Knowledge that Transforms

Problem definition: Most industrial innovations consist of multiple interacting components that must be combined into a final product to form an integrated system. When procuring such complex innovations from their suppliers, buyers face a substantial challenge: Should they tender the integrated system as a whole or, instead, procure the system’s components individually from (possibly) different suppliers? Academic/practical relevance: Suppliers have become a major source of innovation for many firms, so buyers ponder how best to structure their procurement efforts. This paper offers guidance on how complex industrial products can be optimally sourced when the buyer’s primary goal is to identify and incentivize innovation. In contrast, most previous research on procurement has concentrated on cost-reduction efforts. Methodology: We use a game-theoretic model to design and compare different procurement mechanisms that allow a buying firm to tap into the innovation potential of its supplier base. Results: Three factors largely determine whether a buyer should procure an integrated system or individual components—namely, the novelty of the innovation to be sourced, the size of the supplier base, and the extent and distribution of component integration costs. We demonstrate how buyers can improve the effectiveness of procurement mechanisms via active supplier management. Managerial implications: Our study provides clear guidelines for managers seeking to source complex industrial innovation. The recommendations presented here reveal how the optimal procurement mechanism changes with the characteristics of the desired product and of the buyer’s supplier base.

Problem definition: We consider the problem faced by a welfare-maximizing service provider who must make a decision on how to split a fixed quantity of resources between two variants of the service: a standard variant and an expedited variant. The service is mandatory, but customers can choose between the two variants. Choosing the expedited variant requires enrollment that incurs a fixed cost per period. Customers are strategic and have the same cost of waiting but are heterogeneous in the rate at which they use the service. Academic/practical relevance: The option of expedited security at U.S. airports (TSA PreCheck) is an instance where this problem arises. As has been the case with the PreCheck program, providers that offer expedited service may face criticism from customers, with the main concern being that the diversion of resources to expedited services increases wait time for regular customers. This has important policy implications for the provider, especially a government organization such as the TSA. Existing literature has focused on service differentiation as a means to maximize profit or overall social welfare, but its effect on individual customers has received little attention. Methodology: We find customer’s equilibrium decisions for any allocation choice made by the provider. Using the equilibrium result, we solve for the allocation choice that maximizes social welfare. Results: Even when customers behave strategically, an expedited service offered in parallel to a standard service cannot only increase overall welfare, but also do so for each customer individually. We also find that in a scenario where some customers lose out because of the expedited service, improving the efficiency of the expedited service is more effective than decreasing the enrollment cost to help those who are worse off. Managerial implications: The gains from offering expedited service do not have to come at the expense of regular customers. When they do, we provide recommendations for which decision levers are most effective at making the system fair.

Problem definition: Inspired by India’s challenges in importing pulses, we study the negotiation of government-to-government food importing contracts, with a focus on ad hoc and forward negotiations with multiple suppliers (henceforth referred to as multiple-sourcing negotiations). Academic/practical relevance: We are the first to comprehensively study ad hoc and forward multiple-sourcing negotiations for food importing. Such problems are widespread, especially in developing nations, and thus the research can be relevant to the wellbeing of large underprivileged populations. Methodology: We develop an analytical negotiation model in the Nash bargaining framework and adopt the Nash-in-Nash framework to analyze multiple-sourcing negotiations. Results: We find that while forward negotiations are not necessarily better than ad hoc negotiations for the buyer, it would be true with sufficiently many suppliers. When facing a supplier pool, we show that it may be optimal to mix forward and ad hoc suppliers. In general, fewer suppliers should be assigned as ad hoc as the pool size increases. We also find that adding a hybrid supplier (engaged in a forward negotiation with an ad hoc negotiation as the fallback option) may be better or worse than adding a forward supplier in the presence of other suppliers. Managerial implications: Our findings inform how a food importer should strategically utilize ad hoc and forward negotiations with its suppliers to improve the outcome. The work may help countries’ food importing policymaking and strategies and may improve the wellbeing of large underprivileged populations.

Problem definition: Putting customer experience at the heart of service design has become a governing principle of today’s “experience economy.” Echoing this principle, our paper addresses a service designer’s problem of how to select and sequence activities in designing a service package. Academic/practical relevance: Empirical literature shows an ideal sequence often entails an interior peak; that is, the peak (i.e., highest-utility) activity is placed neither at the beginning nor the end of the package. Theoretic literature, by contrast, advocates placing the peak activity either at the beginning or at the end. Our paper bridges this gap by developing a theory accounting for interior peaks. It also provides managerial implications for activity sequencing and selection. Methodology: We model the activity sequencing and selection problem as a nonlinear optimization problem and reformulate its objective as an additive function to generate structural insights. Results: We show that heterogeneity in memory decay explains the phenomenon of interior peaks. The optimal sequence is in either an “IU” or “UI” shape. An interior peak is optimal when the memory decay rate of the peak activity is neither too high nor too low. Managerial implications: Our research sheds light on service sequencing by weighing the phenomenon of interior peaks. In the presence of an interior peak, we show it is optimal to schedule a low point immediately before or after the peak activity, creating a contrast in customer experience. In addition, interior peaks arise partly because the peak activity is more memorable than others. Guided by this logic, as the peak activity becomes even more memorable, one might be tempted to move it to an earlier slot; we show that, counterintuitively, moving it to a later slot can be optimal. Our research also provides implications for activity selection by showing the optimal portfolio may consist of activities with the highest- and lowest-utility values but not those with medium values.

Problem definition: We investigate the effectiveness of different extended producer responsibility (EPR) implementation models for pharmaceuticals. In particular, we study two viable and prevalent models: (1) source reduction (SR), where a form of fee on sale is imposed on producers, and (2) end-of-pipe control (EC), where producers are made responsible for the collection of unused pharmaceuticals. Academic/practical relevance: The existing literature on EPR implementation models has focused primarily on nonconsumable products (e.g., electronics), whereas there is limited research on the effectiveness of different EPR implementation models for pharmaceuticals used in practice. We aim to fill this gap in this study. Methodology: We develop a game-theoretic model to characterize the equilibrium strategies of different stakeholders under both the SR and EC models and compare the resulting producer profit, environmental/social impact, and total welfare. Results: In contrast to the nonconsumable contexts where the SR model is shown to maximize total welfare, the EC model leads to a higher total welfare for certain categories of pharmaceuticals because of its effectiveness in eliminating overprescription. Moreover, we characterize conditions under which stakeholder (e.g., producer, environmental/social advocacy groups) preferences toward EPR implementation model choices are (mis-)aligned. We further show that limiting the social planner’s budget surplus under SR can eliminate the preference misalignment but leads to a loss of total welfare. Managerial implications: (1) Policymakers should be cautious about directly applying preferred EPR models from other product categories to the pharmaceutical setting. (2) The EC model maximizes the objectives of all stakeholders for a salient category of pharmaceuticals with high health benefits, high collection costs, and high environmental/social costs. (3) Policymakers should give thought to differentiating EPR implementation models across pharmaceutical categories. (4) It is important to carefully quantify the health impact of the pharmaceuticals and the operational cost parameters to inform policymaking.

Problem definition: “Slugging,” or casual carpooling, refers to the commuting practice of drivers picking up passengers at designated locations and offering them a free ride in order to qualify for high-occupancy vehicle (HOV) lanes. Academic/practical relevance: It is estimated that tens of thousands of daily commuters rely on slugging to go to work in major U.S. cities. As drivers save commute time and passengers ride for free, slugging can be a promising Smart Mobility solution. However, little is known about the welfare, policy, and environmental implications of slugging. Methodology: We develop a stylized model that captures the essence of slugging. We characterize commuters’ equilibrium behavior in the model. Results: We find that slugging indeed makes commuters better off. However, the widely observed free-ride tradition is socially suboptimal. As compared with the social optimum, commuters always underslug in the free-slugging equilibrium when highway travel time is insensitive to slugging activities but may overslug otherwise. The socially optimal outcome can be achieved by allowing pecuniary exchanges between drivers and passengers. Interestingly, passengers may be better off if they pay for a ride than if they do not under free slugging. We also find that although policy initiatives to expand highway capacity or improve public transportation always increase social welfare in the absence of slugging, they may reduce social welfare in areas where free slugging is a major commuting choice. Nevertheless, these unintended consequences would be mitigated by the introduction of pecuniary exchanges. Finally, contrary to conventional wisdom, slugging as a form of carpooling can result in more cars on the road and thus, more carbon emissions. Managerial implications: Our results call upon the slugging community to rethink the free-ride practice. We also caution that slugging benefits commuters possibly to the detriment of the environment.

Problem definition: This paper studies pricing and assortment management for cross-category products, a common practice in brick-and-mortar retailing and e-tailing. Academic/practical relevance: We investigate the complementarity effects between the main products and the secondary products, in addition to the substitution effects for products in the same category. Methodology: In this paper, we develop a multistage sequential choice model, under which a consumer first chooses a main product and then selects a secondary product. The new model can alleviate the restriction of the independence of irrelevant alternatives property and allows more flexible substitution patterns and also takes into account complementarity effects. Results: We characterize the impact of the magnitude of complementarity effects on pricing and assortment management. For the problems that are hard to solve optimally, we propose simple heuristics and establish performance guarantee. In addition, we develop easy-to-implement estimation algorithms to calibrate the proposed sequential choice model by using sales data. Managerial implications: We show that ignoring or mis-specifying complementarity effects may lead to substantial losses. The methodologies on modeling, optimization, and estimation have potential to make an impact on cross-category retailing management.

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.

Problem Definition: We develop a framework to study purchase behavior from distinct segments of heterogeneous customers and to optimize prices for different policies in a sports ticket market with multiple sales channels. Academic/Practical Relevance: Sports teams face challenges in maintaining or increasing ticket sales levels. With the growth of analytics, they aim to implement data-driven pricing techniques to improve gate revenues; however, they do not have state-of-the-art demand estimation and price optimization tools that take into account the range of valuations across different seat sections and opponent match-ups. Methodology: Partnering with a college football team, we develop a data-driven pricing tool which (1) segments customers in two sales channels, using transaction-level data and anonymous customer profiles; (2) explores the decision-making process of different customers within these segments using the Multinomial Logit and Mixed Multinomial Logit frameworks; and (3) computes optimal or near-optimal prices subject to some business constraints enforced by the team management. In addition, our method takes the sequential arrivals of customers and the capacity constraints of seat categories into account. Results: Our estimation results show that customers differ significantly in their sensitivities to price and distance to the field within each segment, in addition to the differences across segments. We also observe that customers become less likely to choose a seat category as its remaining inventory falls below a certain point. Managerial Implications: By analyzing different policies, we show that price optimization could increase revenue by as much as 7.6%. In addition, better categorization of games and further refinement of seat category differentiation and related pricing may help further boost this figure up to 11.9%.

Problem definition: To approve a novel drug therapy, the U.S. Food and Drug Administration (FDA) requires clinical trial evidence demonstrating efficacy with 2.5% statistical significance, although the agency often uses regulatory discretion when interpreting these standards. Factors including disease severity, prevalence, and availability of existing therapies are qualitatively considered; yet, current guidelines fail to systematically consider such characteristics in approval decisions. Academic/practical relevance: In making approval decisions, the FDA weighs the health benefits of introducing life-saving therapies against the potential risks of approving ineffective or harmful drugs. Tailoring approval standards to individual diseases could improve treatment options for patients with few alternatives and further incentivize pharmaceutical companies to invest in neglected diseases. Methodology: Using a novel queueing framework, we analyze the FDA’s drug approval process to incorporate disease-specific factors and obsolescence—newer drugs replacing older formulas—through a set of pre-emptive M/M/1/1 queues. Based on public data encompassing all registered U.S. clinical trials and FDA-approved drugs, we estimate model parameters for three high-burden diseases (breast cancer, human immunodeficiency virus (HIV), and hypertension) and solve for the optimal policy to maximize net life-years gained following FDA approval. Results: The optimal policy relaxes approval standards for diseases with long trial duration, high attrition, or low research and development intensity. Results indicate that a more lenient policy is warranted for drugs targeting breast cancer or hypertension, and a more stringent policy is recommended for HIV, relative to the FDA’s existing policy. If pharmaceutical firms respond to the new standards by submitting more drugs for approval—leading to an endogenous clinical trial initiation rate—the FDA’s optimal policy modestly decreases for breast cancer and hypertension, with minimal change for HIV. Managerial implications: Our study offers a transparent, quantitative framework that could help the FDA develop disease-specific approval guidelines based on underlying disease-related severity, prevalence, and characteristics of the drug development process and existing market.