Supply Chain and Operations Seminar Series

The Supply Chain and Operations Department & Juran Research Center Seminar Series showcases current research in Management and Operations Science, including topics in operations strategy, technology, quality, new product development, and supply chain management. Speakers are drawn from universities around the world.

 

2021-2022

Fall

 

Hamsa Bastani 
Wharton School, University of Pennsylvania

Date: Friday, October 15th, 2021

Time: 9:30 am - 11:00 am

Location: Virtual

"Efficient and Targeted COVID-19 Border Testing
via Reinforcement Learning"

Abstract: 

Throughout the COVID-19 pandemic, countries relied on a variety of ad-hoc border control protocols to allow for non-essential travel while safeguarding public health: from quarantining all travelers to restricting entry from select nations based on population-level epidemiological metrics such as cases, deaths or testing positivity rates. Here we report the design and performance of a reinforcement learning system, nicknamed ‘Eva’. In the summer of 2020, Eva was deployed across all Greek borders to limit the influx of asymptomatic travelers infected with SARS-CoV-2, and to inform border policies through real-time estimates of COVID-19 prevalence. In contrast to country-wide protocols, Eva allocated Greece’s limited testing resources based upon incoming travelers’ demographic information and testing results from previous travelers. By comparing Eva’s performance against model good would like to save the power or that's even better put that in the corner thing right away where am Ied counterfactual scenarios, we show that Eva identified 1.85 times as many asymptomatic, infected travelers as random surveillance testing, with up to 2-4 times as many during peak travel, and 1.25-1.45 times as many asymptomatic, infected travelers as testing policies that only utilize epidemiological metrics. We demonstrate that this latter benefit arises, at least partially, because population-level epidemiological metrics had limited predictive value for the actual prevalence of SARS-CoV-2 among asymptomatic travelers and exhibited strong country-specific idiosyncrasies in the summer of 2020. Our results raise serious concerns on the effectiveness of country-agnostic internationally proposed border control policies that are based on population-level epidemiological metrics. Instead, our work represents a successful example of the potential of reinforcement learning and real-time data for safeguarding public health.

 

 

Spring

 

Bradley Staats
University of North Carolina, Chapel Hill

Date: Friday, February 4, 2022

Time: 9:30 am - 11:00 am

Location: Virtual

"Task Selection and Patient Pick-up: How Familiarity Encourages Physician
Multitasking in the Emergency Department"

Abstract: 

Throughout the COVID-19 pandemic, countries relied on a variety of ad-hoc border control protocols to allow for non-essential travel while safeguarding public health: from quarantining all travellers to restricting entry from select nations based on population-level epidemiological metrics such as cases, deaths or testing positivity rates. Here we report the design and performance of a reinforcement learning system, nicknamed ‘Eva’. In the summer of 2020, Eva was deployed across all Greek borders to limit the influx of asymptomatic travellers infected with SARS-CoV-2, and to inform border policies through real-time estimates of COVID-19 prevalence. In contrast to country-wide protocols, Eva allocated Greece’s limited testing resources based upon incoming travellers’ demographic information and testing results from previous travellers. By comparing Eva’s performance against modelled counterfactual scenarios, we show that Eva identified 1.85 times as many asymptomatic, infected travellers as random surveillance testing, with up to 2-4 times as many during peak travel, and 1.25-1.45 times as many asymptomatic, infected travellers as testing policies that only utilize epidemiological metrics. We demonstrate that this latter benefit arises, at least partially, because population-level epidemiological metrics had limited predictive value for the actual prevalence of SARS-CoV-2 among asymptomatic travellers and exhibited strong country-specific idiosyncrasies in the summer of 2020. Our results raise serious concerns on the effectiveness of country-agnostic internationally proposed border control policies that are based on population-level epidemiological metrics. Instead, our work represents a successful example of the potential of reinforcement learning and real-time data for safeguarding public health.

 

Masha Shunko
University of Washington

Date: Friday, February 11, 2022

Time: 9:30 am - 11:00 am

Location: Virtual

"Evidence of The Unintended Labor Scheduling Implications of The Minimum Wage"

Abstract: 

The effect of the minimum wage is an important yet controversial topic that has received attention for decades. Our study is the first to take an operational lens and empirically study the impact of the minimum wage on firms' scheduling practices. Using a highly granular dataset from a chain of fashion retail stores, we estimate that a $1 increase in the minimum wage, while having a negligible impact on the total labor hours used by the stores, leads to a 27.7% increase in the number of workers scheduled per week, but a 19.4% reduction in weekly hours per worker. For an average store in California, these changes translate into four extra workers and five fewer hours per worker per week. Such scheduling adjustment not only reduces the total wage compensation per worker but also reduces workers' eligibility for benefits. We also show that the minimum wage increase reduces the consistency of weekly and daily schedules for workers. For example, the absolute (relative) deviation in weekly hours worked by each worker increases by up to 32.9% (6.6%) and by up to 9.7% (2.1%) in daily hours, as the minimum wage increases by $1. Our study empirically identifies and highlights a new operational mechanism through which increasing the minimum wage may negatively impact worker welfare. Our further analysis suggests that the combination of the reduced hours, lower eligibility for benefits, and less consistent schedules (that resulted from the minimum wage increase) may substantially hurt worker welfare, even when the overall employment at the stores stay unchanged. By better understanding the intrinsic trade-off of firms' scheduling decisions, policy makers can better design minimum wage policies that will truly benefit workers.

 

Kris (Johnson) Ferreira
Harvard Business School

 

Date: May 6, 2022

Time: 9:30 am -11:00 am

Location: Virtual

"Unlocking algorithm potential: Overcoming naïve advice weighting with feature transparency"

Abstract:

Although algorithms typically make better forecasts than human decision-makers (HDMs), occasionally HDMs have “private” information which the forecasting algorithm does not have access to - such as social media buzz - that can be used to improve algorithmic forecasts. We first propose a mathematical model that captures how an HDM combines information she directly observes (some of which is private) with an algorithmic prediction to make a final demand forecast. In the model, HDMs take a weighted average between their own forecast and the algorithm’s, where the weights depend only on the aggregate relative historical performances. This leads to HDMs over-adjusting the algorithm’s predictions when it performs well and under-adjusting the algorithm’s predictions when it performs poorly. We validate our model using a lab experiment where 359 participants are tasked with making demand forecasts for 20 products while having access to an algorithm’s recommendations. In a follow-up experiment, we show that providing transparency into the algorithm’s input features can help HDMs use the private information to differentially adjust the algorithm’s forecasts. Our result shows that feature transparency – even when the underlying algorithm is a black box – helps users better incorporate algorithmic recommendations in their decisions.

 

 

2020-2021

Fall

 

Ariel Stern
Harvard Business School

 

Date: November 13, 2020

Time: 9:30 am - 11:00 am

Location: Virtual

"Product Recalls and New Product Development: Own Firm Distractions
 and Competitor Firm Opportunities”

Abstract:

Product recalls create significant challenges for R&D intensive firms, but simultaneously generate potentially lucrative opportunities for competitors. Using the U.S. medical device industry as our setting, we develop predictions and provide evidence that own firm recalls slow new product development activities, while competitor firm recalls accelerate them. We also examine two firm-level moderators that influence the recall and new product development relationship: product scope and ownership structure. We find that own firm recalls slow new product development for all firm types: a single own firm recall slows new product development up to 43 days, equating to more than $10 million in revenue lost in this high-margin and highly competitive setting. We also find that competitor firm recalls are associated with accelerated development times, but only for broad (vs. narrow) product scope firms and public (vs. private) firms. A one standard deviation increase in competitor firm recalls for these firm types accelerates new product development by more than two weeks. Organizational resources and financial incentives are thus key determinants of whether firms can effectively capitalize on the potential market opportunities created by competitor recalls. In post-hoc analyses, we explore whether future product quality is predicted by post-recall submission times, but find no evidence for this relationship. This result suggests that new product development delays following own firm recalls are more likely driven by organizational distractions than by product quality learning, and that firms react strategically and rationally by speeding new products to market after competitor recalls.

 

Bin Hu
University of Texas, Dallas

 

Date: November 20, 2020

Time: 9:30 am - 11:00 am

Location: Virtual

"Curbing Emissions: Environmental Regulations and
Product Offerings Across Markets" 

Abstract:

In order to curb CO2 emissions, the US Environmental Protection Agency (EPA) implements a minimum federal fuel economy standard for new cars, whereas the California Air Resources Board (CARB) implements a stricter standard in California and other voluntary "CARB states". Automakers have historically met the stricter CARB standard with their offerings. The Trump administration's 2018 announcement to freeze the EPA standard threatened to widen its gap from the CARB standard and cause a split market where automakers offer differentiated car models in CARB and non-CARB states. Inspired by this crisis, we model two regulators---of different levels of environmental awareness---who set efficiency standards in their respective markets, and a firm offering product(s) for these markets. We find that there exist equilibria where the firm offers a unified product for both markets, a different product in each market, or only serves one market, and show that a unified market is preferable to a split market in terms of total emissions. We then propose and show horizontal negotiations (between the regulators) and vertical negotiations (between a regulator and the firm) to be effective strategies to unify a split market and reduce emissions. Finally, we analyze a model variation for local pollutants such as NOx and SO2, as opposed to global pollutants such as CO2, and find that different mitigating strategies may be needed to manage local pollutants.

 

Hessam Bavafa
University of Wisconsin

Date: December 11, 2020

Time: 9:30 am - 11:00 am

Location: Virtual

“The Variance Learning Curve”

Abstract:

The expansive learning curve literature in Operations Management has established how various facets of prior experience improve average performance. In this paper, we explore how increased cumulative experience affects performance variability, or consistency. We use a two-stage estimation method of a heteroskedastic learning curve model to examine the relationship between experience and performance variability among paramedics at the London Ambulance Service. We find that for paramedics with lower experience, an increase in experience of 500 jobs reduces the variance of task completion time by 8.7%, in addition to improving average completion times by 2.7%. Similar to prior results on the average learning curve, we find a diminishing impact of additional experience on the variance learning curve. We provide an evidence base for how to model the learning benefits of cumulative experience on performance in service systems. Our findings imply that the benefits of learning are substantially underestimated if the consistency effect is ignored. Specifically, our estimates indicate that queue lengths (or wait times) might be overestimated by as much as 4% by ignoring the impact of the variance learning curve in service systems. Furthermore, our results suggest that previously established drivers of productivity should be revisited to examine how they affect consistency, in addition to average performance.

 

Spring

Felipe Caro
UCLA, Anderson School of Management

Date: January 22, 2021

Time: 9:30 am - 11:00 am

Location: Virtual

"Believing in Analytics: Managers' Adherence to
 Price Recommendations from a DSS"

Abstract

Problem definition: We study the adherence to the recommendations of a decision support system (DSS) for clearance markdowns at Zara, the Spanish fast fashion retailer. Our focus is on behavioral drivers of (i) the decision to deviate from the recommendation, and (ii) the magnitude of the deviation when it occurs. We also study the effect of two related interventions: 1. providing feedback using a revenue metric; and 2. showing a reference point for that metric.  Academic/practical relevance: A major obstacle in the implementation of prescriptive analytics is users' lack of trust in the tool. Understanding the behavioral aspects of managers' usage of these tools is paramount for a successful rollout and deployment.  Methodology: We use data collected by Zara during seven clearance sales campaigns to analyze the operational and behavioral drivers of managers' adherence decisions. Results: Adherence to the DSS's recommendations was higher when such recommendations were aligned with managers' previous coarse heuristic, consistent with algorithm aversion and status quo bias. Adherence was also higher when managers had fewer prices to set, consistent with rational inattention. The magnitude of managers' deviations was larger when inventory levels were higher and sales were slower, and when salvage prices were lower, consistent with the idea that inventory was more salient than revenue, and with loss aversion. Of the two interventions, only the second one was effective in increasing targeted managers' adherence and decreasing their probability of marking down when the DSS recommended leaving a price unchanged. Managerial implications: Our findings provide insights on how to increase voluntary adherence that can be used in any context in which a company wants an analytical tool to be adopted organically by its users. 

This is joint work with Anna Sáez de Tejada Cuenca (IESE Business School).

 

Sridhar Tayur
Carnegie Mellon University, Tepper School of Business

Date: February 5, 2021

Time: 9:30 am - 11:00 am

Location: Virtual

“Quantum Integer Programming (QuIP): An Introduction”

Abstract:  

Many applications in Operations Management, Finance and Cancer Genomics (and others) can be modeled as non-linear (and non-convex) integer programs. Quantum computing, in particular Ising models, provide an alternative way to  tackle these hard problems. In this talk, I will provide an introduction to this new area of Quantum Integer Programming, based on the recent course that I taught at CMU (and was taught at IIT-Madras) last fall, in collaboration with NASA/USRA and Amazon.

 

Nur Sunar
University of North Carolina, Kenan-Flagler Business School

Date: February 12, 2021

Time: 9:30 am - 11:00 am

Location: Virtual

"Net-Metered Distributed Renewable Energy: A Peril for Utilities?"

Abstract: 

Electricity end-users have been increasingly generating their own electricity via rooftop solar panels. We study the impact of such distributed renewable energy (DRE) on utility profits and social welfare under net metering, which is a widespread policy in the United States. Utilities have been lobbying against net-metered distributed solar based on the common belief that it harms utility profits. We find that when wholesale market dynamics are considered, net-metered DRE may be a positive for utilities. That is, net-metered DRE strictly improves the expected utility profit when the utility’s self-supply is below a threshold and the wholesale electricity price is sufficiently responsive to wholesale demand fluctuations. Our paper distinctively considers both downstream and upstream impacts of net-metered DRE on utilities and analyzes the tradeoff between these impacts. Net-metered DRE can increase utilities’ expenses because of their required buyback from generating customers, and reduces their retail sales revenues. In addition, it can either reduce utilities’ wholesale procurement costs or affect their wholesale market revenues. Our results suggest that utilities might benefit from emerging business strategies that motivate their customers to install solar panels. Our numerical study uses data on the distributed solar in California and the CAISO’s wholesale electricity market and demonstrates that our findings hold under realistic parameters.

 Joint work with Jay Swaminathan

 

Aravind Chandrasekaran
Ohio State University, Fisher College of Business

Date: Friday, February 19, 2021

Time: 9:30 am - 11:00 am

Location: Virtual

"Does Fresh Food Improve Health?
Expanding the Care Delivery Boundary in Partnership Models of Care"

Abstract:

Problem definition: To address a root cause of poor quality, healthcare institutions are beginning to partner with community-based organizations (CBOs) such as food pantries. These partnership models of care broaden the healthcare delivery infrastructure and, indirectly, couple the distribution of clinical and non-clinical services to increase care continuity. Academic/practical relevance: The healthcare provider identifies the patient’s health-related, social needs and addresses them using a CBO referral. In this paper, we study a partnership model of care called the Fresh Food Farmacy (FFF) program and identify the treatment effect of participation on health quality and cost for those with chronic diseases. The FFF program uses a food insecurity screening tool to individually assess food status and refer patients to nearby food pantries. The partnership model of care advances our understanding of care continuity by expanding the care delivery boundary beyond the healthcare clinic. Methodology: At the patient-clinic visit level of analysis, we operationalize health quality as weight (lbs.) and total cost of care as the summed Medicaid reimbursement rate for primary care procedure codes. We tracked changes in health quality and cost one-year before and one-year after referral for patients with sufficient clinic health data; otherwise, those that met a patient inclusion protocol. We address differences in the amount of clinical health data and observable covariates using a two-step matching procedure and achieve a nearly balanced sample. Econometrically, we use difference-in-differences to identify the treatment effect on health quality and cost. Our total sample includes 1,179 patients with 9,974 clinic visits from 2015 to 2018 from a network of 8 federally-qualified primary care clinics. Results: For patients that comply with the referral, we use a unique identifier to track food pantry access behaviors one-year after referral. We find that participation in the FFF program confers a quality and cost benefit for a subset of patients that accessed the food pantry almost once per month. Among this group, there was a 3% reduction in weight and a 10% reduction in costs. These reductions are clinically significant and suggest that patients have changed underlying health behaviors, a root cause of poor quality. A post-hoc analysis explores the frequency and consistency of food pantry access after referral as a potential explanation for heterogeneous effects. A patient with poor health (at the time of referral) is more likely to use “food as medicine” and benefit more from participating in the FFF program. Managerial implications: Extending the care delivery boundary to include CBOs offers a favorable quality-cost benefit, however, only for certain patients. Healthcare institutions should implement concurrent interventions that target area level barriers to patient participation. 

 

Javad Nasiry
McGill University, Montreal

Date: March 5, 2021

Time: 9:30 am - 11:OO am

Location: Virtual

"Sustainability in the Fast Fashion Industry"

Abstract:

We establish a much-needed link between the fast fashion business model and its environmental consequences. A fast fashion system allows firms to react quickly to changing consumer demand by replenishing inventory (via quick response) and introducing more fashion styles. We study the environmental impact of the fast fashion business model by analyzing its implications for product quality, variety, and inventory decisions. We find that a key driver of low product quality in the fast fashion industry is the firm's incentive to offer variety to hedge against uncertain fashion trends. When variety is endogenous, quality decreases as consumers become more sensitive to fashion or as the cost of introducing new styles decreases. We assess the effectiveness of three environmental initiatives (waste disposal regulations, consumer education, and production tax schemes) in countering the environmental impact of fast fashion. We show that waste disposal policies and production taxes are effective in reducing the firm's leftover inventory--but may have the unintended consequence of lowering product quality and hence worsening the firm's environmental impact.  We also find that education campaigns that increase consumers' sensitivity to quality strictly benefit the environment in the long run.

 

Greys Sosic
University of Southern California, Marshall School of Business

Date: March 12, 2021

Time: 9:30 am - 11:00 am

Location: Virtual

"Rethinking Salt Supply Chains: Costs and Emissions Analysis
for Co-production of Salt and Freshwater from U.S. Seawater"

Abstract: 

Is it feasible to build desalination plants for the co-production of salt and freshwater from U.S. seawater that could lead to a restructuring of supply chains for salt imports? As water is becoming scarcer worldwide, an increasing number of countries are using desalination plants to generate freshwater. In most such cases, residual concentrates must be disposed off, and the disposal cost is increasing as countries are becoming more environmentally conscious.  Selective salt recovery can help to alleviate this issue, as it reduces the need for concentrate disposal and generates additional revenue. To gain some insights into the costs and benefits of co-production plants, we have collected  data on current desalination practices and salt imports in the U.S., along with the manufacturing costs and energy requirements for co-production plants. We have  used this data to build an optimization model  to determine an optimal number and location of co-production plants in the U.S. and their potential markets for the sale of co-produced salt. In our analysis, we have considered a different total number of co-production facilities, and for each configuration we evaluated the resulting net water cost and carbon emissions impact. Our results indicate that there exists the potential for building several co-production plants in the U.S. that would be both financially competitive with  existing desalination plants and  lead to a reduction in carbon emissions. This information might be of use to both governments and businesses   when they make decisions about the type of desalination facilities  built and the implemented ``polluter pays'' policies.

 

Baris Ata
University of Chicago, Booth School of Business

Date: March 19, 2021

Time: 9:30 am -  11:00 am

Location: Virtual

"Structural Estimation of Kidney Transplant Candidates' Quality of Life Scores:
Improving National Kidney Allocation Policy Under Endogenous Patient Choice and Geographical Structure"

Abstract:

This paper develops a framework for assessing the impact of changes to the deceased-donor kidney allocation policy, taking into account the transplant candidates' (endogenous) organ acceptance behavior. To be specific, it advances a dynamic structural model of the transplant candidates' accept/reject decisions for organ offers. Our formulation models the national list (and its geographic structure) which is important for practical implementations, e.g. for incorporating it in the Kidney Pancreas Simulation Allocation Model (KPSAM). Moreover, it allows various important features of the transplant system such as the degree of tissue matching between the donor and the transplant candidate, changes in the health status of the transplant candidates as they wait on the list, organ quality, geographical sharing and cold-ischemia time of the organs as well as the heterogeneity in transplant candidates' quality of life scores. Using United Network of Organ Sharing (UNOS) data on transplant candidates, donors, organ offers, and follow up results on transplant outcomes, we first estimate the transplant candidates' quality of life scores.  Our estimates are based on patient's revealed preferences and yield similar results on average to what is typically assumed in the medical literature. However, they differ significantly when patient and donor characteristics are considered. We then perform various counterfactual studies for assessing the (unintended) consequences of policy changes. In particular, we find that although the current policy increases the total number of transplants by 2.63% and total life years by 4.45%, it decreases total quality adjusted life years by 1.68%. Moreover, it increases the disparity in probability of getting a transplant for patients of different health scores by 69.3%. These happen due to the current prioritization of healthier patients for kidneys of better quality.  We also show that geographical redistricting of the transplant system, as done for the liver allocation system, does not change the system performance significantly.  However, the brevity matching policy which is a last-come-first served distribution policy based on the health scores of the patients, can further increase the total number of transplants by 1.50%.

 Joint work with: 

John J. Friedewald, Northwestern University Transplant Outcomes Research Center
A. Cem Randa, University of California San Francisco Department of Surgery

 

Ravi Subramanian
Georgia Tech, Scheller College of Business

Date: April 16, 2021

Time: 9:00 am - 11:00 am

Location: Virtual

"An Empirical Investigation of Relationships Between
Locational Demographics and Facility-Level Emissions"

Abstract:

Environmental Justice encompasses the idea of fairness in protecting individuals and communities from environmental and health hazards, regardless of race, color, national origin, or income. Environmental Justice is relevant to the practice of Operations Management in the form of inequities – whether advertent or inadvertent – that result from spatially disparate operational decisions or policies. The broad research question that we aim to address is: How disparate are facility-level emissions and environmental practices across communities with different racial makeups? To empirically address this question, we draw data from the US Census Bureau’s American Community Survey (ACS), and the US EPA’s Toxics Release Inventory (TRI) and Risk-Screening Environmental Indicators Model (RSEI). We employ Coarsened Exact Matching to assess how facilities may differ in their toxicity and exposure-weighted aggregate emissions (outcome) between locations that differ in racial makeup (treatment). Our findings offer evidence for regulatory intervention and opportunities for firms to reframe their broad ESG objectives with local considerations of fairness and equity.

 

Samantha Keppler
University of Michigan

Date: April 23, 2021

Time: 9:30 am - 11:00 am

Location: Virtual

“Crowdfunding the Front Lines: An Empirical Study of
Teacher-Driven School Improvement”

Abstract:

A widespread belief is that the traditional brick-and-mortar K--12 education system in the US is broken. The private sector, specifically education technology (EdTech) companies, have stepped in to try to help. In this paper, we study DonorsChoose, a nonprofit that works to improve the traditional brick-and-mortar system with a teacher crowdfunding platform. Given the constraints of working with the current struggling system, we ask whether DonorsChoose moves the needle on effectiveness and inequality. Combining DonorsChoose data with data on student test scores in Pennsylvania from 2012--2013 to 2017--2018, we find an increase in the number of DonorsChoose projects funded at a school leads to higher student performance, after controlling for selection biases. In high schools, a 10% increase in the number of funded projects leads to a 0.1 to 0.2 percentage point (pp) increase in students scoring basic and above in all tested subjects. A 10% increase in the number of funded projects at an elementary or middle school leads to a 0.06 pp increase in the percentage of students scoring basic and above in language arts and a 0.15 pp increase in science. We find these effects are driven primarily by teacher projects from the lowest income schools, suggesting the platform helps reduce inequality in educational outcomes. Based on a textual analysis of thousands of statements from all funded teachers describing how resources are used, we find two channels of improvement uniquely effective in the lowest income schools. Our study suggests that those in the education sector can harness the wisdom of front-line workers -- teachers -- to improve effectiveness, efficiency, and equity.

 

2019 - 2020

Spring

Ahmet Colak
Clemson University

Date: March 6, 2020

Time: 9:30 am - 11:00 am

Location: CSOM - 1-132

"Bilateral R&D Productivity and Supply Chain Networks"

Abstract:

We study research and development productivity (RDP) transmission between 4,123 global firms across three supply chain tiers. Collecting 153,090 yearly supply chain dyad partnerships from Bloomberg, we construct a two-sided econometric model of supply chain R&D. In our empirical specification, the dependent variable measures return on R&D, and the independent variables measure supply chain partner and network effects. In our sample data, we find that a 1% R&D productivity improvement of (i) an upstream partner can increase a downstream agent’s R&D productivity by 0.14%, and (ii) a downstream partner can increase an upstream agent’s R&D productivity by 0.28%. Our findings show that having R&D-productive partners plays a significant role in transforming an agent’s R&D into revenues. Similarly, we estimate a network’s average R&D productivity elasticity on an agent as 0.23%. We further find that R&D productivity spreads more within smaller, integrated, domestic, and intra-industry networks. In our two-stage estimation, we address supply chain network endogeneity resulting from entanglement, simultaneity, and partner selection. Our findings provide operational and financial insights for R&D practitioners.

 

2018 - 2019

Fall

Dennis Cook
University of Minnesota, School of Statistics

Date: Friday, September 28, 2018

Time: 10:00-11:30 a.m.

Location: CSOM 1-132

Title: Partial Least Squares Regression

Abstract: Partial least squares regression, which has been around for about four decades, is a dimension-reduction algorithm for fitting linear regression models without requiring that the sample size be larger than the number of predictors.  It was developed primarily by the Chemometrics community where it is now ingrained as a core method, and it is apparently used throughout the applied sciences.

And yet it seems fair to conclude that PLS regression has not been embraced by some communities, even as a serviceable method that might be useful occasionally. Nor does there seem to be a common understanding as to why this rather enigmatic method should not be used, although bumptious discussions of PLS failings can be found in some applied areas.  Perhaps this is as it should be — perhaps not.

This talk is intended as a relatively informal overview on PLS regression, including historical context, personal encounters, methodology, relationship to envelopes and, near the end, a few recent asymptotic results for high-dimensional regressions.

 

Gopesh Anand 
Illinois, Gies College of Business

Date: Friday, October 26, 2018

Time: 10:00-11:30 a.m.

Location: CSOM 2-233

 

Tim Kraft 
Massachusetts Institute of Technology

Date: Friday, October 5, 2018

Time: 10:00-11:30 a.m.

Location: CSOM 2-233

Title: Supply Chain Transparency and Social Responsibility 

Abstract: Consumers increasingly expect companies to ensure that their products are made in a socially responsible manner. However, most companies do not have extensive visibility into their supply chains. According to a recent study, 81% of the 1,700 companies surveyed lacked full visibility into the social responsibility (SR) practices of their suppliers. In this talk, I will first overview our work on how improved supply chain visibility into suppliers’ SR practices can impact companies’ interactions with both consumers and suppliers. I will then discuss a specific study (below) that examines the effect of visibility on consumers’ trust of companies’ SR disclosures.

According to a 2015 Nielsen survey, brand trust tops the list of factors that influence socially responsible purchases. At the same time, supply chain transparency has been identified as one of the most effective ways for companies to improve consumers’ trust of SR disclosures. The current literature focuses on the effect that disclosing information has on consumer trust, generally assuming that companies have complete information (i.e., full supply chain visibility) about the SR practices occurring in their supply chains (e.g., working conditions). In this study, we design an incentivized human-subject experiment to examine whether and how visibility impacts consumers’ trust in companies’ SR communications. Our results show that by investing to improve visibility into the SR practices in its supply chain, a company can increase consumer trust in its SR communications. This increased trust can in turn help the company to increase its sales when a good SR claim is made. This is particularly true among consumers with prosocial orientations; i.e., a willingness to sacrifice their own benefit to help others. 

This talk is based on joint work with Leόn Valdés (University of Pittsburgh) and Karen Zheng (MIT).

 

Ruomeng Cui
Emory University, Goizueta Business School

 

Date: Friday, November 16, 2018

Time: 10:00-11:30 a.m.

Location: CSOM 2-233

 

Basak Kalkanci
Georgia Institute of Technology, Scheller College of Business

Date: Friday, November 30, 2018

Time: 10:00-11:30 a.m.

Location: CSOM 2-233

Spring

Chris Tang
University of California, Los Angeles 

Date: Friday, May 17, 2019

Time: 10:00-11:30 a.m.

Location: 2-224 CSOM

Title: Environmental Violations in China: Descriptive, Predictive, and Prescriptive Analysis

Abstract:  Air and water pollution in China has crated public concern.  In this talk, we discuss our empirical findings about the short-term and long-term implications of Chinese manufacturers who violated environmental regulations.   Also, we present a model that can enable Chinese government to use publicly available data to predict which firm is more likely to violate regulations in the future.

*This talk is based on two research papers: 1. Chris KY Lo, Christopher S Tang, Yi Zhou, Andy CL Yeung, Di Fan, 2018. Environmental Incidents and the Market Value of Firms: An Empirical Investigation in the Chinese Context, M&SOM; and 2. Chris KY Lo, Christopher S Tang, Yi Zhou, 2018. Environmental Violations in China: Evaluating Their Long-term Impact and Predicting Future Violations, Working Paper, UCLA.  

2017 - 2018

Fall

Susan Lu
Purdue, Krannert School of Management

Date: Friday, October 6, 2017

Time: 10:00-11:30 a.m.

Location: CSOM 1-132

Title: Do Mandatory Overtime Law Improve Quality? Staffing Decisions and Operational Flexibility of Nursing Homes

Abstract: TBD

 

Soo-Haeng Cho
Carnegie Mellon University, Tepper School of Business

Date: Friday, October 27, 2017

Time: 10:00-11:30 a.m.

Location: CSOM 1-135

Title: The Theory of Crowdsourcing Contests

Abstract: 

     This talk will present papers that concern the theory of innovation tournaments (also called crowdsourcing contests or open innovation). In an innovation tournament, an organizer solicits innovative ideas from a number of independent agents. Agents exert efforts to develop their solutions, but their outcomes are unknown due to technical uncertainty and/or subjective evaluation criteria. We call an agent whose ex-post solution contributes to the organizer's utility a “contributor.”

     The first paper, entitled “Optimal Award Scheme in Innovation Tournaments,” examines optimal award scheme/rule in innovation tournaments. While extant literature either assumes a winner-take-all scheme a priori or shows its optimality under specific distributions for uncertainty, this paper derives necessary and sufficient conditions under which the winner-take-all scheme is optimal. These conditions are violated when agents perceive it very likely that only few agents receive high evaluation or when a tournament does not require substantial increase in agents' marginal cost of effort to develop high-quality solutions. Yet, the winner-take-all scheme is optimal in many practical situations, especially when agents have symmetric beliefs about their evaluation. In this case, the organizer should offer a larger winner prize when he is interested in obtaining a higher number of good solutions, but interestingly the organizer need not necessarily raise the winner prize when anticipating more participants to a tournament.

     The second paper, entitled “Innovation Tournaments with Multiple Contributors,” analyzes a general model of uncertainty and utility functions with multiple contributors, and shows that these factors play a crucial role in decision-making of agents and the organizer. Specifically, contrary to existing theories, increased competition to a tournament can have a positive impact on agents' incentives to exert effort when agents expect good outcomes with high likelihood, and a free-entry open tournament should be encouraged only when the problem is highly uncertain or the organizer seeks diverse solutions from many contributors. Our results are consistent with recent empirical evidence, hence helping close a gap in the extant literature between theory and practice.

 

Spring

John Birge
The University of Chicago, Booth School of Business

Date: Friday, March 30

Time: 10:00-11:30 a.m.

Location: CSOM 2-224

Title: Dynamic Learning in Strategic Pricing Games

Abstract:

In monopoly pricing situations, firms should optimally vary prices to learn demand. The variation must be sufficiently high to ensure complete learning.  In competitive situations, however, varying prices provides information to competitors and may reduce the value of learning. Such situations may arise in the pricing of new products such as pharmaceuticals. This talk will discuss how this effect can be strong enough to stop learning so that firms optimally reduce any variation in prices and choose not to learn demand. The result can be that the selling firms achieve a collaborative outcome instead of a competitive equilibrium. The result has implications for policies that restrict price changes or require disclosures. 

 

Karen Zheng
MIT, Sloan

Date: Friday, April 27

Time: 10:00-11:30 a.m.

Location: CSOM 2-213

Title: Economically Motivated Adulteration in Farming Supply Chains: Data and Models

Abstract: 

Food adulteration is a serious threat to public health. Many incidents of food adulteration are motivated by economic gains, defined as economically motivated adulteration (EMA). In this talk, I will present recent works that examine drivers of EMA risks in farming supply chains. We first present a multi-industry empirical study that demonstrates supply chain dispersion and local governance being two important risk drivers. To do so, we collect farming supply chain data, food sampling data, and socioeconomic data across five different industries (aquatic products, eggs, honey, pork, and poultry) in China. We define supply chain dispersion as the degree to which farming outputs are sourced from a dispersed network of farms -- and develop a method to quantify dispersion in farming supply chains based on field data. We also develop multi-faceted measures to objectively quantify the strength of city-level governance in China based on factual (as opposed to perception) data. Our results show that products made in a more dispersed supply chain and in regions with weaker governance are associated with higher EMA risks. Inspired by the empirical findings, we develop a set of supply network models to structurally analyze farms’ strategic adulteration behavior and the resulting EMA risks in farming supply chains. We examine both preemptive EMA, where farms engage in adulteration to reduce the likelihood of producing low-quality units, and reactive EMA, where farms adulterate low-quality units to increase the perceived quality of their outputs. We fully characterize the farms’ equilibrium adulteration behavior in both types of EMA and examine how quality uncertainty, supply chain dispersion, traceability, and testing sensitivity (in detecting adulteration) jointly impact EMA risks. We validate our models’ predictions with numerical analyses calibrated by field data. We conclude by offering tangible insights regarding how business entities and regulators can more proactively prevent and address EMA risks in farming supply chains.