Examining the Nexus between the Vs of Big Data and the Sustainable Challenges in the Textile Industry

Abstract

Despite its substantial economic power, the textile industry currently faces environmental and social challenges, such as continuous extraction of natural resources, extensive water consumption and contamination, greenhouse gas emissions, increasing generation of waste, and inadequate working conditions. In this context, the literature indicates that Big Data contributes to solving these challenges, enabling the extraction of insights and the improvement of decision-making processes from the volume, variety and velocity of data. However, there is still a gap in the literature regarding the directions of how Big Data must be applied by an organization to achieve this goal. Therefore, this article aims to explore this gap, presenting an analysis regarding the nexus between Big Data and sustainability challenges of the textile industry. To this end, a set of 12 textile industry challenges were extracted from an assessment of 108 case studies. These challenges were categorized and contextualized according to Big Data dimensions, and a discussion of the applicability of Big Data to solving each challenge was presented. From this approach, this article contributes to the textile industry by presenting a categorization of sustainable challenges of the industry and also by providing directions regarding the resolution of such challenges from a data-driven perspective.

1. Introduction

The textile industry is considered one of the oldest industries, and is responsible for the generation of a large number of jobs [1]. Since the first industrial revolution, the industry has undergone significant changes and innovations, adapting to market trends [2]. However, even in the face of such innovations, the linear production model, adopted in the first industrial revolution, still prevails in the industry. Although it can generate economic growth, it is identified that the adoption of this model, combined with factors such as the globalization of the industry and the wide adoption of fast fashion, has resulted in alarming environmental and social challenges [3,4].

Regarding environmental aspects, challenges are identified in the stages of production in the supply chain (e.g., spinning, weaving, dyeing, printing) and in the stages of consumption and post-consumption [4]. Examples include the continuous extraction of virgin raw materials, the high use of water in the manufacturing processes of fibers, fabrics and clothing, and the extensive volume of waste generated, which harm the environment when incinerated or deposited in landfills [5,6]. Concerning social aspects, alarming challenges are also identified, referring to precarious working conditions, low wages, and lack of labor rights [7,8]. Given this scenario, studies are being carried out to identify how the textile industry can solve such challenges, adopting a sustainable production and consumption system, in harmony with economic, environmental and social dimensions [6,9,10]. Thus, such challenges are considered alarming due to the impacts generated, contributing to the negative effects of climate change and increasing social inequality [11,12]. From a business perspective, it also results in increased risks for the company, due to shortages and rising prices of raw materials, as well as pressure from consumers, for a sustainable strategy [13].

Currently, it is also noticeable that we are experiencing the generation of an unprecedented volume of data, especially in the digital world, revealing the possibility of using different data sources to improve products and services, generating insights and improving the decision-making process [14]. This context is often described as Big Data [15]. The concept of Big Data can be understood by “the Vs of Big Data”, coined in 2001 by Doug Laney, in a white paper [16]. These Vs (volume, variety and velocity) are used to describe the challenges and opportunities in data management, and currently are also used to highlight how and why they must be carefully evaluated in a Big Data project. As stated by Gartner, Big Data is defined as large volume, variety and velocity data, which demand innovative and cost-effective ways of processing information, for greater insight and improvement in the decision-making process [15].

After the three Vs had been popularized, a vast number of dimensions were used to characterize Big Data, such as variety and value [17,18]. However, there is still a consensus on including the original three Vs in such proposals. From these dimensions, it becomes possible to identify requirements and opportunities related to factors such as data governance, data ingestion, data analytics, and data monetization [19]. In fact, due to the growing interest in data-driven solutions and the availability of data and technological resources for the development of innovative solutions, it can be perceived that Big Data has been applied for a myriad of purposes [20]. Examples include the applicability of Big Data in medicine, agriculture, banking, and retail [21].

Considering its capability, one of the approaches that have been explored in recent years is the use of Big Data towards sustainability [22,23,24]. Companies, and entire industries, including the textile industry, are creating initiatives to understand how data-driven solutions can help them define, execute, and monitor sustainable practices in their strategies and value propositions. However, considering the fact that the textile industry faces a series of challenges related to sustainability, on the one hand, it is considered a challenge for Big Data researchers who are starting in the area to understand a broad view of such challenges, and to obtain a big picture of this scenario. The challenges are diverse and inherent to different stages of the supply chain, involving operational, technological and strategic aspects. On the other hand, it can be a challenge for professionals in the textile industry to identify how Big Data can contribute to achieving the Sustainable Development Goals (SDGs) in this industry [25].

With the aim of interconnecting aspects of Big Data and sustainability in the textile industry, this article has two main objectives. First, the objective is to extract the sustainability challenges of the textile industry and their categorization in the context of a set of dimensions, in order to further the understanding of such challenges by professionals who are starting in this area of study. The second objective is to present a discussion on how Big Data can contribute to the resolution of each identified challenge, providing insights for future research aimed at exploring this intersection of themes. Therefore, this article seeks to answer the following research questions: How can the volume, variety and velocity dimensions be understood in the context of the textile industry in terms of sustainability?How can Big Data help solve sustainability challenges in the textile industry? In this context, it is important to emphasize that the challenges of the textile industry presented in the article are already known, and there is a wide discussion about them in the literature [4,26,27,28,29]. However, to the best of our knowledge, this is the first work that aims to present a perspective of such challenges from the lens of a set of dimensions, such as the Vs of Big Data, while also featuring a discussion of how Big Data can provide significant resources to address each of these challenges.

To achieve this goal, this research was performed as a two-phased study. First, we conducted an assessment of a set of case studies, seeking to understand the challenges of the textile industry. The selection of case studies was obtained from a public online library, available on a platform called Knowledge Hub [30]. A total number of 108 case studies were analyzed in this study. Second, from these case studies, the challenges existing in this area were extracted, and a literature review was conducted to identify relevant works that discuss and address the challenges identified. As a result of this approach, the article presents how these Vs provide an insight into the sustainability lens in the textile industry. We argue that a perspective from the same dimensions can help in the understanding of the challenges existing in the textile industry, as well as providing insights into the intersection of Big Data, sustainability and the textile industry, stimulating the emergence of new studies based on this perception.

The remainder of this paper is structured as follows. In Section 2, the method applied to conduct the research on the case studies is described. After, in Section 3, the three Vs are explained, considering Big Data and sustainability challenges in the textile industry. A discussion about the obtained results is presented in Section 4, and, finally, the final considerations are described in Section 5.

2. Materials and Methods

This study was first carried out from a set of case studies related to the circular economy in the textile industry, publicly available on the Knowledge Hub platform [30], as a valuable source for understanding sustainability practices in this industry. Although the circular economy is not the only possible approach to achieving sustainable development, with other proposals such as the bioeconomy and green economy existing, this choice is justified by the fact that the circular economy has been considered a promising approach to ensure sustainability [31,32].

Created by the not-for-profit organization Circle Economy, the Knowledge Hub platform offers a digital library of case studies from different areas (e.g., the textile industry, civil construction, food) that address the concept of a circular economy [30]. At the time the study was carried out, a total number of 4222 case studies were available, and regarding the textile industry, the platform presented more than 500 cases studies, and 108 of these were approved by the platform’s curators. The approach of assigning a curator to the evaluation of case studies gives greater credibility to the content of the platform, allowing readers to have access to information previously validated by experts in the field.

The majority of the case studies related to textile industry were captured from a project called World Circular Textiles Day (WCTD) [33], which aims to annually assess the global status of the circular economy in the textile industry, until the year 2050, when the industry is expected to be fully circular. As described in the WCTD website, it “aims to build the world’s largest digital library of circular textiles activities, #TheWCTDCollection, to provide a snapshot of what and where circularity is happening around the globe right now.” Within the WCTD, the Global Mapping Project (GMP) was also carried out, with the participation of 40 researchers and students from 26 countries, who, for 3 months, researched worldwide case studies on circularity in the textile industry.

Each case study approved in the Knowledge Hub contains information related to the following topics: an overview of the case study, the problem that the case study aims to solve, the proposed solution, and the outcome obtained from the case study. In addition, keywords related to the impact caused by the case study and keywords related to circular economy activities.

To conduct this research, the following steps were carried out: initially, all case studies approved by the platform’s curator team were downloaded and tabulated. With the objective of analyzing the existing challenges in the textile industry, the texts referring to the problem section were extracted, to extract the challenges presented in each study. After the extraction, the challenges were categorized, resulting in 12 main topics which were contextualized in the context of volume, variety and velocity dimensions. After extracting the challenges from the case studies, we started a literature review focused on validating the extracted information, verifying if these same challenges are also discussed in the literature. Figure 1 contains a summary of the research method applied.

All case studies used for this research are available for public access. The selection was made using the filters available on the platform, selecting case studies from the “fashion and textiles” area, and approved by the curator.

3. Results

In this section, we present an analysis of how the volume, variety and velocity dimensions can be characterized both in Big Data and in relation to the sustainability challenges of the textile industry. For each dimension, the definition of Big Data is briefly presented, followed by a more broad definition of the dimension within the textile industry context, extracted from the case studies.

3.1. Volume

The volume dimension in the context of Big Data refers to the unprecedented amount of data generated and currently available at a global level. Being the most expressive attribute of this concept, the data volume brings countless possibilities to business companies, offering the ability to extract value from massive amounts of data, improve the decision-making process, better understand their consumers, and develop innovative business models [34]. To achieve these results, the large data volume results in the need for new technologies for collecting, storing, and processing data, such as Hadoop, Spark, and NoSQL technologies, so that data can be processed according to the required performance, even on a terabyte or petabyte scale [17]. In addition, companies need to be innovative in their data collection, storage and management strategies, identifying solutions that would allow them to deal with the growing volume of data in a timely and cost-effective manner [15].

Considering the same dimension, now under the lens of the textile industry and its sustainable challenges, it can be identified that the volume can be characterized by numerous challenges within this industry. The following factors were identified in the case studies, considering the volume dimension:

• The large volume of clothing currently being produced;
• The large volume of raw materials extracted;
• The large volume of polluting gases emitted;
• The large volume of water used;
• The large volume of waste generated.

The first factor included in the volume dimension expresses the current scenario of the textile industry, which comprises an increasing volume of clothes being generated on a daily basis. The number of garments has doubled since 2000, exceeding 100 billion garments for the first time in 2014 [35]. For this production, it is estimated that approximately 111 million tons of fibers were used in 2019, a growth of 50% compared to 2008 [36]. Among the factors identified for this increase is the emergence and wide adoption of the fast fashion concept, which shortened the time and cost of clothing production, allowing for the frequent introduction of new collections [27]. In addition, there are also previous studies that indicate that this increase has been due to the greater competitiveness of the industry [25]. Given the short life cycle of the products, there are difficulties in forecasting the demand for a new collection, and thus, in order to meet the demands of time and desired quantity, the industry ends up generating an excessive production of clothes.

This unprecedented volume of clothing being produced on a global level, despite the potential economic power offered, has resulted in alarming environmental consequences. One example is the second factor, referring to the large volume of raw materials extracted for the manufacture of clothing. The textile industry uses both natural (e.g., cotton) and synthetic (e.g., polyester) resources for the production of clothing, and both generate critical environmental challenges [37]. With regard to the extraction of natural resources, it is identified that in the textile industry, the linear production pattern “take-make-dispose” predominates, resulting in factors such as soil degradation and biodiversity loss. In addition, the growing volume of clothing produced has generated an increase in the extraction of these resources, resulting in resource scarcity [38]. Given the exhaustion of resources and their continued extraction, there is a debate that the linear model is reaching its limit [5].

While extracting natural resources presents challenges in the context of sustainability, the shift towards synthetic fibers does not make the industry more sustainable. The main challenge is the fact that these materials are mostly fossil-based, such as polyester, resulting in the accumulation of micro-plastics that are discharged daily into the oceans, polluting the seas [37]. In this context, circular economy projects have invested in researching new biodegradable materials that can be grown using a sustainable approach and that can be reused from waste, reducing the extraction of new raw materials.

The third factor identified also presents alarming challenges. The textile industry is considered the second largest pollutant in the world [6,39], being responsible for approximately 1.2 billion tons of greenhouse gas emissions [37]. According to a report of 2020, 71% of emissions occur in the production process, 6% in brand operations, while around 23% of emissions are related to consumer actions in the use and end phase of usage [40]. One of the factors identified by the high emissions in the production process is the fact that the production of energy from fossil fuels predominates in this industry.

In line with the third factor, there is also the fourth, referring to the growing volume of water used in fiber production, clothing manufacturing and use phases. It is estimated that 93 billion cubic meters of water are consumed annually by the textile industry [3,29]. Cotton, one of the main raw materials used in clothing production, is one of the most water-intensive materials in its production [41]. For example, it is estimated that approximately 2700 L of water is used in the production of a cotton T-shirt. In addition, it is also estimated that a pair of jeans is responsible for the consumption of 9500 L of water during the manufacturing process, involving steps such as fiber production, dyeing and garment finishing [42]. In this way, it is necessary to create strategies along the supply chain that make it possible to reduce the volume of water used for the manufacture of clothes, as well as the awareness regarding the volume of clothes produced.

In addition, consequently, this mass production and the growing volume of clothes at low prices available on the market have resulted in the last factor presented, referring to the growing volume of textile waste discarded in landfills or being incinerated, generating severe environmental impacts on the environment [3,43]. It is estimated that 92 million tons of textile waste are generated annually, with 134 million tons per year expected by 2030 [37,44]. The waste is generated both in the pre-consumption and post-consumption stages. This large volume of clothing being incinerated or landfilled not only results in environmental impacts, due to the emission of polluting gases, soil deterioration and marine pollution, but also represents a great economic loss, since the clothing materials could be reused for the development of new fibers, garments or even other materials.

Besides the solutions related to recycling, some circular economy initiatives are being proposed and discussed in academia and the business sector, within a perspective of identifying new business models and strategies that can solve these challenges. As an example, one of the case studies raised by WCTD was that of the circular denim brand MUD Jeans [45,46]. Aiming to reduce the volume of jeans produced, and consequently, reduce the volume of extracted resources, used water and discarded clothes, this company proposes the “lease a jeans” model, which allows its customers to exchange their jeans for other ones, thus avoiding the accumulation and improper disposal of clothes. In addition, the jeans received by the brand are recycled and transformed into new items.

From the topics presented, it is possible to identify how the volume dimension is characterized in the textile industry, considering sustainability challenges.

Table 1. Summary of the volume dimension of the textile industry.

Dimension	Description	Global Amount
	Large volume of clothing currently being produced	Billion garments per year
	Large volume of raw materials extracted	Million tons per year
Volume	Large volume of polluting gases emitted	Billion tons per year
	Large volume of water used	Billion cubic meters per year
	Large volume of waste generated	Million tonnes per year

presents an overview of these factors, in order to understand the level of magnitude that the industry presents.

Only from the dimension of the volume is it possible to identify the challenges of the textile industry and its demand for strategies to achieve sustainability. It is possible to note that the challenges presented occur at a global level, however, global, regional and local measures are required to reduce the current impact of the industry. In addition, it is also identified that such challenges are included in specific SDGs. For example, all the challenges presented are related to SGD 12, referring to responsible production and consumption. In addition, the large volume of virgin raw materials extracted is also related to SGD 15, which seeks to promote the sustainable use of terrestrial ecosystems. The challenge regarding the emission of polluting gases is also related to SGD 7, focused on clean and affordable energy, and also SGD 13, related to actions to prevent climate change and its impacts. Finally, SGD 6, referring to clean water and sanitation, and SGD 14, referring to the prevention of marine pollution, are related to the challenge of a large volume of water used.

3.2. Variety

In addition to volume, the variety dimension of Big Data represents this new scenario that has emerged for companies, allowing them to generate and use both internal and external data in their business strategies, coming from a variety of sources and formats, which can be data generated by humans (e.g., social media, messages, reports, emails) or by machines (e.g., logs, sensors) [47].

The variety of data enabled the development of a vast field of data-driven products and services by companies, which began to extract and integrate data with a focus on generating new insights for the decision-making process. In addition, the variety of data also made it possible to improve the understanding of consumers’ needs, based on data available from sources such as social networks and review sites. Now, companies are able to offer personalized experiences based on recommendations generated in algorithms that process the data captured from consumer behavior. Added to this, these insights also make it possible to predict trends and provide data-driven services, such as recommendation systems and sentiment analysis. Pech and Vrchota also highlight that not only Big Data, but also digitalization and industry 4.0 technologies make it possible to integrate data streams from different stages in the design of a product, in addition to enabling sensors and smart products to be used as key resources in the product customization process [14].

Regarding the variety dimension in the context of sustainability challenges in the textile industry, the following factors were identified from the case studies:

• The large variety of stakeholders, steps, and materials involved along the supply chain;
• The large variety of unknown and hidden information;
• The large variety of inappropriate working conditions in the industry;
• The large variety of chemical components used in the production.

The first factor is one of the intrinsic characteristics of the textile industry. The supply chain of this industry is considered highly fragmented, multi-layered, and complex, due to the fact that its chain comprises the stages of production of raw material to the sale of clothing, and because, during these stages, there is the integration of numerous sub-stages, such as yarn manufacturing, fabric creation, manufacturing, transportation, and retail [48]. Along the value chain, there is a wide variety of stakeholders involved, responsible for a specific activity in the production cycle. This scenario makes the objective of achieving a sustainable textile industry even more challenging, because, given the complexity, the data referring to each stage become sometimes obscure, are not disclosed to consumers, and often are not even identified by the large brands, which should be responsible for transparency regarding the supply chain [49].

The linear model adopted, as well as the progress of globalization, caused the supply chain of this industry to become geographically dispersed and with multiple actors [11]. Currently, the clothing manufacturing process is more concentrated in developing countries, such as India, Bangladesh, Sri Lanka and Vietnam [50]. However, the materials to produce these clothes are often obtained from other countries, such as Brazil, and at the end of manufacturing, these clothes are again transported to large retailers in developed countries, such as the United States and Europe. In this context, added to the fact that the industry is fragmented, it becomes even more difficult to identify and track the products used and the socio-environmental impact at each stage of production [48].

The second factor is related to the variety of unknown and hidden information related to environmental and social impacts of the textile industry. The characteristics of the textile industry’s supply chain (fragmented, complex and geographically dispersed), combined with factors such as competitive advantage and marketing strategies, culminate in the lack of transparency and traceability of the life cycle of a garment. However, some initiatives are currently being adopted. For example, to address the possibility of dealing with the wide variety of stakeholders and practices existing in the supply chain, and thus, to identify which practices are and are not sustainable, technologies such as blockchain and Near-Field Communication (NFC) have been adopted [51]. This adoption has made it possible for each stakeholder, including the consumer, to visualize and understand all the steps carried out in a particular piece of clothing, thus boosting awareness and pressing retailers to act in an increasingly sustainable way [52]. An example identified in the case studies investigated is that of the company Lablaco [53], which offers an online platform in which consumers and other stakeholders can, using a QR Code, track and verify the history of clothing manufacturing, identifying the partner suppliers and the impact caused in each step. This is possible through a tag that stores data via blockchain, allowing the information to be immutable and traceable.

The third factor identified also poses an alarming challenge for the textile industry. Just from the economic potential and the number of direct and indirect jobs generated by the industry, a series of social issues are identified that need to be evaluated and corrected. Supported by factors such as a lack of transparency and a lack of stricter legislation, fiber producers, suppliers and clothing manufacturers report poor conditions in their workplaces. This scenario occurs mainly in developing countries, such as Bangladesh, where the textile industry is predominant, with the majority of the workers being women who work in factories with precarious conditions of installations, in addition to receiving low wages and a lack of support on labor rights. These violations and social impacts of the textile industry received greater visibility following the occurrence of incidents in textile factories, such as the one that occurred at the Rana Plaza factory in 2013, resulting in the death of 1134 workers [54]. This tragedy touched many people and NGOs, generating social movements to pressure companies for adequate working conditions in the textile and fashion industries. One of these examples is the social movement based on the use of the hashtag #whomademyclothes, with the aim of bringing greater awareness to consumers, so that they pressure brands and authorities to be more transparent in relation to its supply chain, avoiding the occurrence of new collapses.

However, although there are already initiatives in the social context, one of the criticisms in the circular economy context is that, although its proposal aims to present an approach that considers not only the economic dimension, but also the social and environmental ones, some authors report that most of the proposed solutions rely only on the economic and environmental aspect, with few initiatives aimed at the social scope [26,55]. Considering the fact that the textile industry is characterized as a labor-intensive industry, this dimension requires attention. Mies and Gold argue that sustainability can only truly be achieved within the circular economy if there is a balance between the three dimensions [32]. They state that one of the reasons for the less attention given to this dimension is due to the lack of conceptual clarity, which would allow for a deeper understanding of the needs of society. Based on a significant study with an emphasis on social aspects within the scope of the circular economy, they highlight a series of factors that must be evaluated and measured in this context. Examples include workers’ health and safety, fair wages, access to education and training, equity, and social justice. They also present a study on the barriers that prevent progress towards achieving these goals, such as the need for collaboration and mutual trust between stakeholders, active participation by society, well-established policies and a lack of transparency, as mentioned in the previous factor.

Finally, the last factor identified in the use cases refers to the variety of chemical products used in the manufacturing and processing of clothes. As shown in the volume attribute, in addition to the high consumption of water used in the production cycle of a garment, there is also a concern regarding water pollution during this process [4]. For example, in the traditional dyeing process, a wide variety of chemicals are used which end up being sent to the water, polluting and causing health and environmental impacts [27]. It is estimated that approximately 20% of all industrial water pollution comes from the textile industry [6]. In addition, another impact that this causes is difficulty in recycling fabrics, since it is common to mix different fibers in clothing manufacturing, in addition to the use of different chemical products. In this context, one of the use cases investigated, and one which seeks to solve this sustainability challenge, is that of the company Colorifix, which develops a technique for dyeing clothes that does not use chemical processes, in addition to reducing the amount of water during this process. Instead, the technique applied is based on DNA sequencing from an animal, plant or microbe [56].

Table 2. Summary of the variety dimension of the textile industry.

Dimension	Description	Examples
	Large variety of stakeholders, steps, and materials involved along the supply chain	- Stakeholders (suppliers, yarn manufacturers, designers, retailers, consumers, …) - Steps (fiber production, garment manufacturing, dyeing, …) - Materials (cotton, cotton, wool, leather, polyester, silk, …)
Variety	Large variety of unknown and hidden information	- Lack of information about raw materials used, social conditions, suppliers, production footprints, …)
	Large variety of inappropriate working conditions in the industry	- Low wages - Factories with precarious conditions - Lack of regulation - Child labor
	Large variety of chemical components used in the production	- Pesticides for the production of raw materials - Chemical dyes - Chemicals in the treatment of clothes

contains a summary of the topics related to the variety dimension, as well as examples of values presented in each one. As for the variety of Big Data, it is not possible to have an exact measure of all the items that fall under each topic, given the extent that each represents.

Considering the challenges included in the variety dimension, in addition to SDG 12, it is identified that such challenges are also related to other SDGs. The wide variety of unknown and hidden information is also related to SDG 16, referring to peace, justice and strong institutions, aiming at the development of transparent institutions. The challenge regarding the variety of inappropriate conditions in the textile industry is related, in addition to SGD 12 and 16, to SGD 5, referring to gender equity, and to SGD 8, which seeks initiatives for decent work and economic growth. Finally, it is observed that the challenge regarding the variety of chemical components used in the production of the textile industry is also related to SDG 14, which aims to prevent marine pollution.

3.3. Velocity

Finally, velocity is considered a key factor in the context of Big Data, referring to the velocity with which data is currently generated, as well as the velocity with which it must be analyzed and used in the decision-making process [15]. Real-time processing solutions are increasingly in demand, so that companies can develop strategies that improve the customer experience, reduce their risk and can predict the best course of action [57]. In this context, real-time processing systems and Big Data streaming analytics have received great attention, such as Spark Streaming, Storm, and Flink, as a way to deal with large data in motion, for example, from data stream sources from the Internet of Things (IoT) context [58,59].

When analyzing the case studies, considering the velocity dimension, the following factors were presented:

• Large velocity at which clothes are being produced;
• Large velocity at which clothes are being consumed;
• Large velocity at which clothes are being discarded.

Currently, the textile and clothing industry is dominated by the fast-fashion concept, in which clothes are mass-produced. As a result, the industry has been majorly contributing to the increase in the number of clothes produced in recent decades. One of the changes that occurred and caused this increase is the reduction in the periods with which collections are launched. Some brands have been producing new clothing collections and publishing merchandise every four weeks, generating programmed obsolescence of clothing, encouraging consumers to buy new pieces and discard the old ones [60].

The fast-fashion concept is characterized by factors such as the low-cost production of clothes, low-paid workers, and the short time that consumers wear clothes [7]. From this model, added to the previously mentioned occurrences, it is possible to identify that currently, the volume of clothes produced is increasingly fast. At the same time, this growing volume of clothes has generated the second factor presented, referring to the large amount of clothes that are currently being disposed of in landfills or being incinerated. For example, It is estimated that USD 3 billion worth of clothing and textiles are landfilled every year, causing harmful impacts to the Earth and generating the waste of valuable resources [6].

Just as the volume of clothes is increasing, the second factor identified also occurs, referring to the velocity with which clothes are being consumed. The literature points out several factors that have culminated in this scenario. Researchers point to the fact that marketing strategies have driven the increasing volume of clothing being produced [49]. Through programmed obsolescence, major brands produce collections in increasingly shorter periods, and use marketing strategies to encourage consumers to purchase garments from the new collection. That is, the purchase of a new item has become more practical than the repair of an already purchased garment. In [43], the authors also state that, in recent years, consumers have been losing emotional attachment with their clothes, which favors the fact that they frequently change their clothes for new ones. They also argue that many clothes are currently being discarded due to the fact that new generations do not have knowledge or interest in clothes’ mending. As a result, there is low consumer awareness, in addition to a lack of infrastructure that allows them to receive support from companies for the conversion and repair of their clothes.

In this context, some solutions identified in the case studies are being created to mitigate this challenge. For example, the Fixing Fashion project [61] created by One Army presents a proposal for workshops and an online platform to promote the teaching of repair, renovation and upcycling of clothes, presenting creative alternatives to encourage people to prolong the use of their clothes. Through these resources, the founders of this project hope to promote greater consumer awareness, as well as to stimulate other companies and create solutions beyond the direct sale of clothing.

Finally, directly related to the previous factor, is the velocity with which the clothes are being discarded. Today, not only are people wearing clothes less often, but they are also discarding clothes in landfills or incinerating them at an ever-increasing rate. As shown in the volume dimension, the number of clothes that are reused through recycling processes is still low, with the need for additional research, investments and new business models to make this practice viable [4]. One of the factors observed in the literature is the need to share the responsibility between the consumer and the clothing seller after the clothing is sold, until the end of the clothing item’s life [62]. At the moment, responsibility is passed from the seller to the consumer at the time of sale, so that the company becomes exempt from the need to provide adequate mechanisms and services for the consumer in relation to solutions such as reuse, recycle, repair or disposal of clothes. In this context, some initiatives are being addressed, focusing on the adoption of regulations in the context of extended producer responsibility (EPR) [43]. Another challenge observed in this context is related to the process of sorting and separating fibers, since it is currently common to manufacture mixed fabrics composed of two or more fibers. However, most recycling companies currently do not have adequate technology to automate this process, as well as others such as sorting and disassembly [63]. In this context, the circular economy concept can facilitate this scenario, with clothes being designed to be recycled and thus resources to be reused.

Table 3. Summary of the velocity dimension of the textile industry.

Dimension	Description	Characteristics
	Large velocity at which clothes are being produced	- Fast fashion - New collections every four weeks - Planned obsolescence
Velocity	Large velocity at which clothes are being consumed	- Marketing strategies to encourage the purchase of new clothes - Lack of emotional attachment to clothes - Lack of knowledge to repair and mend clothes
	Large velocity at which clothes are being discarded	- Lack of consumer awareness - Lack of infrastructure for clothing recycling - Lack of support from companies in providing solutions that avoid waste

presents a summary of the factors identified in the velocity dimension. The characteristics observed in each factor are also presented, providing an overview of the main aspects related to each one.

It is observed that the challenges related to the velocity dimension are associated with SGD 12, requiring the implementation of sustainable actions that enable the production and consumption of clothing and other textile products in a responsible and conscious way, which allows for reducing material extraction virgin raw materials, the non-generation of waste and reducing negative impacts on the ecosystem through sustainable management models supported by innovations and technological advances.

4. Discussion

From the volume, variety and velocity dimensions described, it is possible to identify the main factors related to sustainability challenges in the textile industry. Figure 2 contains a summary of the Vs from both perspectives. Similar to the Vs applied to Big Data, the Vs of this industry denote how each of these factors should be considered and evaluated in the development of a business strategy.

From the characteristics identified in this research, it can be seen that the sustainability challenges of the textile industry refer to the large volume, variety and velocity with which natural resources are being extracted, water is being used and polluted, in which polluting gases are emitted, chemical components are used and the inappropriate working conditions are observed, as well as the lack of transparency of supply chain, resulting in an increasing volume of clothing being produced, consumed and disposed of inappropriately. We argue that this definition can inspire future works regarding the adoption of data-driven solutions towards the sustainability challenges of this industry.

Therefore, the challenges presented by the textile industry demonstrate that achieving holistic sustainability is challenging, requiring a paradigm shift throughout the value chain, collaboration and awareness of multiple stakeholders, as well as a balance between economic, environmental and social dimensions [32,55]. For example, a strategy that emphasizes only the environmental dimension through the adoption of biodegradable materials may not consider the working conditions of stakeholders along the supply chain, which may negatively impact the well-being of workers, and thus, may contrast with sustainable development. However, it must be recognized that many organizations are still in the process of transitioning to the circular economy, as identified in the case studies, and that, although such solutions do not fully incorporate circularity, they already present a promising approach towards a more sustainable economic system.

We can also identify that, although the Vs of Big Data do not currently necessarily refer to challenges, but also to opportunities, the Vs of the textile industry presented refer to the challenges, denoting how they need to be evaluated to achieve sustainability goals. For example, the large volume of clothes produced, as well as the large volume of waste generated, must be evaluated, seeking a reconfiguration of the business models that offers economic, social and environmental benefits. This context has been evaluated in the circular economy proposal, which aims to provide an alternative to the “take-make-dispose” model prevailing in the textile industry. Thus, the circular economy proposal introduces alternatives and business models focused on reducing, recycling or repairing clothes [55]. The proposal is to eliminate the generation of waste so that the process along the production chain makes it possible for the outputs of a given phase to be used as input for the next phase, in a closed-loop approach. In this way, it aims to reduce or eliminate the factors of the volume dimension of the textile industry.

Ertz et al. argue that Big Data plays an important role in the development of business models such as product rental and sharing [64]. Awan et al. also identify that Big Data allows for improving the quality of the decision-making process in sustainability strategies, providing insights into the best course of action regarding the reformulation of products, the improvement of material efficiency and the end of the life cycle of these products and materials [65].

With respect to the integration between the challenges identified in the textile industry and the techniques and technologies in the context of Big Data, some observations can be made. For example, regarding the combination of the volume of Big Data with the volume present in the sustainability challenges of the textile industry, the need to collect more information during all stages of the supply chain of this industry is identified, so that Big Data analytics strategies can be applied, including machine learning and artificial intelligence methods, to extract insights and predictions that can help decision makers understand how to reduce the volume of natural resources, water and waste generated by this industry [25]. For example, based on data collection strategies in the clothing design and manufacturing process, machine-learning and computer-vision techniques can be used to determine the best position for cutting clothing patterns to avoid pre-consumer waste [66]. That is, the greater the volume of data, associated with the adoption of Big Data analytics techniques, which enable the integration and analysis of this data, the greater the possibility of identifying strategies that make this industry more sustainable.

In addition, one of the challenges to the textile industry advancing in sustainable development refers to the lack of transparency along the supply chain. In this context, the large volume of data can be used with a focus on contributing to increased transparency, facilitating collaboration between stakeholders and contributing to the sustainable development of business models [64,67,68,69]. From the collection, integration and analysis of data from the entire life cycle of the textile product, together with the ability to monitor the performance of equipment and environmental indicators, it becomes possible to extract insights based on a holistic view, which enables the exchange and integration of data throughout the supply chain [70].

Considering the use of Big Data techniques to meet the sustainability challenges of the textile industry in the context of variety, it was identified that, from solutions such as the integration of data from different stakeholders, the possibility of analyzing and understanding the social and environmental impact becomes easier throughout the entire chain, identifying processes under which it becomes necessary to adopt new sustainable practices [52,67,71]. In this context, the adoption of strategies such as the data lake and data lakehouse can be beneficial, as they facilitate the storage and processing of data from different sources and formats, offering resources for extracting insights from an integrated view of the data [72].

Jabbour et al. also make a significant contribution to the context of circular economy and Big Data [73]. Based on the framework ReSOLVE proposed by the Ellen MacArthur Foundation [74], the authors present a proposal for integrating the framework with the capabilities obtained from large-scale data. This integration is also proposed considering the relationship between the elements of the framework, the dimensions of volume, variety, velocity and veracity of data, in the context of Big Data and the stakeholders, such as suppliers, producers and consumers [73].

In the context of Big Data techniques and the velocity dimension, techniques aimed at behavior analysis and recommendation systems can be highlighted. The faster companies identify the impact of their sustainable strategies on changing the behavior of their consumers, the better they will be able to achieve their goals and recommend actions that can help society to also adopt sustainable practices. In addition, the ability to process a large volume of data from the textile industry’s supply chain can improve clothing demand forecasting, enabling retailers to be more assertive in determining the volume of clothing produced [25].

In addition to the three Vs mentioned, some authors also consider the veracity and value of data to be relevant to understanding the concept of Big Data [19,75]. Data veracity refers to the need, in a Big Data project, to assess to what extent the data are adequate and consistent with the real scenario they are representing. This attribute denotes the need, even in the face of a vast amount of data, to perform a careful analysis of the data before using them to extract insights [73]. Additionally, the importance of using data to create sustainability indicators and to analyze their veracity is also highlighted, improving sustainability awareness within industries [76].

Considering the textile industry context, veracity can be highlighted as the veracity of the practices adopted by companies in the life cycle of clothing production. The lack of transparency is a barrier that is still present in this industry, which makes it impossible for regulatory institutions, governments, NGOs and consumers themselves to have a real perception of how their clothes were produced [28]. Another V also adopted by some researchers refers to the value of data, which aims to identify which data, among so many available, should be used in an integrated way in a given analysis, in order to extract the desired value from them. Considering the textile industry, the value dimension can be evaluated to identify how companies and consumers can extract value from clothes, not only for economic, but also environmental and social thinking. More recently, Munawar, Qayyum and Shahzad presented Big Data from 10 attributes, including, besides the five Vs mentioned, the volatility, validity, variability, vulnerability, and visualization [77]. Nevertheless, the study of such attributes, considering the context of the textile industry, can also be significant, making it possible to assess whether the challenges of the industry can also be evaluated on the same vanguard, as well as identifying how Big Data technologies can contribute to such aspects.

Finally, although the Big Data theme is being applied to solve sustainability challenges, such as those identified in the textile industry, there is still a need to expand studies in this area, identifying strategies to apply Big Data effectively. For example, Ertz et al. emphasize that Big Data solutions in the context of the circular economy focus only on the stage of product use, and it is necessary to expand this study to other stages, such as the design and recovery of [64] products. Nobre and Tavares report the need for additional studies to assess the link between circular economy, Big Data and the IoT [78]. Still in this context, Jabbour et al. highlight twelve research propositions regarding the integration between circular economy and large-scale data, with a focus on expanding theoretical and practical studies regarding the intersection between Big Data and the circular economy [73].

Therefore, the case studies investigated have demonstrated the possibility of rethinking clothing production, consumption and post-consumption in the textile industry, seeking solutions, strategies and business models towards sustainability. Such proposals, added to the introduction of digital technologies such as cloud computing internet of things, blockchain, and artificial intelligence, denote that the large volume, variety and velocity components of the textile industry may be replaced by a large volume, variety and velocity of sustainable practices.

5. Conclusions

This article presented a proposal to identify the sustainability challenges existing in the textile industry, from the perspectives of the volume, variety and velocity dimensions, already widely known in the concept of Big Data. From the analysis of circular economy case studies in the textile industry, it was possible to identify that these dimensions can represent numerous social and environmental challenges that permeate this industry. In summary, the following contributions were identified in this study:

• An investigation of sustainability challenges in the textile industry from a set of case studies, making it possible to identify 12 main challenges, categorized from the perspective of volume, variety and velocity.
• A discussion of the applicability of Big Data to the identified challenges, presenting proposals for technologies and techniques and Big Data that can contribute to their resolution.

It is also important to mention that despite the relevance of the article, this work has some limitations. First, the sustainability challenges identified in the textile industry were identified based on case studies of a single platform, focused on circular economy solutions for the textile industry. Thus, even covering a significant number of case studies, other studies not included in the investigation may present additional challenges. A second limitation refers to the fact that the discussion of the applicability of Big Data to the identified challenges is based on studies presented in the literature, thus there is a lack of studies that evaluate results obtained from practical applications. Therefore, to advance this research, empirical research on the literature is suggested for future work, to identify how Big Data techniques and technologies are being adopted in terms of each of the sustainability challenges identified in this article, as well as to identify which other Vs can be observed in the textile industry, which is important considering the expansion of the Vs of Big Data.

Considering that there is currently a growing number of professionals working with Big Data and other interconnected areas such as data science, analytics, business intelligence, and artificial intelligence, the perspective presented, based on the Vs of Big Data, can contribute to encouraging researchers and practitioners in these areas to explore, debate and develop solutions towards solving the challenges of this remarkable but challenging industry in terms of sustainability.