I am an Assistant Professor and Claude Marion Endowed Faculty Scholar in the Operations, Information and Decisions Department at the Wharton School, University of Pennsylvania.
My research focuses on how technology and data are re-shaping the organization and management of workers, service platforms, and supply chains—all of which call for actively managing decentralized incentives and information. I use data to study the principled design of such incentives and information flows.
I am always open to new collaborations with academics and data partners. Current endeavors, which I am happy to talk about, include partnerships to enhance worker productivity and welfare within complex operations by collecting and leveraging better big data. Examples include (1) using biosensors to track and manage stress and rapid burnout among hospital intensive care providers, (2) ongoing initiatives with the Apple Worker Exit Study to understand disruptive worker turnover in the supply chain, and (3) using blockchain technology to tailor incentives to improve the welfare of first-mile producers in global, agricultural supply chains. I also study and collaborate extensively with online platforms managing flexible work arrangements.
I completed my Ph.D. at the Stanford Graduate School of Business and also hold a J.D. from the Harvard Law School and undergraduate degrees from Stanford University.
Please see my personal website for latest updates.
Gad Allon, Georgios Askalidis, Randall Berry, Nicole Immorlica, Ken Moon, Amandeep Singh (2021), When to Be Agile: Ratings and Version Updates in Mobile Apps, Management Science, Forthcoming.
Abstract: Lean and agile models of product development organize flexible capacity to rapidly update individual products in response to customer feedback. While agile operations have been adopted across numerous industries, neither the benefits nor the factors explaining when firms choose to become agile are validated and understood. We study these questions using data on the development of mobile apps, which occurs through the dynamic release of new versions into the mobile app marketplace, in conjunction with customer ratings. We develop a structural model estimating the dependence of product versioning on (A) market feedback in the form of customer ratings, against (B) project and work-based considerations, such as development timelines, scale economies, and operational constraints. In contrast to when they actually benefit from operational agility, firms become agile when launching riskier products (in terms of uncertainty in initial customer reception) and less agile when able to exploit scale economies from coordinating development over a portfolio of apps. Agile operations increase firm payoffs by margins of 20-80%, and interestingly partial agility is often sufficient to capture the bulk of these returns. Finally turning to a question of marketplace design, we study how the mobile app marketplace should design the display of ratings to incentivize quality (increasing app categories’ average user satisfaction rates by as much as 26%).
Abstract: Service networks with open routing by self-interested customers have drawn attention in the theoretical literature (Arlotto et al. 2019, Parlakturk and Kumar 2004). However, these networks, which range from shopping centers to amusement parks, remain challenging to explore empirically. Customers' physical-movement trajectories simultaneously reflect their reactions to congestion, demand for complementary groups of stations, and dynamic choices about the order of station visits. As such, large-scale trajectory datasets offer tremendous opportunities to understand customer motivations and behaviors but are complex to analyze. We develop structural empirical methods to recover customer demand preferences and congestion sensitivities from diverse trajectory patterns using machine learning. Specifically, we employ adversarial neural networks to handle the high-dimensional space of (combinatorially many) trajectory types. Key innovations collapse the dynamics of customer trajectory choices into static trajectory market shares and derive theoretically efficient incentive-compatibility bounds on customers' preferences.
Pia Ramchandani, Hamsa Bastani, Ken Moon (Under Revision), Responsible Sourcing: The First Step Is the Hardest.
Abstract: Responsible sourcing is a priority for companies and consumers concerned with corporate social responsibility (CSR) in global supply chains. Most brands' product lines contain just a few products certified by third parties- which suggests that brands limit their efforts at ensuring that suppliers behave responsibly. In this paper, we examine a previously under-appreciated role of certifications: that certifications enable brands to learn about how to source responsibly. By successfully certifying even a single product, the certifying brand may enjoy positive, knowledge-based spillovers encouraging responsible sourcing throughout its product line. Using data on the responsible sourcing decisions of coffee brands in the $48B US consumer market, we find that certifying brands' rates of CSR violations (adjusted for disparities in production volume and detection) are similarly low regardless of whether the brand's portfolio is 3% certified or 100% certified- consistent with learning-based spillover effects. Certifying brands' violation rates are an estimated 61-78% lower than for comparable brands that make no CSR claims. While we find that brands making their own uncertified, on-packaging CSR claims also exhibit low CSR violation rates, their low violation rates are nearly entirely explained by the countries from which they source. In contrast, certifying brands appear uniquely able to source responsibly even from within "high-risk" countries. Our work novelly suggests that prevalent dual-sourcing may surprisingly amplify, rather than limit, responsible sourcing in supply chains, and that certified sourcing valuably develops the pool of responsible suppliers in high-risk countries.
Ken Moon, P. Loyalka, P. Bergemann, J. Cohen (Under Revision), When Work Becomes Traceable in the Supply Chain: Connecting Product Reliability to the Turnover of Factory Workers.
Abstract: Traceability enables manufacturers to link the work performed by individual workers with downstream effects in a supply chain. With good traceability, firms may be able to observe the downstream quality of specific product units, recognize lapses in product reliability, connect those lapses to workforce dynamics occurring upstream in the supply chain, and correct the problems by improving those dynamics. To study this potential, we focus on the impact of worker turnover on product reliability. We collect and integrate (1) data reporting factory worker staffing and turnover within a major consumer electronics producer's supply chain and (2) traceable data covering the component quality and field failures—i.e., replacements and repairs—of nearly 50M consumer mobile devices over four years of customer usage. Devices are individually traced back to the factory conditions and staffing, down to the assembly line-week, under which they were produced. Despite the manufacturer's extensive quality-control efforts, each percentage-point increase in the weekly rate of workers quitting from an assembly line (its weekly worker turnover) is found to increase field failures by 0.74-0.79%. In the high-turnover weeks following paydays, eventual field failures are strikingly 10.2% more common than for devices produced during the lowest-turnover weeks immediately before paydays. In other weeks, the assembly lines experiencing higher turnover produce an estimated 2-3% more field failures on average. The associated costs amount to hundreds of millions USD. We demonstrate that staffing and retaining a stable factory workforce critically underlies product reliability and showcase the value of work traceability in supply chain operations.
Abstract: We explore marketplace design in the context of a business-to-business platform specializing in liquidation auctions. Even when the platform’s aggregate levels of supply and demand remain fixed, we establish that the platform’s ability to use its design levers to manage the availability of supply over time yields significant value. We study two such levers, each using the platform’s availability of supply as a means to incentivize participation from buyers who decide strategically when/how often to participate. First, the platform’s listing policy sets the ending times of incoming auctions (hence, the frequency of market clearing). Exploiting a natural experiment, we illustrate that consolidating auctions’ ending times to certain weekdays increases the platform’s revenues by 7.3% mainly by inducing a higher level of bidder participation. The second lever is a recommendation system that can be used to reveal information about real-time market thickness to potential bidders. The optimization of these levers highlights a novel trade-off. Namely, when the platform consolidates auctions’ ending times, more bidders may participate in the marketplace (demand-side competition); but ultimately auctions for substitutable goods cannibalize one another (supply-side competition). To optimize these design decisions, we estimate a structural model that endogenizes bidders’ dynamic behavior, that is, their decisions on whether/how often to participate in the marketplace and how much to bid. We find that appropriately designing a recommendation system yields an additional revenue increase (on top of the benefits obtained by optimizing the platform’s listing policy) by reducing supply-side cannibalization and altering the composition of participating bidders.
Haim Mendelson, Ken Moon, Yuanyuan Shen (Working), Behavioral and Social Motivations in a Crowdfunding Marketplace.
Haim Mendelson and Ken Moon (2018), Modeling Success and Engagement for the App Economy, ACM The Web Conference (WWW'18).
Elena Belavina, Karan Girotra, Ken Moon, Jiding Zhang (Working), Matching in Labor Marketplaces: The Role of Experiential Information.
Abstract: Online labor marketplaces assign workers to short-term jobs. For some jobs, the choice of the best worker is based on ex-ante observable information (e.g., driver assignment based on location in ride-hailing). In others, the assignment is driven by experiential information, that is information obtained privately only through the worker performing the job (e.g., the fit of a childcare provider with a family). This study develops an empirical framework to impute the relative importance of each kind of information from participants' past hiring choices. Our moment inequality approach accommodates high worker turnover, varying choice sets, and limited observations of a very large number of market participants -- all key characteristics of online labor markets. We apply our framework to two markets, exploiting a natural experiment that changed marketplace commissions. Based on over 1.2M hiring decisions, we estimate that experiential information is a key driver of hiring choices, while ex-ante observable fit is relevant only for the simplest jobs. Using our estimates, we propose and evaluate alternate assignment policies. The best-performing policies prioritize repeat work and, surprisingly, ignore ex-ante observable information to instead experiment with new workers and generate experiential information. Such policies can increase buyer welfare by as much as 45.3% (47.1%) of gross revenue in the Data Entry (Web Development) market compared to the current practice of skills-based matching. Policies exploiting buyers' past revealed preferences (in repeat work) without incorporating exploration still under-perform by 18.9% in Data Entry and 8.7% in Web Development.
OIDD 101 explores a variety of common quantitative modeling problems that arise frequently in business settings, and discusses how they can be formally modeled and solved with a combination of business insight and computer-based tools. The key topics covered include capacity management, service operations, inventory control, structured decision making, constrained optimization and simulation. This course teaches how to model complex business situations and how to master tools to improve business performance. The goal is to provide a set of foundational skills useful for future coursework atWharton as well as providing an overview of problems and techniques that characterize disciplines that comprise Operations and Information Management.
Matching supply with demand is an enormous challenge for firms: excess supply is too costly, inadequate supply irritates customers. In the course, we will explore how firms can better organize their operations so that they more effectively align their supply with the demand for their products and services. Throughout the course, we illustrate mathematical analysis applied to real operational challenges--we seek rigor and relevance. Our aim is to provide both tactical knowledge and high-level insights needed by general managers and management consultants. We will demonstrate that companies can use (and have used) the principles from this course to significantly enhance their competitiveness.
Operations strategy is about organizing people and resources to gain a competitive advantage in the delivery of products (both goods and services) to customers. This course approaches this challenge primarily from two perspectives: 1) how should a firm design their products so that they can be profitably offered; 2) how can a firm best organize and acquire resources to deliver its portfolio of products to customers. To be able to make intelligent decisions regarding these high-level choices, this course also provides a foundation of analytical methods. These methods give students a conceptual framework for understanding the linkage between how a firm manages its supply and how well that supply matches the firm's resulting demand. Specific course topics include designing service systems, managing inventory and product variety, capacity planning, approaches to sourcing and supplier management, constructing global supply chains, managing sustainability initiatives, and revenue management. This course emphasizes both quantitative tools and qualitative frameworks. Neither is more important than the other.
High worker turnover rates in manufacturing can cost companies hundreds of millions of dollars. A new paper co-authored by Wharton’s Ken Moon looks at how firms can keep employees on the job longer.Knowledge @ Wharton - 12/4/2018