Purpose: The purpose of this paper is to investigate remanufacturing operational strategies considering uncertain quality of end-of-life (EOL) products and differential consumers’ willingness-to-pay (WTP) for new products and provide suggestions on the remanufacturing mode selection for the original equipment manufacturer (OEM).Design/methodology/approach: This study considers three remanufacturing modes, i.e. in-house, outsourcing and authorization modes. By establishing and comparing decision models of three modes from the perspectives of profit, consumer surplus and environment, the optimal remanufacturing mode is discussed.Findings: The results suggest that if the OEM’s remanufacturing capability is high, the in-house mode brings to the highest environmental performance, OEM’s profit and consumer surplus. Otherwise, the outsourcing mode (authorization) is the best benefit to environment (consumers if the unit production cost of new products is not too high). As for the preference of two decision-makers to outsourcing and authorization modes, if the difference of consumers’ WTP for new products is low, the OEM prefers the outsourcing mode; otherwise, the OEM prefers the authorization mode. The preference of the third-party remanufacturer (TPR) to remanufacturing mode is affected by consumers’ WTP for remanufactured products, WTP difference for new products and remanufacturing quality level standard.Practical implications: These results can provide operational insights into how to select remanufacturing mode when the quality of EOL products is uncertain and consumers’ WTP for new products is different under three remanufacturing modes.Originality/value: This paper is among the first to investigate the joint effects of EOL products’ uncertain quality and differential consumers’ WTP for new products on the operational strategies and performance under different remanufacturing modes.

Largely motivated by the industrial practice in which a platform giant will encroach online retailing by introducing private label (PL) products, this paper aims to investigate the optimal introduction decision for a platform and identify the best sales format, between the reselling format and the agency format, for a manufacturer in an e-commerce platform supply chain. In response to these two sales formats, this paper characterizes and proposes three different PL product introduction strategies, including No Introduction, Partial Introduction, and Full Introduction. By developing a game-theoretic framework and applying the Karush–Kuhn–Tucker optimality, this paper examines the optimal PL product introduction decision and the best sales format. With analytical studies and numerical experiments, several significant implications are derived in this paper. For example, it is first found that the consumer preference for the platform and the quality of the PL products are two key factors influencing the platform’s PL product introduction, with associated effects differing notably. Secondly, improving the PL products’ quality does not necessarily lead to an increased profit for the platform. It will also not lead to a loss in profit for the manufacturer. Lastly, the best sales formats for the manufacturer are significantly influenced by the PL products’ introduction strategy chosen by the platform.

Uncertain factors in modern multi-variety and small-lot manufacturing make it extremely challenging to optimise and control the production process. Researchers propose a bottleneck-based optimisation method to reduce perplexity and enhance optimisation. Detecting bottlenecks is a crucial first step in this method and its accuracy has great impacts on production optimisation. This study proposes an independent bottleneck degree to describe the probability of a manufacturing cell becoming a system bottleneck, and model it using capacity and demand observations from the perspectives of capability, quality, and cost. Based on the independent bottleneck degree, we design a closed-loop multi-bottleneck prediction method, which can solve the responsibility cognisance problem resulting from correlation among manufacturing cells. Therefore, it can predict bottlenecks, especially multiple bottlenecks, accurately compared to existing methods.