Used product take-back plays a significant role in the transition to a Circular Economy. Many businesses are introducing retailer-level take-back schemes for low and medium-value products by deploying collection bins for customers to drop off used products. However, the literature has not given much attention to these schemes compared to those with high recovery value products. The viability of such schemes depends on several factors, including the volume of returns and the contamination in the collection bins. We consider a two-echelon reverse supply chain consisting of a retailer taking back low and medium-value used products and delivering them to a recycler for value recovery. We mathematically model the optimal retailer decisions, including the return incentive paid to customers, investment in contamination reduction, the number and capacity of bins, their product assignment, and time between shipments from the retailer to the recycler, and apply the optimisation model to a case study. In addition, our numerical experiments with globally representative parameter value combinations provide generalisable insights for theory and practice. For example, recycling low-value products like plastic containers is economically infeasible for retailers under all conditions. However, including at least one medium-value product, such as apparel, in a take-back programme aids in offsetting its costs. Investing in reducing contamination for any product type is financially unwarranted unless the costs of separation and disposal exceed that of contamination reduction investment. Furthermore, combining multiple product streams into fewer bins could improve financial results, but keeping bins separate may offer psychological benefits to users.

Businesses are increasingly adopting product take-back programmes as part of their environmental initiatives. A frequently used method involves setting up bins where consumers can return their used products. Despite their growing popularity, these programmes have received limited research attention. This research extends take-back literature by incorporating novel bin configuration considerations and evaluating the economic benefits of volume reduction technologies. We develop a detailed mathematical framework to determine the optimal reverse supply chain (RSC) strategies for take-back. We validate our model through a real-world case study implementation. Furthermore, by testing it across diverse global scenarios with various parameter combinations, we generate broadly applicable findings that advance both theoretical understanding and practical implementation. Our analysis reveals that optimal bin configuration varies by returns volume: mixed product streams in a single bin prove more economical at low-volume sites, while separate collection bins become advantageous at high-volume locations. The implementation of shredding technology emerges as a crucial cost-reduction strategy, that is particularly effective when deployed at high-volume storage facilities. For a wide range of experiments, transportation costs represent approximately half of optimised total expenses. Higher contamination levels in bins correlate with reduced transportation expenditure, but this brings increased disposal fees and negative environmental impact. Our findings recommend maintaining a mixed inventory of shredded and non-shredded materials at storage facilities, with selective transportation to recycling centres. The proposed models and recommendations provide practical decision-making tools for businesses seeking to optimise take-back operations, contributing to the broader implementation of circular economy principles for environmental performance.

The vision of a circular economy (CE) inspires firms, governments, and scholars alike. The transition is underway in both practice and the literature, but success depends on the effective implementation of circular supply chains (CSCs), which encompass acquiring used products, sorting them by type and quality, and deciding which to dispose to various processing options. We review 131 high-impact journal articles on returns acquisition, sorting, and disposition (ASD) over the decade 2012–2021 to assess the current status of ASD research for CSCs and to discuss important research directions for supporting the transition to a CE. Uniquely synthesising the state of the art on all these three overarching decision areas, we find aspects of CSCs prominent in the decade's research agenda, such as closed loop supply chain coordination and ASD for remanufacturing, and highlight growing coverage of behavioural considerations. Research applicability has been constrained by a lack of empirical studies, limited practical validation of mathematical models, a focus on economic objectives, and restrictive modelling assumptions about behaviour and uncertainty in returns. We recommend further research in each part of ASD to facilitate a CSC, and as a whole, for transitioning to a CE. CE concepts such as joint decision-making between product design and returns management, cross-sector collaboration, and product-service systems should inform the agenda for CSC research.

Omnichannel retailing and digitalization result in considerable challenges for the management and optimization of retail operations. The continued demand of quantitative insights, their practical need, and the growing availability of data motivates an increasing number of scientists and practitioners to intensify research on demand and supply-related issues in retailing. This featured cluster provides the state-of-the art literature on forecasting and digitalization technologies, channel structures and delivery concepts as well as logistics in omnichannel and online retailing. The featured cluster contains 17 articles that deal with such topics.

Remanufacturing is a common production mode for firms taking part in sustainable operations and the circular economy because it can improve environmental performance, especially via third-party remanufacturing. This paper studies third-party remanufacturing by developing game models for a closed-loop supply chain with one manufacturer and one third-party remanufacturer. Here the manufacturer has two strategies for third-party remanufacturing: outsourcing and authorization. The third-party remanufacturer collects end-of-life products to produce remanufactured products. Unlike the extant literature, this paper takes environmentally responsible behaviors of consumers and firms into consideration and focuses on how green consumption behavior and environmentally responsible behavior affect the manufacturer’s strategy. It finds that when a large gap exists between green consumer preferences for new and remanufactured products, both the manufacturer and the third-party remanufacturer prefer authorization; otherwise, they prefer outsourcing. In addition, it is difficult for them to select the same strategy for third-party remanufacturing when the environmental cost (the cost of not collecting end-of-life products) is high. However, they can reach an agreement in outsourcing scenario in the case of sequentially identical game.

Food Loss and Waste (FLW) is a major problem for humanity, with a third of all food grown for human consumption being wasted. While FLW happens at every stage of the food supply chain, in developed economies, 40% of the wastage happens in households (FAO 2011). The extant literature identifies several factors that drive wastage in households, but scalable interventions to reduce this wastage remain elusive. We contend that the idiosyncratic nature of households' behavior in regards purchase, consumption, and wastage makes such interventions difficult, and that an analytical model characterizing these behaviors will be helpful for furthering work in this direction. With this goal, we develop an analytical model incorporating factors (parameters) that influence consumption and wastage in households. While our model applies generally to perishable items including dairy products, we focus on Fresh Fruit and Vegetables (FFV) as it constitutes a large fraction of food wasted in households, even as many people consume far less FFV than the amount recommended for a healthy diet. Using numerical experiments for a range of parameter values obtained from the extant literature and from a survey, we identify the parameter combinations that result in larger volumes of wastage. Our findings will be useful for future research focusing on recommendations for reducing wastage and improving intake of FFV in households.

We study the quality disclosure strategy of a manufacturer/retailer in a supply chain with consumer returns for new products. In particular, we explicitly model the effect of quality disclosure on the reduction of the returns rate. We identify the condition in which the manufacturer/retailer should reveal product quality in the presence of consumer returns. We find that (1) the manufacturer/retailer has an incentive to choose quality disclosure strategy when the disclosure quality is above a lower bound; (2) if the returns rate of new products is low, the retailer-disclosure scenario leads to reveal more quality information than that of the manufacturer-disclosure scenario; otherwise, the manufacturer-disclosure scenario leads to reveal more quality information; and (3) the manufacturer prefers to reveal a higher level of product quality in the supply chain with a higher refund amount or a lower salvage value per unit of returned product.

Aligning staff with changing customer and store needs is key to store operations management. There is considerable research on personnel scheduling, but little on the common phenomenon of Real-Time Labour Allocation (RTLA), where mismatches between workforce supply and demand are addressed by allocating potentially cross-trained employees in real time. Our interviews with retail practitioners confirm that RTLA decisions lack analytical justification. In view of this, we design a generalisable stylised Retail Store Simulator (RSS) and instantiate the RSS using data from a gourmet supermarket. Simulation results show substantial long-term benefits to store performance from RTLA – a potential 6.6% increase in market share compared with No-RTLA. We further discuss RTLA's benefits under various employee cross-training configurations, answering a question from the collaborating retailer – "given the benefits of RTLA, how should we manage workforce flexibility?" We conduct extensive "what-if" analysis and find that broadening employee skill range and deepening employee proficiency increase the benefits of RTLA. This research helps understand workforce management at the execution stage.

One-third of nations have adopted some form of Daylight Saving Time (DST). Associated costs and benefits include impacts on accident rates. Using data from 12.6 million accident claims in New Zealand during 2005-2016, we model accident rates as a function of various date-based predictors including days before/after the start and end of DST, holidays, day of week, and month of year. This is the first study to consider multiple accident categories (Road, Work, Falls and Home & Community), and the first in the southern hemisphere. The start of DST is associated with significantly higher rates of road accidents (first day +16% and second day +12%). Evidence that accident rates for Falls and Home & Community decline (increase) prior to the start (end) of DST suggest potential behavioural adaption from anticipating the change. While Work accidents show limited impact from DST changes, they exhibit a significant decline over the course of the week (Friday 13% lower than Monday), whereas Road accidents exhibit a significant increase (Friday 19% higher than Monday). Our results have implications for both DST implementation and policy.

In the digital age, retail store operations face a variety of novel challenges and complexities. We review 255 papers on retail store operations from 32 operations research, management science, retailing, and general management journals over the period 2008-2016. We assess the current state of research within the context of retail store operations. By discussing the limitations present in these papers, we identify a number of research gaps and propose several opportunities for advancing retail expertise in the operations management community.(C) 2017 Elsevier B.V. All rights reserved.

We ask whether, in China, geographic location has explanatory power for firms' inventory turn, and why. To do this, we undertake a variance component analysis (VCA) of firm-level inventory turn, using a panel dataset of 1,531 unique Chinese firms spanning 1999-2008. Our identification arises from the fact that many locations have multiple firms and some firms have multiple locations. We find that city and province effects explain 18.4% and 6.3% of the variation in inventory turn respectively, constituting the most important effects after firm effects (50.0%) and ahead of year and industry effects. To understand why, we use seemingly unrelated regressions (SUR) to identify six city-specific effects that explain inventory turn. We then check how these effects impact inventory turn, by estimating how the effects-turn relationship is mediated by known drivers of inventory turn such as gross margin and lead time. For example, we find that distance from Beijing is associated with higher inventory turn via lower gross margins.

In this article, we investigate the ( R, S) periodic review, order‐up‐to level inventory control system with stochastic demand and variable leadtimes. Variable leadtimes can lead to order crossover, in which some orders arrive out of sequence. Most theoretical studies of order‐up‐to inventory systems under variable leadtimes assume that crossovers do not occur and, in so doing, overestimate the standard deviation of the realized leadtime distribution and prescribe policies that can inflate inventory costs. We develop a new analytic model of the expected costs associated with this system, making use of a novel approximation of the realized (reduced) leadtime standard deviation resulting from order crossovers. Extensive experimentation through simulation shows that our model closely approximates the true expected cost and can be used to find values of R and S that provide an expected cost close to the minimum cost. Taking account of, as opposed to ignoring, crossovers leads, on average, to substantial improvements in accuracy and significant cost reductions. Our results are particularly useful for managers seeking to reduce inventory costs in supply chains with variable leadtimes.

Stored value cards (SVCs) are ubiquitous, but little investigation has been undertaken into actual and desired replenishment behaviour, or the economic impact of card consolidation on consumers and service providers (such as retailers and public transit authorities). We develop classic and joint replenishment economic order quantity models for reloadable SVCs where the card value may be lost, for example, through theft or breakage. We extend the models to consider potential benefits of discounts, and providing refunds upon card loss (via external recording of card value). We also show how SVC consolidation and service expansion decisions can benefit consumers and service providers. As a case study, we utilise the Beijing subway system ‘YiKaTong’ card to demonstrate cost optimisation at the consumer and enterprise level. Direct observations of recharging and a survey suggest that commuters take the likelihood of card loss into account and are risk averse. The results of the work help to explain and predict consumer recharge behaviour, and should assist service providers evaluating decisions relating to their market scope, technology, pricing, and service capacity.

Companies undertaking operations improvement in supply chains face many alternatives. This work seeks to assist practitioners to prioritize improvement actions by developing analytical expressions for the marginal values of three parameters – (i) lead time mean, (ii) lead time variance, and (iii) demand variance – which measure the marginal cost of an incremental change in a parameter. The relative effectiveness of reducing lead time mean versus lead time variance is captured by the ratio of the marginal value of lead time mean to that of lead time variance. We find that this ratio strongly depends on whether the lead time mean and variance are independent or correlated. We illustrate the application of the results with a numerical example from an industrial setting. The insights can help managers determine the optimal investment decision to modify demand and supply characteristics in their supply chain, e.g., by switching suppliers, factory layout, or investing in information systems.

We review inventories in mainland China by evaluating the trajectory of aggregate inventories in recent decades, and then modelling the relationship of inventories in some 300,000 manufacturers with respect to volume (using cost of goods sold), industry (using SIC codes), and geographical location (using the 31 regions of China). We find that inventories generally exhibit economies of scale (in terms of cost of goods sold) in all but one industry (tobacco), and differ widely by province, with relatively high inventories in remote regions.We provide explanations for apparent diseconomies of scale for large unlisted firms, and reflect on why publicly listed manufacturers have significantly higher inventories than do unlisted firms. We note that manufacturing inventories as a proportion of manufacturing value-added are substantially higher in China than in the US The results may be employed for benchmarking and auditing of firms and managers, as well as for conducting due diligence for investment, mergers and acquisitions. (C) 2011 Elsevier B.V. All rights reserved.

This paper considers a single-product inventory system that serves both online and traditional customers. Demands from both channels follow independent stationary Poisson processes. Traditional customers have their demands fulfilled upon arrival if the retailer has stock on hand; otherwise the demand is lost. However, online customers place their orders in advance, and delivery is flexible in the sense that early shipment is allowed. In particular, an online order placed at time t should be fulfilled by time t + T, where T is the customer's demand lead time [1]; late fulfillment incurs a time-dependent backorder cost. A (Q,r) replenishment policy is used and replenishment lead times are assumed to be constant. We develop an approximation for the expected annual cost for the retailer, and compare analytically and simulated results. The optimal parameters of the system are derived by minimizing the expected annual cost. We illustrate the model with numerical examples, and discuss the sensitivity of the results to variables such as demand lead time and the split between online and traditional orders.

Encouraging interest in inventory management necessitates that instructors overcome concerns that the subject is too abstract or conceptual. To aid in this process, we describe a competition engaging students in concepts, including demand estimation, demand uncertainty, and costs of inventory and shortages. The competition simulates a multi-item newsvendor problem employing participant-generated data. We present results from use of the exercise in multiple class settings over the past decade. A number of possible extensions of the basic competition are discussed. Data collection and analysis materials are available to interested readers.