Reusable packaging has emerged as a promising strategy to mitigate plastic waste, yet practical implementation remains challenging. Consumers face upfront deposit payments and the inconvenience of returning empty packaging, while firms incur additional costs related to collection and cleaning. This paper sheds light on a firm's decision to introduce products in reusable packaging under a deposit-refund system. We model a firm that offers products in both disposable and reusable formats and jointly chooses the product price and the deposit fee for the reusable option. The model incorporates consumer heterogeneity in both product valuations and return-related hassle costs. We characterize the firm's optimal pricing policy and identify the market conditions under which offering reusable packaging is profitable. We show that product price-beyond deposit fees-affects the return rate of reusable packaging. A sufficiently high price enables the firm to segment the market by directing high-hassle consumers toward the disposable option while offering the reusable option only to low-hassle consumers. This mechanism enables the firm to achieve full return rates without imposing large deposit fees. When consumers' willingness to pay for reusable solutions is limited, the firm relies on this segmentation strategy and restricts the reusable option to low-hassle consumers. As willingness to pay a premium increases, however, the firm expands reusable offerings to high-hassle consumers, increasing adoption but reducing return rates and diminishing environmental outcomes. We also examine policy interventions aimed at promoting reuse and show that banning or taxing disposable packaging can unintentionally lower return rates for reusable packaging and undermine environmental benefits.

As consumer concern about labor conditions in global supply chains grows, firms face increasing pressure to disclose fair wage and labor-related information. Yet, transparency can be risky because it may expose unfavorable supplier practices and cause reputational harm. Drawing on signaling theory, we examine how fair wage and labor-related supply chain transparency (SCT) influences consumer word-of-mouth (WOM), particularly when disclosures contain both positive and negative information. Across two vignette-based experiments in an online shopping context, we test whether distributive justice and trustworthiness explain consumer responses to SCT and leverage these mechanisms to further explain responses to mixed-valence SCT disclosures. Study 1 shows that a uniformly positive SCT disclosure, relative to nondisclosure, increases WOM through both distributive justice and trustworthiness. Study 2 examines a mixed-valence disclosure combining positive and negative information. Although mixed-valence disclosures lower distributive justice perceptions, they increase trustworthiness relative to nondisclosure, yielding a positive total effect on WOM. Together, the findings show that SCT functions as a costly and credible signal: even when transparency reveals negative wage and labor information, mixed-valence disclosure can strengthen trustworthiness enough to enhance consumer advocacy.

Problem definition: Artificial intelligence (AI) is rapidly transforming the research and practice of supply chain management. Yet its impact depends on how effectively it is integrated with the theories, methods, and fundamental principles of operations management (OM), which must also evolve to account for the informational, incentive, and institutional changes brought by AI. The OM community has an important role and responsibility to lead in shaping not only how AI transforms supply chains but also how the supply chains that enable AI are designed to be sustainable, resilient, and equitable. Methodology/results: This vision statement organizes the discussion around five layers of the interaction between AI and supply chain management: intelligence, execution, strategy, human, and infrastructure. It synthesizes recent research and industry practice to show how AI enhances forecasting, planning, decision making, risk management, and human-machine collaboration and also examines the supply chains that support AI. Finally, it highlights persistent challenges in data quality, model integration, governance, and workforce adaptation. Managerial implications: Realizing AI's promise in supply chain management requires reliable data and infrastructure, integration of learning and optimization, transparent and explainable decision systems, and a long-term commitment to human-AI collaboration. Together, these elements form the foundation for resilient, adaptive, and trustworthy supply chains in the AI era.

The transportation sector has been the main contributor to emissions growth in the last decade. The type of truck and its delivery characteristics largely explain the transportation carbon dioxide (CO2) emissions and carbon intensity factors. This article introduces a novel methodology for the allocation of a fleet of vehicles to certain regions aimed at minimizing total transportation-related CO2 emissions. Our methodology employs geospatial analysis and machine learning to assess the fuel efficiency and CO2 emissions performance of a vehicle fleet by analyzing historical GPS data, cargo, and fuel use. Subsequently, we include these variables into a mathematical model to obtain an optimal allocation that minimizes total transportation CO2 emissions. Our approach extends the current literature by considering detailed data for operation, such as gradient variability (road hilliness), vehicle speed, elevation/altitude, and distance between stops. We applied our methodology in Coppel, one of the largest retailers in Mexico, which operates its own fleet. Our results showed that, by exchanging 10 vehicles for one month, we observed 8% savings in fuel efficiency and transportation CO2 emissions.

Problem definition: Informal credit is extended by small independent grocery retailers ("nanostores") to billions of base-of-the-pyramid consumers. We study whether supplier trade credit enables liquidity-constrained nanostore shopkeepers to extend more informal credit to such liquidity-constrained consumers. Methodology/results: Through a field experiment with nanostores in Lima, Peru, we find that access to trade credit partly increases the likelihood of consumer credit provision. To understand the behavioral underpinnings of this result, we develop an analytical model in which shopkeepers allocate limited working capital between base consumers, who always pay immediately upon arrival, and additional credit consumers, who pay later but carry default risk. The model predicts that relaxing working capital constraints through trade credit should increase optimal consumer credit provision, with negligible risk of supplier default when credit consumers are profitable on average. We subsequently test these predictions in a behavioral experiment and find systematic deviations from the theoretical benchmark. Participants exhibit mental accounting so that they become more conservative when working capital includes trade credit, discount trade credit more heavily than their own funds, fail to calibrate consumer credit provision - offering too little (much) when the risk is low (high) - and fail to expand consumer credit provision even when effective capital increases. Examining the heterogeneity of the effect, we find that participants with a positive attitude toward credit are more willing to allocate both trade credit funds and their own working capital toward consumer credit provision. Finally, a simple change in repayment-penalty framing mitigates this mental-accounting effect. Managerial implications: Together, the results highlight the challenges shopkeepers face when extending consumer credit and point to behavioral frictions-particularly a repayment shortfall aversion-that would need to be addressed to enhance the pass-through of trade credit to end consumers.

Problem definition: Nanostores—small, independent grocery retailers serving Bottom-of-the-Pyramid consumers—constitute the largest retail channel in many developing countries, yet they increasingly face competition from convenience store chains that differentiate themselves through value-added digital services. Although such services can attract traffic and strengthen customer loyalty, most nanostores lack the financial and technological capabilities to invest in them independently. Because nanostores remain a strategically important channel for Consumer Packaged Goods manufacturers due to their scale and relatively high margins, manufacturers have begun supporting the digital transformation of these retailers. This paper investigates whether manufacturer-sponsored digital services effectively strengthen the competitiveness of the nanostore channel and increase manufacturers’ sales. Methodology/results: We collaborate with a large CPG manufacturer in Latin America that introduced a digital platform enabling nanostores to offer value-added services such as bill payments and mobile top-ups. Exploiting quasi-experimental variation and conducting econometric analyses that account for local retail competition, we examine whether adoption of the platform increases manufacturers’ sales to participating nanostores and whether any observed gains arise from cannibalization of neighboring stores. We find that adopting nanostores experience a significant increase in ordering volume, with evidence of cannibalization from nearby convenience stores over a very granular radius of 100 meters, but not from nearby nanostores. Overall, we find that value-added digital services organically expand total average sales within proximity retail channels, shifting market share to the more profitable nanostore channel. Managerial implications: Our findings suggest that manufacturer-sponsored digital services can organically expand the competitiveness and market share of the nanostore channel, generating benefits for both retailers and manufacturers. More broadly, the study highlights how CPG manufacturers in developing economies can strategically invest in retailer digitalization to strengthen traditional retail channels and sustain growth in underserved markets.

A fundamental principle in operations management holds that increasing the number of servers reduces delays in service systems. To date, no mechanism has been identified that could reverse this effect. We propose, however, that risk aversion can cause increased service capacity to intensify congestion. We study an unobservable M / M / s queue where risk-averse customers choose whether to join based on their anticipated waiting time. We show that, in equilibrium, demand, expected waiting time, expected sojourn time, and the probability of waiting all increase with the number of servers, and that these effects are stronger for more risk-averse customers. We further uncover the mechanism behind this phenomenon: adding servers makes delays less risky (in the sense of second-order stochastic dominance), which increases the sensitivity of demand to capacity as customers become more risk-averse. These patterns are more prevalent in small systems and fade as the system grows. They can also persist when customers differ in their degree of risk aversion, when capacity is increased by raising service speed, and when the system is observable. Our findings reveal a novel trade-off created by customer risk aversion: expanding capacity attracts more customers, but also exacerbates congestion. A manager aiming to reduce waiting times may therefore prefer to de-pool service capacity instead of following the standard approach of pooling, while increasing capacity in the resulting smaller systems to preserve total throughput. When the objective is to maximize profitability, our results further suggest that the cost of additional servers may be offset by the associated increase in revenue when customers are sufficiently risk-averse.

We study the optimal online service for grocery retailers operating both physical and online stores. The challenge lies in optimizing the size of the online assortment and the delivery fees to maximize profitability across channels, while considering customer, operational, and market dynamics. Using transaction data from a major grocery retailer, we employ an alternative-specific conditional logit model to investigate how delivery fees, assortment size, network characteristics, and customer needs influence store choice and spending across physical and online channels. We develop a profitability model that incorporates online service variables, customer behavior, and operational costs, enabling us to explore optimal strategies under various conditions. By identifying favorable conditions for the online store and analyzing optimal service variables, we provide actionable insights for retailers. Our findings challenge common practices in omnichannel retail. We show that delivery fees should not merely cover costs but can be strategically set higher, particularly for retailers with strong offline presence. Additionally, while reducing fulfillment costs improves profitability, its impact is smaller than expected. Multichannel retailers can offset these costs by passing them on to customers, with minimal overall demand loss, as some customers opt to shop in physical stores rather than abandoning the retailer entirely. Lastly, maximizing the online assortment may not always be optimal, particularly if the operational inefficiencies and costs outweigh the value customers place on variety. Our methodological framework provides retailers the opportunity to align their online services with customer preferences and operational constraints and to leverage customer data in shaping their omnichannel strategies.

This paper presents a modeling framework that integrates facility location-allocation and inventory control decisions, accounting for the potential presence of the bullwhip effect. Since incorporating the bullwhip effect adds complexity to the optimization problem, we apply linearization and approximation techniques in our solution approach. Through numerical experiments, we assess how the bullwhip effect influences optimal network design, focusing on various design parameters. The results suggest that the bullwhip effect can significantly influence network design, with lead time playing a particularly critical role in differentiating designs for supply chains with and without the bullwhip effect. Additionally, through a simulation study, we explore how incorporating the bullwhip effect may affect the resilience of supply chain networks when facing supply disruptions.

We propose user-centric booking platforms for end-to-end freight transport as a requirement for the scaling of synchromodal transport and a new avenue for transport and logistics research. We start with the assertion that synchromodal transport is still an unapplied concept due to the strong heterogeneity and disconnection of the transport offer and the huge variety of cargo requests. We suggest that open digital platforms with a focus on shippers can help create transparency that benefits shippers and carriers, and may increase the efficiency in the use of network capacity. We denote the concept Freight Mobility as a Service (FMaaS). Current digital platforms predominantly operate under the assumption that transport services are on-demand, often with flexible lead times, overlooking the structured nature of most actual transport operations. FMaaS challenges this paradigm by recognizing that a significant portion of transport - such as rail, barge, and short sea shipping - is inherently scheduled, not chartered, and must be integrated accordingly. Finally, FMaaS is an open market where the visibility of the transport service offer for the shipper is global and not limited to contracts between the platform operator and the service suppliers. The applicability of FMaaS presents barriers and questions that open possibilities for a rich multidisciplinary research agenda. One of the main barriers to this concept is the acceptance of the actors involved, along with the lack of scientific evidence on how a user-centric platform system can help achieve the sustainability challenge. Also, the development of centralized platforms may pose serious commercial and legal threats. This paper aims to describe the requirements and possible research avenues of this new paradigm in the wake of an emerging market.

Global supply chains are increasingly vulnerable to disruptions, underscoring the importance of effective resilience strategies. This study examines how agility, adaptability, and alignment (AAA capabilities) mitigate the negative impacts of major disruptions on corporate performance and influence capability enhancement during recovery. Using a longitudinal survey conducted across two phases of the COVID-19 pandemic, we find that agility significantly enhances resilience in conditions of high demand uncertainty, whereas adaptability is particularly effective under high supply uncertainty. Conversely, alignment demonstrates limited effectiveness during acute disruptions but remains critical for post-disruption collaboration and recovery. Interestingly, firms often respond to severe disruptions by broadly investing in all three capabilities, potentially overlooking their distinct, context-specific advantages. This study advances supply chain resilience theory by clarifying the contingent roles of AAA capabilities, guiding managers in strategically prioritizing resilience investments based on specific disruption scenarios and environmental uncertainties.

Reducing food waste in supply chains (SCs) with multiple decision-makers is challenging. A common approach grocery retailers use to reduce waste is requiring manufacturers to only send products with along remaining shelf life ("minimum life on receipt"-MLOR). However, its impact on manufacturers remains unclear. To evaluate the effectiveness of MLOR agreements on food waste, we investigate two strategies: (1) collaborating on setting the MLOR level and (2) coordinating the SC via contract. Through collaboration, we analytically show that if the MLOR agreement does not demand solely fresh products, it raises manufacturer profits, enabling potential wholesale price reduction. This might incentivize retailers to collaborate to reduce the MLOR level. We demonstrate that the coordinating strategy can reduce waste in the SC and is most beneficial when the wholesale price is high, and the issuing policy is FIFO. We introduce possible coordination contracts and show that in coordinated SCs, manufacturers always provide the highest MLOR level without requiring any restrictive MLOR agreements. Governments mainly focus on reducing retail waste and promoting retailers to request higher MLOR. However, these efforts can backfire by creating more waste for manufacturers. Reducing the MLOR allows retailers to negotiate lower wholesale prices, increasing profitability while reducing waste. Although SC coordination is known for reducing inefficiency, it may not be the best strategy for reducing waste, especially when the issuing policy is more LIFO than FIFO. Specifically, while coordination might be abetter strategy for online retailers, collaboration can be abetter strategy for brick-and-mortar retailers.

Purpose: We propose a framework focusing on logistics space in peri-urban regions and the associated planning decisions. Our approach explicitly incorporates social and environmental sustainability considerations to address growing concerns of large-scale warehouse spatial developments. Design/methodology/approach: We conceptualize warehousification by combining the perspectives of logistics management and spatial planning research with examples of industry practice and develop a framework to deal with the associated challenges. Findings: Challenges exist for policymakers and logistics practitioners attempting to mitigate the negative externalities of the growth in the development of big box warehouses (i.e. warehousification). While some mitigation strategies have been used in practice, they are not yet widespread. Research limitations/implications: Both empirical and model-based research in physical distribution and logistics management can help develop a better understanding of the complexities of these trade-offs. However, data availability remains a limitation for future research. Thus, novel data-collection methods may be a promising path forward. Practical implications: Our study helps address a contentious debate between the logistics industry and locals where big box warehouses have been planned or developed, which have created considerable resistance and limited development. It also helps policymakers trapped between these two groups. Social implications: Our work addresses how advances in the logistics industry can support economic development while at the same time reducing the associated negative environmental and social externalities. Originality/value: Research in logistics planning and spatial planning approaches sustainability from distinct perspectives, which results in a research gap, and limits the practical impact. This work offers a foundation from which researchers can explore the interconnectedness of the problems and policymakers can consider real-world practices and their trade-offs.