Knowledge that Transforms

This paper studies how retailers can effectively deliver online and offline information to omnichannel consumers who strategically choose whether to gather information online or offline and whether to buy products online or offline. Information resolves two types of uncertainty: product value uncertainty (i.e., consumers realize valuations when they inspect the product in store, but may end up returning the product when they purchase online) and availability uncertainty (i.e., store visits are futile when consumers encounter stockouts). We consider three information mechanisms: physical showrooms allow consumers to learn valuations anytime they visit the store, even during stockouts; virtual showrooms give consumers online access to an imperfect signal of their valuations; availability information provides real-time information about whether the store has a product in stock. Our main results follow. First, physical showrooms may prompt retailers to reduce store inventory, which increases availability risk and discourages store patronage. Second, virtual showrooms may increase online returns and hurt profits, if they induce excessive customer migration from store to online channels. Third, availability information may be redundant when availability risk is low and may render physical showrooms ineffective when implemented jointly. Finally, when customers are homogeneous, these mechanisms may not exhibit significant complementarities and the optimal information structure may involve choosing only one of the three.

The goal of this paper is to describe, model, and optimize inventory in a reverse logistics system that supports the warranty returns and replacements for a consumer electronic device. The context and motivation for this work stem from a collaboration with an industrial partner, a Fortune 100 company that sells consumer electronics. The reverse logistics system is a closed-loop supply chain: failed devices are returned for repair and refurbishing; this inventory is then used to serve warranty claims or sold through a side sales channel. Managing inventory in this system is challenging because of the short life cycle of these devices and the rapidly declining value for the inventory. We examine an inventory model that captures these dynamics. We characterize the structure of the optimal policy for this problem for stochastic demand and introduce an algorithm to calculate optimal sell-down levels. We also provide a closed-form policy for the deterministic version of the problem, and we use this policy as a certainty-equivalent approximation to the stochastic optimal policy. Finally, using numerical experiments, we analyze the sensitivity of this system to changes in various parameters, and we also evaluate the performance of the certainty-equivalent approximation using data from our industrial partner. The online appendix is available at https://doi.org/10.1287/msom.2017.0622 .

Online purchases constitute about one-tenth of U.S. retail sales. The supply chains that support online retailing are fundamentally different from those that support traditional brick-and-mortar stores. Traditional solutions are not always appropriate to solve online retailing’s operations problems; thus, there is an opportunity to understand and improve these novel supply chains. One key characteristic of the inventory systems for online retailing is demand spillover, whereby a stockout at a fulfillment center (FC) results in demand spilling over to another FC. For this context we examine how to allocate inventory to the FCs under a periodic-review joint-replenishment policy, with an objective to minimize outbound shipping costs for a fixed amount of inventory. We first show how traditional decentralized allocation policies may perform suboptimally and induce dynamics (whiplash) that result in costly spillover. We find that this phenomenon increases with the prevalence of local stockouts and with the level of inventory imbalance. We then describe that inventory imbalance occurs in online retailing because of operational realities and provide evidence based on real data. Finally, we propose a heuristic to allocate inventory accounting for possible spillover during the lead time. We test the heuristic by a simulation and show that it performs better than the status quo policy, is robust to operational realities, and captures over 90% of the possible improvement as compared to a pseudo-optimal policy. The online appendix is available at https://doi.org/10.1287/msom.2016.0614 .

Plant inspections enable firms to manage their quality risk in global supply chains. However, surprisingly little research examines the relationship between such inspections and future product quality. In this paper, we study how well plant inspection outcomes predict the hazard of a future recall and analyze how investigator experience affects this predictive relationship. Using secondary data spanning a seven-year period in the medical device industry and a recurrent-event Cox proportional hazard model, our analysis shows that inspection outcomes reliably predict future product recalls. However, inspection outcomes become an unreliable predictor of recalls with an increase in site-specific investigator experience. Through post hoc analysis, we also show that the hazard of a recall at a plant increases with site-specific experience, independent of the inspection outcome. Compared to the first visit by an investigator, the recall hazard increases by 21% the second time the investigator visits a specific plant, and by 57% on the third visit. We propose investigator rotation and investigator sequencing as two policies to help mitigate this increased recall risk. The online appendix is available at https://doi.org/10.1287/msom.2017.0661 .

To analyze incentives for investing in the capacity to generate renewable electricity, we model the trade-off between renewable (e.g., wind) and nonrenewable (e.g., natural gas) technology. Renewable technology has a higher investment cost and yields only an intermittent supply of electricity; nonrenewable technology is reliable and has lower investment cost but entails both fuel expenditures and carbon emission costs. With reference to existing electricity markets, we model several interrelated contexts—the vertically integrated electricity supplier, market competition, and partial market competition with long-term fixed-price contracts for renewable electricity—and examine the effect of carbon taxes on the cost and share of wind capacity in an energy portfolio. We find that the intermittency of renewable technologies drives the effectiveness of carbon pricing mechanisms, which suggests that charging more for emissions could unexpectedly discourage investment in renewables. We also show that market liberalization may reduce investment in renewable capacity while increasing the overall system’s cost and emissions. Fixed-price contracts with renewable generators can mitigate these detrimental effects, but not without possibly creating other problems. In short, actions to reduce the intermittency of renewable sources may be more effective than carbon taxes alone at promoting investment in renewable generation capacity. The online appendix is available at https://doi.org/10.1287/msom.2017.0621 .

Several auto manufacturers have recently introduced car sharing programs. Although the structure of most programs is the same, there is no clear dominant strategy for the type of vehicles that should be provided through car sharing. In this paper, we consider an original equipment manufacturer (OEM) that contemplates car sharing and designs its product line by accounting for the trade-off between driving performance and fuel efficiency under CAFE standards. Customers have different valuations of driving performance and decide whether to buy, join car sharing or rely on their outside options. We find that the OEM increases the fuel efficiency of the vehicles it provides through car sharing. This higher efficiency enables the OEM to charge a higher selling price to the higher end of the market, thus increasing its profit. This is especially beneficial to higher-end OEMs that face greater cannibalization and can explain why Daimler and BMW have been particularly active in introducing car sharing. Offering car sharing is not always environmentally beneficial. Even when it is, we find that doing so may reduce the OEM’s Corporate Average Fuel Economy (CAFE) level. In such cases, incentive multipliers should be granted for each shared car. Finally, if anticipating aggressive CAFE standards, OEMs may introduce car sharing to better absorb the increase in the production cost. The online appendix is available at https://doi.org/10.1287/msom.2016.0605 .