With an increasingly open global economy and advanced technologies, some third-party logistics providers (3PLs), such as Eternal Asia, have emerged as supply chain orchestrators, linking buyers with manufacturers worldwide. In addition to their traditional transportation services, these orchestrators provide procurement and financial assistance to buyers in the supply network, especially small- and medium-sized enterprises (SMEs) in developing countries. Oftentimes, the 3PLs can obtain payment delay arrangements from the financially stronger manufacturers, which in turn can be partially extended to the SME buyers, alleviating their high costs of capital. To illustrate the efficiency improvements of the aforementioned practice, we use a model to explicitly capture the cash-flow dynamics in a supply chain consisting of a manufacturer, a buyer, and a 3PL firm and explore the conditions under which this innovation benefits all parties in the supply chain so that the business model is sustainable. We characterize these conditions and show that the supply chain profit can be higher under leadership by the 3PL than by the manufacturer. The intermediary role of the 3PL is crucial, in that its benefit may vanish if the manufacturer chooses to directly grant payment delay to the buyers. We demonstrate that the benefit is more likely to occur with more buyers. We further identify the unique Nash bargaining solution for the transportation time and the payment delay grace period. The online appendix is available at https://doi.org/10.1287/msom.2017.0667 . This paper has been accepted for the Manufacturing & Service Operations Management Special Issue on Value Chain Innovations in Developing Economies.

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In cargo logistics, a key performance measure is transport risk, defined as the deviation of the actual arrival time from the planned arrival time. Neither earliness nor tardiness is desirable for the customer and freight forwarder. In this paper, we investigate ways to assess and forecast transport risks using a half-year of air cargo data, provided by a leading forwarder on 1336 routes served by 20 airlines. Interestingly, our preliminary data analysis shows a strong multimodal feature in the transport risks, driven by unobserved events, such as cargo missing flights. To accommodate this feature, we introduce a Bayesian nonparametric model -the probit stick-breaking process (PSBP) mixture model -for flexible estimation of the conditional (i.e., state-dependent) density function of transport risk. We demonstrate that using simpler methods, such as OLS linear regression, can lead to misleading inferences. Our model provides a tool for the forwarder to offer customized price and service quotes. It can also generate baseline airline performance to enable fair supplier evaluation. Furthermore, the method allows us to separate recurrent risks from disruption risks. This is important, because hedging strategies for these two kinds of risks are often drastically different.iv

We present a general framework to study the design of spare parts logistics in the presence of three-dimensional (3-D) printing technology. We consider multiple parts facing stochastic demands and adopt procure/manufacture-to-stock versus print-on-demand to highlight the main difference of production modes featured in traditional manufacturing and 3-D printing. To minimize long-run average system cost, our model determines which parts to stock and which to print. We find that the optimal 3-D printer’s utilization increases as the additional unit cost of printing declines and the printing speed improves. The rate of increase, however, decays, demonstrating the well-known diminishing returns effect. We also find the optimal utilization to increase in part variety and decrease in part criticality, suggesting the value of 3-D technology in tolerating large part variety and the value of inventory for critical parts. By examining the percentage cost savings enabled by 3-D printing, we find that, although the reduction in printing cost continuously adds to the value of 3-D printing in a linear fashion, the impact of the improvement of printing speed exhibits S-shaped growth. We also derive various structural properties of the problem and devise an efficient algorithm to obtain near optimal solutions. Finally, our numerical study shows that the 3-D printer is, in general, lightly used under realistic parameter settings but results in significant cost savings, suggesting complementarity between stock and print in cost minimization. This paper was accepted by Victor Martínez-de-Albéniz, operations management.

We consider a retailer that sells a perishable product, making joint pricing and inventory ordering decisions over a finite time horizon of T periods with lost sales. Exploring a real-life data set from a leading supermarket chain, we identify several distinctive challenges faced by such a retailer that have not been jointly studied in the literature: the retailer does not have perfect information on (1) the demand-price relationship, (2) the demand noise distribution, (3) the inventory perishability rate, and (4) how the demand-price relationship changes over time. Furthermore, the demand noise distribution is nonparametric for some products but parametric for others. To tackle these challenges, we design two types of data-driven pricing and ordering (DDPO) policies for the cases of nonparametric and parametric noise distributions. Measuring performance by regret, that is, the profit loss caused by not knowing (1)–(4), we prove that the T-period regret of our DDPO policies are in the order of [Formula: see text] and [Formula: see text] in the cases of nonparametric and parametric noise distributions, respectively. These are the best achievable growth rates of regret in these settings (up to logarithmic terms). Implementing our policies in the context of the aforementioned real-life data set, we show that our approach significantly outperforms the historical decisions made by the supermarket chain. Moreover, we characterize parameter regimes that quantify the relative significance of the changing environment and product perishability. Finally, we extend our model to allow for age-dependent perishability and demand censoring and modify our policies to address these issues. This paper was accepted by David Simchi-Levi, Management Science Special Section on Data-Driven Prescriptive Analytics.