Papers are listed alphabetically by UCLA Anderson faculty. To search by keyword, use the Find feature on your browser.
Double-Counting of Emissions in Carbon-Neutral and Carbon-Optimal Supply Chains
F. Caro, C.J. Corbett, T. Tan & R. Zuidwijk
Carbon footprinting is a tool for firms to determine the total greenhouse gas (GHG) emissions associated with their supply chain or with a unit of final product or service. Carbon footprinting typically aims to identify where best to invest in emission reduction efforts, and/or to determine the proportion of total emissions that an individual firm is accountable for, whether financially and/or operationally. A major and under-recognized challenge in determining the appropriate allocation stems from the high degree to which GHG emissions are the result of joint efforts by multiple firms. As more firms make (part of) their supply chains carbon neutral, by choice or by regulation, these allocation questions become more critical. We introduce a simple but effective model of joint production of GHG emissions in general supply chains, decomposing the total footprint into processes, each of which can be influenced by any combination of firms. A supply chain in which all firms exert their first-best emissions reduction effort levels is "carbon-optimal", while one which offsets all emissions is "carbon-neutral". With this structure, we examine conditions under which a carbon-neutral supply chain will also be carbon-optimal. We find that, in order to induce the carbon-optimal effort levels, the emissions need to be over-allocated, in contrast to the usual focus in the life cycle assessment (LCA) and carbon footprinting literatures on avoiding double-counting. We analyze the problem from the perspective of the social planner as well as that of a "carbon leader", a single firm that offsets all supply chain emissions and that can contract with other firms to encourage them to help reduce emissions. We show that even when the carbon leader can only contract on emissions, she can still induce the same effort levels and profits as when she can contract directly on effort. Our work aims to lay the foundation for a framework to integrate the economics- and LCA-based perspectives on supply chain carbon footprinting.
The Assortment Packing Problem: Multiperiod Assortment Planning for Short-Lived Products
F. Caro, V. Martínez-de-Albéniz, P. Rusmevichientong
Motivated by retailers' frequent introduction of new items to refresh product lines and maintain their market share, we present the assortment packing problem in which a firm must decide, in advance, the release date of each product in a given collection over a selling season. Our formulation models the trade-offs among profit margins, preference weights, and limited life cycles. A key aspect of the problem is that each product is short-lived in the sense that, once introduced, its attractiveness lasts only a few periods and vanishes over time. The objective is to determine when to introduce each product to maximize the total profit over the selling season. Even for two periods, the corresponding optimization problem is shown to be NP-complete. As a result, we study a continuous relaxation of the problem that approximates the problem well when the number of products is large. When margins are identical and product preferences decay exponentially, its solution can be characterized: it is optimal to introduce products with slower decays earlier. The relaxation also helps us to develop several heuristics, for which we establish performance guarantees. Numerical experiments show that these heuristics perform very well, yielding profits within 1% of the optimal in most cases.
Clearance Pricing Optimization for a Fast-Fashion Retailer
F. Caro, J. Gallien
Fast-fashion retailers such as Zara offer continuously changing assortments and use minimal in-season promotions. Their clearance pricing problem is thus challenging because it involves comparatively more different articles of unsold inventory with less historical price data points. Until 2007, Zara used a manual and informal decision-making process for determining price markdowns. In collaboration with their pricing team, we designed and implemented since an alternative process relying on a formal forecasting model feeding a price optimization model. As part of a controlled field experiment conducted in all Belgian and Irish stores during the 2008 Fall-Winter season, this new process increased clearance revenues by approximately 6%. Zara is currently using this process worldwide for its markdown decisions during clearance sales.
Robust Control of the Multi-armed Bandit Problem
F. Caro, A. Das Gupta
We study a robust model of the multi-armed bandit (MAB) problem in which the transition probabilities are ambiguous and belong to subsets of the probability simplex. We characterize the optimal policy as a project-by-project retirement policy but we show that arms become dependent so the Gittins index is not optimal. We propose a Lagrangian index policy that is computationally equivalent to evaluating the indices of a non-robust MAB. For a project selection problem we find that it performs near optimal.
Optimal Static Pricing for a Tree Network
F. Caro, D. Simchi-Levi
We study the static pricing problem for a network service provider in a loss system with a tree structure. In the network, multiple classes share a common inbound link and then have dedicated outbound links. The motivation is from a company that sells phone cards and needs to price calls to different destinations. We characterize the optimal static prices in order to maximize the steady-state revenue. We report new structural findings as well as alternative proofs for some known results. We compare the optimal static prices versus prices that are asymptotically optimal, and through a set of illustrative numerical examples we show that in certain cases the loss in revenue can be significant. Finally, we show that static prices obtained using the reduced load approximation of the blocking probabilities can be easily obtained and have near-optimal performance, which makes them more attractive for applications.
Optimal Pricing Strategy in the Case of Price Dispersion: New Evidence from the Tokyo Housing Market
Stuart A. Gabriel, Yongheng Deng, Kiyohiko G. Nishimura, Diehang (Della) Zheng
In the wake of recent pronounced cycles in housing, substantial media and professional debate have focused on house price determination, and in particular, optimal seller pricing strategies. In this paper, we adopt a multistage search model, in which the home seller's reservation price is determined by her opportunity cost, search cost, discount rate, and additional market parameters including the anticipated offer arrival rate and the offer price distribution. The optimal asking price is chosen so as to maximize the return from search. Theoretical results indicate that a greater dispersion in offer prices leads to higher reservation and asking prices, which in turn result in a higher expected transaction price. Under the assumption that offer prices are normally distributed, a higher dispersion of offer prices also reduces time on the market for overpriced properties. A unique dataset from the Tokyo condominium re-sale market enables us to test those modeled hypotheses. Empirical results indicate that the standard deviation of transaction prices for each submarket, a proxy of offer price dispersion, is an important determinant of both pricing strategy and pricing outcomes. A one percentage point increase in the standard deviation of sub-market transaction prices results in a two-tenths of a percent increase in the initial asking price and in the final transaction price. Although overpriced properties stay on the market longer, increases in the dispersion of market prices enhance the probabilities of a successful transaction and/or an accelerated sale. Moreover, less well-informed sellers are more likely to list their properties at significantly higher prices and later reduce their list price. Those properties stay on the market longer and sell at about a three percent discount relative to the properties of better-informed sellers.
Neighborhoods: A Multiple Attribute Social Structure Connecting Individuals and Organizations
Barbara S. Lawrence & Michael J. Zyphur
We propose and inductively explore neighborhoods, a tacit social structure connecting individuals and organizations. Neighborhoods are clusters of individuals' organizational reference groups, in which the people each individual knows are demographically-similar to the people other individuals know. Because of their internal similarity, neighborhoods circumscribe the social information individuals receive and thus plausibly generate shared perceptions and meaning. Using latent class cluster analysis on data from a large organization, we induce five neighborhoods. While individuals' own attributes are related to their neighborhood, they frequently differ from those in their neighborhood. Neighborhoods discriminate between individuals' career-related perceptions and social network attributes.
Do people with specific skills want more social insurance? Not in the United States
Jerry Nickelsburg & Jeffrey F. Timmons
Iversen and Soskice's asset-based theory of social preferences predicts that people with more specific-skills will prefer more social insurance. Market-based theories of social preferences, by contrast, suggest that specific skills should have no effect on preferences when labor markets use wages to adjust for differences in risks and investment costs. The Dictionary of Occupational Titles (DOT) has a vast quantity of high-quality data on specific and general skills. We use the DOT data found in the United States General Social Survey (GSS) and an updated series on skills to test whether or not individuals with more specific skills prefer more social insurance/redistribution. Our results with a variety of questions from the GSS show that individuals in the United States with more specific skills do not have a greater demand for social insurance/redistribution than those with more general skills. These results are not consistent with either Iversen and Soskice's asset-based theory of social preferences or their empirical findings for the United States. The results are consistent with theories that emphasize the role that wages perform in adjusting for risks and investment costs, however.
Improving Access and Customer Service Times at the Ronald Reagan UCLA Medical Center
C. Duda, K. Rajaram, C. Barz, T. Rosenthal
There has been an increasing emphasis on health care efficiency and costs, and improving quality at several stages in health care settings (such as hospitals or clinics) using the idea of Total Quality Management (TQM). Despite a broad emphasis on TQM, there has not been sufficient work on methods to improve access and customer service times for a given level of clinical quality in health care settings.
Determinants of Experienced Utility: Laws and Implications
M. Baucells, R.K. Sarin
Satisfaction in experiencing the future depends on decisions made today. We consider six well-known psychological laws governing satisfaction. The laws capture habit formation, social comparison, and satiation. We show it is possible to formalize these laws by means of a utility model, and to derive implications from the laws: wanting vs. liking, crescendo, recharge periods, variety seeking, and craving. The discussion combines mathematical propositions, experimental findings in psychology, and time-honored wisdom. We discuss how the sixth law, presentism, may lead to incorrect predictions of experienced utility and suboptimal life-balance choices.
Gender Differences in Risk Aversion: When and Why?
A. Wieland, R.K. Sarin
It has become well-accepted that women are more risk averse than men. This research investigates when gender differences in risk aversion are likely to occur and when they are less likely to manifest. We find that gender differences in risk aversion are likely to occur in decisions under risk, where the probability of outcomes is known and objectively quantified, such as games of chance, and less likely to occur in decisions under uncertainty, where one must rely on their own internal subjective expectancies of the probabilities of outcomes - the kind of decisions that dominate our day-to-day decision-making. We propose and test the mechanism that is responsible for producing gender differences in risk aversion: one's subjective expectancy of the outcome. In decisions under risk, when subjective expectancies are accounted for, the gender difference in risk aversion disappears; while in decisions under uncertainty, we do not observe any gender differences in risk aversion, but instead find one's subjective expectancies of the outcome to be the only reliable predicator of valuation.