Knowledge that Transforms

Although influenza vaccine shortage is often attributed to low supply, it has been observed that even with abundant supply, a major shortage can still occur because of late delivery. In this paper, motivated by the influenza vaccine industry, we study a supply chain contracting problem in the presence of uncertainties surrounding design, delivery, and demand of the influenza vaccine. In this supply chain, a manufacturer has insufficient incentive to initiate at-risk early production prior to the design freeze because it is a retailer who reaps the most benefits from selling more vaccines delivered on time. Anticipating that late delivery will lead to potential loss in demand, the retailer tends to reduce the order size, which further discourages the manufacturer from making an effort to improve its delivery performance. To break this negative feedback loop, a supply contract needs to achieve two objectives: incentivize at-risk early production and eliminate double marginalization. We find that two commonly observed supply contracts in practice, the delivery-time-dependent quantity flexibility (D-QF) contract and the late-rebate (LR) contract, may fail to coordinate the supply chain because of the tension between these two objectives. To resolve such a tension, we construct a buyback-and-late-rebate (BLR) contract and show that a properly designed BLR contract can not only coordinate the supply chain but also can provide full flexibility of profit division between members of the supply chain. Numerical experiments further demonstrate that the BLR contract significantly improves supply chain efficiency compared to the contracts used in the industry.

Prominent buyers’ brands have been damaged because their suppliers caused major harm to workers or the environment, e.g., through a deadly factory fire or release of toxic chemicals. How can buyers motivate suppliers to exert greater care to prevent such harm? This paper characterizes a “backfiring condition” under which actions taken by prominent buyers (increasing auditing, publicizing negative audit reports, providing loans to suppliers) motivate a supplier to exert greater effort to pass the buyer’s audit by hiding information and less care to prevent harm. Intuitively appealing actions for a buyer (penalizing a supplier for harming workers or the environment, or for trying to deceive an auditor) may be similarly counterproductive. Contrary to conventional wisdom, squeezing a supplier’s margin (by reducing the price paid to the supplier or increasing wages for workers) motivates the supplier to exert greater care to prevent harm—under the backfiring condition. Whereas the necessary and sufficient condition depends on the relative convexity of the supplier’s hiding cost function, a simple sufficient condition is that the supplier is likely to successfully hide information from the auditor, in equilibrium. Anecdotal evidence suggests that the backfiring condition is prevalent or becoming increasingly so. Similar insights apply to mitigation of unauthorized subcontracting.

We investigate information flow in two-tier supply chains, where retailers order from suppliers and sell in a market with uncertain demand. The retailers each have access to a demand signal and can exchange signals (horizontal information sharing). The suppliers can offer the retailers differential payments to gain access to their signals (vertical information acquisition). We demonstrate that retailer competition is a necessary condition to sustain information flow, whereas supplier competition precludes vertical information acquisition. Facing horizontal competition, the retailers can have an incentive to exchange signals if competition is less intense; and this incentive is stronger when they order from independent suppliers than when they order from a monopolist supplier. In the setting where two retailers order from a monopolist supplier, once the retailers exchange signals, the supplier will acquire signals from them both; otherwise, it will have an incentive to acquire signals if the signals are sufficiently correlated. It can be incentive compatible for horizontal information sharing and vertical information acquisition to coexist so that the retailers’ signals are available to all parties. Under this circumstance, the supplier will profit from information flow and the retailers can earn profit gains as well, whereas the consumers will be worse off.

We study a supply chain with manufacturer encroachment in which product quality is endogenous and customers have heterogeneous preferences for quality. It is known that, when quality is exogenous, encroachment could make the retailer better off. Yet, when quality is endogenous and the manufacturer has enough flexibility in adjusting quality, we find that encroachment always makes the retailer worse off in a large variety of scenarios. We also establish that, while a higher manufacturer’s cost of quality hurts the retailer in absence of encroachment, it could benefit the retailer with encroachment. In addition, we show that a manufacturer offering differentiated products through two channels prefers to sell its high-quality product through the direct channel. Contrary to conventional wisdom, quality differentiation does not always benefit either manufacturer or retailer. Our results may explain why, despite extant theoretical predictions, retailers almost always resent encroachment. These findings also suggest that firms must be cautious when adopting quality differentiation as a strategy to ease channel conflict caused by encroachment.

We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts on designer apparel and accessories. One of the retailer’s main challenges is pricing and predicting demand for products that it has never sold before, which account for the majority of sales and revenue. To tackle this challenge, we use machine learning techniques to estimate historical lost sales and predict future demand of new products. The nonparametric structure of our demand prediction model, along with the dependence of a product’s demand on the price of competing products, pose new challenges on translating the demand forecasts into a pricing policy. We develop an algorithm to efficiently solve the subsequent multiproduct price optimization that incorporates reference price effects, and we create and implement this algorithm into a pricing decision support tool for Rue La La’s daily use. We conduct a field experiment and find that sales does not decrease because of implementing tool recommended price increases for medium and high price point products. Finally, we estimate an increase in revenue of the test group by approximately 9.7% with an associated 90% confidence interval of [2.3%, 17.8%].