Labor Automation and its Impact on Sourcing Decisions and Lead Times

The business world throughout the last decade has been evolving to integrate automation and independent programs in most aspects of business. For the common consumer, there may not be an understanding of how these decisions may change the way they purchase or receive items. This paper seeks to inform on what labor automation is, and how it affects both sourcing decisions and lead times through an array of mediums.

Understanding Labor Automation

Labor automation is slowly being introduced to the apparel industry as advancements are being made in AI and spatial reasoning. Yet for all of the progress in advanced manufacturing, apparel remains one of the least automated industries in the world. Many aspects of garment construction that rely on skilled hands, such as sewing, have very unique technical challenges that historically have been very difficult to solve (Tsang, 2025). In addition, another challenge is that the apparel industry is not a single-step process. There are dozens of steps that go into creating garments, which makes it difficult to introduce labor automation and figure out where it would be most beneficial in the production line.

Since manual work has always been integral to apparel production, the point of introducing labor automation isn’t to replace every job that these skilled workers do. In apparel, the focus has been placed on domain-specific automation, which focuses on a single step that will make the following steps quicker and easier for workers to accomplish. While these examples focus on apparel manufacturing, automation also affects the broader labor market in similar ways. “The future of apparel manufacturing won’t simply be humanoids replacing workers to automate the manual steps of today. Like any other legacy sector, real progress depends on applying new technologies within the broader economic context” (Tsang, 2025).

Key Research Findings

Armas-Castañeda et al. (2025) explained how a Peruvian Shipbuilding Company used RPA (Robotic Process Automation) to decrease production time, which ultimately makes lead times shorter. According to Armas-Castañeda et al., RPA is software “robots” that can communicate with applications and systems that already exist. They can do tasks like data entry, report generation, and transaction management (p. 534). When companies use this, they can free up human resources so they can focus on higher priority tasks, which can improve customer satisfaction and business agility. In this case study it explains that the RPA fully transformed the Peruvian company. It significantly changed the workflow, helped with cost reduction, and enhanced efficiency.

This case study also showed a before-and-after of RPA within the company. There were two tasks that showed a big change. One was proposal registration in Unisys, which took 2 hours before RPA; now it takes 10 minutes. That was a 92% reduction in time and a 10% reduction in human supervision. The next task was payment process management, which took 1 hour before and now takes 10 minutes. That was a 90%-time reduction and reduced assistant workload by 90% (Armas-Castañeda et al., 2025). This case study demonstrates that labor automation can really help lead times because it helps with the production side of the supply chain. Similarly, using machines in the textile industry helps speed up production.

Trend Analysis

In the article, “Automation and New Task: How technology Displaces and Reinstates Labor”, labor automation is explained by being split into 2 key forces: the displacement effect and the reinstatement effect. The displacement effect occurs when machines replace human workers in certain tasks, reducing the need for human labor and thereby eliminating certain roles. On the other hand, the reinstatement effect is the creation of new jobs and tasks specifically designed for human workers, as technology continues to advance. The article states, “The effects of automation are counterbalanced by the creation of new tasks in which labor has a comparative advantage.” (Acemoglu/Restrepo, 2019) The authors of the article argue that jobs are made up of many tasks and technology is primarily what changes what tasks are completed. This research shows that while automation is increasing, the creation of human tasks has also progressed, just not as fast. The imbalance in this explains the current labor market and weak job growth in certain areas.

In the last decade, India has advanced its apparel industry by implementing more modern equipment, with stitching and knitting now done by machines rather than humans. These new additions allow manufacturers to produce textiles much faster than they could with manual labor. Automation reduces human error, reduces the need for rework, and delays in production.

Machines can also operate constantly, unlike human workers, further accelerating output. As a result, orders can be completed and delivered more quickly. This is particularly important as countries like the United States rely on global suppliers such as India for textile products, making faster production and shorter lead times essential for maintaining competitiveness in the global market. The shift from manual to automatic was further supported when the Indian Government introduced the Amended Technology Upgradation Fund Scheme, which would help pay for new machinery and aid companies investing in automatic machines (Vani, 2025).

Implications for Sourcing Decisions

Labor automation in apparel manufacturing is not primarily driven by the replacement of human workers with humanoid robots, but rather through the gradual implementation of task-specific technologies. Similar to broader supply chain applications of artificial intelligence, automation tends to focus on improving efficiency within specific processes rather than fully replacing human labor. AI technologies such as predictive analytics, robotic process automation (RPA), and autonomous systems are commonly used to streamline operations, reduce errors, and enhance productivity across supply chain functions (Tarihal, 2024). This incremental approach allows companies to reduce reliance on manual labor step by step while improving responsiveness and operational flexibility. Although automation can increase efficiency and reduce costs, it also raises concerns about workforce impacts, as many routine tasks become more easily automated.

Currently, automation in transportation and supply chains is a powerful force shaping sourcing decisions. It reduces reliance on low-cost labor countries and promotes production closer to consumer markets. Innovations like robotics, AI, and automated warehouse systems enhance efficiency, minimize errors, and shorten delivery times, enabling companies to fulfill client demands more quickly. This shift has prompted several companies to consider near-shoring or reshoring over offshore production. Automation also fosters more consistent production processes and redirects sourcing decisions away from labor cost savings toward speed, flexibility, and supply chain dependability (Dekhne et al., 2019).