Knowledge that Transforms

Problem definition: A critical decision made by firms is whether to adopt a responsive supply chain (prioritizing speed) or an efficient supply chain (prioritizing cost). We consider the environmental implications of this choice, distinguishing between responsiveness achieved via three pathways: responsive offshore supply chains increase speed by using expedited production and distribution methods; responsive nearshore supply chains increase speed by reducing the physical distance between source and destination for all production; and hybrid nearshore supply chains produce in multiple locations simultaneously, increasing speed by reducing distance on some portion of production. Methodology/results: Using a model wherein responsiveness increases fixed and marginal costs, decreases leadtimes, and changes the per-unit environmental impact of production and distribution, we identify several results. First, all types of responsiveness can decrease environmental impact relative to an efficient supply chain, showing any form of responsiveness has potential to improve sustainability. Second, despite this, all types of responsiveness can also increase environmental impact relative to an efficient supply chain, particularly if demand variability is high. This is precisely when responsiveness is most profitable to the firm, indicating a tension between firm and environmental preferences. Third, a win-win outcome in which responsiveness both maximizes firm profit and minimizes environmental impact is most likely to occur when demand variability is high and unsatisfied customers substitute with a product that generates high environmental impact. Fourth, the firm may have incentive to choose a supply chain that does not minimize (and may maximize) environmental impact, especially at low-to-moderate demand variability. Managerial implications: While responsive supply chains can improve sustainability, they also generate the potential for misalignment of profit and environmental performance. We discuss the implications of this for firms and for policymakers seeking to encourage firms to use supply chains that generate the least environmental impact.

Problem definition: Overproduction is often cited as the fashion industry’s biggest environmental issue, as textile production is notoriously resource intensive and pollutive, and much of the textile produced may end up as “deadstock” fabric or finished goods that do not sell. In this paper, we study two major approaches to address this issue: quick response, whereby finished goods inventory is replenished on demand, and upcycling, whereby deadstock fabric is reused to make new clothes. Proponents of these strategies typically focus on their positive environmental impact in downstream supply chain stages (e.g., finished goods production and waste disposal). Less is known, however, about their impact on upstream activities such as raw material acquisition, which we investigate in this work. Methodology/results: We analyze the effect of quick response and upcycling options on firms’ fabric acquisition and production decisions, as well as firms’ incentives to adopt these strategies. We then assess these strategies’ environmental impact in a life cycle framework. Our results show that quick response—when implemented in isolation—reduces deadstock of finished goods, but could increase the amount of fabric acquired. This not only results in more total deadstock (in both finished goods and fabric form), but also aggravates the environmental burden associated with fabric production in the upstream of the fashion supply chain, and could lead to a worse overall environmental impact for the industry. Upcycling together with quick response could alleviate total deadstock generation, but further increases the firm’s demand for fabric. We analyze the effectiveness of two types of policies—subsidizing quick response/upcycling and banning deadstock destruction—in reducing deadstock and curbing firms’ need for fabric. Managerial implications: Our work highlights a tradeoff between downstream deadstock reduction and upstream fabric acquisition, and suggests that regional policies that aim to reduce local deadstock could often have adverse global impacts.

Problem definition: Increasing costs of healthcare highlight the importance of effective disease prevention. However, decision models for allocating preventive care are lacking. Methodology/results: In this paper, we develop a data-driven decision model for determining a cost-effective allocation of preventive treatments to patients at risk. Specifically, we combine counterfactual inference, machine learning, and optimization techniques to build a scalable decision model that can exploit high-dimensional medical data, such as the data found in modern electronic health records. Our decision model is evaluated based on electronic health records from 89,191 prediabetic patients. We compare the allocation of preventive treatments (metformin) prescribed by our data-driven decision model with that of current practice. We find that if our approach is applied to the U.S. population, it can yield annual savings of $1.1 billion. Finally, we analyze the cost-effectiveness under varying budget levels. Managerial implications: Our work supports decision making in health management, with the goal of achieving effective disease prevention at lower costs. Importantly, our decision model is generic and can thus be used for effective allocation of preventive care for other preventable diseases.

Problem definition: Algorithm-enabled decision support has an increasingly important role in supporting the day-to-day operations of healthcare organizations. Yet, fully realizing the value of algorithmic decision support lies critically in the opportunity to re-engineer the related processes and redefine roles in ways that make organizations more effective. We study how and when algorithm-enabled process innovation (AEPI) creates value in light of dynamic operational environments (i.e., workload) and behavioral responses to algorithmic predictions (i.e., algorithmic accuracy). Our context is an AEPI effort around a rule-based decision-support algorithm for early detection of sepsis—a costly condition that is the leading cause of death for hospitalized patients. We collaborated with a large U.S.-based hospital system and examined whether AEPI developed for sepsis care (sepsis AEPI) impacts patient mortality and when this impact is stronger or weaker. Methodology/results: We utilize a rich set of clinical and nonclinical data in empirically examining the impact of sepsis AEPI on patient mortality. We leverage the staggered implementation of sepsis AEPI across hospital units and conduct our estimation on a carefully matched sample. The matching utilizes data on patient vitals and the logic behind the algorithm to create a robust comparison group consisting of patient visits for which sepsis AEPI would have triggered an alert if it had been in place. Our empirical analysis shows that sepsis AEPI reduces the likelihood of death from sepsis (45% relative reduction in mortality risk due to sepsis). A higher-than-usual workload and an increase in the average number of inaccurate alert experience at a hospital unit (e.g., an oncology unit, which provides care for cancer patients), in general, reduces the effectiveness of AEPI. We also identify diminishing mortality benefits over prolonged periods of adoption; evaluation of the moderators over time helps explain this diminishing impact. Managerial implications: Our findings suggest that streamlining sepsis-care processes through a predictive algorithm (i.e., algorithm-based monitoring of real-time patient data and providing predictions, streamlined communication channels for coordinating care for a patient with sepsis prediction, and a more standardized process for sepsis diagnosis and treatment) can reduce the loss of life from sepsis. For the 3,739 sepsis patients in our study period, AEPI’s benefits would translate to 181 lives saved. We show that such value, however, is sensitive to operational and behavioral factors as the algorithm becomes a routine part of the day-to-day operations of the hospital.

Problem definition: Online platforms that provide on-demand services are often threatened by the phenomenon of leakage, where customer-provider pairs may decide to transact “off-platform” to avoid paying commissions to the platform. This paper investigates properties of services that make them vulnerable or resistant to leakage. Academic/practical relevance: In practice, much attention has been given to platform leakage, with platforms experimenting with multiple approaches to alleviate leakage and maintain their customer and provider bases. Yet, there is a current dearth of studies in the operations literature that systematically analyze the key factors behind platform leakage. Our work fills this gap and answers practical questions regarding the sustainability of platform. Methodology: We develop two game-theoretical models that capture service providers’ and customers’ decisions whether to conduct transactions on or off the platform. In the first (“perfect information”) model, we assume that customers are equipped with information to select their desired providers on the platform, whereas in the second (“imperfect information”) model, we assume customers are randomly matched with available providers by the platform. Results: For profit maximizing platforms, we show that leakage occurs if and only if the value of the counterparty risk from off-platform transactions exceeds a threshold. Across both models, platforms tend to be more immunized against leakage as provider pool sizes increase, customer valuations for service increase, their waiting costs decrease, or variability in service times are reduced. Finally, by comparing the degree of leakage between both settings, we find that neither model dominates the other across all parameter combinations. Managerial implications: Our results provide guidance to existing platform managers or entrepreneurs who are considering “platforming” their services. Namely, based on a few key features of the operating environment, managers can assess the severity of the threat of platform leakage for their specific business context. Our results also suggest how redesigning the waiting process, reducing service time variability, upskilling providers can reduce the threat of leakage. They also suggest the conditions under which revealing provider quality information to customers can help to curb leakage.

Problem definition: The undesirable but inevitable consequence of running promotions is that consumers can be trained to time their purchases strategically. In this paper, we study randomized promotions, where the firm randomly offers discounts over time, as an alternative strategy of intertemporal price discrimination. Methodology/results: We consider a base model where a monopolist sells a single product to a market with a constant stream of two market segments. The segments are heterogeneous in both their product valuations and patience levels. The firm precommits to a price distribution, and in each period, a price is randomly drawn from the committed distribution. We characterize the optimal price distribution as a randomized promotion policy and show that it serves as an intertemporal price discrimination mechanism such that high-valuation customers would make a purchase immediately at a regular price upon arrival, and low-valuation customers would wait for a random promotion. Compared against the optimal cyclic pricing policy, which is optimal within the strategy space of all deterministic pricing policies, the optimal randomized pricing policy beats it if low-valuation customers are sufficiently patient and the absolute discrepancy between high and low customer valuations is large enough. We extend the model in three directions. First, we consider the case where a portion of customers are myopic and would never wait. We show that the existence of myopic customers is detrimental to the firm’s profitability, and the expected profit from an optimal randomized pricing policy decreases as the proportion of myopic customers in the population increases. Second, we consider Markovian pricing policies where prices are allowed to be intertemporally correlated in a Markovian fashion. This additional maneuver allows the firm to reap an even higher profit when low-valuation customers are sufficiently patient by avoiding consecutive promotions but, on average, running the promotion more frequently with a smaller discount size. Lastly, we consider a model with multiple customer segments and show that a two-point price distribution remains optimal, and our conclusion from the two-segment base model still holds under certain conditions that are adopted in the literature. Managerial implications: Our results imply that the firm may want to deliberately randomize promotions in the presence of forward-looking customers.

Problem definition: Approximately 11,000 alleged illicit massage businesses (IMBs) exist across the United States hidden in plain sight among legitimate businesses. These illicit businesses frequently exploit workers, many of whom are victims of human trafficking, forced or coerced to provide commercial sex. Academic/practical relevance: Although IMB review boards like Rubmaps.ch can provide first-hand information to identify IMBs, these sites are likely to be closed by law enforcement. Open websites like Yelp.com provide more accessible and detailed information about a larger set of massage businesses. Reviews from these sites can be screened for risk factors of trafficking. Methodology: We develop a natural language processing approach to detect online customer reviews that indicate a massage business is likely engaged in human trafficking. We label data sets of Yelp reviews using knowledge of known IMBs. We develop a lexicon of key words/phrases related to human trafficking and commercial sex acts. We then build two classification models based on this lexicon. We also train two classification models using embeddings from the bidirectional encoder representations from transformers (BERT) model and the Doc2Vec model. Results: We evaluate the performance of these classification models and various ensemble models. The lexicon-based models achieve high precision, whereas the embedding-based models have relatively high recall. The ensemble models provide a compromise and achieve the best performance on the out-of-sample test. Our results verify the usefulness of ensemble methods for building robust models to detect risk factors of human trafficking in reviews on open websites like Yelp. Managerial implications: The proposed models can save countless hours in IMB investigations by automatically sorting through large quantities of data to flag potential illicit activity, eliminating the need for manual screening of these reviews by law enforcement and other stakeholders.

Problem definition: Customer reviews are essential to online marketplaces. However, reviews typically vary; ratings of a product or service are rarely the same. In many service marketplaces, including the ones for solar panel installations, supply-side participants are active. That is, a seller must make a proposal to serve each customer. In such marketplaces, it is not clear how (or if) the dispersion in customer reviews affects the seller activity level and number of matches in the marketplace. Our paper examines this by considering both ratings and text reviews. To our knowledge, this is the first paper that empirically studies how the review dispersion affects a seller’s activity level and the number of matches in an online marketplace with active sellers. Distinct from literature, we examine the relationship between the review dispersion and supply-side activities in an online service marketplace. Methodology/results: We collaborated with one of the largest online solar marketplaces in the United States that connects potential solar panel adopters with installers. We obtained a unique data set from the marketplace for 2013 − 2018. We complement this with public data sets. Our analysis uses traditional econometrics methods, a clustering method, and the deep-learning-based natural-language-processing model BERT developed by Google AI. We find that the dispersion in customer reviews has a significant and inverted U-shaped relationship with an installer’s marketplace activity level. Intuitively, a marketplace operator would favor having more sellers with perfect ratings. In contrast, we identify a significant and inverted U-shaped relationship between the market-level review dispersion and transactions. Managerial implications: Our paper provides key insights to marketplace operators and sellers. We find that in contrast to general belief, an operator can improve its market transactions by keeping/promoting sellers with low ratings or avoiding (negative) review filtering. Furthermore, sellers’ implementation of “rating gating” to avoid negative reviews may backfire for them by reducing their matches.

Problem definition: Firms often delay the decision to recall faulty medical devices long after they become aware of a defect, thereby putting public safety at heightened risk. However, the factors contributing to these delays are not well-understood. To help address this gap, we examine whether and how CEO stock ownership influences the speed with which faulty medical devices are recalled and whether this influence varies with recall severity. We then examine whether the stock market penalizes firms differently based on recall decision-making speed and whether this penalty also varies with recall severity. Methodology/results: We collect data on 2,144 medical device recalls across 50 public medical device firms from 2002 to 2015. We use accelerated failure time models to test the effects of CEO stock ownership on the time-to-recall and event study methodology to examine how the time-to-recall influences stock market returns. Supplementary analyses shed further light on underlying mechanisms. Robustness checks demonstrate consistent results, including coarsened exact matching, reverse causality tests, Cox proportional hazard models, generalized linear regression models, and a mediation analysis. Firms whose CEOs possess greater ownership stakes recall medical devices more slowly, and this recall-slowing effect is accentuated for high-severity recalls. Delaying recalls magnifies the stock market penalty attributable to the recall, particularly for high-severity recalls. Managerial implications: Our study highlights an ownership characteristic of firms that are more likely to delay recalling faulty medical devices. Boards of directors can use insights from our study as they oversee product-quality decisions and determine the level and form of CEO compensation, and the FDA can use our findings to identify firms that might warrant extra scrutiny and better allocate its limited monitoring resources.

Problem definition: Online retailers often receive customer orders comprising several products of differing origins. To fulfill these orders, retailers must ship multiple parcels from different locations and—unless they are grouped somewhere along the supply chain—these may reach the customer’s doorstep one by one. Academic/practical relevance: We conjecture here that receiving products sequentially instead of all together affects a consumer’s reaction to her purchases, possibly influencing—for good or ill—her decision to return products, as well as her overall service satisfaction. We use two-year granular data from an online fashion marketplace to test this hypothesis and characterize consumer behavioral responses to delivery consolidation and examine how it impacts supply chain stakeholders. Methodology: To achieve causal inference, we exploit the fact that the couriers used by the focal marketplace gather together certain parcels for reasons related more to the timing of their arrival than their actual customers, thereby exogenously consolidating the delivery of some orders. We construct a balanced sample of matched twin multiproduct orders that are alike in all respects except their delivery: consolidated (all parcels delivered jointly) versus otherwise (split). Results: We find that delivery consolidation benefits the marketplace and all its suppliers. By eliminating the stress associated with split deliveries, delivery consolidation pleases consumers as it leads to fewer returns and higher overall satisfaction. Managerial implications: Delivering all products in an order together, even if later, reduces the probability of a return, which improves the financial performance of the marketplace and its suppliers and reduces reverse logistics. Our results suggest that in our context, delivery speed matters less than the convenience of receiving all ordered goods in a single delivery, and we provide directions for adapting logistics strategies accordingly. Our empirical findings also imply that the return decisions of multiple products purchased at once should not be considered to be independent. Finding tractable ways of modeling this feature will be necessary in further driving retail practice through theoretical research that accounts for the behavioral implications of delivery consolidation when optimizing fulfillment decisions.