Antonio Moreno - Faculty & Research - Harvard Business School
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Antonio Moreno

Sicupira Family Associate Professor of Business Administration

Technology and Operations Management

Antonio (Toni) Moreno is the Sicupira Family Associate Professor in the Technology and Operations Management Unit, teaching empirical technology and operations management. Before joining HBS, he was an associate professor in the Kellogg School of Management.

Professor Moreno uses data and empirical approaches to study operations management, with a main focus on retail. His primary aims include exploring operational consequences of innovative business models, addressing fundamental questions in operations strategy, and developing models to help firms make better operational decisions. Most of his work uses novel data sets that he has obtained through collaboration with companies or collected himself. Professor Moreno’s work has appeared in journals such as Management ScienceMarketing ScienceManufacturing & Service Operations ManagementInformation Systems Research, and Sloan Management Review, and has been covered by several media outlets. He has also received the Wickham Skinner Early-Career Research Accomplishments Award from the Production and Operations Management Society.

Professor Moreno earned a B.Sc. in electrical engineering, an M.Sc. in industrial engineering, and an M.Sc. in electrical engineering from Technical University of Catalonia. He has an MA in statistics and a PhD in operations and information management from the Wharton School of the University of Pennsylvania.

Journal Articles
  1. Offline Showrooms in Omni-channel Retail: Demand and Operational Benefits

    David R. Bell, Santiago Gallino and Antonio Moreno

    Omnichannel environments where customers shop online and offline at the same retailer are ubiquitous, and are deployed by online-first and traditional retailers alike. We focus on the relatively understudied domain of online-first retailers and the engagement of a key omnichannel tactic; specifically, introduction of showrooms (physical locations where customers can view and try products) in combination with online fulfillment that uses centralized inventory management. We ask whether, and if so, how, showrooms benefit the two most basic retail objectives: demand generation and operational efficiency. Using quasi-experimental data on showroom openings by, the leading and iconic online-first eyewear retailer, we find that showrooms: (1) increase demand overall and in the online channel as well; (2) generate operational spillovers to the other channels by attracting customers who, on average, have a higher cost-to-serve; (3) improve overall operational efficiency by increasing conversion in a sampling channel and by decreasing returns; and (4) amplify these demand and operational benefits in dealing with customers who have the most acute need for the firm’s products. Moreover, the effects we document strengthen with time as showrooms contribute not only to brand awareness but also to what we term channel awareness as well. We conclude by elaborating the underlying customer dynamics driving our findings and by offering implications for how online-first retailers might deploy omnichannel tactics.

    Keywords: experience attributes; marketing–operations interface; omnichannel retailing; quasi-experimental methods; Retail operations; showrooms; Marketing Channels; Demand and Consumers; Performance Efficiency; Retail Industry;


    Bell, David R., Santiago Gallino, and Antonio Moreno. "Offline Showrooms in Omni-channel Retail: Demand and Operational Benefits." Management Science (forthcoming). (Winner of the 2014 POMS Applied Research Challenge. Workshop on Information Systems Economics Overall Best Paper Award 2014.)  View Details
  2. Channel Integration, Sales Dispersion, and Inventory Management

    Santiago Gallino, Antonio Moreno and Ioannis Stamatopoulos

    We study the effects of the introduction of cross-channel functionalities on the overall sales dispersion of retailers and the implications of these effects for inventory management. To do that, we analyze data from a leading U.S. retailer who introduced a “ship-to-store” (STS) functionality that allows customers to ship products to their local store free of charge when those products are not available in their local store. Based on the fact that stores prioritize carrying products for which local demand is high, we test the hypothesis that introducing the STS functionality increased the retailer’s overall sales dispersion. We find that, on average, the contribution of the 90% lowest-selling products to total sales increased by 0.75 percentage points, increasing sales dispersion. Calibrating conventional inventory-ordering models, we show that to respond optimally to the observed increase in dispersion, the retailer would need to increase its cycle and safety inventories by approximately 2.7%. Our paper points out the effect of an increasingly important retail phenomenon (channel integration) on a key factor for inventory management (sales dispersion).

    Keywords: Retail operations; online retail; channel integration; sales dispersion; long tail; empirical operations; Inventory management; omnichannel retail; Marketing Channels; Integration; Sales; Logistics; Operations; Management; Retail Industry;


    Gallino, Santiago, Antonio Moreno, and Ioannis Stamatopoulos. "Channel Integration, Sales Dispersion, and Inventory Management." Management Science 63, no. 9 (September 2017): 2813–2831.  View Details
  3. The Operational Value of Social Media Information

    Ruomeng Cui, Santiago Gallino, Antonio Moreno and Dennis J. Zhang

    While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study attempts to do that by empirically studying whether using publicly available social media information can improve the accuracy of daily sales forecasts. We collaborated with an online apparel retailer to assemble a dataset that combines (1) detailed internal operational information, including data on sales, advertising, and promotions, as well as (2) publicly available social media information obtained from Facebook. We implement a variety of machine learning methods to forecast daily sales. We find that using social media information results in statistically significant improvements in the out-of-sample accuracy of the forecasts, with relative improvements ranging from 12.85% to 23.23% over different forecast horizons. We also demonstrate that nonlinear boosting models with feature selection, such as random forests, perform significantly better than traditional linear models. The best-performing method (random forest) yields an out-of-sample MAPE of 7.21% when not using social media information and 5.73% when using social media information is used. In both cases, this significantly improves the accuracy of the company's internal forecasts (a MAPE of 11.97%). Combining these empirical results, we provide recommendations for forecasting sales in general as well as with social media information.

    Keywords: Information; Sales; Forecasting and Prediction;


    Cui, Ruomeng, Santiago Gallino, Antonio Moreno, and Dennis J. Zhang. "The Operational Value of Social Media Information." Production and Operations Management (forthcoming).  View Details
  4. The Effects of Product Line Breadth: Evidence from the Automotive Industry

    Antonio Moreno and Christian Terwiesch

    Using a detailed data set from the U.S. automotive industry, we enrich the existing literature on product line breadth with new results that highlight previously unexplored operational aspects of its benefits and costs. We find that expanding product line breadth has a significant effect on increasing mismatch costs arising from the increased demand uncertainty associated with product proliferation. These mismatch costs are manifested through additional discounts and inventories. The effect of product line breadth on mismatch costs is comparable in magnitude to the effect on production costs, suggesting that the operational benefits of inventory pooling achievable by rationalizing product lines can be very substantial. Furthermore, we quantify the benefit of using a platform strategy to mitigate the effects of a broad product line on production costs. Finally, we propose an additional, attribute-based measure of product line breadth and find that product line breadth can work as a hedge against changes in demand conditions. For example, automakers that offer a broader range of fuel economy levels increase their market share and reduce their average discounts as gas prices become more volatile.

    Keywords: variety; pricing; automotive industry; marketing/operations interface; platforms; empirical operations management; Product Marketing; Production; Management; Market Platforms; Auto Industry;


    Moreno, Antonio, and Christian Terwiesch. "The Effects of Product Line Breadth: Evidence from the Automotive Industry." Marketing Science 36, no. 2 (March–April 2017): 254–271.  View Details
  5. Pricing and Production Flexibility: An Empirical Analysis of the U.S. Automotive Industry

    Antonio Moreno and Christian Terwiesch

    We use a detailed data set from the U.S. auto industry spanning from 2002 to 2009 and a variety of econometric methods to characterize the relationship between the availability of production mix flexibility and firms’ use of responsive pricing. We find that production mix flexibility is associated with reductions in observed manufacturer discounts, resulting from the increased ability to match supply and demand. Under the observed market conditions, mix flexibility accounts for substantial average savings by reducing price discounting by approximately 10% of the average industry discount. We test three supplementary hypotheses and find that the reduction in discounts for vehicles manufactured at flexible plants is (1) higher for higher demand uncertainty, (2) higher for vehicles coproduced with vehicles that belong to a different segment, and (3) lower in situations with higher local competition.

    Keywords: empirical operations management; Flexibility; pricing; automotive industry; Production; Price; Management; Analysis; Auto Industry; United States;


    Moreno, Antonio, and Christian Terwiesch. "Pricing and Production Flexibility: An Empirical Analysis of the U.S. Automotive Industry." Manufacturing & Service Operations Management 17, no. 4 (Fall 2015): 428–444.  View Details
  6. How to Win in an Omnichannel World

    David R. Bell, Santiago Gallino and Antonio Moreno

    The omnichannel environment presents new challenges and opportunities for both information and product fulfillment. While all retailers need to effectively and efficiently manage fulfillment and information provision, there are important nuances to how this happens, depending on where and how the retailer got started and what kinds of improvement create the most leverage. This article delivers a customer-focused framework showing how to win in the omni-channel environment through critical innovations in information delivery and product fulfillment. The framework emerged from our research with both traditional and nontraditional retailers. To thrive in the new environment, retailers of all stripes and origins need to deploy information and fulfillment strategies that reduce friction in every phase of the buying process. This means simultaneously providing, in a cost-effective and narrative-enhancing way.

    Keywords: Supply Chain Management; Customer Relationship Management; Marketing; Marketing Strategy; United States;


    Bell, David R., Santiago Gallino, and Antonio Moreno. "How to Win in an Omnichannel World." MIT Sloan Management Review 56, no. 1 (Fall 2014): 45–53.  View Details
  7. Doing Business with Strangers: Reputation in Online Service Marketplaces

    Antonio Moreno and Christian Terwiesch

    Online service marketplaces allow service buyers to post their project requests and service providers to bid for them. To reduce the transactional risks, marketplaces typically track and publish previous seller performance. By analyzing a detailed transactional data set with more than 1,800,000 bids corresponding to 270,000 projects posted between 2001 and 2010 in a leading online intermediary for software development services, we empirically study the effects of the reputation system on market outcomes. We consider both a structured measure summarized in a numerical reputation score and an unstructured measure based on the verbal praise left by previous buyers, which we encode using text mining techniques. We find that buyers trade off reputation (both structured and unstructured) and price and are willing to accept higher bids posted by more reputable bidders. Sellers also respond to changes in their own reputation through three different channels. They increase their bids with their reputation score (price effect) but primarily use a superior reputation to increase their probability of being selected (volume effect) as opposed to increasing their bid prices. Negative shocks in seller reputation are associated to an increase in the probability of seller exit (exit effect), but this effect is moderated by the investment that the seller has made in the site. We conclude that participants in this market are very responsive to the numerical reputation score and also to the unstructured reputational information, which behaves in a similar way to the structured numerical reputation score but provides complementary information.

    Keywords: online service marketplace; procurement; Auctions; Reputation; Bids and Bidding;


    Moreno, Antonio, and Christian Terwiesch. "Doing Business with Strangers: Reputation in Online Service Marketplaces." Information Systems Research 25, no. 4 (December 2014): 865–886.  View Details
  8. Integration of Online and Offline Channels in Retail: The Impact of Sharing Reliable Inventory Availability Information

    Santiago Gallino and Antonio Moreno

    Using a proprietary data set, we analyze the impact of the implementation of a “buy-online, pick-up-in-store” (BOPS) project. The implementation of this project is associated with a reduction in online sales and an increase in store sales and traffic. These results can be explained by two simultaneous phenomena: (1) additional store sales from customers who use the BOPS functionality and buy additional products in the stores (cross-selling effect) and (2) the shift of some customers from the online to the brick-and-mortar channel and the conversion of noncustomers into store customers (channel-shift effect). We explain these channel-shift patterns as an increase in “research online, purchase offline” behavior enabled by BOPS implementation, and we validate this explanation with evidence from the change of cart abandonment and conversion rates of the brick-and-mortar and online channels. We interpret these results in light of recent operations management literature that analyzes the impact of sharing inventory availability information. Our analysis illustrates the limitations of drawing conclusions about complex interventions using single-channel data.

    Keywords: Retail operations; inventory availability; empirical operations management; business analytics; online retail; ecommerce; Operations; Management; Distribution Channels; Consumer Behavior; Retail Industry;


    Gallino, Santiago, and Antonio Moreno. "Integration of Online and Offline Channels in Retail: The Impact of Sharing Reliable Inventory Availability Information." Management Science 60, no. 6 (June 2014): 1434–1451. (Finalist of Management Science Best Paper award in Operations Management.)  View Details
Working Papers
  1. The Effects of Menu Costs on Supply Chain Efficiency: Evidence from Adoption of the Electronic Shelf Label Technology

    Ioannis Stamatopoulos, Achal Bassamboo and Antonio Moreno

    We use the adoption of electronic shelf labels (ESLs) by a major international grocery retailer in 2015 in the United Kingdom to identify the effects of reducing physical menu costs (operational costs of price adjustment) on supply chain efficiency. The ESL technology essentially eliminates the physical costs associated with price adjustment (e.g., costs of printing and distributing price tags). We find that the elimination of physical menu costs benefits all supply chain stakeholders (retailer, consumers, suppliers). In our setting, daily revenues increased, the average price per unit sold decreased, and daily sales volumes increased as a result of ESLs. We also find that ESL adoption increased price-adjustment volume, decreased the average size of a price adjustment, and decreased the batching of price changes across different products. Finally, we find that ESL adoption had a statistically significant effect on the volume of downward price changes, but not on the volume of upward price changes, which explains the direction of the change in operational outcomes.

    Keywords: menu cost; dynamic pricing; ordering; revenue management; welfare analysis; Technology Adoption; Price; Supply Chain; Performance Efficiency;


    Stamatopoulos, Ioannis, Achal Bassamboo, and Antonio Moreno. "The Effects of Menu Costs on Supply Chain Efficiency: Evidence from Adoption of the Electronic Shelf Label Technology." Working Paper, July 2017.  View Details
  2. The Hidden Costs of Dynamic Pricing: Empirical Evidence from Online Retailing in Emerging Markets

    Chaithanya Bandi, Antonio Moreno and Zhiji Xu

    We investigate how dynamic pricing can lead to higher operational costs through more product returns in the online retail industry. Dynamic pricing has been widely applied by many online retailers. Research has shown that, in response to dynamic pricing, some customers choose to wait strategically in anticipation of a future discount. Using detailed sales data of more than 2 million transactions with return information from the Indian online retail market, we document two types of strategic customer behavior that have not been considered by previous research. First, customers who keep monitoring the price after purchase may initiate opportunistic returns because of price drops. Second, customers who anticipate a future return may strategically choose a payment method with a lower return cost. Our logistic regression model indicates that: (1) realized post-purchase price drops lead to higher probability of returns; (2) anticipated price drops after purchase lead to higher probability of using cash-on-delivery, a payment method with a lower return cost. Our findings are robust to alternative model specifications and sample selection procedures. In conclusion, we demonstrate that an optimal pricing policy should take into consideration the potential operational costs of two types of strategic customer behavior, opportunistic returns and strategic choice of payment method.

    Keywords: Price; Policy; Consumer Behavior; Cost Management; Emerging Markets; Retail Industry;


    Bandi, Chaithanya, Antonio Moreno, and Zhiji Xu. "The Hidden Costs of Dynamic Pricing: Empirical Evidence from Online Retailing in Emerging Markets." Working Paper, May 2017.  View Details
  3. The Wisdom of Crowds in Operations: Forecasting Using Prediction Markets

    Achal Bassamboo, Ruomeng Cui and Antonio Moreno

    Prediction is an important activity in various business processes, but it becomes difficult when historical information is not available, such as forecasting demand of a new product. One approach that can be applied in such situations is to crowdsource opinions from employees and the public. Our paper studies the application of crowd forecasting in operations management. In particular, we study how efficient crowds are in estimating parameters important for operational decisions that companies make, including sales forecasts, price commodity forecasts, and predictions of popular product features. We focus on a widely adopted class of crowd-based forecasting tools, referred to as prediction markets. These are virtual markets created to aggregate crowds' opinions and operate in a way similar to stock markets. We partnered with Cultivate Labs, a leading company that provides a prediction market engine, to test the forecast accuracy of prediction markets using the firm's data from its public markets and several corporate prediction markets, including a chemical company, a retail company and an automotive company. Using information extracted from employees and public crowds, we show that prediction markets produce well-calibrated forecasting results. In addition, we run a field experiment to study the conditions under which groups work well. Specifically, we explore how group size plays a role in the accuracy of the forecast and find that large groups (e.g., 18 participants) perform substantially better than smaller groups (e.g., 8 participants), highlighting the importance of group size and quantifying the right sizes needed to produce a good forecast using such mechanisms.

    Keywords: wisdom of crowds; demand forecasting; price forecasting; Forecasting and Prediction; Social and Collaborative Networks; Size; Performance;


    Bassamboo, Achal, Ruomeng Cui, and Antonio Moreno. "The Wisdom of Crowds in Operations: Forecasting Using Prediction Markets." Working Paper, October 2015.  View Details
  4. Inventory Auditing and Replenishment Using Point-of-sales Data

    Achal Bassamboo, Antonio Moreno and Ioannis Stamatopoulos

    We study how inventory managers can fully utilize point-of-sales (POS) data for the design of inventory auditing and replenishment strategies that account for the existence of phantom inventories. Events such as spoilage, expiration, employee theft, and customer shoplifting reduce available inventories in retail stores without these reductions being reflected in inventory records. As a result, inventory records often include phantom inventories, i.e., units of good present on inventory records but not actually available for sale. These phantom inventories cause replenishment delays and stockouts which ultimately hurt service levels. The optimal policy in the presence of phantom inventories is complex. We analyze the structure of the problem using tools from discrete mathematics and dynamic programming to derive a simple policy which performs close to the optimal policy and rarely acts sub-optimally. We propose a simple policy based on a threshold on the estimated fraction of demand to be met on a given day conditional on the POS data up to that day, a statistic that we refer to as the daily expected service level. Our policy is easy to compute and interpret, and can thus offer an attractive solution for inventory managers.

    Keywords: shelf availability; inventory-record inaccuracy; POMDP; optimal replenishment; retail analytics; Performance Effectiveness; Analysis; Mathematical Methods;


    Bassamboo, Achal, Antonio Moreno, and Ioannis Stamatopoulos. "Inventory Auditing and Replenishment Using Point-of-sales Data." Working Paper, July 2017.  View Details
  5. Operations in the On-Demand Economy: Staffing Services with Self-Scheduling Capacity

    Itai Gurvich, Martin Lariviere and Antonio Moreno

    Motivated by recent innovations in service delivery such as ride-sharing services and work-from-home call centers, we study capacity management when workers self-schedule. Our service provider chooses capacity to maximize its profit (revenue from served customers minus capacity costs) over a horizon. Because demand varies over the horizon, the provider benefits from flexibility to adjust its capacity from period to period. However, the firm controls its capacity only indirectly through compensation. The agents have the flexibility to choose when they will or will not work and they optimize their schedules based on the compensation offered and their individual availability. To guarantee adequate capacity, the firm must offer sufficiently high compensation. The goal of this paper is to examine how a firm that allows its agents to self schedule solves this problem.

    An augmented newsvendor formula captures the tradeoffs for the firm and the agents. If the firm could keep the flexibility but summon as many agents as it wants (i.e,. have direct control) for the same wages it would not only generate higher profit, as is expected, but would also provide better service levels to its customers. If the agents require a "minimum wage" to remain in the agent pool they will have to relinquish some of their flexibility. To pay a minimum wage the firm must restrict the number of agents that can work in some time intervals.

    The costs to the firm are countered by the self-scheduling firm's flexibility to match supply to varying demand. If the pool of agents is sufficiently large relative to peak demand, the firm earns more than it would if it had control of agents' schedules but had to maintain a fixed staffing level over the horizon.

    Keywords: strategic servers; on-demand economy; independent capacity; distributed systems; service operations; Uber; Service Operations; Performance Capacity;


    Gurvich, Itai, Martin Lariviere, and Antonio Moreno. "Operations in the On-Demand Economy: Staffing Services with Self-Scheduling Capacity." Working Paper, June 2016.  View Details
  6. Pros vs Joes: Agent Pricing Behavior in the Sharing Economy

    Jun Li, Antonio Moreno and Dennis J. Zhang

    One of the major differences between markets that follow a “sharing economy” paradigm and traditional two-sided markets is that the supply side in the sharing economy often includes individual nonprofessional decision makers, in addition to firms and professional agents. Using a data set of prices and availability of listings on Airbnb, we find that there exist substantial differences in the operational and financial performance of professional and nonprofessional hosts. In particular, properties managed by professional hosts earn 16.9% more in daily revenue, have 15.5% higher occupancy rates, and are 13.6% less likely to exit the market compared with properties owned by nonprofessional hosts, while controlling for property and market characteristics. We demonstrate that these performance differences between professionals and nonprofessionals can be partly explained by pricing inefficiencies. Specifically, we provide empirical evidence that nonprofessional hosts are less likely to offer different rates across stay dates based on the underlying demand patterns, such as those created by major holidays and conventions. We develop a parsimonious model to analyze the implications of having two such different host groups for a profit-maximizing platform operator and for a social planner. While a profit-maximizing platform operator should charge lower prices to nonprofessional hosts, a social planner would charge the same prices to professionals and nonprofessionals.

    Keywords: two-sided market; sharing economy; Behavioral economics; revenue management; hospitality; Two-Sided Platforms; Price; Behavior; Experience and Expertise;


    Li, Jun, Antonio Moreno, and Dennis J. Zhang. "Pros vs Joes: Agent Pricing Behavior in the Sharing Economy." Michigan Ross School of Business Working Paper, No. 1298, August 2016.  View Details