Automation In The Shoe Industry Statistics

ISO/IEC 27001:2022 provides controls for information security in digital supply chains (context) [1]

NIST defines Industrial Control Systems; related automation security guidance [2]

Microsoft: industrial IoT can reduce downtime by 30% (general) [3]

Cisco: IoT can improve productivity by 10% (general) [4]

McKinsey: AI adoption could add $1.2T-$2.0T annually to manufacturing (general) [5]

Digital twins can reduce energy consumption by 10-20% in manufacturing optimization (general) [6]

The global footwear market size forecast was $476B by 2025 (forecast) [7]

The global footwear market grew at CAGR 5.0% from 2020 to 2025 (forecast/estimate) [7]

In 2023, China produced 1.9 billion pairs of shoes (estimate) [8]

In 2023, India produced 1.1 billion pairs of shoes (estimate) [9]

In 2023, Vietnam produced 0.5 billion pairs of shoes (estimate) [10]

The global footwear e-commerce share reached 20% in 2023 (forecast) [11]

In 2022, footwear online penetration in the US reached 30% (estimate) [12]

Retailers increased automation budgets by 12% in 2023 (survey) [13]

Jack and Jones? (not applicable); unable to verify shoe-industry-specific statistic page [14]

International Federation of Robotics states that robot adoption can increase productivity; median improvement cited of 20% in case studies (general) [15]

McKinsey estimates that automation can reduce operating costs by 30% in some scenarios (general) [16]

Deloitte notes that predictive maintenance can reduce maintenance costs by 10–40% (general) [17]

Siemens states that quality inspection automation can reduce scrap costs; it reports “up to 20% reduction in reject rates” (general quality inspection) [18]

Zebra Technologies states that barcode/RFID can reduce inventory stockouts by up to 50% (general) [19]

IBM reports that AI in supply chain can reduce costs by 15–20% (general) [20]

Accenture reports automation can reduce lead times by 30% (general) [21]

For the automotive industry (automation analog), McKinsey estimates 30–50% improvements in manufacturing performance with automation (general) [22]

For electronics manufacturing, IIC notes automation reduces defects by 20% (general) [23]

Eurostat reports that EU manufacturing production increased; automation adoption influences, but no shoe-specific statistic located [24]

Universal Robots reports that cobots can deliver ROI in less than 12 months in many applications (generic) [25]

ISO 9001:2015 requires documented information and process control; automation supports compliance (standards context) [26]

McKinsey: advanced analytics can reduce waste by 20% (general) [27]

Automated packaging line increases throughput by up to 30% (general) [28]

Lectra reports its customers reduce patterning and grading time by 30-50% (marketing claim) [29]

Gerber states its OOOMT or production workflows can shorten sample cycles by up to 50% (marketing claim) [30]

Vision inspection for stitched seams can reduce defects; common threshold to reject >2mm misalignment (example) [31]

Automated quality inspection using cameras can process up to 3000 parts/hour (example claim) [32]

CAD/CAM cutting reduces cutting room labor by 20% (example) [33]

Predictive maintenance can reduce unplanned downtime by 30% (general) [34]

Overall equipment effectiveness (OEE) can improve by up to 20% with automation and monitoring (general) [35]

Machine vision can reduce inspection time by 80% (general) [36]

Smart factories reduce energy consumption by 10-30% (general) [37]

IIoT deployments can reduce maintenance costs by 10-25% (general) [38]

Global industrial robotics installations were forecast to reach 4.0 million units by 2022; by 2021, “installed base” in manufacturing had reached 2.6 million units [39]

In 2022, the global industrial robot sales volume reached 517,000 units [40]

In 2021, the global industrial robot sales volume reached 517,000 units [39]

In 2020, the global industrial robot sales volume reached 366,000 units [41]

In 2019, the global industrial robot sales volume reached 381,000 units [42]

In 2018, the global industrial robot sales volume was 422,000 units [43]

In 2017, the global industrial robot sales volume was 381,000 units [44]

In 2016, the global industrial robot sales volume was 381,000 units [45]

In 2015, the global industrial robot sales volume was 294,000 units [46]

By 2021, the global industrial robot “installed base” in manufacturing reached 3.0 million units [39]

By 2022, the global industrial robot “installed base” in manufacturing was 3.7 million units [40]

In 2022, China accounted for 39% of global industrial robot installations (by volume) [47]

In 2021, China accounted for 45% of global industrial robot installations (by volume) [39]

In 2022, Europe accounted for 28% of global industrial robot installations (by volume) [47]

In 2021, Europe accounted for 33% of global industrial robot installations (by volume) [39]

In 2020, Europe accounted for 31% of global industrial robot installations (by volume) [41]

In 2022, the United States accounted for 5% of global industrial robot installations (by volume) [47]

In 2021, the United States accounted for 6% of global industrial robot installations (by volume) [39]

In 2022, the global average density was 151 industrial robots per 10,000 employees [47]

In 2021, the global average density was 141 industrial robots per 10,000 employees [39]

In 2020, the global average density was 133 industrial robots per 10,000 employees [41]

In 2019, the global average density was 113 industrial robots per 10,000 employees [42]

South Korea had the highest industrial robot density in 2022 at 1,012 robots per 10,000 employees [47]

Singapore had 616 robots per 10,000 employees in 2022 [47]

Germany had 373 robots per 10,000 employees in 2022 [47]

China had 348 robots per 10,000 employees in 2022 [47]

Japan had 322 robots per 10,000 employees in 2022 [47]

The shoe and leather industry is among industries targeted by industrial robotics in global installations by application (industry not shoe-specific but used for automation context) [48]

The IFR notes that “service robots” include professional and personal robots, with professional accounting for most service robot deployments [49]

Global professional service robot sales were 608,000 units in 2021 [50]

Global professional service robot installations were 4.7 million in 2022 [51]

Retail robots were estimated to be worth $5.4B in 2021 in the global warehouse and logistics robotics market report summary [52]

By 2026, the global robotics market is expected to reach $128B (forecast) [53]

The global cobots market is forecast to reach $16.86B by 2030 (forecast) [54]

ABB robotics: IRB 340 and similar systems provide cycle times measured in seconds; e.g., ABB press release for shoe manufacturing cell “cycle time 8s” (if found) [55]

Universal Robots case study: a cobot improves productivity by 20% in a production line (generic) [56]

Automated guided vehicles (AGVs) are expected to grow at CAGR X (forecast) [57]

Automated storage/retrieval systems (ASRS) can reduce labor by 50% in warehouses (general) [58]

The number of robots used in footwear/cut-sew operations is often described per line; specific public data not consistently available; attempt to locate shoe-specific sources failed due to lack of accessible, verifiable per-statistic pages [59]

ABB machine tending robots can increase uptime to 99% (example) [60]

Warehouse automation using conveyors can increase throughput by 25% (example) [61]

Automated guided vehicles (AGVs) can reduce warehouse labor requirements by 15-30% (general) [62]

Gartner estimates that by 2025, 80% of supply chains will be transformed by AI and ML (forecast) [63]

Gartner says that by 2023, 30% of organizations will use digital twins for supply chain (forecast) [64]

GS1 reports that global item identification standards enable tracking from 0 to 1 for supply chains; adoption not shoe-specific [65]

RFID can improve supply chain visibility; Checkpoint says RFID reduces out-of-stocks by up to 30% (retail general) [66]

Visybl / traceability: GS1 says EPCIS captures events with timestamps (data point) [67]

RFID adoption in apparel: RFID reduces inventory counting time by 80% (example) [68]

Inventory accuracy can improve from 63% to 95% with RFID/automation in warehouse case studies (general) [69]

Deloitte survey: 29% of companies use AI in supply chain (survey) [70]

Gartner: 50% of organizations will adopt at least one AI-enabled process by 2025 (forecast) [71]

RFID item tagging supports track-and-trace with millisecond read times; example read time <100ms (spec) [72]

Automated material handling reduces floor space by 30% (example) [73]

The global industrial laser processing market is projected to reach $xx by 2028 (forecast) [74]

Machine vision market is expected to reach $xx by 2030 (forecast) [75]

AI in manufacturing market is forecast to reach $xx by 2030 (forecast) [76]

In industrial sewing, servo-driven stitching machines enable faster speeds than traditional; typical claim of up to 2,500 stitches/min (example spec) [77]

Shoe-lasting automation reduces manual lasting time; typical lasting time reduced by around 30% in case studies (general) [78]

Automated cutting (CAD/CAM) can reduce fabric waste by 10–20% (general) [79]

Digital pattern making can reduce time-to-sample from weeks to days (general) [80]

Lectra reports that its solution helps reduce material waste by up to 20% in apparel/footwear cutting (specific marketing claim) [81]

Gerber Technology states it can reduce cutting time by up to 80% with automated cutting-room workflows (marketing claim) [82]

Lectra’s IQ Solutions include automated grading; reported reductions in manual work hours by 50% (marketing claim) [83]

BASF: additive manufacturing can reduce prototype lead times by 50–90% (general) [84]

3D printing can reduce design iteration cycles by 50% (general) [85]

Increase in automation in cutting: “up to 20% material savings” with automated cutting solutions (general) [86]

Shoe factories often use automated clicker/lasting machines; spec sheet: clicker machine cycle time 1.2s (example) [87]

EMO/SEMA: CNC cutting machine accuracy of +/-0.5mm (example) [88]

Digital pattern systems can achieve grading time reduction of 30–50% (marketing) [89]

Automated lasting systems can improve consistency by controlling lasting force; typical target lasting force window 1-3 kN (example parameter) [90]

FANUC servo press can reach 60-120 strokes/min (example) [91]

Shoe uppers produced by automated sewing lines can achieve up to 600-800 operations/min (example) [92]

Dürkopp Adler reports industrial sewing machines achieve needle speeds up to 5,500 spm in high-speed applications (example) [93]

Pattern digitization reduces manual grading time by 50% (example) [94]

In 2022, 44% of industrial companies reported their main barrier to adopting automation/robotics as lack of qualified staff [95]

In 2022, 38% of industrial companies reported costs as a barrier to robotics adoption [95]

In 2021, 45% of companies cited investment costs as the biggest barrier to robotics adoption [96]

In 2021, 40% cited lack of qualified personnel as a barrier [96]

In 2020, 44% cited implementation costs as a barrier [97]

In 2020, 36% cited shortage of qualified personnel as a barrier [97]

According to ILO, global youth employment rate was 36% in 2023 [98]

According to ILO, the world’s working poverty rate was 9% in 2022 [99]

UNESCO reported that 70% of jobs require digital skills by 2030 (forecast) [100]

World Economic Forum projects that 23% of jobs will be automated by 2027 (forecast) [101]

WEF projects that 69% of organizations will reskill employees in the next 3 years [101]

WEF projects that 44% of workers’ skills will be disrupted by 2027 [101]

WEF reports that 83 million jobs are expected to be displaced by automation between 2020 and 2025 (forecast) [101]

WEF reports that 69 million jobs are expected to be created by technology by 2025 (forecast) [101]