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Faculty and Staff Profile

Ahmet Colak

Assistant Professor of Management


Office: 123 Sirrine Hall
Phone: (864) 656-0591
Email: ACOLAK@clemson.edu
Vita: https://tinyurl.com/ycvcxzga
 

 Educational Background

Ph.D. Operations Management
Kellogg School of Management, Northwestern University 2017

M.A. Economics
Northwestern University 2013

B.S. Industrial Engineering
Bilkent University 2011

 Courses Taught

Clemson University, College of Business:

MGT 3170 Logistics Management

MGT 4120 Sourcing and Supplier Management

 Profile

Ahmet is an Assistant Professor of Management in the Clemson College of Business. His research and teaching areas lie in the Operations and Supply Chain Management area. Prior to Clemson, he obtained his doctoral degree in Operations Management from Kellogg School of Management and his master’s degree in Economics from Northwestern University. His research studies service and supply chain operations from a strategic view and addresses both theory and practice. Methodologically, he uses empirical frameworks such as structural econometrics and machine learning. His current research focuses on strategic recall and sourcing decisions in the auto industry – studying novel data sets from public agents, private companies, consumer reports, and online reviews. Ahmet has held research presentations at prestigious business schools, companies, and conferences, including INFORMS, MSOM, and POMS. Ahmet teaches operations management with an emphasis on supply chains and services. His applied teaching areas cover a rich domain of practice-driven operational strategies across industries.

 Research Interests

Data Analytics, Empirical Operations Management, Supply Chain Management

 Research Publications

1. "Empirical Studies on Auto Recalls"
PhD Dissertation, 2017
Northwestern University

Abstract: In the last two decades, the federal U.S. government have mandated 5.68 thousand distinct product recalls associated with 0.70 billion faulty auto components, and the auto firms have voluntarily recalled 8.44 thousand distinct auto products associated with 0.73 billion faulty components. And in each year, the National Highway Traffic Safety Administration (NHTSA) receives around 50,000 defect reports for potentially defective auto products. These defect reports spur automakers and NHTSA to recall nearly 70 million auto components annually.

While there is a substantial amount of empirical research on the negative consequences of auto recalls and product defects---faulty products might hurt brand equity significantly---and on the positive aspects of auto recalls---auto recalls can reduce accident rates and improve product reliability significantly---there is little evidence on two important questions: First, how do the federal government and auto manufacturers initiate recalls? And second, what type of operational factors cause such quality problems?

To study these important questions, we combine together and analyze novel auto industry data in this dissertation. Using text-matching algorithms, we merge a rich collection of automotive data relating to product recalls, consumer complaints, defect reports, defect statements, defect sources, defect keywords, vehicle sales, vehicle scraps, vehicle registrations, supplier-manufacturer networks, supply chain geography, supplier characteristics, and product characteristics.

This dissertation studies the distinct aspects of our quality-related automotive datasets across three chapters: Chapter 1 studies defect rates as independent variables to endogenous recalls decisions with a structural model, Chapter 2 studies supply chain distance as independent variables to endogenous defect rates with a selection-corrected model, Chapter 3 studies defect characteristics as independent variables to endogenous recalls rates with a longitudinal sample.

2. "Supply Chain Proximity and Product Quality"
Forthcoming at Management Science, 2018
With Robert Bray and Juan Camilo Serpa

Abstract: We explore the effect of supply chain proximity on product quality by merging four independent data sources from the automotive industry, collecting: (i) auto component defect rates, (ii) upstream component factory locations, (iii) downstream assembly plant locations, and (iv) product-level links connecting the upstream and downstream factories. Combining these four datasets allows us to trace the flow of 27,807 products through 529 supplier factories and 275 assembly plants. We estimate that increasing the distance between an upstream component factory and a downstream plant by an order of magnitude increases the component's expected defect rate by 3.9%. We also find that shorter inter-factory spans are associated with more rapid product quality improvements, and that supply chain distance is more detrimental to quality when automakers: (i) produce early generation models or (ii) high-end products, (iii) when they buy components with more complex configurations, or (iv) when they source from suppliers who invest relatively little in research and development.

3. "Why Do Automakers Initiate Recalls? A Structural Econometric Game"
Major Revision at Management Science, 2017
With Robert Bray

Abstract: We model a manufacturer's and regulator's joint recall decisions as an asymmetric dynamic discrete choice game. In each quarter, the agents observe a product's defect reports and update their beliefs about its failure rate. The agents face an optimal stopping problem: they decide whether or not to recall the product. The agents trade off between current recall costs and future failure costs; they respond to these intertemporal costs with Markov-perfect equilibrium. We estimate our model with auto industry data comprising 14,124 recalls and 976,062 defect reports. We reverse-engineer the structural primitives that underlie our model: (i) the evolution of the failure rates, and (ii) the failure and recall cost parameters. Since our model is a regenerative process---a recall resets the future failure rate---we implement a myopic policy estimator to circumvent the curse of dimensionality. Our counterfactual study establishes that both agents initiate recalls to avoid future failures but not to preempt the other agent's anticipated recalls. Indeed, we find that the regulator's recalls have no significant deterrence effect on the manufacturers.


Last Updated: June 2018

 Links

LinkedIn
SSRN
Recalls
Proximity
Thesis