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This paper aims to assess the links among these demand chain constructs by conducting a full-scale systematic review of all demand chain management (DCM) literature reviews published in marketing and operations management journals from 2013 to 2020. Marketing and supply chain management are central to DCM; thus, this study briefly describes the contributions to knowledge provided by the papers contained in this issue. In addition, some additional areas of research in which the DCM can be gainfully deployed are outlined.

This paper makes a systematic literature review of 70 literature samples by means of content analysis and comprehensive analysis. These approaches guarantee a replicable, rigorous and transparent research process and minimize researcher bias. The analytical categories required for the content analysis are defined along the constructs of marketing and supply chain management.

As can be expected, this paper highlights the key role of the two constructs in the strategy of DCM. In this light, the paper claims to provide evidence of a link between the constructs of marketing and supply chain management. This paper reviews the connotation of DCM through literature review, distinguishes the relationship between DCM and supply chain management from a strategic management perspective and discusses the future research direction.

This study assesses the link between the strategic constructs of marketing and supply chain management through research embedded in literature reviews, pinpointing research gaps and potential future research directions in the field. Contributing to DCM theory building, a thorough review provides qualitative comparison of the link between marketing and supply chain management.

Although some literature reviews have been conducted in the past on the constructs of DCM, no full review of literature reviews aiming to test a strategic theoretical link in the demand chain related to supply chain and marketing.

Despite the importance of demand forecasting in retail industry, its influence on supply chain agility has not been sufficiently examined. From a total information technology (IT) capability perspective, the purpose of this paper is to examine the antecedent of supply chain agility through retail demand forecasting.

Combining the literature reviews, the quantitative method of algorithm analysis was targeted at, and the firm data were processed on MATLAB.

This paper summarizes IT dimensions of demand forecasting in retail industry and distinguishes the relationship of supply chain agility and demand forecasting from an IT capability view.

Managers can derive a better understanding and measurement of operating activities that appropriately balance among supply chain agility, IT capability and demand forecast practice. Demand forecasting should be integrated into the firm operations to determine the agility level of supply chain in marketplace.

This paper constructs new theoretical grounds for research into the relationship of demand forecasting-supply chain agility and provides an empirical assessment of the essential components for the means to prioritize IT-supply chain.

The purpose of this study is to explore the relationship between big data and supply chain platform in China’s retail industry. With the emergence of big data resources and technologies, the business pattern of new retail advocates the combination of online and offline channels. Supply chain platform plays a key role in the implementation of retail activities, which has gradually become a research hotspot in the cross field of operations management and information system.

Through the method of literature review and case study, this study empirically explores how big data shapes supply chain platform to support new forms of online retail by grounded theory.

The model framework is validated by reliability test and coding method to process survey materials. The results identify the overall antecedents of supply chain platform and reveal positive effects between big data and new retail. The findings help firm managers build a big data-driven supply chain to support new retail.

There are insufficient studies on theoretical frameworks and interaction relationships among big data, supply chain platform and new retail.

Classifying the types of fabric defects in the textile industry requires a way to effectively detect. The traditional textile fabric defects detection method is human eyes, which performs very low efficiency and high cost. Therefore, how to improve the classification accuracy of textile fabric defects by using current artificial intelligence and to better meet the needs in the textile industry, the purpose of this article is to develop a method to improve the accuracy of textile fabric defects classification.

To improve the accuracy of textile fabric defects classification, an ensemble learning-based convolutional neural network (CNN) method in terms of textile fabric defects classification (short for ECTFDC) on an enhanced TILDA database is used. ECTFDC first adopts ensemble learning-based model to classify five types of fabric defects from TILDA. Subsequently, ECTFDC extracts features of fabric defects via an ensemble multiple convolutional neural network model and obtains parameters by using transfer learning method.

The authors applied ECTFDC on an enhanced TILDA database to improve the robustness and generalization ability of the proposed networks. Experimental results show that ECTFDC outperforms the other networks, the precision and recall rates are 97.8%, 97.68%, respectively.

The ensemble convolutional neural network textile fabric defect classification method in this paper can quickly and effectively classify textile fabric defect categories; it can reduce the production cost of textiles and it can alleviate the visual fatigue of inspectors working for a long time.

Principals' well-being worldwide is under increasing threat due to the challenging and complex nature of their work and growing demands. This paper aimed at developing and validating a multidimensional Principal Well-being Inventory (PWI) and examining the state and consequences of principal well-being.

This paper involves four independent samples of principals working in schools from Hong Kong and Mainland China. The research design consisted of four phases with four sequential empirical studies. Phase 1 was to establish the content validity (literature review and Study 1); Phase 2 was to test the construct validity (Study 2 and Study 3); Phase 3 was to build the criterion validity (re-use the data from Study 3) and Phase 4 was to test the cross-validity of the PWI (Study 4).

Based on published literature and four successive empirical studies, a 24-item PWI was created via a theoretical-empirical approach of test construction. Validity was confirmed through construct-, content-, criterion- and cross-validity testing. The PWI covers the six important well-being dimensions – physical, cognitive, emotional, psychological, social and spiritual – to present a general picture of principals' occupational well-being associated with job nature, well-being literacy, leadership and context.

The inventory will aid efforts to promote principal well-being as an essential component of schoolwide well-being, quality education and a wellness society.

During the post-COVID-19 period, this project is deemed both critical and timely so that quality education will not be sacrificed due to factors affecting principal well-being.

This theoretically and empirically validated inventory serves as a robust tool for comprehensively understanding principal well-being and a fuller exploration of their well-being literacy, drivers and outcomes.

The purpose of this paper is to examine the indigenous Chinese concept of suzhi (素质) with the aim of furthering the development of Chinese human resource management (HRM) research and practice.

An extensive review of the literature on suzhi, published in the West, as well as in China, is the basis for proffering an organizational-level conceptualization of suzhi in the Chinese context.

Instead of understanding it as a free-floating signifier, we argue that suzhi can be considered as a criterion-based framework for HRM research and practice. Suzhi research is classified into two major sources – indigenous Chinese and indigenized Western constructs. We further make a distinction between intrinsic and extrinsic suzhi, and analyze a popular set of suzhi criteria, considering de (morality) and cai (talent), while focusing on de in HRM selection (德才兼备, 以德为先). As multilevel and multidimensional framework, suzhi criteria may form different gestalts in different organizations and industries.

From a social cultural and historical perspective, HRM research that incorporates a combination of indigenous and indigenized suzhi characteristics may receive better acceptance by individuals, organizations and the society in the Chinese context. Accordingly, the reconstruction of suzhi into manageable and measurable dimensions can be undertaken for more effective HRM practice in the Chinese context.

The HRM literature is advanced by linking the indigenous suzhi discourse to Chinese indigenous HRM research and practice.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

Cables are widely used, and they play a key role in complex electromechanical products such as vehicles, ships, aircraft and satellites. Cable design and assembly significantly impact the development cycle and assembly quality, which is be-coming a key element affecting the function of a product. However, there are various kinds of cables, with complex geo-metric configurations and a narrow assembly space, which can easily result in improper or missed assembly, an unreasonable layout or interference. Traditional serial design methods are inefficient and costly, and they cannot predict problems in installation and use. Based on physical modeling, computer-aided cable design and assembly can effectively solve these problems. This paper aims to address virtual assembly (VA) of flexible cables based on physical modeling.

Much research has focused recently on virtual design and assembly-process planning for cables. This paper systematically reviews the research progress and the current state of mechanical models, virtual design, assembly-process planning, collision detection and geometric configuration and proposes areas for further research.

In the first instance, the main research groups and typical systems are investigated, followed by extensive exploration of the major research issues. The latter can be reviewed from five perspectives: the current state of mechanical models, virtual design, assembly-process planning, collision detection and geometric configuration. Finally, the barriers that prevent successful application of VA are also discussed, and the future research directions are summarized.

This paper presents a comprehensive survey of the topics of VA of flexible cables based on physical modeling and investigates some new ideas and recent advances in the area.