Marcelo Olivares

Structural Estimation of the Newsvendor Model: An Application to Reserving Operating Room Time

The newsvendor model captures the trade-off faced by a decision maker that needs to place a firm bet prior to the occurrence of a random event. Previous research in operations management has mostly focused on deriving the decision that minimizes the expected mismatch costs. In contrast, we present two methods that estimate the unobservable cost parameters characterizing the mismatch cost function. We present a structural estimation framework that accounts for heterogeneity in the uncertainty faced by the newsvendor as well as in the cost parameters. We develop statistical methods that give consistent estimates of the model primitives, and derive their asymptotic distribution, which is useful to do hypothesis testing. We apply our econometric model to a hospital that balances the costs of reserving too much versus too little operating room capacity to cardiac surgery cases. Our results reveal that the hospital places more emphasis on the tangible costs of having idle capacity than on the costs of schedule overrun and long working hours for the staff. We also extend our structural models to incorporate external information on forecasting biases and mismatch costs reported by the medical literature. Our analysis suggests that overconfidence and incentive conflicts are important drivers of the frequency of schedule overruns observed in our sample.

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 Toyotas 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 manufacturers distribution network and a manufacturers production flexibility, explain essentially all of the difference in finished-goods inventory between Toyota and three other manufacturers: Chrysler, Ford, and General Motors.

Structural Estimation of the Effect of Out-of-Stocks

We develop a structural demand model that endogenously captures the effect of out-of-stocks on customer choice by simulating a time-varying set of available alternatives. Our estimation method uses store-level data on sales and partial information on product availability. Our model allows for flexible substitution patterns, which are based on utility maximization principles and can accommodate categorical and continuous product characteristics. The methodology can be applied to data from multiple markets and in categories with a relatively large number of alternatives, slow-moving products, and frequent out-of-stocks (unlike many existing approaches). In addition, we illustrate how the model can be used to assist the decisions of a store manager in two ways. First, we show how to quantify the lost sales induced by out-of-stock products. Second, we provide insights on the financial consequences of out-of-stocks and suggest price promotion policies that can be used to help mitigate their negative economic impact, which run counter to simple commonly used heuristics.

Severe Weather and Automobile Assembly Productivity

It is apparent that severe weather should hamper the productivity of work that occurs outside. But what is the effect of extreme rain, snow, heat and wind on work that occurs indoors, such as the production of automobiles? Using weekly production data from 64 automobile plants in the United States over a ten-year period, we find that adverse weather conditions lead to a significant reduction in production. For example, a week with six or more days of heat exceeding 90F reduces production in that week by 8% on average. The location impacted the least by weather (Princeton, IN) loses on average 0.5% of its production due to severe weather and the location with the most adverse weather (Montgomery, AL) suffers a production loss of 3.0%. Across our sample of plants, severe weather reduces production on average by 1.5%. While it is possible that plants are able to recover these losses at some later date, we do not find evidence that recovery occurs in the week after the event. Furthermore, even if recovery does occur at some point, at the very least, these shocks are costly as they increase the volatility of production. Our findings are useful both for assessing the potential productivity shock associated with inclement weather as well as guiding managers on where to locate a new production facility - in addition to the traditional factors considered in plant location (e.g., labor costs, local regulations, proximity to customers, access to suppliers), we add the prevalence of bad weather. These results can be expected to become more relevant as climate change may increase the severity and frequency of severe weather.

Drivers of Finished Goods Inventory Performance 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 Toyotas 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 manufacturers distribution network and a manufacturers production flexibility, explain essentially all of the difference in finished goods inventory between Toyota and three other makes, Chrysler, Ford and General Motors.

Retail in High Definition: Monitoring Customer Assistance through Video Analytics

Abstract Staffing decisions typically account for a large portion of a retailers operational costs. The effectiveness of these decisions has often been analyzed by relating staffing levels to revenues. However, such approach does not explicitly consider the mechanisms by which the staff can contribute to generate revenues, such as customer assistance. This motivates the development of a fast, efficient, high-frequency method to measure customer assistance in real time. The method relies on the use of short videos that track only a portion of a customers shopping path. The recorded videos may not track all the relevant information to identify a customer-employee interaction, i.e. they might be censored. Accordingly, we develop a survival model to analyze these data, defining unbiased estimates of customer assistance. This methodology also gives insights into how staffing decisions translate into different levels of customer assistance under different congestion scenarios. For example, when the store is congested, increasing the staff from one to four employees can increase the fraction of customers receiving assistance from 38% to 45%. Furthermore, these assistance rate measures can in turn be used to assess the economic impact of assisting customers in terms of conversion or basket size. This introduces important estimation challenges related to the endogeneity of customer assistance (e.g., if the customers that are more likely to purchase are also more likely to seek assistance) and the measurement error in customer assistance rates. We address both issues using an instrumental variables approach that relies on variations on service capacity as a driver of exogenous variance in customer assistance. In particular, we find that raising the assistance rate from 50% to 60% (a one standard deviation increase from the average) increases conversion by about 5 percentage points, corresponding to a 18.5% increase in transaction volume. Finally, we show that the approach developed in this work is useful to support store staffing decisions.

Identifying Competitors in Markets with Fixed Product Offerings

We develop a novel methodology to identify competitors in markets where spatial location is an important factor of differentiation. In differentiated product markets, determining the key competitors of a focal product requires a characterization of customer demand that includes the identification of heterogeneous customer segments. The prevalent approach to characterize such a demand system is through a random utility model of customer choice. The existing methods used to estimate these models based on aggregate product sales data exploit variation in the set of product offerings to identify customer heterogeneity. Our proposed methodology uses a different empirical strategy: we use variation in observable attributes that determine the size of the distinct customer segments. This approach is useful to study markets where the set of product offerings and their characteristics are fixed or change infrequently. We apply our methodology in the hotel travel industry, where the characterization of key competitors is commonly used in practice to benchmark hotel performance. We collect publicly available data on local events to explain variation in customer segments, differentiating events by location and business/leisure purposes. Our methodology can be used effectively to characterize customer heterogeneity and identify the closest competitors to any focal hotel.

Productivity Analysis in Services Using Timing Studies

We develop a novel empirical approach to analyze workforce productivity in service systems via timing studies – detailed time-stamped data recording relevant activities performed by the employees processing service requests. Our econometric approach, which is based on models from survival analysis, takes advantage of the detailed information provided by timing study data to capture the time-varying factors that affect productivity, such as the workload level, switching among different tasks and temporary work-relief from breaks during the working shift. We apply our framework in an information technology service delivery system and use the estimated results to evaluate alternative designs of the service system in terms of workforce productivity. Specifically, our methodology can inform decisions regarding workload allocation, routing, prioritization, and working schedule design in a service system.