A joint pricing and network design model for a closed-loop supply chain under disruption (Glass Industry)

Closed-loop supply chains have attracted more attention by researchers and practitioners due to strong government regulations, environmental issues, social responsibilities and natural resource constraints over past few years. This paper presents a mixed-integer linear programming model to design a closed-loop supply chain network and optimizing pricing policies under random disruption. Reusing the returned products is applied as a resilience strategy to cope with the waste of energy and improving supply efficiency. Moreover, it is necessary to find the optimal prices for both final and returned products. Therefore, the model is formulated based on demand function and it maximizes total supply chain’s profit. Finally, its application is explored through using the real data of an industrial company in glass industry.

Learning-based dynamic capabilities in closed-loop supply chains: an expert study

Purpose Product returns information gives firms an opportunity for continuous strategic adaptation by allowing them to understand the reasons for product returns, learning from them and improving their products and processes accordingly. By applying the Dynamic Capabilities (DCs) view in the context of closed-loop supply chains (CLSC), this study explores how firms can continuously learn from product returns information.Design/methodology/approach This study adopts a qualitative Delphi study-inspired approach. Experts from industry and academia are interviewed in two interview rounds. First round of interviews are based on extant research, while the second round allows the experts to elaborate and correct the results.Findings This study culminates into a conceptual model for incremental learning from product returns information. The results indicate incremental learning from product returns can potentially lead to a competitive advantage. Additionally, the authors identify the sources of information, capabilities along with their microfoundations and the manifestations of product return information. Three propositions are formulated embedding the findings in DC theory.Research limitations/implications This study supports extant literature in confirming the value of product returns information and opens concrete avenues for research by providing several propositions.Practical implications This research elucidates the practices, processes and resources required for firms to utilize product returns information for continuous strategic adaptation. Practitioners can use these results while implementing continuous learning practices in their organizations.Originality/value This study presents the first systematic framework for incremental learning from product returns information. The authors apply the DC framework to a new functional domain, namely CLSC management and product returns management. Furthermore, the authors offer a concrete example of how organizational learning and DC intersect, thus advancing DC theoretical knowledge.

Urban Mining of e-Waste and the Role of Consumers

This chapter adds to the body of literature on the Circular Economy (CE), urban mining, and their intersection with consumer behaviour, by first providing a review of existing and emergent EU regulations aimed towards enhancing the collection rate of household WEEE. The fast growth of the EEE waste stream and its potential for Urban Mining as well as the inability of WEEE collection to keep up with the growth of the EEE industry is showcased with statistical data. The final section critically analyses the literature the intersection between consumer behaviour and closed-loop supply chains for EEE, identified through a systematic keyword search to ensure replicability. The findings point at a lack of theoretical, methodological and product-case heterogeneity among the identified sources, with most of them employing the Theory of Planned Behaviour and survey methods and focusing on mobile phones or general WEEE. While the literature suggests important behavioural differences across EEE categories, this was not representatively explored. The final section contributes to filling this gap by developing a taxonomy of EEE categories based on characteristics that may predispose consumer behaviour. The identified dimensions are: size, involvement, long-term reliability expectations, value type, internet access, multifunctionality, the quality of being outdated and social meaning.