Purpose The aim of this paper is to provide a comprehensive and detailed review of the state-of-the-art mechanisms of knowledge sharing (KS) in the supply chain (SC) field, as well as directions for future research. Briefly, this paper tries to offer a systematic and methodical review of the KS mechanisms in the SC to provide a comparative summary of the selected articles, to collect and describe the factors that have the influence on KS and SC, to explore some main challenges in this field and to present the guidelines to face the existing challenges and outlining the key areas where the KS mechanisms in SC can be improved. Design/methodology/approach In the current study, a systematic literature review up to 2018 is presented on the supply chain’s mechanisms of KS. The authors identified 21,907 papers, which are reduced to 25 primary studies through the paper-selection process. Findings The results showed that the KS in SC helps to increase the success of the organizations, improve employee performance, increase competitive advantage, enhance innovation and improve relationships between supplier and consumer. However, there were some weaknesses, such as staff resistance to share knowledge in the SC because of fear of job loss. Research limitations/implications There are several limitations to this study. This study limited the search to Google Scholar. There might be other academic journals where Google does not find their paper and they can offer a more complete picture of the related articles. Finally, non-English publications were omitted from this study. It is possible that the research about the application of KS in SC can also be published in other languages. In addition, more studies need to be carried out using other methodologies such as interviews. Originality/value The paper presents a comprehensive structured literature review of the articles’ mechanisms of KS in SC. The paper’s findings can offer insights into future research needs. By providing comparative information and analyzing the current developments in this area, this paper will directly support academics and practicing professionals for better knowing the progress in KS mechanisms.

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Energy-efficient buildings have attracted vast attention as a key component of sustainable development. Thermal load analysis is a pivotal step for the proper design of heating, ventilation, and air conditioning (HVAC) systems for increasing thermal comfort in energy-efficient buildings. In this work, novel a methodology is proposed to predict the cooling load (LC) of residential buildings based on their geometrical characteristics. Multi-layer perceptron (MLP) neural network was coupled with metaheuristic algorithms to attain its optimum hyperparameter values. According to the results, the LC pattern can be promisingly captured and predicted by all developed hybrid models. Nevertheless, the comparison analysis revealed that the electrostatic discharge algorithm (ESDA) achieved the most powerful MLP model. Hence, utilizing the proposed methodology would give new insights into the thermal load analysis method and bridge the existing gap between the most recently developed computational intelligence techniques and energy performance analysis in the sustainable design of energy-efficient residential buildings.

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