TTF is committed to becoming the leader and enabler of ODM in the textile and garment industry. In 2021, TTF has achieved digitally driven fabric R&D and technology in terms of fabric productivity, forming over 6,000 differentiated new fabrics and over 20,000 reproducible printing and dyeing digital craft package. Simple improvement of production relations, such as "matching" and "gathering orders", has little value and cannot solve the problem of inflexible production in the textile and garment industry. Starting from the direction of productivity, TTF has built an intelligent printing and dyeing factory as a “central kitchen”. Through the "central kitchen + cloud factory" model, TTF has solved the core pain point of flexible fabric production. At the same time, TTF starts from the standardization direction. Since dyed fabrics and garments are non-standardized products in the industrial chain, how to standardize them is the second curve of industry growth. TTF is firmly committed to product standardization, process standardization, and production capacity standardization.

At present, the pain points of the garment industry are obvious. First of all, the garment industry as a whole is still based on production and sales. The production and sales data based on this mechanism are not interoperable, as well as other factors, making large inventory the biggest problem in the garment industry. The average inventory rate in the garment industry exceeds 30%, which needs to be solved urgently. Most garment factories are still in the "man-machine collaboration" stage, and labor is the main production cost. Up to now, fully automated production facilities have serious problems in their applicability and economy, but the continuous increase in labor costs has resulted in almost no profit for enterprises operating in accordance with regulations. Management upgrades and efficiency improvements are urgently needed. The degree of digitalization of production in the garment industry is less than 10%. The fundamental reason is that there is very little accumulation of big data in the industry, and the further reason is that no one has established data standards for the textile industry. Without standardized data, it cannot be generalized and cannot be used for data application. The lack of the foundation and means of data analysis resulting in heavy reliance on personal experience; the lack of effective quality control tools and means cannot guarantee the quality of products produced in the "quick response" mode.

TTF's exploration in digitalization of the textile and garment industry follows a principle, that is, digitalization creates incremental demand, rather than simply absorbing stock demand; digitalization optimizes stock supply, rather than simply adding new supply. In the flexible garment modules of vertical integration, TTF still adheres to the ODM business model, and creates new demand by recommending products. TTF achieves the effect of "specialized division of labor + network collaboration" by building the model of "central sample house + central BOM + cloud factory", optimizes the supply of garments, and realizes flexible production capacity.

TTF's central sample house is the starting point for the transformation of customer needs. The function of the sample room is to transform the garment design sketch into the plane structure drawing, and then the paper pattern, for garment production. The quality of the pattern determines whether the front-end customer accepts and places an order, and determines the process cost of the back-end garment production. Furthermore, for traditional garment brands, expanding the category sometimes means rebuilding the sample team. The production of sample garment is similar to garment customization, with the cost ranging from 500 to 3000 yuan and the research and development period from 48 to 72 hours. In addition to the problem of R&D resource matching and cost, the more fatal problem is that it is difficult to accurately communicate design requirements. There is often a language barrier between designers and pattern makers and sample makers with industrial background, which leads to inaccurate information transmission. In the end, samples need to be revised and developed repeatedly, resulting in waste of resources and very high communication costs in the entire process. TTF's central sample room has the function of translating the designer's design language into an industrialized language that can be read by sample makers and pattern makers, and establishes a database, for example, AI can automatically recommend different optimal size ratios between sleeve length and body length for long-sleeved T-shirts and long-sleeved hoodies, thereby reducing communication costs; TTF's central sample room outputs "digital process package-patterns" to each production unit to form a reproducible sewing production standard.

TTF's central cutting and central finishing system is the key quality section. Garment production enterprises have the highest equipment investment intensity in front cutting unit and back process, but at the same time cutting and back process often appear "idle capacity in front and back channel" because of insufficient capacity. Sewing plants typically organize production by investing in low-cost and inefficient mechanical cutting beds and entrusting the "last process" to external factories, there may be large risks in quality and efficiency. TTF integrates and classifies the sewing processing plants through the platform, invests in smart computer cutting bed in the front process, self-develops special equipment in the back process, and shares cutting and finishing processes capacity on the platform, which effectively controls the quality, greatly improves efficiency and reduces costs.

TTF has created an intelligent garment cloud factory and a "G-work" model as well as an AI network collaboration model. Through the three-in-one AI intelligence of "equipment + worker + process", flexible and quick response of orders is realized. Based on worker big data (historical process, historical process operation efficiency value and good process ranking), equipment big data (equipment parameters related to production category and efficiency, intelligent adjustment according to the difference of processed fabrics), process big data (samples data, working hours, etc.), by learning curve model and parameters, AI automatically plans and arranges production, generates man-machine position map, and monitors on-site through MES to achieve dynamic production balance. In the next step, TTF will consider building a group of smart garment factories through BOT and TOT models, providing complete sets of IOT sewing equipment, certified workers can flexibly receive orders through mobile phones, enter the nearest factory for production, and flexibly deliver, and finally achieve "G-work (Garment WORK)" according to the "We-work" model.

TTF’s style development is the core key capability of ODM, and the core strategy to seize the initiative in the supply chain is ODM. By designing, recommending styles to customers, and  assisting the planning department and design department of brand customers to intervene in the earlier stage of the supply chain cycle, it will effectively reduce the pressure of rapid response in the later supply chain. TTF gathers discrete designer studios (non-independent designers) in the industry to implement crowdsourcing, relying on the self-built "central sample house" and "XIBU fabric library" and other infrastructures to connect the "supply and demand" of style development, creating flexible design and pattern-making capabilities to improve efficiency and reduce costs.

TTF's flexible garment modules always adhere to the essence of value creation. Three months after the implementation, the production cycle has been shortened by 2 times, the average efficiency has increased by more than 3 times, and the inventory has dropped by 80%, which has greatly reduced the cost of the garment supply chain. From the launch in January 2022, within 3 months, the TTF garment module has achieved break-even in economic benefits, and expects to achieve large-scale replication!