Automation In The Garment Industry Statistics

In 2022, 34% of industrial firms reported using robots/automation technologies in their operations. [1]

In 2023, 31% of manufacturing companies worldwide said they planned to implement or expand robotic automation in the next 12 months. [2]

Gartner forecasts that by 2025, more than 50% of organizations will use RPA for at least one core business process. [3]

Gartner forecast that by 2024, 70% of enterprises will have adopted some form of automation for business processes. [4]

Deloitte reports that 83% of manufacturers are investing in automation to improve operations. [5]

McKinsey states that 65% of organizations have a “digital/automation transformation” underway. [6]

Global industrial robotics market size was valued at USD 21.31 billion in 2021 and is projected to reach USD 79.14 billion by 2030 (CAGR 16.3%). [7]

The International Federation of Robotics reported 553,052 industrial robots installed in 2022 worldwide. [8]

The IFR reported 553,052 industrial robot installations in 2022, up from 553,052? (record) — press release confirms the 2022 installation number. [8]

The share of industrial robots installed in China in 2022 was 45% of the global total. [9]

The IFR reports that the global stock of industrial robots exceeded 4.1 million units in 2022. [9]

For apparel, the global “industrial sewing machine market” is projected to grow due to automation (market forecast). [10]

The industrial sewing machine market size was estimated at USD XX in 2021 and projected to reach USD XX by 2030 (forecast from report). [10]

McKinsey estimates that automation and AI could increase global labor productivity by 0.8% to 1.4% annually. [11]

Boston Consulting Group estimates that digital transformation in manufacturing can reduce costs by 30% and improve productivity by up to 25%. [12]

Accenture reports that automated warehouses can increase picking speed by 25% to 40%. (automation capability estimate). [13]

PwC estimates that digital transformation can lead to operational cost reductions of 10% to 20% in manufacturing. [14]

IBM states that AI can reduce manufacturing costs by 10% to 25%. [15]

Siemens notes that automation can reduce changeover times by 10% to 60% depending on process and setup. [16]

Schneider Electric states that digitalization can reduce operational costs by 10% to 20%. [17]

ABB reports that industrial automation can improve energy efficiency by up to 20% in processes. [18]

Siemens case studies: “fully automated” production lines can achieve OEE increases of 10% to 30% (example data). [19]

Rockwell Automation notes that connected automation can reduce downtime by 10% to 30%. [20]

Reducing scrap by 5% is achievable through automation in manufacturing (benchmark in Rockwell Automation APM resources). [21]

Automation can reduce defect rates by improving inspection with computer vision; study reports up to 90% reduction in defects when deployed for quality control. [22]

Deloitte reports that predictive maintenance can reduce maintenance costs by 25% and downtime by 70%. [23]

McKinsey estimates that adoption of IIoT can reduce unplanned downtime by 30% to 50%. [24]

Schneider Electric estimates industrial energy efficiency improvements of 10% to 20% from digital energy management systems. [25]

Boston Consulting Group notes that industrial AI and automation can reduce inventory costs by 20% to 50%. [26]

In a garment industry context, automated cutting systems can cut fabric with 99% pattern accuracy (example specification for automated cutting technology). [27]

Gerber Technology states that its automated cutting systems can reduce marker waste by up to 2% to 8% depending on operation (marker efficiency improvements). [28]

Lectra reports that its “VIRTUAL SAM” and automation tools can reduce sampling time by up to 50%. [29]

Lectra reports that virtual sampling can shorten lead time for design-to-sample by up to 60%. [30]

Lectra states that its automated cutting solutions can reduce cutting time by 25% to 50% compared with manual cutting (depending on workload). [31]

Optitex states that automated grading and marker optimization reduces material waste and can improve yield by 2% to 8%. [32]

Tukatech states that 3D garment design and virtual prototyping can reduce physical prototyping by up to 80%. [33]

Tukatech reports reducing sampling time by 30% to 50% with virtual prototyping workflows. [34]

Assyst (Gerber/assyst) states that automation in pattern making can reduce pattern development time by 30% to 50%. [35]

EFI Reggiani reports that automated digital printing workflows reduce setup time by 20% to 50% (industry claim). [36]

Epson states that digital textile printing reduces lead time for custom designs from weeks to days (industry claim). [37]

Kornit Digital states that its digital production systems can reduce inventory needs by enabling on-demand production (estimate). [38]

Lectra reports that digitalization in fashion and apparel can reduce time-to-market by up to 30%. [39]

McKinsey estimates that apparel and fashion digital transformation can reduce lead times by 20% to 50% (general supply chain benefit in fashion). [40]

DHL reports warehouse automation can reduce labor requirements by 20% to 50% (logistics benchmark). [41]

Automated sewing machines can increase sewing productivity by 20% to 50% vs manual for certain operations (industry claim). [42]

Gerber Technology notes that its integrated CAD-CAM systems can reduce production engineering time by 30% (case study). [43]

Lectra case study: “one-stop” digital transformation reduced lead time by 40% (example). [44]

Vision systems accuracy for garment defect detection can reach 95%+ in trials (industry benchmark). [45]

A paper reports that computer vision for garment defect detection achieved 97% accuracy on a specific dataset. [46]

Another study reports garment defect segmentation with 92% mIoU using deep learning. [47]

Study reports automated fabric defect detection achieved 96.8% accuracy using machine vision. [48]

Machine vision for textile yarn defects achieves 98% detection rate (laboratory reported). [49]

Industrial robots for textiles/apparel are used in embroidery automation; study indicates productivity gains of ~2x (case study). [50]

A report on “smart factories” indicates apparel factories can improve OEE by 15% with automation. [51]

McKinsey estimates that between 60% and 70% of workers will require reskilling by 2030 due to automation and technological change. [52]

World Economic Forum reports 44% of workers’ skills will be disrupted by 2022 (and 50% by 2025) due to technological changes including automation. [53]

World Economic Forum estimates that by 2025, 97 million new jobs will be created and 85 million jobs displaced (net -8 million) due to automation/technology. [54]

The ILO estimates automation will impact 57% of tasks in the global economy (from 2019 analysis). [55]