The state of the apparel and footwear industry

Oct 23, 2023
The state of the apparel and footwear industry

1.1 An important sector for women’s employment

The global supply chain in the apparel and footwear sector has been an important source of employment. This has been especially so for women who may have previously had difficulties accessing paid employment, and particularly wage work.

In 2019, the textiles and garment sectors combined employed approximately 91 million workers globally, 50 million of which were women (ILO 2020b). In garment manufacturing more specifically, it has been estimated that women account for about 80 per cent of employment (ILO 2019). Not only are women prevalent in the industry’s workforce, but this sector also often accounts for a large share of women’s total employment, particularly in developing economies. In Asia and the

Pacific, for instance, more than 5 per cent of working women engaged in this industry in 2019, making it the largesst employer of women among all industrial sectors and fourth largest overall (ILO 2020b; Lowell Jackson, Judd, and Viegelahn 2020).

Employment in the apparel and footwear industry is associated with both benefits as well as challenges. On the one hand, the sector is one of the largest providers of formal employment for women – and arguably often their first opportunity for paid work in many developing countries. On the other hand, poor working conditions and violations of labour rights are well documented (ILO and IFC 2018; BSR 2017; Anner 2018; Barrientos, Gereffi, and Rossi 2011). Although many of these

issues affect all workers in the sector (including both men and women), it is women who are the majority in low-skilled low-wage jobs, and who also face additional challenges related to social norms and power dynamics that disadvantage them (ILO and IFC 2018; BSR 2017).

According to the literature, the main benefits of employment in the garment industry for women include opportunities to earn an income, particularly where formal employment opportunities are limited, and social safety nets are weak (ILO 2021). Where this employment is formal, it also provides rights and protection to workers, as well as skilling opportunities to at least some of the workforce (Barrientos, Gereffi, and Rossi 2011). This access to paid employment increases women’s

bargaining power within the household and is critical for enhancing women’s economic and social status and women’s empowerment (Bárcia de Mattos and Dasgupta 2017). But there are several deficits associated with garment work. These encompass low pay, long working hours, poor working conditions, lack of access to benefits (such as health insurance and maternity leave), gender-based harassment and violence, systemic discrimination (including on the grounds of pregnancy), limited opportunities for skills development and career advancement, and barriers to participation in

leadership positions and decision-making (ILO 2021; ILO and IFC 2018; BSR 2017). In addition, employment for many women in the sector is frequently informal, piece-rate and hourly work performed outside of factories, often home-based, with even less income, legal and social security (Anner 2019). Moreover, there are several persistent gender gaps in the industry. On average, women earn less than men, are segregated in the lowest skill, lowest wage occupations and are underrepresented in supervisory and management roles relative to their share in total employment in the sector (ILO and IFC 2018). A critical question is whether these decent work deficits and gender gaps will be exacerbated by technological upgrading or whether developments in the industry hold promise towards greater gender equality, and how best to

harness this potential and capitalize on change.

These benefits and deficits are also linked to the structure of the global industry. Generally, the establishment and expansion of global and regional supply chains and production networks have been associated with opportunities for

economic and social upgrading. However, the value chains’ structure and the power dynamics across firms, particularly in buyer-driven chains such as garments, have been the subject of debate to the extent that they shape, to a large degree working conditions and labour standards (Barrientos, Gereffi, and Rossi 2011; Kucera 2021; Bamber and Staritz 2016). The debate was particularly intense following the collapse of Rana Plaza in Bangladesh in 2013 (Kucera 2021). One strand of literature suggests that there is an opposition between participation in global supply chains and trade on the one hand and labour standards, on the other, especially in labour-intensive and price-sensitive industries like garments (Kucera 2021; Kabeer 2004). Some argue that the garment sector has benefitted many, particularly poorer women from rural areas in developing countries, and that it provides better wages and working conditions than most available alternatives forsemployment (Kabeer 2004; Kabeer and Mahmud 2004). Others, while recognizing that the sector has generated many jobs, suggest that competitiveness could be maintained with better working conditions and wages (through the coordinated implementation of international labour standards across competing countries) by offsetting associated cost increases with industrial policy-enabled reductions in other production costs (Berik 2017; Seguino 2006). In this context the importance of freedom of association and networking with international unions is underscored (Berik 2017). This literature recognizes that there are critical issues related to purchasing practices of brands and retailers in supply chains which frequently prioritize lower costs and shorter delivery times, thus pressuring suppliers (Kucera 2021; Berik 2017). It also stresses the social and institutional embeddedness of production and power relations across actors in the supply chain (Barrientos, Gereffi, and Rossi 2011).

Power relations became clear in 2020, when orders abruptly cancelled by brands, led to factory closures, layoffs and wage cuts (Tejani and Fukuda-Parr 2021). The COVID-19 pandemic resulted in millions of temporary and permanent jobs being lost, which compromised women’s ability to meet basic needs and threatened their bargaining power and status in the household, particularly in contexts of limited social protection and unequal access to public and financial services (ILO 2020c; 2020a; Tejani and Fukuda-Parr, 2021). Moreover, those women who do return to factories face risk of infection at the workplace, during commutes and in employer-provided dormitories (Tejani and Fukuda-Parr 2021). Business has yet not returned to pre-crisis levels, resulting in uncertain prospects for women. Fewer opportunities in apparel and footwear could lead to women being pushed into informal employment (in and outside of the industry) due to their status as secondary earners, with employers often giving preference to male workers in periods of retrenchment (ILO 2020c). There

is also a risk of women leaving the labour force, given that finding alternative employment can be particularly challenging as a result of skills mismatches and social norms and expectations in regard to what constitutes appropriate work for women. A critical concern for the future relates to the potential acceleration of technological upgrading and the adoption of automation, and the associated impacts on the low-skilled jobs of many women in apparel manufacturing. More specifically, and particularly for developing regions where the industry is characterized by labour-intensive processes, a key issue relates to the potential defeminization of the workforce which often accompanies increases in capital intensity and productivity (Kucera and Tejani 2014; Tejani and Kucera 2021).

1.2 Technology and employment in apparel and footwear manufacturing

The future of the apparel and footwear industry, its supply chain, its geography of production, and workforce depend not only on the capabilities of new technologies, but on a host of other factors such as relative capital and labour costs, labour availability, skills, infrastructure, logistics, trade policies, and the broader institutional setting where production take place. Nevertheless, literature on automation and employment often focuses solely on the availability of technology and its potential to displace workers, leaving aside other factors that would lead to (or hinder) the adoption of different technologies and other dimensions that also contribute to employment outcomes.

Technology is quickly changing and is increasingly able to automate work previously performed by workers, yet employment impacts are uncertain. Some empirical studies examining recent data associate technology adoption wit negative employment impacts (Acemoglu and Restrepo 2017; Chiacchio, Petropoulos, and Pichler 2018), while others do not find automation to be labour-displacing (Dauth et al. 2017; Graetz and Michaels 2018; Autor and Salomons 2018).

Forward-looking attempts to assess potential future impacts of emerging technologies based exclusively on technical feasibility and task characteristics tend to indicate a high degree of risk of worker displacement – particularly in routin repetitive work (Frey and Osborne 2013; World Bank 2016; McKinsey Global Institute 2017; Arntz, Gregory, and Zierahn 2016; Nedelkoska and Quintini 2018). However, historically, automation has not led to higher long-term unemployment but rather to a new composition of employment (Levy and Murnane 2005; Minian and Martinez Monroy 2018; ADB 2018 Nubler 2016). This is because job destruction dynamics coexist with job creation dynamics. For instance, new Technologies could result in less new hires rather than job loss, could result in the emergence of new occupations, and could trigger job creation elsewhere in the economy (Acemoglu and Restrepo 2018; Gregory, Salomons, and Zierahn 2019; Kucera 2017 Nubler 2016). Adjustments are not, however, instantaneous or costless, and impacts are uneven.

Another layer of complexity in assessing technology impacts on employment is added when one considers the implications of human agency and social and organizational aspects of production (Fernández-Macías 2018; Fana, Villani, and Bisello 2021; Anzolin 2021). In addition to impacts on the number of jobs, tasks and occupations, new technologies affect conditions of work and employment, workers’ autonomy and dynamics of control as well as industrial relations. While tasks and occupations impacts are more easily observable, technology impacts on conditions of employment and institutions are less direct and harder to determine (Fernández-Macías 2018). Moreover, issues related to social structure may be of particular concern when viewed through a gendered lens. Fana, Villani, and Bisello (2021) find disparities between the tasks performed by women and men in similar jobs even after controlling for characteristics such as education and seniority. They also find that gender matters in regard to work organization and distribution of power, identifying gender asymmetries in autonomy and authority.

The digitization and automation of industrial processes interact with local social structures, cultural and institutional systems, and structures of industrial relations and workplace organization to produce uneven employment outcomes.

There is a growing literature indicating that automation affects women and men’s employment differently, associating technological upgrading with the defeminization of employment. A number of studies show that feminized sectors of  manufacturing experience declines in the proportion of women workers as production becomes more technologically advanced (Tejani and Milberg 2016; Tejani and Kucera 2021; Seguino and Braunstein 2018; Caraway 2006; Kucera and Tejani 2014). In regard to apparel and footwear manufacturing specifically, Tejani and Kucera (2021) find negative impacts of labour productivity increases on women’s employment within the industry.

This outcome has been attributed to the systematic effects of gendered norms and institutions in the economy. These studies invoke the lesser importance of low-wage women’s labour in more capital-intensive production; gender norm designating men as breadwinners and women as secondary workers, with men more likely to be hired for higher paying jobs; and the different skills requirements of new industrial jobs combined with the purportedly different skills of men and women workers – regardless of whether these differences are real or perceived (Tejani and Milberg 2016; Tejani and

Kucera 2021; Seguino and Braunstein 2018; Caraway 2006; Kucera and Tejani 2014). For instance, breadwinner norm tend to award men with higher value-added jobs that have the potential for wage increases while women are preferred for labour-intensive jobs as they are considered secondary workers. Gender stereotypes about workers’ abilities, or tropes of “feminine productivity” about women’s suitability for labour-intensive, routine and manual work (Salzinger 2003)

disadvantage women during technological upgrading. Institutional factors such as gender segregation in vocational training and technical education place women at a disadvantage even before they enter the labour market and intensify unequal outcomes in the context of automation (Tejani and Milberg 2016). These studies link up to the broader debate in the GVC literature on whether technological upgrading within GVCs leads to social upgrading, including in garments

(Barrientos 2019). At the level of the firm, Tomascovic-Devey (2014, p. 52) reminds us that: “…organizations are inequality regimes embedded in social structures populated by culturally infused people”.

There is limited research directly examining evidence for the competing hypotheses outlined above, and little is known about the decision-making process behind these gendered outcomes, including the role of different actors and institutions in shaping them. There is also little research assessing the gendered impacts of automation in specific industries and how this may vary according to the capital intensity of production, the amenability of production to automation and the prevalence of women in the workforce. The main goal of this project is to address some of these knowledge gaps.

1.3 Technology advances in apparel and footwear manufacturing

As Altenburg et al. (2020) summarize, new technologies will change the way in which companies produce goods and services, compete with one another, engage in international trade and supply chains, adapt business models, and interact with consumers. Yet, for the foreseeable future, technical bottlenecks suggest that new and old production systems in apparel and footwear manufacturing will coexist (Altenburg et al. 2020; Bárcia de Mattos et al. 2021; Kucera and Bárcia de Mattos 2020).

So-called factory 4.0 innovations comprise enabling digital technologies (such as big data and cloud computing) as well as new production systems, including digital sensors, sophisticated vision systems, advanced material handling tools robotics and many other technologies, operating either autonomously or in collaboration with workers (Nayak and Padhy 2018; Altenburg et al. 2020). Usage and availability of manufacturing technology varies across the different stages of the

apparel and footwear supply chain (figure 1). Automation technologies are predominantly present at the ends of the supply chain, particularly in textile manufacturing on one end, and logistics and retail on the other (Bárcia de Mattos et al 2020). The focus of this study (and research project) is apparel and footwear manufacturing, which remains labourintensive with little presence of advanced manufacturing technologies, despite some variations, as footwear lends itself

more easily to automation. In addition, this research project will explore digitization in the management of supply chains and how this affects work organization and intensity, with potentially differential employment impacts on women and men. It is posited that technologies focused on the automation of production processes, digitization of processes and supply chain management, and reshoring not only are themselves interrelated, but they are also alternative approaches to the same end goal: to increase speed to market, reduce excessive inventories and price markdowns, and to respond to the growing demand for customized products. Moreover, this would also mean addressing greater concerns over environmental sustainability while at the same time responding to increased public awareness over human rights and working conditions in garment manufacturing.

It is in sewing that labour inputs remain concentrated, engaging about two-thirds of workers (Chang, Huynh, and Rynhart 2016). It is, therefore, advancements in sewing technology that hold the most promise in terms of reducing labour (and production) costs in apparel manufacturing – estimated at about 20 per cent of total costs in developing Asia and 30 per cent in the US (Chang and Rynhart 2017). It is also sewing which remains most challenging to automate. Despite being

considered a routine repetitive task (characteristics associated with amenability to automation), handling pliable fabrics of various weights and grades and perfectly aligning them for sewing remains a challenge to existing machinery (Kucera 2020; Kucera and Bárcia de Mattos 2020; Bárcia de Mattos et al. 2021). It has been estimated that material handling accounts for about 80 per cent of overall production time and cost in apparel assembly (Gries and Lutz 2019). However, there have been noteworthy advancements in recent years.

Technology developers endeavouring to sew with robots are dealing in very different ways with common technological challenges. In this regard, it is worth reviewing the characteristics of Sewbo, SoftWear Automation and Grabit (Kucera and Bárcia de Mattos 2020; Kucera 2020). At the same time, a high degree of automation is possible in apparel sewing even when fabric handling remains largely done by hand. In this regard, we also consider below MAICA, one of the companies

producing semi-automated machinery to sew shirts (Kucera and Bárcia de Mattos 2020; Kucera 2020). Sewbo’s approach makes use of conventional, off-the-shelf collaborative robots and sewing machines. Its innovation is not with automation machinery but rather in the treatment of pieces of fabric, making them temporarily rigid with a watersoluble chemical. That is, Sewbo’s approach is to make pieces of fabric similarly manipulable to pieces of metal, thus making apparel sewing akin to a conventional assembly operation that is able to take advantage of the ready reprogrammability of state-of-the-art collaborative robots. In contrast with Sewbo, SoftWear Automation designs and builds robots specifically for sewing – Sewbots, the company calls them. The company deals with the challenges posed by the pliability of fabrics through the development of sensors and accompanying visual enhancement software that count individual threads and intersections of threads in fabric. These sensors enable its robots to guide fabrics through conventional sewing machines with a high degree of precision, and the company has also developed robotic sewing machines. Grabit developed a robotic hand that uses electroadhesion (a type of static electricity) and can pick up andhandle a wider range of objects – including fabrics – than conventional robotic grippers or suction cup hands. When combined with a customized Toshiba Machine robot, Grabit’s hand is reportedly able to arrange the pieces for a sports shoe upper 20 times faster than a human, after which the pieces are heat-fused. Rather than attempting to overcome the challenges posed by the pliability of fabrics, as with Sewbo and SoftWear Automation, MAICA’s strategy is to work within these constraints, with workers hand-feeding fabrics into a series of machines that break down the shirt-making process into discrete steps. Each machine is specialized for each step, with some of the steps using conventional sewing machine integrated with MAICA’s auxiliary machinery.  

Other technologies, like 3D sewing machines have also been developed to overcome barriers to automation resulting  from handling various types of fabric. 3D sewing machines are complementary to sewbots in that they allow the garments to be place on a 3D mould which enables automated sewing to autonomously move around it. This type of technology can be used to manufacture wearing apparel, such as trousers, jackets and shirts, as well as other products like car seats, even if limited to certain applications within these production process (Nayak and Padhye 2018; Gries and Lutz 2019). Still, to date, producing garments necessitates many semiautomatic machines and steps, and it remains therefore difficult to achieve economical and flexible production with automated systems (Gries and Lutz 2019).

An area in which automation is largely absent is fabric pressing, although different kinds of automated machinery with varying degrees of autonomy have been developed in recent years. As noted by Fergusson (2015), pressing requires full and precise control of different factors (i.e, heat, moisture and pressure) and significant risks of error persist. Within this context, automation in the pressing stage could improve quality and consistency (Nayak and Padhye 2018).

Original design manufacturers may use 3D printing for rapid prototyping, while this may not be the case for cut-makeand-trim companies. According to the qualitative study conducted by Bárcia de Mattos et al. (2021), 3D printing appearsas “a good option for prototyping, sample development and product customization” and its diffusion should spread in thefuture as the technology is still rapidly evolving. Yet, the technology is still not able to deal with high volumes and a Wide range of materials. There are several other new technological advancements. This includes, for instance, new technologies in laser cutting, radio-frequency identification (RFDI) technologies to trace production through the entire manufacturing process, digital design and sampling technologies, as well as in ancillary operations in warehousing, distribution, marketing and sales.

The push towards a digital transformation has been renewed in the context of COVID-19, with the need to improve management and forecasting capabilities to ensure business continuity, as well as to stay connected and engaged with consumers virtually (Gonzalo et al., n.d.).

Digitization and automation development in apparel and footwear manufacturing is currently rife with experimentation. With no clear dominant approach, as evidenced by the company examples above, future pathways are still to be defined. 

In addition, qualitative studies with informants from the industry (such as Kucera 2020; Bárcia de Mattos et al. 2021; Altenburg et al. 2020) indicate that automation adoption is likely to be a gradual and incremental process, rather than as sudden and radical disruption.

The question of technological upgrading is closely linked to that of the geography of production and the landscape of the supply chain. A critical concern is whether automation will erode the labour cost advantage that led to the development of apparel and footwear manufacturing in developing and emerging regions, creating millions of jobs for women. And if that is the case, whether this is likely to lead to a reversal of offshoring processes, to the detriment of women workers who may be constrained in accessing other employment opportunities. In such a scenario, automation Technologies implemented in developed countries – which are home to lead brands and central destinations of exports from manufacturing regions – could lead to job-displacement in developing and emerging economies.

However, decisions regarding automation and the geography of production are not solely reliant on the relative costs of capital and labour, or on the unit of production. Speed to market, consistent quality, environmental sustainability, customization, and the availability of material inputs are amongst many of the other factors affecting the uptake of new technologies and the location of production (Bárcia de Mattos et al. 2021).

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