Thought Leadership

Estimating the hazard function of customer patience time has become a necessary component of effective operational planning such as workforce staffing and scheduling in call centers. When customers get served, their patience times are right-censored. In addition, the exact event times in call centers are sometimes unobserved and naturally binned into time intervals, due to the design of data collection systems. We develop a TunT (Transform-unTransform) estimator that turns the difficult problem of nonparametric hazard function estimation into a regression problem on binned and right-censored data. Our approach starts with binning event times and transforming event count data with a mean-matching transformation, which enables a simpler characterization of the heteroscedastic variance function. A nonparametric regression technique is then applied to the transformed data. Finally, the estimated regression function is back-transformed to yield an estimator for the original hazard function. The proposed estimation procedure is illustrated using call center data to reveal interesting customer patience behavior, and health insurance plan trial data to compare the effect between treatment and control groups. The numerical study shows that our approach yields more accurate estimates and better staffing decisions than existing methods.

Using a comprehensive sample of credit card data from a leading Chinese bank, we show that government bureaucrats receive 16% higher credit lines than non-bureaucrats with similar income and demographics, but their accounts experience a significantly higher likelihood of delinquency and debt forgiveness. Regions associated with greater credit provision to bureaucrats open more branches and receive more deposits from the local government. After staggered corruption crackdowns of provincial-level political officials, the new credit cards originated to bureaucrats in exposed regions do not enjoy a credit line premium, and bureaucrats’ delinquency and reinstatement rates are similar to those of non-bureaucrats.

We consider a firm consisting of two divisions, one responsible for designing and manufacturing new products and the other responsible for remanufacturing operations. The firm will sell these new and remanufactured products either directly to the consumer (direct selling) or through an independent retailer (indirect selling). Our study demonstrates that a firm’s organizational structure can affect its marketing decisions. Specifically, a decentralized firm with separate manufacturing and remanufacturing divisions can benefit from indirect selling with higher firm profit, supply chain profit, and total consumer demand than direct selling. Moreover, this structure also induces a remanufacturable product design. In contrast, a centralized firm in which the manufacturing and remanufacturing divisions are consolidated is intuitively better off by choosing direct selling than indirect selling. Furthermore, we show that, surprisingly, when the focal firm sells through an independent retailer, a decentralized internal structure can result in higher supply chain profit than a centralized internal structure. We further investigate the case of dual dedicated channels and conclude that, while direct selling of remanufactured products and indirect selling of new products can better induce a remanufacturable product design and higher supply chain profit, it is not in the best interest of the firm in terms of total sales and firm profit.