Explore the vast range of titles available.

Washington Post fashion critic Rachel Tashjian shares her cheat sheet for common words and phrases that come up when shopping for used clothing.

Under a consignment contract with revenue sharing, a supplier decides on the retail price and delivery quantity for his product, and retains ownership of the goods; for each item sold, the retailer deducts a percentage from the selling price and remits the balance to the supplier. In this paper we show that, under such a contract, both the overall channel performance and the performance of individual firms depend critically on demand price elasticity and on the retailer's share of channel cost. In particular, the (expected) channel profit loss, compared with that of a centralized system, increases with demand price elasticity and decreases with retailer's cost share, while the profit share extracted by the retailer decreases with price elasticity and increases with retailer's cost share. With an iso-price-elastic demand model, we show that the channel profit loss cannot exceed 26.4%, and that the retailer's profit share cannot be below 50%. When price elasticity is low, or when the retailer's cost share approaches 100%, or both, the retailer can extract nearly all the channel profit that is almost equal to the centralized channel profit.

Consignment contracts are widely implemented in virtual market. We consider a supplier produces product and sells it to market through a retailer by consignment contract. In a unified model assumption, considering random demand and customers’ uncertain return behavior, we focus on two popular consignment contracts: vendor managed consignment inventory contract and consignment contract with revenue sharing. Comparing analytical results between the two contracts, we find that (1) the retailer and whole supply chain will benefit more from the consignment contract with revenue sharing, (2) the supplier will prefer to vendor managed consignment inventory contract, (3) both the retailer and supply chain will share a higher risk under the consignment contract with revenue sharing, the supplier’s shares of risk and profit highly depend on his share of the total expected cost, (4) all the profit and risk functions are decrease in uncertainty of customer return. We believe that these managerial insights can advance the understanding about use of the two consignment contracts in supply chain management with both benefit and risk considerations.

This work develops a general model of a two-echelon supply chain in which a dominant retailer interacts with a manufacturer via a consignment contract with revenue sharing. The manufacturer’s cost function is known only to him, whereas the retailer has only an estimation of this function, which is based on common knowledge. We formulate the interaction between the parties as a Stackelberg game in which the less informed party (the retailer) moves first. We investigate a strategic information-sharing policy of the manufacturer under general formulations of (i) the supply chain’s revenue and cost functions, and (ii) the manufacturer’s decision functions. Two models are considered: (i) a point-estimation model—the retailer relies on a single-valued estimation of the manufacturer’s cost function, based on her “best belief”; and (ii) an interval-estimation model—the retailer faces uncertainty with regard to the cost function and thus estimates its parameter values within intervals. We find a condition that distinguishes between a case in which it is optimal for both parties for the manufacturer to share his exact cost function and a case in which such information-sharing is not optimal for the manufacturer but is optimal for the retailer. In the interval-estimation model, equilibrium is obtained using a normative (probabilistic) approach as well as behavioral-decision criteria (max–max, max–min and regret minimization). Under a normative approach both hidden and known superiority of the manufacturer are considered. Finally, we use our model to analyze a supply chain of a mobile application.

Purpose This study aims to comprehensively examine generative artificial intelligence (GenAI) applications in the fashion retail value chain and propose a practical framework to guide effective GenAI innovation for fashion retailers. Design/methodology/approach An extensive literature review was conducted, including use cases of GenAI within the fashion industry. Given GenAI’s evolving nature, a forward-looking perspective is applied to explore its potential and challenges within fashion retail. The technology-organization-environment (TOE) theory was used as the foundation for the proposed framework. Findings This study presented a systematic, future-oriented view of GenAI’s opportunities and challenges across the fashion retail value chain. GenAI enhances productivity across the fashion retail value chain by streamlining design, enhancing operations, improving marketing and customer experiences and optimizing decision-making. However, its implementation presents key challenges in four areas: data issues, technical constraints, output reliability and social concerns. A TOE-based practical framework and actionable insights were proposed to help fashion retailers navigate GenAI’s complexities, harness its potential and address related challenges. Originality/value Our study provides a holistic view and actionable guidance for fashion retailers on effectively and responsibly implementing GenAI across the retail value chain. These insights can also benefit GenAI practitioners in other industries.

To further reduce carbon emissions, supply chain members implement the low-carbon production process and use varieties of contracts to coordinate the channel. Considering the effect of emission reduction, this paper studies a two-echelon supply chain consisting of one manufacturer and one retailer. Two supply chain members dedicate to maximize profits by reducing their products’ carbon emissions under two different contracts: the wholesale price contract and the consignment contract. The Stackelberg differential game is used, and the optimal strategies of emission reduction effort, wholesale, and retail price in the two situations are studied. The results show that the Pareto improvement for the whole supply chain can be reached under the consignment contract. However, the specific impacts on the retailer and the manufacturer are different. When consumers have a higher level of environmental awareness, the retailer tends to decrease her proportion of sales revenue under the consignment contract. At that time, choosing the wholesale price contract is more favorable for the retailer. However, as the retailer’s proportion of sales revenue becomes lower, the proportion of revenue belonging to the manufacturer will increase. It would be better for the manufacturer to choose the consignment contract.

PurposeThe demand for second-hand clothing has risen rapidly in the past few years. Yet, the understanding of the motivations of consumers buying second-hand clothing is very limited. The purpose of this study is to propose and empirically test an integrated model of the theory of planned behaviour and the theory of consumption values to explain consumers' intention to buy second-hand clothing.Design/methodology/approachData (n = 290) are collected from consumers in Malaysia and analysed by partial least squares structural equation modelling (PLS-SEM).FindingsResults of this study show that attitudes towards second-hand clothing, injunctive norms, descriptive norms, moral norms, and perceived behavioural control have a significant positive influence on consumers' intention to buy second-hand clothing. Furthermore, emotional value and environmental value are found to have a significant positive influence on attitudes. However, no support is found for the positive influence of social value and epistemic value on attitudes.Originality/valueThe study confirms that the integrated model is useful in explaining consumers' intention to buy second-hand clothing. Furthermore, this study also provides some valuable suggestions to practitioners.

Because of uncertainty in customer demand and lack of understanding in customer reactions to price changes, it is a challenge for many companies, such as manufacturers and retailers, to match supply and demand. Most of the models in the operations literature, however, have focused on the case in which the underlying customer demand information is known a priori, which is not true in many applications. In “Coordinating Pricing and Inventory Replenishment with Nonparametric Demand Learning,” B. Chen, X. Chao, and H. Ahn develop a data-driven algorithm for pricing and inventory decisions that learns the demand and customer information from sales data on the fly, and they show that the profit generated from the algorithm converges to the clairvoyant optimal profit at the quickest possible rate. We consider a firm (e.g., retailer) selling a single nonperishable product over a finite-period planning horizon. Demand in each period is stochastic and price sensitive, and unsatisfied demands are backlogged. At the beginning of each period, the firm determines its selling price and inventory replenishment quantity with the objective of maximizing total profit, but it knows neither the average demand (as a function of price) nor the distribution of demand uncertainty a priori; hence, it has to make pricing and ordering decisions based on observed demand data. We propose a nonparametric, data-driven algorithm that learns about the demand on the fly and, concurrently, applies learned information to make replenishment and pricing decisions. The algorithm integrates learning and action in a sense that the firm actively experiments on pricing and inventory levels to collect demand information with minimum profit loss. Besides convergence of optimal policies, we show that the regret of the algorithm, defined as the average profit loss compared with that of the optimal solution had the firm known the underlying demand information, vanishes at the fastest possible rate as the planning horizon increases.

Selling fresh food products can be challenging due to their perishability, which often results in significant losses. To address this issue and maximize profits, we have developed a contract that takes into account the costs of investing in preservation technology over a finite planning horizon. The arrangements incentivize the supplier and the retailer to establish coordination and determine not only the optimal price and schedule for replenishment but also the optimal investment required in preservation technology. We investigate the effectiveness of vendor managed inventory (VMI) and consignment with revenue-sharing models through an analysis of pricing and inventory decisions, followed by evaluating the channel performance and the distribution of profits. Contract parameters are defined under the equilibrium state to achieve advantageous relationships among supply chain partners by improving profits for both channel members. The finding shows that a VMI and consignment mechanism with a side payment arrangement could help coordinate channels in a non-cooperative setting. Still, performing the contract is easier in a cooperative setting. Both members could achieve optimal decisions for the wide-channel system without any additional payments, leading to increased profitability for all supply chain members. In an alternative approach, the retailer has the option to offer a subsidy alongside the revenue-sharing-only trading terms within a VMI and consignment by incorporating a generalized revenue-sharing scheme to facilitate coordination with the supplier.

The article analyses the use of modern technologies in rail freight transport with a specific focus on the process of transferring information from transport documents through automatic identification technology. The main transport document in this context is the consignment note, where automatic identification is required. The automatic identification technologies investigated are RFID/NFC and QR code, with the analysis suggesting that QR code is more accessible and convenient in the current railway environment. However, in practice, limitations have emerged regarding the capacity of the stored information and the readability of the QR code data. It concludes that the use of modern technologies in rail freight transport brings new opportunities and increases the efficiency of information transfer associated with transport documents. Despite the advantages of QR code technology, limitations have been identified, in particular in terms of capacity and readability. The results also show that users with access to modern technologies, such as internet-enabled mobile phones or computers with internet access, are able to handle this technology efficiently, suggesting potential process improvements in rail freight transport. Overall, the paper highlights the importance of integrated technology solutions within this sectoral area and suggests that technological advances are opening up new opportunities for more efficient management and monitoring of rail transport processes.