Clothing is a typical seasonal and fashionable product; it is very easy to cause inventory backlog problems. This paper addresses the determination of pricing and production of a fashion clothing brand under both presale and regular sales stages. More specifically, we analyze the effect of the fashion degree of clothing and price on the demand. Demand during different market periods (peak sales season, low sales season, and stable market) change with time. The static pricing decision of presale price is made at the presale stage, but the regular sales price under static and dynamic pricing decisions is compared. We build both static and dynamic pricing models, with the objective of finding a pricing and production strategy that maximizes total expected profit of the clothing brand under two-stage sale. The influences of the fashion degree attenuation factor and the initial fashion degree on the brand's optimal pricing and revenue are analyzed. We show that when the market is in the off season or the market size is stable, the brand can benefit from the dynamic pricing policy. When the market is in peak sales season, the static price is better than the dynamic price strategy. No matter how the market size changes, in the regular sales stage, the optimal price of the brand always decreases with the fashion degree attenuation factor, and increases with the fashion degree sensitivity coefficient.

Frequent problems of counterfeiting have spawned consumer demands to monitor the entire supply chain. The application of blockchain technology with anti-counterfeiting and traceability can improve the reliability and authenticity of product information and eliminate consumer doubts about product quality. Furthermore, based on the transparency of blockchain technology, brand suppliers can independently obtain the market demand information through information sharing. This paper introduces a consumer suspicion coefficient to illustrate the application of blockchain technology in the supply chain. Considering product authenticity verification and information sharing, we study the optimal pricing and product quality decisions in a two-level supply chain under the following three scenarios: (1) no blockchain technology, a traditional supply chain, and no information sharing (case TN); (2) no blockchain technology but a traditional supply chain with information sharing (case TS); and (3) a supply chain based on blockchain technology (case BT). We find that when the consumer suspicion coefficient increases, consumers will have limited faith in the authenticity of the product, which will affect the retailer’s optimal decision and profit. By comparing the equilibrium results of several cases, we also find that demand information sharing by the retailer may not achieve a win-win outcome in a decentralized channel in the absence of blockchain technology. Under demand information sharing based on blockchain technology, however, if the consumer suspicion coefficient exceeds a certain threshold, the brand supplier and retailer can achieve a win–win outcome. In addition, the extended models reveal that in a centralized supply chain, regardless of the state of market demand, blockchain technology can always improve product quality and retail price and optimize supply chain profit.

Increasing attention is being paid to the pricing decisions of ride-hailing platforms. These platforms usually face market demand fluctuation and reflect supply and demand imbalances. Unlike existing studies, we focus on the optimal dynamic pricing of the platforms under imbalance between supply and demand caused by market fluctuation. Dynamic models are constructed based on the state change of supply and demand by using optimal control theory, with the aim of maximizing the platform’s total profit. We obtain the optimal trajectories of price, supply, and demand under three ride demand situations. The effects of some key parameters on pricing decisions, such as coefficient of demand fluctuation, service quality, and fixed commission rate, are examined. We find the optimal dynamic price can improve the match of supply-demand in ride-hailing market and enhance the revenue of platform.

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The isolation requirements of the coronavirus epidemic and the intuitive display advantages of live-streaming have led to an increasing number of retailers shifting to social live-streaming platforms and e-commerce live-streaming platforms to promote and sell their products in real time. However, the provision of live-streaming services will also incur high live-streaming effort costs. In this paper, we develop two decision models for retailers to sell goods through a single online shop and both online shop and live-streaming room; we also present the optimal decisions of pricing and live-streaming efforts. Furthermore, we identify the profitability conditions for retailers to determine when to provide live-streaming services. In addition, we examine the impact of the provision of live-streaming services on the optimal price and live-streaming effort. We obtain three findings. First, there is a unique optimal decision on the price and live-streaming effort under certain conditions. Second, when the effect coefficient of the live-streaming room reaches a certain threshold, there are enough customers who enter the live-streaming room to watch and buy and it is profitable for retailers to provide live-streaming service. Finally, the optimal price and live-streaming effort increase with the increase in average return loss, the effect coefficient of live-streaming effort, and the extra return rate and decrease with the increase in the proportion of customers who choose to buy in the online shop and the price discount coefficient in the live-streaming room.