optimal policy and insights into the sensitivity of value function to the dropout rate and prior probability distribution. A firm can have a sizable gain in the drug’s profit by managing its trials using the optimal policies and the properties of value function. We illustrated that firms can use the ADP algorithms to develop their patient enrollment strategies.
{"title":"Adaptive Learning of Drug Quality and Optimization of Patient Recruitment for Clinical Trials with Dropouts","authors":"Zhili Tian, Weidong Han, Warrren B Powell","doi":"10.1287/MSOM.2020.0936","DOIUrl":"https://doi.org/10.1287/MSOM.2020.0936","url":null,"abstract":"Problem definition: Clinical trials are crucial to new drug development. This study investigates optimal patient enrollment in clinical trials with interim analyses, which are analyses of treatment responses from patients at intermediate points. Our model considers uncertainties in patient enrollment and drug treatment effectiveness. We consider the benefits of completing a trial early and the cost of accelerating a trial by maximizing the net present value of drug cumulative profit. Academic/practical relevance: Clinical trials frequently account for the largest cost in drug development, and patient enrollment is an important problem in trial management. Our study develops a dynamic program, accurately capturing the dynamics of the problem, to optimize patient enrollment while learning the treatment effectiveness of an investigated drug. Methodology: The model explicitly captures both the physical state (enrolled patients) and belief states about the effectiveness of the investigated drug and a standard treatment drug. Using Bayesian updates and dynamic programming, we establish monotonicity of the value function in state variables and characterize an optimal enrollment policy. We also introduce, for the first time, the use of backward approximate dynamic programming (ADP) for this problem class. We illustrate the findings using a clinical trial program from a leading firm. Our study performs sensitivity analyses of the input parameters on the optimal enrollment policy. Results: The value function is monotonic in cumulative patient enrollment and the average responses of treatment for the investigated drug and standard treatment drug. The optimal enrollment policy is nondecreasing in the average response from patients using the investigated drug and is nonincreasing in cumulative patient enrollment in periods between two successive interim analyses. The forward ADP algorithm (or backward ADP algorithm) exploiting the monotonicity of the value function reduced the run time from 1.5 months using the exact method to a day (or 20 minutes) within 4% of the exact method. Through an application to a leading firm’s clinical trial program, the study demonstrates that the firm can have a sizable gain of drug profit following the optimal policy that our model provides. Managerial implications: We developed a new model for improving the management of clinical trials. Our study provides insights of an optimal policy and insights into the sensitivity of value function to the dropout rate and prior probability distribution. A firm can have a sizable gain in the drug’s profit by managing its trials using the optimal policies and the properties of value function. We illustrated that firms can use the ADP algorithms to develop their patient enrollment strategies.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"37 1 1","pages":"580-599"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75945889","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: For the standard newsvendor problem with an unknown demand distribution, we develop an approach that uses data input to construct a distribution ambiguity set with the nonparametric characteristics of the true distribution, and we use it to make robust decisions. Academic/practical relevance: Empirical approach relies on historical data to estimate the true distribution. Although the estimated distribution converges to the true distribution, its performance with limited data is not guaranteed. Our approach generates robust decisions from a distribution ambiguity set that is constructed by data-driven estimators for nonparametric characteristics and includes the true distribution with the desired probability. It fits situations where data size is small. Methodology: We apply a robust optimization approach with nonparametric information. Results: Under a fixed method to partition the support of the demand, we construct a distribution ambiguity set, build a protection curve as a proxy for the worst-case distribution in the set, and use it to obtain a robust stocking quantity in closed form. Implementation-wise, we develop an adaptive method to continuously feed data to update partitions with a prespecified confidence level in their unbiasedness and adjust the protection curve to obtain robust decisions. We theoretically and experimentally compare the proposed approach with existing approaches. Managerial implications: Our nonparametric approach under adaptive partitioning guarantees that the realized average profit exceeds the worst-case expected profit with a high probability. Using real data sets from Kaggle.com, it can outperform existing approaches in yielding profit rate and stabilizing the generated profits, and the advantages are more prominent as the service ratio decreases. Nonparametric information is more valuable than parametric information in profit generation provided that the service requirement is not too high. Moreover, our proposed approach provides a means of combining nonparametric and parametric information in a robust optimization framework.
{"title":"A Robust Data-Driven Approach for the Newsvendor Problem with Nonparametric Information","authors":"Liang Xu, Yi Zheng, Li Jiang","doi":"10.1287/MSOM.2020.0961","DOIUrl":"https://doi.org/10.1287/MSOM.2020.0961","url":null,"abstract":"Problem definition: For the standard newsvendor problem with an unknown demand distribution, we develop an approach that uses data input to construct a distribution ambiguity set with the nonparametric characteristics of the true distribution, and we use it to make robust decisions. Academic/practical relevance: Empirical approach relies on historical data to estimate the true distribution. Although the estimated distribution converges to the true distribution, its performance with limited data is not guaranteed. Our approach generates robust decisions from a distribution ambiguity set that is constructed by data-driven estimators for nonparametric characteristics and includes the true distribution with the desired probability. It fits situations where data size is small. Methodology: We apply a robust optimization approach with nonparametric information. Results: Under a fixed method to partition the support of the demand, we construct a distribution ambiguity set, build a protection curve as a proxy for the worst-case distribution in the set, and use it to obtain a robust stocking quantity in closed form. Implementation-wise, we develop an adaptive method to continuously feed data to update partitions with a prespecified confidence level in their unbiasedness and adjust the protection curve to obtain robust decisions. We theoretically and experimentally compare the proposed approach with existing approaches. Managerial implications: Our nonparametric approach under adaptive partitioning guarantees that the realized average profit exceeds the worst-case expected profit with a high probability. Using real data sets from Kaggle.com, it can outperform existing approaches in yielding profit rate and stabilizing the generated profits, and the advantages are more prominent as the service ratio decreases. Nonparametric information is more valuable than parametric information in profit generation provided that the service requirement is not too high. Moreover, our proposed approach provides a means of combining nonparametric and parametric information in a robust optimization framework.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"57 1","pages":"504-523"},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80438874","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: Considering a mix of prepositioning and local purchasing, common to cover humanitarian demands in the aftermath of a rapid-onset disaster, we propose policies to determine preposition stock. These formulations are developed in the presence of demand, budget, and local supply uncertainties and for single-items delivery. Academic/practical relevance: The immediate period aftermath of a disaster is the most crucial period during which humanitarian organizations must supply relief items to beneficiaries. Yet, because of many unknowns such as time, place, and magnitude of a disaster, supply management is a significant challenge, and these decisions are made intuitively. The features and complexities we examine have not been studied in the literature. Methodology: We derive properties of the optimal solution, identify exact solution methods, and determine approximate methods that are easy to implement. Results: We (i) characterize the interplay of supply, demand, and budget uncertainties, as well as the impact of product characteristics on optimal prepo stock levels; (ii) show in what conditions the prepo stock is a simple newsvendor solution; and (iii) discuss the value of emergency funds. Managerial implications: We show that budget level is a key determinant of the optimal policy. When it is above a threshold, inventory increases in disaster frequency and severity, but the reverse is true otherwise. When budget is limited, the rate of savings from improved forecasts is amplified (attenuated) for critical (noncritical) items, reflecting opposing directional effects of mismatch cost and cost of insufficient funding. Our model can also be used to estimate the value of initiatives to mitigate constraints on local spend (e.g., a line of credit underwritten by large donors that is available during the immediate relief period).
{"title":"Prepositioning and Local Purchasing for Emergency Operations Under Budget, Demand, and Supply Uncertainty","authors":"Mahyar Eftekhar, Jing-Sheng Song, S. Webster","doi":"10.1287/MSOM.2020.0956","DOIUrl":"https://doi.org/10.1287/MSOM.2020.0956","url":null,"abstract":"Problem definition: Considering a mix of prepositioning and local purchasing, common to cover humanitarian demands in the aftermath of a rapid-onset disaster, we propose policies to determine preposition stock. These formulations are developed in the presence of demand, budget, and local supply uncertainties and for single-items delivery. Academic/practical relevance: The immediate period aftermath of a disaster is the most crucial period during which humanitarian organizations must supply relief items to beneficiaries. Yet, because of many unknowns such as time, place, and magnitude of a disaster, supply management is a significant challenge, and these decisions are made intuitively. The features and complexities we examine have not been studied in the literature. Methodology: We derive properties of the optimal solution, identify exact solution methods, and determine approximate methods that are easy to implement. Results: We (i) characterize the interplay of supply, demand, and budget uncertainties, as well as the impact of product characteristics on optimal prepo stock levels; (ii) show in what conditions the prepo stock is a simple newsvendor solution; and (iii) discuss the value of emergency funds. Managerial implications: We show that budget level is a key determinant of the optimal policy. When it is above a threshold, inventory increases in disaster frequency and severity, but the reverse is true otherwise. When budget is limited, the rate of savings from improved forecasts is amplified (attenuated) for critical (noncritical) items, reflecting opposing directional effects of mismatch cost and cost of insufficient funding. Our model can also be used to estimate the value of initiatives to mitigate constraints on local spend (e.g., a line of credit underwritten by large donors that is available during the immediate relief period).","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"56 1","pages":"315-332"},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85685778","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: To improve the poor performance of supply chains caused by misaligned incentives under the wholesale price contract, theory proposes coordinating contracts. However, a common finding of experimental studies testing such contracts is that they tend to yield only a marginal, if any, performance improvement over wholesale pricing. These studies identify several behavioral factors that are at play but none accounted for by the theory proposing coordinating contracts. Among them, identified as the single most detrimental for the supply chain performance, is incomplete information about preferences for fairness causing contract rejections. Can the supply chain performance be improved with a contract designed allowing for this type of information asymmetry? What does this contract (mechanism) look like? Academic/practical relevance: The extant research characterized the optimal contracting mechanisms for such important practical cases as the suppliers’ private information about production cost or the retailers’ private information about the end customer demand. The present study addresses the gap in another important practical case: when the source of information asymmetry is the private information about preferences for fairness. Methodology: The underlying research method is mechanism design. Results: We prove that the optimal mechanism consists of a single contract positioned on the Pareto frontier and characterize the optimal profit split between the supplier and the retailer. We show that, under a wide range of preferences for fairness, the efficiency loss because of private information is strictly positive, but exceptions are possible. We also show that the optimal mechanism can be implemented with a variety of commonly used in practice and widely studied in academic literature contracts, including the minimum order quantity and the two-part tariff ones. Managerial implications: We establish a direct link between a large volume of theoretical and empirical literature on social preferences with the research on supply chain contracts. Because rejections that are because of incomplete information are an important cause of contract inefficiency observed in the laboratory, managers should avoid take it or leave it offers when they negotiate contracts. Instead, the bargaining process should be geared toward discovering the extent of the fairness preferences of the contracting parties.
{"title":"Optimal Contract Under Asymmetric Information About Fairness","authors":"V. Pavlov, Elena Katok, Wen Zhang","doi":"10.1287/MSOM.2020.0945","DOIUrl":"https://doi.org/10.1287/MSOM.2020.0945","url":null,"abstract":"Problem definition: To improve the poor performance of supply chains caused by misaligned incentives under the wholesale price contract, theory proposes coordinating contracts. However, a common finding of experimental studies testing such contracts is that they tend to yield only a marginal, if any, performance improvement over wholesale pricing. These studies identify several behavioral factors that are at play but none accounted for by the theory proposing coordinating contracts. Among them, identified as the single most detrimental for the supply chain performance, is incomplete information about preferences for fairness causing contract rejections. Can the supply chain performance be improved with a contract designed allowing for this type of information asymmetry? What does this contract (mechanism) look like? Academic/practical relevance: The extant research characterized the optimal contracting mechanisms for such important practical cases as the suppliers’ private information about production cost or the retailers’ private information about the end customer demand. The present study addresses the gap in another important practical case: when the source of information asymmetry is the private information about preferences for fairness. Methodology: The underlying research method is mechanism design. Results: We prove that the optimal mechanism consists of a single contract positioned on the Pareto frontier and characterize the optimal profit split between the supplier and the retailer. We show that, under a wide range of preferences for fairness, the efficiency loss because of private information is strictly positive, but exceptions are possible. We also show that the optimal mechanism can be implemented with a variety of commonly used in practice and widely studied in academic literature contracts, including the minimum order quantity and the two-part tariff ones. Managerial implications: We establish a direct link between a large volume of theoretical and empirical literature on social preferences with the research on supply chain contracts. Because rejections that are because of incomplete information are an important cause of contract inefficiency observed in the laboratory, managers should avoid take it or leave it offers when they negotiate contracts. Instead, the bargaining process should be geared toward discovering the extent of the fairness preferences of the contracting parties.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"49 1","pages":"305-314"},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91353253","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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