Inventory management of deteriorating drugs has attracted considerable attention recently in hospitals. Drugs are a kind of special product. Two characteristics of some drugs are the shorter shelf life and high service level. This causes hospitals a great deal of difficulty in inventory management of perishable drugs. On one hand, hospitals should increase the drug inventory to achieve a higher service level. On the other hand, hospitals should decrease the drug inventory because of the short shelf life of drugs. An effective management of pharmaceuticals is required to ensure 100% product availability at the right time, at the right cost, in good conditions to the right customers. This requires a trade-off between shelf-life and service level. In addition, many uncontrollable factors can lead to random lead time of drugs. This paper focuses on deteriorating drugs with stochastic lead time. We have established a stochastic lead time inventory model for deteriorating drugs with fixed demand. The lead time obeyed a certain distribution function and shortages were allowed. This model also considered constraints on service level, stock space and drug shelf life. Through the analysis of the model, the shelf life of drugs and service level were weighted in different lead time distributions. Empirical analysis and sensitivity analysis were given to get reach important conclusions and enlightenment.

For cross-border e-commerce companies with high shipping costs, the existing retailer and the new entrant retailer on the platform are usually concerned with information sharing and free shipping due to the uncertainty of market demand. For this, by establishing a Stackelberg game model between two competing retailers, we analyze the strategy of retailers and explore the business strategies of the cross-border e-commerce platform. The study shows that regarding information-sharing strategies, retailer A’s willingness to share information is positively related to initial market potential and negatively related to market competition intensity. Moreover, retailer B is willing to spend higher information costs to purchase information when the necessity of the product is more elevated. As for a free shipping strategy, if the existing retailer offers free shipping, the new entrant retailer should also offer free shipping service to consumers when the initial market potential is larger. Conversely, when the initial market potential is smaller, the retailer’s willingness to offer free shipping decreases when the intensity of competition in the market increases. When the market tends to be perfectly competitive, the new entrant retailer will not choose a free shipping strategy, and the platform is most profitable when information sharing and free shipping occur simultaneously. However, when the carrier charges a higher shipping fee to customers, the existing retailer is more profitable when the new entrant does not offer free shipping. Therefore, in order to achieve a win-win situation for all parties, the platform needs to develop appropriate operational strategies to influence the decisions of retailers and carriers. Some numerical experiments are made to test the validity of the model and the effect of the parameters involved in the model.

For the service-oriented manufacturing supply chain with uncertain demand, based on maximizing each participates' profits, this paper investigates the interaction between pricing mechanism and demand signal acquisition by establishing several signal acquisition models. It is shown that the service-oriented manufacturer always benefits from the additional signal acquired, and the signal releasing does not affect its profitability. While the crowdsourcing intermediary is reluctant to share his private signal with the self-operated crowdsourcing platform, and it is not necessarily beneficial to predict additional signal so as to obtain more accurate demand forecast. As for service preference, it is shown that the impact of consumer's preference on profits is not related to signal acquisition, but is subjected to the service-oriented manufacturer's unit service operating cost. Some numerical experiments are provided to test the validity of the obtained results.

To address user privacy concerns and improve user trust levels, sharing platforms are commencing to focus on investing in blockchain technology. This study focuses on blockchain technology investment and pricing strategies for two asymmetric sharing platforms. By constructing a Hotelling model, we investigate the investment strategies of the two asymmetric platforms regarding blockchain technology under different user attribution behaviours, i.e., single-homing or multi-homing, and the optimal pricing under different investment decisions. Afterwards, we compare and analyse the investment strategies under different conditions, obtain the influence of relevant market factors on the pricing strategies of the platforms, and finally determine the optimal timing of blockchain technology investment for asymmetric sharing platforms. The results indicate that when users’ perception of blockchain value is high, both platforms are motivated to introduce blockchain technology, and, conversely, a stronger platform exits the blockchain market. In multi-homing markets, platforms are more likely to implement blockchain strategies and the cost of technology investment is significantly higher than in a single-homing market. In addition, we also find that the degree of differentiation has a significant impact on the blockchain strategies of weaker platform under multi-homing market.

Some capital-constrained and risk-averse retailers may unable to obtain financing from banks due to insufficient collateral and high loan costs, so some retailers tend to use trade credit financing to ease their financial pressure. For the two echelon supply chain composed of a well-funded supplier and a capital-constrained retailer with risk-averse preference, a trade credit strategy model with the supplier-led is established in this paper. By analyzing both parties’ benefits, we derive the model solution and provide optimal decisions to all petitioners. The results obtained in this paper show that the optimum order quantity under the Conditional Value-at-Risk (CVaR) criterion declines w.r.t. the confidence level, and the wholesale price of the supplier increases w.r.t. the confidence level. The reason is that when the retailer makes fewer orders, the supplier will correspondingly increase the wholesale price to maximize their profit. On the other hand, the ordering policy with allowing backorder will make the retailer place fewer orders. Finally, the proposed model is indicated by the given numerical experiments.