Although research regarding supply chain collaboration has been increasing, studies reviewing supply chain collaboration remains limited. This review paper categorises supply chain collaboration and identifies different themes in this research field. Articles were collected based on a designed keywords list and specific subject restrictions in citation databases. Text-mining technology and conceptual criteria were used to categorise and screen articles into different categories. Finally, cluster analysis was used to group articles and identify emerging themes. From the year 1999 to 2017, a total of 678 articles regarding supply chain collaboration were found. These were sorted into six categories: levels of collaboration, industries, supply chain stages, company scales, forms/methods of collaboration and typology of article, and into corresponding sub-categories. 380 articles employing modelling approach where collaboration forms the core of analysis were screened for hierarchical cluster analysis, resulting in four clusters: information sharing paradigm, joint decision-making paradigm, resource sharing paradigm and coordinating contract paradigm. Eleven themes were finally identified based on the four clusters. The number of articles in the field of supply chain collaboration shows an increasing trend by year. However, most of the collaborations discussed were still at a low level. It is expected to explore a higher level of collaborations and related themes in future research.

As fashion mass customization has been developing rapidly in the recent decade, supply chain in apparel industry was required to be more flexible for meeting the need of quick response toward the dynamic changes in the fashion market. Forecast-based make-to-stock production strategy was not suitable anymore. Make-to-order strategy was employed by more and more apparel companies. However, there were still many defects in traditional make-to-order apparel supply chain, e.g., sustainability is still at a low level. In this study, we proposed a collaborative cloud service platform to develop a novel model of sustainable make-to-order apparel supply chain. A service provider selection heuristic was designed for this platform to optimally select suppliers corresponding to each received demand. Multi-agent-based simulation technology was utilized to build the proposed platform and to evaluate the new sustainable supply chain model. Experiment was conducted in the simulation to compare our new model to traditional apparel make-to-order model and model with outsourcing mechanism. Based on simulation results, the remarkable improvement in terms of sustainability of the proposed platform and corresponding supply chain model was demonstrated.

With the development of mass customization, the traditional garment production model needs to be optimized to have a more sustainable structure. To meet demand for flexibility, low-cost, and high-efficiency, an innovative resource sharing mechanism was proposed in this paper to form a new sustainable type of garment production. Different from the individual production in traditional models, the new mechanism involves resources being shared among various manufacturers. The tradeoff between positive and negative effects of the proposed mechanism is a key issue for sustainable production. In the present study, an overall sustainable index, integrating four production performance indicators, was defined on the basis of an Analytical Network Process to assess various production scenarios. According to the discrete-event simulation results of the different scenarios, we found that garment manufacturers could obtain comprehensive improvements in sustainable production by implementing the proposed resource sharing mechanism under the threshold of an increasing production failure rate.

In this paper we proposed a central order processing system under resource sharing strategy for demand-driven garment supply chains to increase supply chain performances. We examined this system by using simulation technology. Simulation results showed that significant improvement in various performance indicators was obtained in new collaborative model with proposed system.