长寿裁缝店运营瓶颈分析与传统服装定制业运营转型策略

针对剧组服装定制周期长、成本高、文化适配性不足等痛点，本文构建基于AI技术的柔性服装定制数字生态。通过生成式AI设计引擎、高保真3D虚拟试衣、工业级自动打版及版权保护四大核心技术，实现从创意设计到生产交付的全链路数字化。该生态深度适配剧组场景需求，支持快速生成符合历史形制或创意风格的服装方案，通过虚拟试穿验证上身效果，结合柔性供应链实现高效交付。实践表明，该生态可将剧组服装定制周期缩短40%以上，面料浪费率降至 4.8%，同时通过非遗元素数字化赋能文化传承，为影视服装定制提供高效、绿色、个性化的解决方案，推动行业数字化转型。

… Simply put, in the case of flow shops, both the standardized and tailor-made codes have to … stream loads the specific buffer–capacity bottlenecks that govern acceptance, switching, and …

ABSTRACT Throughput bottlenecks remain a main concern for managers in practice since they affect production output and throughput times. A large literature on bottleneck detection and prediction consequently emerged. Bottleneck prediction is specifically important in context where bottlenecks shift since it allows for counteracting the potential impact. The literature on throughput bottleneck prediction largely focusses on temporal aspects. Although this reflects the relation among stations if the routing of jobs is fairly directed, the relative station position constantly changes for more complex routings. A station maybe upstream, downstream or have no relation to another station dependent on the mix of jobs currently on the shop floor. In these high-variety contexts, both temporal and spatial features should be considered when predicting bottlenecks. In response, this study proposes a new neural network model that systematically connects multiple independent workstations into a system by extracting the spatial features between workstations. The new approach is different from traditional stacking mechanisms applied in the literature, and it allows for a better integration of spatial and temporal neural networks. Experimental results show that the proposed model outperforms alternative models and provides good prediction performance. Findings have important implications for research and practice.

Abstract This study uses simulation to assess the performance of alternative methods for detecting momentary bottlenecks in high-variety contexts that produce on a to-order basis. The results suggest that using the utilisation level of a station to detect bottlenecks leads to the best performance, but that this method suffers from high nervousness. Using the active period of a station appears to be a better overall choice for practice given its good performance and low nervousness. Meanwhile, methods that focus on the workload at a station are a viable alternative, but they may become dysfunctional in shops with directed routings and a limit on the queue. This negative effect is even stronger if the corrected workload measure is used, as recently suggested in the literature on short term capacity adjustments. Finally, using the inter-departure time detection method leads to the worst performance since: (i) it counterintuitively detects non-bottlenecks instead of bottlenecks; and, (ii) it is based on historical data, leading to a response delay.

Uncertain factors in modern multi-variety and small-lot manufacturing make it extremely challenging to optimise and control the production process. Researchers propose a bottleneck-based optimisation method to reduce perplexity and enhance optimisation. Detecting bottlenecks is a crucial first step in this method and its accuracy has great impacts on production optimisation. This study proposes an independent bottleneck degree to describe the probability of a manufacturing cell becoming a system bottleneck, and model it using capacity and demand observations from the perspectives of capability, quality, and cost. Based on the independent bottleneck degree, we design a closed-loop multi-bottleneck prediction method, which can solve the responsibility cognisance problem resulting from correlation among manufacturing cells. Therefore, it can predict bottlenecks, especially multiple bottlenecks, accurately compared to existing methods.

… Manufacturing capacity has been closely linked to the technological development available … This strategy will make it easy to visualize where downtime and bottlenecks are generated to …

This research article proposes an integrated AI platform designed to revolutionise the apparel industry. The platform, envisioned as a comprehensive ecosystem, aims to enhance every stage of the apparel value chain, from design ideation to marketing and supply chain management. The architecture is built around three core components: an AI-Powered Design Studio, an Intelligent Production & Supply Chain Backbone, and a Hyper-Personalised Marketing & Engagement Engine. The AI-Powered Design Studio leverages generative AI, deep learning, and computer vision to transform the design process. A Trend Forecasting Engine utilises diverse data sources (social media, e-commerce, runway shows) to predict trends with high accuracy, providing data-driven insights that directly inform the AI Co-Creation Suite. This suite employs GANs, diffusion models, and sketch-to-image translation to generate design variations, refine concepts, and simulate fabric drape and fit, resulting in faster design cycles and commercially viable products. A Hyper-Personalisation Module further enhances this by generating personalised designs tailored to individual customer styles and body measurements, bridging the "aspiration gap" between desired and attainable styles. The Intelligent Production & Supply Chain Backbone focuses on efficiency and sustainability. A Material Optimisation and Waste Reduction System uses computer vision to detect fabric defects and optimise cutting layouts, minimising waste. Predictive inventory management and AI-powered logistics orchestration, combined with blockchain technology for enhanced traceability, create a responsive and transparent supply chain. Automated quality control, utilising computer vision, reduces defects and enables predictive maintenance of machinery, optimising production efficiency. A Sustainability and Circularity Management Dashboard provides a holistic view of environmental and social impact, facilitating data-driven decision-making and transparency for consumers. Finally, the Hyper-Personalised Marketing & Engagement Engine uses AI to deliver tailored experiences. A Personalised Marketing and Dynamic Campaign Engine, powered by a Customer Data Platform (CDP) and a "Latent Style" algorithm, provides personalised product recommendations, marketing messages, and promotions. An Automated Content Generation Engine generates marketing assets (product descriptions, social media posts, email copy) at scale, while a Unified Consumer Insights Platform provides real-time market analysis. Conversational AI, through chatbots and virtual stylists, enhances customer support and creates personalised interactions. The use of 3D digital twins and virtual prototyping, including virtual try-on (VTO) capabilities, enhances consumer engagement and reduces return rates. The article concludes with a phased implementation roadmap, prioritising data infrastructure, key modules with high ROI (such as waste reduction), and subsequent integration of generative design and personalisation. The overall goal is a symbiotic relationship between human creativity and AI's efficiency, resulting in a future-ready apparel industry characterised by enhanced speed, sustainability, and personalisation. Case studies of industry leaders like Zara, Stitch Fix, H&M, and Nike illustrate the successful application of similar AI strategies.

Technologies that are ready-to-use and adaptable in real time to customers’ individual needs are influencing the supply chain of the future. This study proposes a supply chain framework for an innovative and sustainable real-time fashion system (RTFS) between enterprises, designers, and consumers in 3D clothing production systems, using information communication technology, artificial intelligence (AI), and virtual environments. In particular, the RTFS is targeted at customers actively involved in product purchasing, personalising, co-designing, and manufacturing planning. The fashion industry is oriented towards 3D services as a service model, owing to the automation and democratisation of product customisation and personalisation processes. Furthermore, AI offers referral services to prosumers or/and customers and companies, and proposes individual designs with perfect styles and measurements using new 3D computer aided design and AI-based product design technologies for fashion and design companies and customers. Consequently, 3D fashion products in the RTFS supply chain are entirely digital, saving time and money with sampling and tracking capabilities, secured, and trusted with personalised service delivery.

… Contemporarily, the MtM (Made-to-Measure) and bespoke apparel can better … -bespoke that can digitally and virtually accomplish the process. However, the existing e-bespoke apparel …

… shift to digital product creation (DPC). It then focuses on the generation of patterns for apparel … Employing these methods in bespoke tailoring leads to patterns still influenced by average …

Despite advancements in manufacturing and information technologies along with innovative operating models and engineering designs, apparel mass customization (MC) has mostly not lived up to its expectations. The main goal of this study is to explore digital native apparel MC companies and establish relevant insights concerning development and technology implementation. The study starts by facilitating a comprehensive literature review to explore apparel MC and related technologies, combined with exploring its implementation in different successful digital native cases. Following a descriptive-analytical approach, this paper offered insights into current technology implementation and utilization in the apparel MC industry, and then classifies their practices from low to high technology adoption. Moreover, by exploring real world cases, the paper developed insights on technology application of MC, which can guide strategic directions in order to accelerate a successful implementation of MC in the apparel industry.

This chapter examines how micro, small, and medium enterprises (MSMEs) in developing countries adapt to digital transformation to drive innovation and international expansion. Theoretically, the chapter draws on Daniel Isenberg’s Entrepreneurial Ecosystem Framework, highlighting the interactions between human capital, policy, finance, markets, support, and culture. We conducted a case study at the Tailor Store, a Sri Lankan-origin and Swedish-based SME, gathering data through in-depth interviews with the founder, personal reflections, online customer reviews, and secondary sources. The findings suggest that the company’s success was built on early digital integration and proactive investment in both human capital and technology. The Tailor Store’s proactive strategy enabled the company to access international markets and remain competitive through bespoke, made-to-measure, and e-commerce tailoring solutions. Challenges did not hinder Tailor Store’s vision; instead, they became integral to its identity, fostering resilience and providing a competitive advantage. More than a business achievement, the case represents a compelling story of MSMEs’ digital transformation in a resource-constrained environment within a developing country.

Chapter 1.1 focuses on introducing the evolution of digital technologies, data sources and their … Key terms associated with digital transformation, Industry 4.0 components and principles …

Abstract The literature on digital disruption has a gap in understanding the capabilities that incumbents develop or enhance to defend or counter-attack against digital attackers. We examine examples of incumbent fashion retail-manufacturers, both high and poor performers, from a systematic review of publicly available data. We uncover the capabilities that underpin the performance outcomes from the incumbents’ defence or counter attack against disruption from digital attackers. We show that the higher performing incumbents have developed new and enhanced capabilities across the whole range of capability categories in order to out-perform the digital attacker. In addition they focus on two specific categories: to further enhance a strong capability around their unique differentiation based around existing resources - their physical stores; also to focus on one of the attackers’ strongest capabilities - a rapid response to changing GENz customer trends. The strategic choice of which capabilities to enhance is driven by a goal to increase an existing advantage, or match the attacker's advantage, or both. We contribute to theory on the dynamic capability of strategic agility which includes the speed and scale of pivoting to implement new initiatives and the capability to shape, and not just respond to, uncertainties in the external environment.

… ation initiatives, which has prompted others to move more rapidly to digital transformation. However, the functional profile of retail apparel PLM is much broader than more mature PLM …

: In the rapidly evolving global apparel industry, small and medium-sized enterprises (SMEs) are facing unprecedented challenges. This study delves into the challenges and strategies faced by small and medium-sized enterprises in the global apparel industry amidst technological advancements and shifting market demands. Although SMEs play a crucial role in the global economy, especially in major apparel-producing countries like Bangladesh, India, and China, their size and resource limitations often hinder their ability to enhance production efficiency and market competitiveness. This study discusses the necessity for strategic development and significant investment to overcome transformation’s high costs and technical complexities. It highlights the importance of government support, industry collaboration, and partnerships with technology providers to help SMEs navigate these challenges and thrive. Additionally, the study explores how SMEs can leverage digital transformation and sustainable practices to improve business efficiency and respond quickly to market pressures, thus maintaining competitiveness in a rapidly changing market environment.

The COVID-19 pandemic has put fashion manufacturers’ needs for optimization in the spotlight. This study argues that mass customization is becoming increasingly instrumental for offering consumers individualized solutions and that suppliers of fashion have to look for more sophisticated solutions in order to face the increasing demand for more sustainable products. With the deduction of a mathematical model derived from production sequencing it became evident that sustainability can be associated with a level production schedule and that cost-based production optimization is useful in achieving holistic sustainability in the fashion industry. The flexibility in the conceived mathematical model specifications allows for a generalizable approach, not limited to a single branch of the fashion industry. This paper additionally delivers a cost-based optimization approach which fashion companies, operating in a mass customization production layout, can easily implement without extensive know-how. The proposed two-stage algorithm is based on the concept of level scheduling. In a first stage, the algorithm determines a feasible production sequence in a time-efficient way while, in the second stage, it further advances the efficiency of the solution. Thus, it offers a framework to optimize a production in a mass customization environment and can contribute to a company taking major steps towards a holistic sustainable orientation as available resources are used more (cost) efficiently.

… , the production mode can complete the rapid production of the customized garments at a reasonable production … a new RCPA for customized clothing production based on the concept …

The shift of traditional mass producing industries towards mass customisation practices is nowadays evident. However, if not implemented properly, mass customisation can lead to disturbances in material flow and severe reduction in productivity. This paper discusses the design and development of a Cloud-based production planning and control system for discrete manufacturing environments, referred to as i-MRP. The proposed approach takes into consideration capacity constraints, lot sizing and priority control in a ‘bucket-less’ manufacturing environment. The i-MRP system offers simultaneous shop scheduling and material planning, where material and capacity constraints are considered together in a continuous time environment. A number of feasible alternative shop schedules and material plan combinations are formed and are evaluated on the Cloud platform where the i-MRP engine is hosted. The Cloud platform enables mobility, since it is device and location independent, as well as it minimises the cost of IT infrastructure ownership, which is especially important for SMEs. The performance of the i-MRP system has been studied in an SME from the textile sector, using real production data. The system demonstrates high performance in cases of short production times, high value inventory and frequent, small deliveries by suppliers. The i-MRP can be easily integrated with legacy IT systems as an interfaced functional module under the Software as a Service (SaaS) architecture.

At this stage, the requirements for refined production management of enterprises are getting higher and higher, and the amount of data has also increased significantly. The traditional management model has been unable to meet the requirements of the new era. In order to further improve the efficiency of enterprise management, enterprises have begun to introduce and apply new technologies such as networking and sensors are used to build a clothing intelligent manufacturing system. Aiming at the complexity and demand uncertainty of multiple batches and types of fabrics in clothing enterprises, a method for mining association rules of clothing fabrics based on a priori algorithm is proposed. First, multi-dimensionally merge the fabric attribute information of multiple batches and types of enterprises. Then, traverse the frequent itemsets generated by the fabric collection and calculate the support respectively. At the same time, applying artificial intelligence technology to clothing manufacturing can achieve the goal of saving human and financial resources and improving labor efficiency by combining artificial intelligence technology with machine sewing. It is believed that in the next few years, artificial intelligence will become the main interactive interface between enterprises and customers, with artificial intelligence dynamic adaptive intelligent manufacturing as the platform side, and modular intelligent manufacturing units as the manufacturing side, to achieve dynamic interaction with the customer demand side, Improve customer satisfaction and achieve real smart clothing manufacturing. It is revealed that the new business model of the clothing industry brought by cloud customization services brings new changes to enterprises and customers, making customers change from passive consumption to active consumption, allowing enterprises to take customer needs as the starting point, actively select consumer groups, and respond to the possible occurrences in practice. To put forward suggestions to promote Chinese clothing enterprises to innovate service concepts, pay attention to responsibility and experience, enhance brand building, and seek greater enterprise benefits.

… scheduling and sequencing problems in apparel production … the standard sizing chart used in mass production (MP) and … ) system adapted to customized garment patterns generated …

Following the development of the Industrial Revolution 4.0, many new types of systems are being designed, introduced, or attempted, even in almost every traditional industry. The clothing industry is no exception. The use of continuously developing production equipment and Information and Communication Technology (ICT) has a single objective, providing a customized service to all customers. Thus, in this study, the primary research task was to identify ill-balanced aspects or disadvantages of the services previously analyzed to construct a more complete online customized service. This was accomplished by analyzing an automated Computer-Aided Design (CAD) output file containing customer requirements regarding individual clothing items. The secondary research task was to plan and design a clothing manufacturing process to which a one-person one-item mass production system has been applied to achieve a customized service. As a result, for the primary research task, the customers’ requirements for each dress were reflected in attributes, such as color, pattern, or size, and it was possible to obtain an automated CAD output file for each element. Such CAD output files can be used in the production process directly. To find the possibility of upgrading the existing dressmaking process and implement the one-person one-item system, the entire manufacturing process was simulated for the test.

PurposeThe objective of the research is to develop implementation strategies for producers at the fashion apparel supply chain upstream, in order to move towards a more coordinated, streamlined and responsive process.Design/methodology/approachQualitative action research was conducted using non‐participatory observations on sampled producers, following a literature review on the design process and mass customization.FindingsMain activities with contributing factors that funnel in and out of this crucial junction are mapped and broken down into a series of processes that involve producers' selection and customers' choice, where decisions are currently made based on informal correlation of supply push and demand pull, months ahead of end‐users' (“customers” hereon) real demand. Key “integrated decision points” where customers' input is identified and can be introduced into the outbound supply chain.Research limitations/implicationsThis conceptual model offers the possibilities for implementing collaborative mass customization with reduced risk for producers and increased satisfaction for customers. However, producers' resistance to change from existing work methods may present potential obstacles. Further work is to be done on collecting, utilizing, and transforming customers' data in order to inform the total design process effectively and comprehensively.Originality/valueThe results of the “integrated decision pulse point map” proposed by this paper provide a threshold to the benefits of mass customization at the heart of the fashion system.

… customization clothing, and realized the whole process control and production scheduling … layer focuses on the customized requirements of the presewing production scenario and …

… Customized product—on designing and producing one of a … production schedule to calculate the optimum quantity of material needed to be stored to avoid any delay in the production. …

… The results show that the perceived benefits associated with a customized product can lead to greater emotional attachment to that product, a more positive attitude toward the …

… In our work we propose for an immediate custom fit dress … can also view the making of his product directly. The paper has been … ; we will plan for the next system following this scheduling …

Complete garment knitting technology is a method of producing products, generally fashion garments, readymade directly in the knitting machine without operations such as cutting and sewing. This makes it possible to manufacture a fashion garment with fewer processes then with conventional methods. Mass customisation is a customer co-design process of products and that tries to meets the needs of an individual customer's demand. The customer can order a garment with a customised style, colour, size, and other personal preferences. Co-design is a collaborative process between the customer, the retailer, and the manufacturer by which a product is customised to fulfil the customer's requirements. This paper is based on the results of a doctoral thesis. The process of codesign and manufacture of a customised complete fashion product is examined. Research was conducted by a retail concept simulation and three case studies. A cross-case analysis was done to analyse the data. The main findings are a description of two kinds of retail concepts for knitted customized fashion products. A knitted garment can be customized, produced, and delivered to the customer in three to five hours. In the Co-design process two kinds of interactions are feasible between the company and the customer: manual or digital co-design. A manual process has advantages such as: high service level for customers, no requirement of advanced technical equipment. However, manual co-design is labour intensive, a shop assistant can only serve one client at a time. It is also only pplicable to brick-and-mortar stores and not transferable to the Internet. Digital codesign, on the other hand, encourages customers to do the customisation on their own, without the aid of sales personnel and little risk of queues. Moreover, this technique is ideal for the Internet. Disadvantages to date have included limited design options and problem of taking body measurements.

… on bi-directional fusion for the custom apparel production system is proposed. With one … of the custom apparel production process. Finally, taking the suit production process of a custom …

This research explores the interrelationships between internal and external factors, business process management (BPM) capability of garment‐based SMEs, and the IT that matches this capability. Multiple case studies are conducted in three SMEs in East Java, Indonesia. The data is collected using in‐depth interviews with the owners and employees of SMEs. A grounded theory approach is used to analyze the findings. Analysis of the three cases found two main factors that affect the BPM in the garment SMEs. The first is the owner's business aptitude and expertise, and the second is the connection between external factors, business context and business strategy. Business strategy is closely related to the way SMEs define their business process, organize their employees, and develop culture. The BPM capabilities needed by SMEs in Indonesia include business process documentation and business process improvement. The business process and business process capability determine the IT needs of garment SMEs. IT is used to support sales, marketing, and payment processes in the three case companies. SMEs are very careful about their IT investment decisions due to resource constraints. SMEs will choose IT systems requiring minimal investment which are less risky to implement, such as social media and the e‐marketplace.

Purpose An ageing population in the developed world has become a significant topic in the contemporary research agenda. The purpose of this paper is to report on the development of a new small-scale business model based on facilitating in-depth understanding and responding to mature female consumers’ needs and expectations towards fashionable clothing. Design/methodology/approach Two complementary approaches are used: interpretative phenomenological analysis allows the researchers to employ the life-course perspective and to develop in-depth understanding of individuals’ present experiences in relation to their past. Action research offers the possibility to develop participatory, co-design processes based on collective creativity and mutual knowledge exchange between the stakeholders. Findings The research finds a strong interest in fashionable clothing by women, irrespective of their age. The action-based co-design process involving collaborative encounters with mature consumers creates a dynamic capability for alternative fashion design methodologies. This approach can contribute to a small-scale fashion business model for the mature women’s fashion market. Practical implications The women in the study stress the need for a more inclusive design process and expressed a willingness to buy from a brand/retailer who would offer them such a collaborative opportunity. There are practical implications for how a more flexible sizing approach to the design of fashion for older women could be implemented. Originality/value This research makes a contribution to practice-based design solutions for mature women and a new inclusive business model based on emotional durability. The innovative methodological approach contributes to the field of ethical and sustainable fashion design.

: The aim of the research study is to carve sustainable business strategies for the fashion communities in Ethi- opia and Ukraine which are suffering today due to ever-increasing share of fast fashion consumerism. Fashion houses and international brands propagandize sustainability and consumption for better consumer base, where as originally sus- tainable local-based craftsmen still stay in the shade. Four communities/local designers are selected from the countries through the method of purposive sampling. Qualitative analysis is the basis of the research as we performed personal interviews and in-depth analysis of the communities to diagnose the problems and subsequently devise the solutions. In this research, we have studied and analyzed the problems faced by hereditary communities and ethnic designers in small and medium scale enterprise sector from two emerging economies. After the grounding the difficulties faced we advised strategies for sustainable future growth to the companies. The current academic literature on small and medium scale enterprises highlights the problems and solutions for general industry sectors. This paper brings attention to fashion communities and designers who promote national heritage and are struggling to survive in emerging economies due to indus- trialization and globalization. Moreover the comparison of the two geographies is unique in nature.

This research was conducted to analyze the mindset of small-scale garment business actors from the perspectives of production and marketing, considering that these two aspects have an important role in creating added value in the value chain of small-scale garment businesses. This research uses a qualitative descriptive method. The type of data required is primary data obtained from in-depth interviews with actors from garment businesses located in Bandung City and Bandung Regency. The research results find that the mindset of small-scale garment entrepreneurs from a production perspective shows that most of them are oriented toward always looking for the best production methods and never feeling satisfied (creativity is not a methodology but a mindset). Given that the market for garment products wants to have a relatively fast product life cycle, the emergence of market responses to the products offered is an important input for garment businesses to produce market-oriented products. In relation to the production mindset, garment businesses pay attention to the marketing mindset, where customer loyalty is considered crucial since customers are valued as an asset that supports the business instead of a burden, and marketing is considered a margin center, not a cost center. Keywords: business, marketing, mindset, production, small-scale garment

… Based on the nature of business improvement models and the … the garment enterprises, a five-phase model has been developed. The model incorporates the key activities of business …

This study examines how apparel enterprises systematically construct and communicate their brand core values through marketing strategies, thereby driving consumer purchase decisions based on cultural identification. Moving beyond traditional consumer behavior analysis, it explores how management translates abstract cultural concepts into actionable product strategies and marketing activities. Employing Bourdieu’s “Cultural Capital” theory and McCracken’s “Meaning Transfer Model” as its analytical framework, this research conducts in-depth case studies of multiple brands spanning both sportswear and luxury segments. The findings reveal that successful apparel brands essentially function as effective “cultural capital operators” and “meaning management experts.” By anchoring themselves within specific social fields through precise positioning, these brands adopt integrated strategies—including product innovation, content narrative, community engagement, and channel orchestration—to systematically encode and transfer cultural meaning, thereby fostering strong value recognition and a sense of identity among consumers. This study argues that value-identification-oriented brand management constitutes a strategic alignment with and empowerment of consumers’ identity projects. This process requires firms to maintain strategic focus while leveraging data-driven precision and operational agility to build sustainable competitive differentiation.