Motivated by the emerging mixed autonomous paradigm in cobotic order picking operations, we investigate the optimal information design to navigate human workers (HWs) who cooperate with autonomous mobile robots (AMRs) within an intralogistics system. We incorporate asymmetric information between AMRs and HWs in a routing game where connected AMRs are informed of the congestion state while HWs rely on information provided by the system. The system designs a communication policy aiming to navigate HWs away from congestion. Without strategic communications, we show that the deployment of AMRs cannot mitigate congestion unless the automation level reaches a threshold. Interestingly, we illustrate a substitution effect between automation and strategic communications when information distortion is mild. In contrast, severe information distortion complements automation due to exacerbated congestion. Furthermore, an in‐house AMR fleet is economically more efficient than a third‐party logistics service. Consequently, in‐house automation can be achieved with mild information distortion, while severe information distortion is required to complement the lack of efficiency in the third‐party AMR fleet. With simulated numerical examples to complement the analytical results, we provide managerial insights concerning the optimal information policies under different levels of automation, guiding warehouse managers in their communications with workers to achieve the best performance of the cobotic system.

We evaluate economic immunity to the COVID-19 pandemic accorded by a sector’s exposure to artificial intelligence (AI). Using an event study design, we causally estimate the impact of AI industry exposure (AIIE) on shifts in economic activity following the first pandemic-induced lockdown in India. We use payments data from one of India’s largest payment gateways and find that a one-standard-deviation increase in AIIE arrests the post-pandemic decline in industry payments by a third. High AIIE of neighboring sectors further improves a sector’s economic resilience, emphasizing the importance of supply chain partners in technology adoption and change. Further analyses speak to the mechanisms underlying the documented impact of AI on economic activity. Notably, we present evidence suggesting that the observed effects of AI are distinct from those attributable to IT investments or digitization, that AI investments are associated with superior information capabilities prior to the pandemic that arguably enabled agility and responsiveness, and that AI-enabled adaptation occurs in a manner that complements labor rather than displacing it. We discuss the implications of our findings in terms of the assessment of the business value of AI and management of disruptions associated with crises events.

Crises are accompanied by resource shortages, operational challenges, and shifts in demand for resources. During crises, nonprofit organizations have historically played a crucial role in mitigating these challenges, with crowdfunding platforms emerging as a new solution for resource mobilization. In this study, we examine how individual and institutional donors respond to the sudden shifts in demand that accompany a global crisis. We analyze rich, granular data from multiple sources, including donation data from a leading education crowdfunding platform, before and after the onset of the COVID-19 pandemic, and find notable contrasts between individual and institutional responses. While there was a significant increase in institutional donations during the pandemic, individual donations remained relatively stable overall. Adopting a motivation-opportunity-ability (MOA) framework, we center donor heterogeneity in our approach, deriving nuanced insights. Donations from individuals in economically disadvantaged communities, which were the worst impacted by the crisis, decreased; however, these communities prioritized immediate and time-sensitive needs. Small and local institutions played a pivotal role in supporting high-need schools during the crisis. These findings make meaningful contributions to the operations literature on crowdfunding and have practical implications for platform designers. By distinguishing between individual and institutional donors and identifying the factors that drive their behavior, our study offers important implications for platform designers to improve crisis-time resource mobilization through more targeted engagement strategies.

This paper investigates the supplier’s pricing problem under upfront reservation discount (URD) contracts where the buyer reserves products in advance and then adjusts the purchase quantity based on realized end-market demand. A key challenge is that the supplier typically has limited data to estimate the demand distribution and possesses inferior information compared to the buyer. To address the challenges of distributional ambiguity and information asymmetry, we develop a refined distributionally robust optimization model for the supplier’s URD pricing to maximize her worst-case profit. To better infer true demand patterns, beyond the conventional reliance on historical demand data, our approach leverages past transaction records involving supplier–buyer interactions through the inverse optimization underlying the first-order conditions of the buyer’s newsvendor behavior. Then, a general Wasserstein p -distance minimization problem for p ≥ 1 is developed to generate a Refined Empirical Distribution (RED) in the enhanced set. We prove that the RED provides a superior estimation of the true demand distribution compared to the classical empirical distribution when the buyer holds an informational advantage. Although identifying the RED leads to an intractable semi-infinite program, we show that the RED admits a closed-form solution. To obtain the supplier’s worst-case profit involving a nonconvex distributionally optimistic optimization problem with a decision-dependent uncertainty set, we exploit the monotone transport structure between univariate distributions to truncate the distributions and convert the decision-dependent quantile constraints, which results in a finite-dimensional convex model that can be efficiently solved. Moreover, we extend the model to accommodate data noise, volatile market prices, evolving market conditions, and multi-item settings. Numerical experiments based on a virtual machine reservation problem in the cloud service market demonstrate the effectiveness and robustness of the proposed approach.

In light of the recent frequency of global emergencies that result in overseas delivery delays and cost surges, many multinational brands consider reshaping their sourcing configurations by incorporating local suppliers that may be of lower quality than globally selected counterparts. In this global context, pre-committed emergency renegotiation clauses have gained recognition as a common practice in international commercial contracts, which enable contractual parties to adjust key terms, such as the wholesale price and order quantity, during ex-post emergencies. This work aims to investigate how the presence of overseas emergency renegotiation affects the brand’s strategy preference for dual versus single sourcing and the decision-making of supply chain parties. By comparing with two widely observed benchmark scenarios—contract termination and contract continuation at post-recovery cost—that exclude overseas emergency renegotiation, we show that in the presence of emergency renegotiation, the overseas supplier under both sourcing strategies may lower its wholesale price for the brand in response to a higher delay probability, which would not be the case in the contract termination benchmark. This price-cutting effect encourages the brand to order more from the overseas supplier, even if the delay probability is high. Furthermore, the brand favors dual sourcing if the overseas post-recovery unit cost is either low or high, driven by a distinct incentive under renegotiation: the local supplier may suppress the local rival by setting a high wholesale price, a scenario not possible in the contract continuation benchmark. Our study highlights the value of emergency contract renegotiation in reshaping resilient sourcing strategies for multinational brands facing global emergencies, even with ex-post local reorder or overseas quality deterioration during recovery.

Motivated by algorithmic targeting and data management, we explore a scenario where the seller holds an advantage over consumers regarding match-related information about products. The seller optimizes a product line consisting of two vertically differentiated products alongside an opaque product resulting from their mixture, strategically recommending these products to potential consumers. We model algorithmic targeting using an information design framework, and our investigation revolves around understanding how algorithmic targeting shapes consumer purchasing behaviors and influences market equilibrium. Furthermore, we explore the potential orchestration between algorithmic targeting and opaque selling, facilitated by product-line design. These two closely related instruments coincide in ex-ante manipulating information while differing in their targeting objects. Interestingly, only when the basic products exhibit intermediate differentiation does the seller use both instruments. This is because, when the disparity between the two primary products is extreme (either too large or too small), algorithmic targeting makes opaque selling ineffective at increasing profits. However, when these differences are moderate, the two strategies can complement each other. Opaque selling enhances profitability by introducing intermediate product variety, enabling more nuanced market segmentation, while algorithmic targeting is more flexible in promoting the willingness-to-pay of a wider range of consumers. Furthermore, when conducting welfare analysis, the adoption of algorithmic targeting is found sometimes to reduce consumer surplus but can enhance overall social welfare, highlighting the need for careful regulatory oversight in this domain.

We study the sample complexity of offline learning for a class of structured Markov decision processes (MDPs) describing the inventory control system with fixed ordering cost/setup cost, a fundamental problem in supply chains. We find that a naive plug-in sampling-based approach applied to the inventory MDPs leads to strictly lower sample complexity bounds compared to the optimal bounds recently obtained for the general MDPs. More specifically, in the infinite-horizon discounted cost setting, we obtain an O ~ ( min { ( S ¯ − s _ ) 2 ( 1 − γ ) 2 ϵ 2 , 1 ( 1 − γ ) 4 ϵ 2 } ) sample complexity bound, where ( S ¯ − s _ ) 2 corresponds to the number of state-action pairs in a generic MDP with state space S and action space A . As such, O ~ ( ( S ¯ − s _ ) 2 ( 1 − γ ) 2 ϵ 2 ) improves on the optimal generic reinforcement learning (RL) bound Θ ~ ( ( S ¯ − s _ ) 2 ( 1 − γ ) 3 ϵ 2 ) (when directly applying Θ ~ ( | S | | A | ( 1 − γ ) 3 ϵ 2 ) here) by a factor of ( 1 − γ ) − 1 , and O ~ ( 1 ( 1 − γ ) 4 ϵ 2 ) is able to completely remove the dependence on state and action cardinality. In the infinite-horizon average cost setting, we obtain an O ~ ( ( S ¯ − s _ ) 2 ϵ 2 ) bound, improving on the generic optimal RL bound Θ ~ ( ( S ¯ − s _ ) 2 t m i x ϵ 2 ) (when directly applying Θ ~ ( | S | | A | t m i x ϵ 2 ) here) by a factor of t m i x , and hence removing the mixing time dependence. By carefully leveraging the structural properties of the inventory dynamics in various settings, we are able to improve on those “best-possible” bounds developed in the RL literature. Our results demonstrate the drawbacks one could face by blindly following RL algorithms and the necessity of designing sample efficient algorithms that properly incorporate the special structures of the inventory systems.

Getting downstream firms in a supply chain to share private information about consumer demand with their suppliers is generally considered a prerequisite to dampening the bullwhip effect, that is, the amplification of demand shocks as they pass upstream in a supply chain. If downstream firms are reluctant to share their demand information, how can they best be incentivized to do so, and what does the optimal incentive scheme imply for the bullwhip effect and supply chain efficiency? We examine these questions by developing a supply chain model with asymmetric information, and show that the supplier should optimally follow a two-pronged strategy. One prong consists of the optimal incentive contract to induce information revelation. Information revelation requires a distortion in the downstream firm’s orders that makes them more responsive to demand shocks, which tends to strengthen the bullwhip effect. The other prong helps to reduce this distortion and dampen the bullwhip effect. It consists of getting the downstream firm to increase its initial order and stock up on inventory before it learns about demand. A larger initial order is sufficient to eliminate the bullwhip effect if demand shocks are not too persistent. Our results hold if the marginal production cost does not increase too quickly with output and the inventory holding cost is not too high.

Many online labor markets face challenges in matching service providers with employers because of the high uncertainty in online environments compared to traditional ones. While prior studies have mostly focused on the uncertainty that employers face with service providers, our study explores the uncertainty service providers face when selecting projects on which to bid. Specifically, our study examines the role of project description in the Call for Bids (CFB) that describes the services requested by employers, in shaping service providers’ bidding behavior and the matching between service providers and employers. Drawing upon transaction cost economics, we examine three key dimensions of project descriptions: (a) c odifiability (provisions of detailed procedures), (b) flexibility (provisions of renegotiation opportunities), and (c) provisions of outcome standards . We first use machine learning algorithms to quantify the three key dimensions based on unstructured textual information. Second, we use Bayesian mediation analysis and a randomized experiment to examine the role of these three key dimensions in matching efficiency and to explore the mediating role of three key bid attributes (i.e., number of bids, average price of bids, average quality of bids). Findings reveal: First , codifiability is positively associated with matching efficiency by reducing the number of bids received by employers. Second , flexibility is negatively associated with matching efficiency by raising the average price of bids. Third , outcome standards are positively linked to matching efficiency by reducing the number, albeit improving the average quality, of bids. By focusing on the project description and analyzing the textual content of CFB, our study contributes to the literature by enhancing the matching efficiency of online labor markets and facilitating the contracting of IT services in online labor markets.

We study a supply chain comprising a downstream retailer and multiple complementary suppliers. Suppliers can form coalitions to jointly invest in sustainable production technologies and enhance the product’s corporate social responsibility (CSR) level. Coalition members benefit from efficiency gains through shared knowledge and information about green production. The retailer sources from these coalitions and sells the product to heterogeneous consumers. We develop a game-theoretical model and use the notion of Nash stability to characterize equilibrium coalition structures and firms’ optimal decisions under three profit allocation rules. Our key findings are as follows: (a) Forming supplier coalitions can benefit all stakeholders (including the retailer, suppliers, and society) but achieving such a win–win–win outcome hinges on two factors: the market’s consciousness rate (i.e., the proportion of socially conscious consumers) and the efficiency gains from coalition formation. (b) The grand coalition that maximizes the retailer’s profit and CSR level is not always stable due to free-riding behaviors. In particular, less efficient suppliers may defect to avoid the cost of sustainable innovation while still benefiting from the high innovation efforts of others (i.e., cross-coalition free-riding). (c) On the supply side, adopting the Shapley value allocation rule exacerbates within-coalition free-riding by assigning greater profit shares to less efficient suppliers. However, it also reduces their incentive to defect, thus enhancing coalition stability. Moreover, greater total efficiency and a smaller efficiency gap among suppliers further improve the stability of the grand coalition. (d) On the demand side, the impact of increasing the market’s consciousness rate is non-monotonic. Improperly influencing consumer awareness may destabilize coalitions and produce unintended consequences, suggesting that the retailer must proceed with caution.