The Future of Fashion Tech: How Technology is Transforming the Apparel Industry

In recent years, the fashion apparel industry has seen growing interest in sustainability and product transparency, as well as increasingly diverse consumer needs. Amid this shift, fashion tech—through advanced technologies such as AI (artificial intelligence) and AR (augmented reality)—has been gaining significant attention. Currently poised to fundamentally transform processes from design and manufacturing to sales, fashion tech is recognized as an important element in building a sustainable future. In this article, we will delve into the definition and importance of fashion tech, explore current trends, and examine its impact on the industry. How are new technologies reshaping the fashion world? Let's find out!

What is Fashion Tech?

Definition and Background

The History of Fashion Tech and Today’s Trends

Fashion tech has been creating new value by blending the worlds of fashion and technology. Here, we’ll take a look at the historical development of fashion tech and highlight some of the key technological advancements shaping the industry today.

1990s: The Birth of E-commerce and the Rise of Online Shopping
With the spread of the internet, e-commerce was born, and online shopping quickly made its way into the fashion world. Suddenly, customers could shop for their favorite styles anytime, anywhere—breaking free from traditional time and location limits. This marked a major shift and can be seen as the first real tech revolution in the fashion industry.

Late 2000s: The Emergence of Virtual Fitting
With the advent of AR technology, virtual fitting became possible—allowing customers to “try on” clothes online without physically wearing them. This innovation helps shoppers check size and style virtually, leading to fewer returns and higher customer satisfaction. As online shopping grew and technology advanced, virtual fitting quickly gained widespread adoption across the fashion industry.

Late 2010s to Early 2020s: Practical Use of AI and Data Analytics
The fashion industry saw a significant rise in the use of AI and data analytics during this period. Leveraging big data, companies began conducting detailed customer analyses and trend forecasting, enabling more personalized product recommendations and accurate demand predictions. Additionally, AI-powered design assistants and pattern generation tools improved product development efficiency, advancing customization options tailored to individual consumer preferences.

Late 2010s: 3D Printing and On-Demand Production
Advancements in 3D printing technology made it possible to produce clothing and accessories on demand, leading to the spread of business models that cater to individual orders. This technology has reduced prototyping costs and increased manufacturing speed. Additionally, by producing only what is needed, it helps prevent overstock and waste, significantly contributing to the promotion of sustainable fashion.

Fashion tech has evolved from e-commerce to virtual fitting, AI, 3D printing, and digital fashion technologies, becoming an indispensable part of the modern fashion industry. As technological innovation continues, we can expect further advancements in customer experience (CX), enhanced functionality of products and services, and accelerated progress toward sustainable fashion through improved manufacturing processes.

For a more detailed explanation of digital fashion, please be sure to check out this the following article.

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Key Categories of Fashion Tech

Here, we will explore the key categories of fashion tech in detail: "digital prototyping," "virtual fitting and AR try-ons," and "advancements in design and marketing through AI and data analytics." These technologies not only enhance the consumer experience but also offer new possibilities for production efficiency and marketing strategies. Let’s take a closer look at how the fashion industry is adopting these innovative technologies and evolving toward the future.

Digital Prototyping

Digital design tools* and 3D prototyping technologies significantly shorten the process of creating prototypes and reduce the wasteful use of resources. This allows designers to quickly bring their ideas to life and develop products efficiently without wasting unnecessary materials or time.
*Digital design tools: Software or applications used to visualize product concepts and images, creating design data. These tools are utilized across various fields such as fashion, interior design, industrial products, graphics, and websites.

In the fashion and apparel industry, a large number of product samples are created before deciding which items will be commercialized. During this sampling stage, substantial amounts of materials are used, and products that are not commercialized often end up being discarded. By replacing physical samples with virtual samples created on computers, it is possible to reduce the amount of clothing waste generated.

Virtual Samples: The Savior of the Apparel and Fashion Industry?

Today, let's dive into the game-changing world of "virtual samples," which is key to making our industry more sustainable by cutting down on sampling time and costs.

 

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APEXFiz^® software

Subscription service for design software that overhauls the supply chain from planning and design to virtual sampling, link to production and even E-Commerce, for fashion and other design-related industries.
View the page

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For a detailed explanation of sustainable initiatives in the fashion industry, please check out this article as well.

What is Sustainable Fashion? From Industry Issues to Corporate Initiatives and What We As Individuals Can Do

The term "sustainable" has become increasingly common in our everyday lives. Numerous initiatives in various industries are underway for achieving a sustainable society, and the fashion industry is no exception. 

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Virtual Fitting with AR

The technology of virtual fitting using AR holds great potential to significantly transform the consumer experience when purchasing products. Here, we will explore how virtual fitting works as well as its benefits, and consider the challenges faced when implementing virtual try-ons and AR technology, along with possible solutions.

What is Virtual Try-On? 

Virtual try-on is a technology that allows consumers to "try on" clothes and accessories from the comfort of their own homes, primarily utilizing AR and 3D modeling technologies. This system creates a digital avatar based on the user's body measurements and shape, then overlays products onto the avatar to provide an experience similar to physically trying them on.

Mechanism of Virtual Try-On
1.  Data Collection 
First, the user's body data is collected. This involves measuring body shape and size using devices such as smartphone cameras or specialized body scanners.

2.  Avatar Generation 
Based on the collected data, a personalized 3D avatar of the user is created. Since this avatar closely represents the actual body shape, it enables a realistic try-on experience. Some services also allow users to upload their own photos for try-on.

3. Product Display
The clothes or accessories selected by the user are displayed on the avatar. As the avatar moves, the sense of size and appearance of the products adjust accordingly, providing a more natural try-on experience.

Benefits
Time and Cost Savings
Consumers no longer need to visit physical stores and can easily check products from the comfort of their homes. This not only saves travel time and transportation costs but also better accommodates busy lifestyles.

Personalized Experience
Since product recommendations can be tailored to each user’s unique body shape and preferences, a shopping experience optimized for each individual is provided. This leads to increased customer satisfaction.

Diverse Options
Virtual fitting allows users to easily compare many products. For example, multiple garments can be tried on at once, making it easier to find preferred styles and coordinate outfits.

Reduced Return Rates
By enabling consumers to confirm size and style in advance through virtual fitting, return rates decrease. This offers significant economic benefits for sellers as well.

Challenges and Solutions 

The introduction of virtual try-on and AR technologies brings innovation to the fashion industry, but it also faces several challenges. Let us consider the main challenges and their solutions.

Challenge 1
Lack of Accuracy
If the appearance and fit of virtual try-on do not match the actual product, users may feel dissatisfied. Especially since the look varies depending on body shape and posture, improving accuracy is essential.

Solution
It is important to utilize AI technology to analyze users’ body data more precisely and provide personalized fitting information. Additionally, introducing high-quality 3D modeling technology that reproduces the texture and movement of real products can enhance realism.

Challenge 2
User Experience
Although AR technology is attractive, users may feel stressed if the operation is complicated or the application is difficult to use. This barrier is particularly high for people unfamiliar with digital technology or elderly users.

Solution
By designing a simple and intuitive interface, it becomes possible to accommodate a diverse range of users. Providing tutorial videos and guide functions also helps reduce resistance to new technologies and supports smooth usage.

Challenge 3
Data Privacy
AR technology handles a large amount of personal information such as users’ body data and images, raising concerns about privacy protection.

Solution
It is crucial to establish transparent data management policies and collect/use data only after obtaining explicit user consent. Implementing secure data encryption and anonymization techniques is also necessary to protect against unauthorized access.

Challenge 4
Technical Barriers
AR features may not be fully utilized without high-performance smartphones or dedicated devices, making them inaccessible to some consumers.

Solution
Focusing on web-based AR solutions and developing lightweight applications that run on low-spec devices can create an environment accessible to many users. Installing dedicated terminals for in-store experiences is also effective.

By carefully considering these challenges and their solutions, virtual try-on and AR technologies will become more widely accepted by consumers, further expanding their convenience and appeal.

Evolution of Design and Marketing Through AI and Data Analysis

Data analysis has become an indispensable element in the modern fashion industry. Especially in the field of sustainable fashion, data is utilized to understand consumer behavior and preferences, enabling the construction of effective marketing strategies. Here, we explain how data analysis influences marketing strategies.

• Consumer Insight Acquisition
  Data analysis enables a detailed understanding of consumers’ purchase histories and online behaviors. This allows for a deep comprehension of target needs and preferences, facilitating product development and promotional activities based on these insights. For example, by identifying consumer segments with a strong interest in specific materials or styles, companies can develop product lines tailored to those needs.
• Personalized Marketing
  AI-powered data analysis makes it possible to conduct personalized marketing campaigns. By providing individually tailored product recommendations and promotional information, engagement rates* improve, leading to increased purchase intent. 
  * Engagement rate: The proportion of users who interact with posts or advertisements on social media through likes, clicks, shares, and other forms of engagement.
• Trend Forecasting
  By analyzing past sales data and market trends, new trends and consumer demands can be predicted. This enables companies to make swift decisions and maintain market competitiveness. In sustainable fashion especially, new trends may emerge as responses to environmental issues; therefore, being sensitive to such changes makes trend forecasting an essential factor.
  
• Effective Advertising Strategies
  Digital advertising platforms perform real-time data analysis that allows for measuring and optimizing campaign effectiveness. By reviewing various metrics such as target audience reach rate *1 and conversion rate *2, more  efficient allocation of resources can be achieved.
  *1  Reach rate: The number of users who have viewed an advertisement.
  *2  Conversion rate: The percentage of users who took desired actions such as requesting information or making purchases after viewing an advertisement.
  

What Is Data-Driven Marketing? 

Data-driven marketing is an approach that optimizes marketing strategies and initiatives based on multiple types of collected data, such as user behavior history, sales information, and big data. Its goal is to deploy effective advertisements and campaigns grounded in these results.

The process begins with gathering data from various sources, including website visit histories, interactions on social media platforms, and purchase records. This information is then analyzed to clarify market trends and customer segments. Based on these insights, marketing strategies tailored to target audiences are formulated, along with adjustments to the timing of advertising campaigns and content delivery.

Furthermore, the effectiveness of implemented initiatives is continuously measured, and the results are incorporated into future strategies to form a PDCA (Plan-Do-Check-Act) cycle. The advantages of this method include highly accurate targeting due to its data foundation. It also reduces costs spent on ineffective advertisements by concentrating investments on truly relevant customer groups. Additionally, it enables rapid responses to changes in the market and consumer needs.

In the fashion industry, data-driven marketing is particularly effective; companies predict new trends from user-generated content on social media and link these insights to new product planning and development. Personalized product recommendations based on individual past purchase and browsing histories also enhance customer satisfaction. Moreover, historical data supports inventory management and demand forecasting, leading to more efficient production planning and stock control.

Thus, data-driven marketing not only strengthens competitiveness in the fashion industry but also promotes the realization of sustainable business models.

Customer Insight Analysis 

Customer insight analysis is a method used to gain a deep understanding of consumer behavior and preferences. The insights obtained through this analysis are highly valuable for marketing strategies and product development.

There are several techniques in insight analysis. In data collection, quantitative data (such as purchase history and website visit data) is combined with qualitative data (such as surveys and interviews) to grasp consumers’ opinions and emotions. Next, customer segments are defined based on factors like age and gender to clarify the specific needs of each group. Creating representative customer profiles (personas) enables more concrete marketing initiatives. Additionally, trend analysis is conducted using past sales data to respond swiftly to emerging consumer needs. Alongside this, a feedback loop continuously analyzes responses to implemented campaigns, forming a PDCA (Plan-Do-Check-Act) cycle that reflects results in future strategies.

The benefits of customer insight analysis include improved targeting accuracy, leading to higher engagement rates and conversion rates. It also enables product development based on consumer needs, enhancing brand loyalty. Furthermore, it allows for efficient allocation of resources within limited budgets.

While both the previously mentioned data-driven marketing and customer insight analysis utilize data to optimize business strategies, they serve different roles and purposes: data-driven marketing aims at optimizing broad marketing initiatives overall, whereas customer insight analysis focuses on deepening understanding of consumers. These two approaches complement each other, and their combined use yields more effective results.

Use Cases and Latest Trends in Fashion Tech

In the previous chapter, we introduced the main categories of fashion tech. Here, we will present actual use cases.

Resource Conservation and Efficiency Through Virtual Sampling 

One example of digital prototyping is the virtual sampling initiative utilizing Shima Seiki Mfg., Ltd.’s SDS^®-ONE APEX series. By using design software to create virtual samples, companies have successfully achieved resource conservation and improved efficiency. Examples of such companies are introduced here.

Shopping Experience with AR Technology 

We also introduce companies that offer virtual try-on experiences using AR technology.

Walmart (Be Your Own Model) 

Walmart, the world’s largest supermarket chain headquartered in Arkansas, USA, offers a service where users can upload their own photos to virtually try on various clothing items. This allows customers to check the fit and style before purchasing, aiming to enhance customer satisfaction.

From Walmart’s official website

Amazon（Virtual Try-On for Shoes）

Introduced in 2022 for the North American market, Amazon’s virtual try-on service allows users to use their camera to view their feet and virtually try on shoes using AR technology. By virtually trying on shoes from various brands to check designs and fit, customers can reduce anxiety before making a purchase.

From Amazon’s official YouTube channel

JINS（Virtual Try On）

JINS, a Japanese eyewear brand specializing in glasses and sunglasses, offers an online service that allows users to virtually try on glasses. By using the smartphone camera, you can perform AR fitting and check various frame designs and sizes in real time.

From JINS’s official website

Zalando（Virtual Fitting Room）

Based in Berlin, Germany, Zalando is an online retailer operating an e-commerce site focused on fashion and lifestyle. They have introduced a feature that allows users to create 3D avatars based on their body shape, enabling more accurate checking of sizes and styles when shopping online.

From Zalando’s official website

Data Analysis and Marketing Strategies 

The following article introduces examples of companies that enhance their competitiveness and promote the realization of more sustainable business models by advancing digitalization. It provides insights into the future direction of the fashion and apparel industry.

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Challenges in Implementing Fashion Tech 

As mentioned above, the introduction of fashion tech brings significant benefits to both consumers and companies. Additionally, it contributes to promoting sustainability and has a positive impact on society as a whole. However, there are also several challenges to consider. One major challenge is the high cost of implementation. Utilizing cutting-edge technology requires substantial initial investment, which can be a heavy burden for companies. Moreover, operating such technology demands specialized skills, making internal resource shortages and the need for human resource development important issues.

Next, data privacy concerns must be taken into account. As noted in the “Challenges and Solutions” section regarding AR virtual fitting, personal information collected during virtual fitting or customer insight analysis needs to be handled with great care. Inadequate data protection measures can undermine consumer trust and damage brand image.

Furthermore, technical hurdles pose significant challenges. AR virtual fitting requires high-performance devices and stable internet environments; however, not all consumers meet these conditions. This results in accessibility issues for some users.

Finally, cultural acceptance and consumers’ understanding of technology are also challenges. Some consumers may feel resistance toward new technologies, which can hinder widespread adoption. Therefore, raising awareness and conducting educational activities targeting consumers are essential.

Outlook for the Future of Fashion Tech

The development of fashion tech holds the potential to fundamentally transform the entire industry structure. As integration with digital fashion progresses, virtual fashion experiences in addition to traditional physical products are expected to establish themselves as a new market. This will enable fashion brands to offer added value beyond physical products and explore new revenue streams.

Moreover, advancements in AI technology are anticipated to enable full automation of the design process and real-time product development that reflects consumer demand. Such technological innovations play a crucial role especially in realizing sustainable fashion. For example, alongside on-demand production, designs utilizing recycled materials and those facilitating easy recycling will become more widespread, significantly reducing environmental impact.

Furthermore, AI can overcome language barriers and facilitate easier access to global markets through enhanced market strategies based on data analysis and improved logistics efficiency. This will increase opportunities for emerging country brands and consumers to benefit from fashion tech. While competition in the global market is expected to intensify, it will also create fertile ground for new collaborations and innovations.

In this way, fashion tech will not only expand the possibilities of the entire industry but also continue evolving as a technology that delivers value exceeding consumer expectations.

As we have seen, fashion tech is transforming the apparel industry and serves as the key to a sustainable future, with its influence expected to continue expanding. AI-powered data analysis and AR-based virtual try-ons can greatly enhance customer satisfaction and shopping convenience. Additionally, digitalization optimizes and streamlines manufacturing processes, reducing waste. As the fashion industry will likely require flexible strategies to meet diverse consumer needs, companies must keep pace with these trends and strive to continue creating new value.