Retail Store Density and the Cost of Greenhouse Gas Emissions

The density, size, and location of stores in a retailer\u27s network influences both the retailer\u27s and the consumers\u27 costs. With stores few and far between, consumers must travel a long distance to shop, whereas shopping trips are shorter with a dense network of stores. The layout of the retail supply chain is of interest to retailers who have emission reduction targets and urban planners concerned with sprawl. Are small local shops preferred over large, “big-box” retailers? A model of the retail supply chain is presented that includes operating costs (such as fuel and rent for floor space) as well as a cost for environmental externalities associated with carbon emissions. A focus on exclusively minimizing operating costs may substantially increase emissions (by 67% in one scenario) relative to the minimum level of emissions. A price on carbon is an ineffective mechanism for reducing emissions. The most attractive option is to improve consumer fuel efficiency—doubling the fuel efficiency of cars reduces long-run emissions by about one-third, whereas an improvement in truck fuel efficiency has a marginal impact on total emissions

Competing Retailers and Inventory: An Empirical Investigation of General Motors\u27 Dealerships in Isolated U.S. Markets

We study the following question: How does competition influence the inventory holdings of General Motors’ dealerships operating in isolated U.S. markets? We wish to disentangle two mechanisms by which local competition influences a dealer’s inventory: (1) the entry or exit of a competitor can change a retailer’s demand (a sales effect); and (2) the entry or exit of a competitor can change the amount of buffer stock a retailer holds, which influences the probability that a consumer finds a desired product in stock (a service-level effect). Theory is clear on the sales effect—an increase in sales leads to an increase in inventory (albeit a less than proportional increase). However, theoretical models of inventory competition are ambiguous on the expected sign of the service-level effect. Via a Web crawler, we obtained data on inventory and sales for more than 200 dealerships over a six-month period. Using cross-sectional variation, we estimated the effect of the number and type of local competitors on inventory holdings. We used several instrumental variables to control for the endogeneity of market entry decisions. Our results suggest that the service-level effect is strong, nonlinear, and positive. Hence, we observe that dealers carry more inventory (controlling for sales) when they face additional competition

Category Management and Coordination in Retail Assortment Planning in the Presence of Basket Shopping Consumers

This paper studies the assortment planning problem with multiple merchandise categories and basket shopping consumers (i.e., consumers who desire to purchase from multiple categories). We present a duopoly model in which retailers choose prices and variety level in each category and consumers make their store choice between retail stores and a no-purchase alternative based on their utilities from each category. The common practice of category management (CM) is an example of a decentralized regime for controlling assortment because each category manager is responsible for maximizing his or her assigned category’s profit. Alternatively, a retailer can make category decisions across the store with a centralized regime. We show that CM never finds the optimal solution and provides both less variety and higher prices than optimal. In a numerical study, we demonstrate that profit loss due to CM can be significant. Finally, we propose a decentralized regime that uses basket profits, a new metric, rather than accounting profits. Basket profits are easily evaluated using point-of-sale data, and the proposed method produces near-optimal solutions

Obtaining Fast Service in a Queueing System Via Performance-Based Allocation of Demand

Any buyer that depends on suppliers for the delivery of a service or the production of a make-to-order component should pay close attention to the suppliers’ service or delivery lead times. This paper studies a queueing model in which two strategic servers choose their capacities/processing rates and faster service is costly. The buyer allocates demand to the servers based on their performance; the faster a server works, the more demand the server is allocated. The buyer’s objective is to minimize the average lead time received from the servers. There are two important attributes to consider in the design of an allocation policy: the degree to which the allocation policy effectively utilizes the servers’ capacities and the strength of the incentives the allocation policy provides for the servers to work quickly. Previous research suggests that there exists a trade-off between efficiency and incentives, i.e., in the choice between two allocation policies a buyer may prefer the less efficient one because it provides stronger incentives. We find considerable variation in the performance of allocation policies: Some intuitively reasonable policies generate essentially no competition among servers to work quickly, whereas others generate too much competition, thereby causing some servers to refuse to work with the buyer. Nevertheless, the trade-off between efficiency and incentives need not exist: It is possible to design an allocation policy that is efficient and also induces the servers to work quickly. We conclude that performance-based allocation can be an effective procurement strategy for a buyer as long as the buyer explicitly accounts for the servers’ strategic behavior

Drivers of Finished Goods Inventory in the U.S. Automobile Industry

Automobile manufacturers in the U.S. supply chain exhibit significant differences in their days of supply of finished vehicles (average inventory divided by average daily sales rate). For example, from 1995 to 2004, Toyota consistently carried approximately 30 fewer days of supply than General Motors. This suggests that Toyota’s well-documented advantage in manufacturing efficiency, product design, and upstream supply chain management extends to their finished-goods inventory in their downstream supply chain from their assembly plants to their dealerships. Our objective in this research is to measure for this industry the effect of several factors on inventory holdings. We find that two factors, the number of dealerships in a manufacturer’s distribution network and a manufacturer’s production flexibility, explain essentially all of the difference in finished-goods inventory between Toyota and three other manufacturers: Chrysler, Ford, and General Motors

Does Adding Inventory Increase Sales? Evidence of a Scarcity Effect in U.S. Automobile Dealerships

What is the relationship between inventory and sales? Clearly, inventory could increase sales: expanding inventory creates more choice (options, colors, etc.) and might signal a popular/desirable product. Or, inventory might encourage a consumer to continue her search (e.g., on the theory that she can return if nothing better is found), thereby decreasing sales (a scarcity effect). We seek to identify these effects in U.S. automobile sales. Our primary research challenge is the endogenous relationship between inventory and sales — e.g., dealers influence their inventory in anticipation of demand. Hence, our estimation strategy relies on weather shocks at upstream production facilities to create exogenous variation in downstream dealership inventory. We find that the impact of adding a vehicle of a particular model to a dealer’s lot depends on which cars the dealer already has. If the added vehicle expands the available set of sub-models (e.g., adding a four-door among a set that is exclusively two-door), then sales increase. But if the added vehicle is of the same sub-model as an existing vehicle, then sales actually decrease. Hence, expanding variety across sub-models should be the first priority when adding inventory—adding inventory within a sub-model is actually detrimental. In fact, given how vehicles were allocated to dealerships in practice, we find that adding inventory actually lowered sales. However, our data indicate that there could be a substantial benefit from the implementation of a “maximizes variety, minimize duplication” allocation strategy: sales increase by 4.4 percent without changing the number of vehicles at each dealership, and a 5.2 percent is possible if inventory is allowed to decrease by 2.8 percent (and no more than 10 percent at any one dealer)

Capacity Investment Timing by Start-ups and Established Firms in New Markets

We analyze the competitive capacity investment timing decisions of both established firms and start-ups entering new markets, which have a high degree of demand uncertainty. Firms may invest in capacity early (when uncertainty is high) or late (when uncertainty has been resolved), possibly at different costs. Established firms choose an investment timing and capacity level to maximize expected profits, whereas start-ups make those choices to maximize the probability of survival. When a start-up competes against an established firm, we find that when demand uncertainty is high and costs do not decline too severely over time, the start-up takes a leadership role and invests first in capacity, whereas the established firm follows; by contrast, when two established firms compete in an otherwise identical game, both firms invest late. We conclude that the threat of firm failure significantly impacts the dynamics of competition involving start-ups

The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity

Recent platforms, like Uber and Lyft, offer service to consumers via “self-scheduling” providers who decide for themselves how often to work. These platforms may charge consumers prices and pay providers wages that both adjust based on prevailing demand conditions. For example, Uber uses a “surge pricing” policy, which pays providers a fixed commission of its dynamic price. With a stylized model that yields analytical and numerical results, we study several pricing schemes that could be implemented on a service platform, including surge pricing. We find that the optimal contract substantially increases the platform’s profit relative to contracts that have a fixed price or fixed wage (or both), and although surge pricing is not optimal, it generally achieves nearly the optimal profit. Despite its merits for the platform, surge pricing has been criticized because of concerns for the welfare of providers and consumers. In our model, as labor becomes more expensive, providers and consumers are better off with surge pricing because providers are better utilized and consumers benefit both from lower prices during normal demand and expanded access to service during peak demand. We conclude, in contrast to popular criticism, that all stakeholders can benefit from the use of surge pricing on a platform with self-scheduling capacity