½ÃÀ庸°í¼­
»óǰÄÚµå
1802988

¼¼°èÀÇ ÀÚÀ²Çü ¶ó½ºÆ® ¸¶ÀÏ ¹è¼Û ½ÃÀå ¿¹Ãø : Ç÷§Æûº°, ¼Ö·ç¼Çº°, ¹üÀ§º°, ¿ëµµº°, ÃÖÁ¾ »ç¿ëÀÚº°, Áö¿ªº° ºÐ¼®(-2032³â)

Autonomous Last-Mile Delivery Market Forecasts to 2032 - Global Analysis By Platform (Aerial Delivery Drones, Ground Delivery Vehicles and Self-Driving Trucks & Vans), Solution, Range, Application, End User and By Geography

¹ßÇàÀÏ: | ¸®¼­Ä¡»ç: Stratistics Market Research Consulting | ÆäÀÌÁö Á¤º¸: ¿µ¹® 200+ Pages | ¹è¼Û¾È³» : 2-3ÀÏ (¿µ¾÷ÀÏ ±âÁØ)

    
    
    



¡Ø º» »óǰÀº ¿µ¹® ÀÚ·á·Î Çѱ۰ú ¿µ¹® ¸ñÂ÷¿¡ ºÒÀÏÄ¡ÇÏ´Â ³»¿ëÀÌ ÀÖÀ» °æ¿ì ¿µ¹®À» ¿ì¼±ÇÕ´Ï´Ù. Á¤È®ÇÑ °ËÅ並 À§ÇØ ¿µ¹® ¸ñÂ÷¸¦ Âü°íÇØÁֽñ⠹ٶø´Ï´Ù.

Stratistics MRC¿¡ µû¸£¸é ¼¼°èÀÇ ÀÚÀ²Çü ¶ó½ºÆ® ¸¶ÀÏ ¹è¼Û ½ÃÀåÀº 2025³â 259¾ï ´Þ·¯¸¦ Â÷ÁöÇϸç, ¿¹Ãø ±â°£ µ¿¾È CAGRÀº 26.5%¸¦ ³ªÅ¸³» 2032³â¿¡´Â 1,346¾ï ´Þ·¯¿¡ À̸¦ °ÍÀ¸·Î ¿¹»óµË´Ï´Ù.

ÀÚÀ²Çü ¶ó½ºÆ® ¸¶ÀÏ ¹è¼ÛÀº ÀÚµ¿ ¿îÀü Â÷·®, ¹«ÀÎ Ç×°ø±â ¹× ·Îº¿À» »ç¿ëÇÏ¿© ¹è¼Û Çãºê ¹× Áö¿ª ½Ã¼³¿¡¼­ °í°´ ÀÔ±¸±îÁö »ç¶÷ÀÇ ¼ÕÀ» ÅëÇÏÁö ¾Ê°í »óǰÀ» Á÷Á¢ ¿î¼ÛÇÏ´Â °ÍÀ» ÀǹÌÇÕ´Ï´Ù. ÀÌ ±â¼úÀº °í±Þ ³×ºñ°ÔÀÌ¼Ç ½Ã½ºÅÛ, ÀΰøÁö´É, ¼¾¼­ ¹× ¿¬°á ¼Ö·ç¼ÇÀ» Ȱ¿ëÇÏ¿© Àû½Ã¿¡ È¿À²ÀûÀÌ°í ºñÁ¢ÃË½Ä ¹è¼ÛÀ» Á¦°øÇÕ´Ï´Ù. ¿î¿ë ºñ¿ë Àý°¨, ¹è¼Û ½Ã°£ ÃÖ´ÜÈ­, ¿îÀüÀÚ ºÎÁ·, µµ½Ã Áö¿ª È¥Àâ µî °úÁ¦ ÇØ°áÀ» ¸ñÇ¥·Î Çϰí ÀÖ½À´Ï´Ù. ÀüÀÚ»ó°Å·¡, ½Äǰ ¹× ¹°·ù ºÐ¾ß¿¡¼­ ³Î¸® äÅÃµÈ ÀÚÀ²Çü ¶ó½ºÆ® ¸¶ÀÏ ¹è¼ÛÀº ¼ÒºñÀÚÀÇ ±â´ëÄ¡¸¦ ³ôÀ̱â À§ÇØ È®Àå °¡´ÉÇϰí Áö¼Ó°¡´ÉÇÏ¸ç °í°´ Áß½ÉÀÇ ¼Ö·ç¼ÇÀ» Á¦°øÇÔÀ¸·Î½á °ø±Þ¸ÁÀ» À籸¼ºÇϰí ÀÖ½À´Ï´Ù.

Áõ°¡ÇÏ´Â ÀüÀÚ»ó°Å·¡ ¼ºÀå

±ÞÁõÇÏ´Â ÀüÀÚ»ó°Å·¡ ¼ö¿ä´Â ÀÚÀ²Çü ¶ó½ºÆ® ¸¶ÀÏ ¹è¼ÛÀÇ ±Þ¼ÓÇÑ Çõ½ÅÀ» ÃËÁøÇÏ¿© È¿À²¼º, È®À强 ¹× ºñ¿ë Àý°¨À» ÃËÁøÇϰí ÀÖ½À´Ï´Ù. ¿Â¶óÀÎ ¼Ò¸Å°¡ µµ½Ã ¹× ¹Ýµµ½Ã Áö¿ªÀ¸·Î È®ÀåµÇ´Â µ¿¾È ÀÚÀ² ÁÖÇà Â÷·®°ú ¹«ÀÎ Ç×°ø±â´Â ´õ ºü¸£°í ºñÁ¢ÃË½Ä ¹è¼Û ¼Ö·ç¼ÇÀ» Á¦°øÇÏ¿© »ç¶÷ÀÇ ÀÇÁ¸¼º°ú ¿î¿µ»óÀÇ º´¸ñ Çö»óÀ» ÁÙÀÏ ¼ö ÀÖ½À´Ï´Ù. ÀÌ º¯È­´Â AI ÁÖµµ ¹°·ù, ½º¸¶Æ® ¶ó¿ìÆÃ ¹× ±ÔÁ¦ ÇÁ·¹ÀÓ¿öÅ©¿¡ ´ëÇÑ ÅõÀÚ¸¦ °¡¼ÓÈ­ÇÏ°í ¶ó½ºÆ® ¸¶ÀÏ ¹è¼Û¸¦ °í¼ºÀå ÇÁ·ÐƼ¾î·Î º¯¸ð½Ãŵ´Ï´Ù. ÀüÀÚ»ó°Å·¡¿Í ÀÚµ¿È­ÀÇ ½Ã³ÊÁö È¿°ú´Â ¼¼°è ¼ÒºñÀÚµéÀÇ ±â´ë¿Í ¹°·ù ÀÎÇÁ¶ó¸¦ À籸¼ºÇϰí ÀÖ½À´Ï´Ù.

±ÔÁ¦ º®

±ÔÁ¦ À庮Àº ÆÄÀÏ·µ ÇÁ·Î±×·¥À» Áö¿¬½ÃŰ°í ±¹°æÀ» ³Ñ¾î¼± ¿î¿µÀ» º¹ÀâÇÏ°Ô Çϸç ÄÄÇöóÀ̾𽺠ºñ¿ëÀ» Áõ°¡½ÃÄÑ ÀÚÀ²Çü ¶ó½ºÆ® ¸¶ÀÏ ¹è¼Û ½ÃÀåÀ» Å©°Ô ¹æÇØÇÕ´Ï´Ù. Áö¿ª¿¡ µû¶ó Á¤Ã¥ÀÌ ºÐ¸®µÇ¾î ºÒÈ®½Ç¼ºÀÌ »ý°Ü ÅõÀÚ¿Í Çõ½ÅÀÌ ÀúÇØµË´Ï´Ù. µ¥ÀÌÅÍ ÀÌ¿ë, Â÷·® ºÐ·ù, µµ½Ã Áö¿ªÀÇ Á¢±Ù ±¸¿ª¿¡ ´ëÇÑ Á¦ÇÑÀº È®À强À» ´õ¿í Á¦ÇÑÇÕ´Ï´Ù. ÀÌ·¯ÇÑ Àå¾Ö¹°Àº »ó¾÷È­¸¦ ´ÊÃß°í °æÀï ¿ìÀ§¸¦ Á¦ÇÑÇÏ°í »ýŰèÀÇ ¼ºÀåÀ» ¹æÇØÇϸç Àα¸ ¹ÐÁýÁö¿¡¼­ ¹°·ù È¿À²¼º°ú ¼ÒºñÀÚ ÆíÀÇ¿¡ Çõ¸íÀ» ÀÏÀ¸Å³ °¡´É¼ºÀ» ¹æÇØÇϰí ÀÖ½À´Ï´Ù.

±â¼ú Áøº¸

±â¼úÀÇ Áøº¸´Â È¿À²¼º, È®À强, ¼ÒºñÀÚ ¸¸Á·µµ¸¦ ³ô¿© ÀÚÀ²Çü ¶ó½ºÆ® ¸¶ÀÏ ¹è¼Û ½ÃÀå¿¡ Çõ¸íÀ» ÀÏÀ¸Å°°í ÀÖ½À´Ï´Ù. AI, ·Îº¿ ¿£Áö´Ï¾î¸µ, IoTÀÇ Çõ½ÅÀº Á¤È®ÇÑ ³×ºñ°ÔÀ̼Ç, ½Ç½Ã°£ ÃßÀû, ÀûÀÀÀûÀÎ °æ·Î ÃÖÀûÈ­¸¦ °¡´ÉÇÏ°Ô ÇÏ¿© ¹è¼Û ½Ã°£°ú ¿î¿µ ºñ¿ëÀ» Àý°¨ÇÕ´Ï´Ù. 5G¿Í ¿§Áö ÄÄÇ»ÆÃÀÇ ÅëÇÕÀº µ¥ÀÌÅÍ Ã³¸®¸¦ °¡¼ÓÈ­Çϰí Áö¼Ó °¡´ÉÇÑ Àü±â Â÷·®Àº ģȯ°æ ¹°·ù¸¦ Áö¿øÇÕ´Ï´Ù. ÀÌ·¯ÇÑ È¹±âÀûÀÎ ±â¼úÀº µµ½Ã¿Í ±³¿Ü¿¡¼­ÀÇ µµÀÔÀ» È®´ëÇÏ°í º¸´Ù ¶È¶ÈÇÏ°í ¾ÈÀüÇÏ¸ç ½Å¼ÓÇÑ ¼Ö·ç¼ÇÀ» ÅëÇØ ¼Ò¸Å, ½Äǰ ¹× ÀÇ·á ¹è¼ÛÀ» À籸¼ºÇϰí ÀÖ½À´Ï´Ù.

³ôÀº Ãʱâ ÅõÀÚ

°í¾×ÀÇ Ãʱâ ÅõÀÚ´Â Á߼ұԸ𠹰·ù ±â¾÷ÀÇ Ã¤¿ë ÀÇ¿åÀ» ±ð¾Æ ÀÚÀ²Çü ¶ó½ºÆ® ¸¶ÀÏ ¸¶ÀÏ ¹è¼Û ½ÃÀå ¼ºÀåÀ» Å©°Ô ÀúÇØÇÕ´Ï´Ù. ÷´Ü ·Îº¿ °øÇÐ, AI ÅëÇÕ, ±ÔÁ¦ Áؼö¿¡ °É¸®´Â ¸·´ëÇÑ ºñ¿ëÀÌ ÁøÀÔ À庮ÀÌ µÇ¾î È®À强°ú Çõ½ÅÀ» ´ÊÃß°í ÀÖ½À´Ï´Ù. ÀÌ °æÁ¦Àû ºÎ´ãÀº ROI¸¦ ´ÊÃß°í, ½ÃÇèÀû µµÀÔÀ» Á¦ÇÑÇϰí, ƯÈ÷ ½ÅÈï±¹ ½ÃÀå¿¡¼­ÀÇ Áö¸®Àû È®ÀåÀ» Á¦ÇÑÇϸç, °á±¹ Àü¹ÝÀûÀÎ ¹ë·ù üÀÎÀÇ ±¤¹üÀ§ÇÑ »ó¾÷È­¿Í »ýÅÂ°è °³Ã´À» Á¤Ã¼½Ãŵ´Ï´Ù.

COVID-19ÀÇ ¿µÇâ

COVID-19ÀÇ ´ëÀ¯ÇàÀ¸·Î ÀÎÇØ ºñÁ¢ÃË½Ä ¹°·ù°¡ ÇʼöÀûÀÌ µÇ¾ú°í ÀÚÀ²Çü ¶ó½ºÆ® ¸¶ÀÏ ¹è¼ÛÀÇ Ã¤¿ëÀÌ °¡¼ÓÈ­µÇ¾ú½À´Ï´Ù. ¶ô´Ù¿î°ú ÀüÀÚ»ó°Å·¡ ¼ö¿äÀÇ ±ÞÁõÀº ¹è¼Û¿ë µå·Ð°ú ·Îº¿ÀÇ ±â¼ú Çõ½ÅÀ» ÃßÁøÇÏ¿© ÀÎü¿¡ ´ëÇÑ ³ëÃâ À§ÇèÀ» ÁÙ¿´½À´Ï´Ù. Ãʱâ È¥¶õ¿¡µµ ºÒ±¸Çϰí ÅõÀÚ´Â Áõ°¡ÇÏ°í ±ÔÁ¦ ´ç±¹ÀÇ Áö¿øµµ ÀÖ¾ú°í ½ÃÀåÀº Àå±âÀûÀÎ ±¸Á¶ ÀüȯÀ» ÀÌ·ç¾ú½À´Ï´Ù. ÀÚÀ² ¼Ö·ç¼ÇÀº ½Ä·áǰ ¹× °Ç°­ °ü¸® ºÐ¾ß¿¡¼­ ÁöÁö¸¦ ¸ðÀ¸°í ¼ÒºñÀÚÀÇ ±â´ë¸¦ À籸¼ºÇϰí ź·ÂÀûÀÌ°í ¹Ì·¡¸¦ Áö¿øÇÏ´Â °ø±Þ¸Á¿¡¼­ÀÇ ¿ªÇÒÀ» È®°íÈ÷ Çß½À´Ï´Ù.

¿¹Ãø ±â°£ µ¿¾È ¼ÒÇÁÆ®¿þ¾î ºÎ¹®ÀÌ ÃÖ´ë°¡ µÉ Àü¸Á

¼ÒÇÁÆ®¿þ¾î ºÐ¾ß´Â È®À强°ú ½Ç½Ã°£ ÃÖÀûÈ­¸¦ ÅëÇØ ¿¹Ãø ±â°£ µ¿¾È ÃÖ´ë ½ÃÀå Á¡À¯À²À» Â÷ÁöÇÒ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù. °í±Þ ¶ó¿ìÆÃ ¾Ë°í¸®Áò, Çø´ °ü¸® Ç÷§Æû, AI¸¦ Ȱ¿ëÇÑ ÀÇ»ç°áÁ¤ ½Ã½ºÅÛÀº ¹è¼Û ¼Óµµ¸¦ ³ôÀÌ°í ¿î¿µ ºñ¿ëÀ» ÁÙÀÌ°í °í°´ ¸¸Á·µµ¸¦ Çâ»ó½Ãŵ´Ï´Ù. IoT ¹× Ŭ¶ó¿ìµå ÀÎÇÁ¶ó¿ÍÀÇ ÅëÇÕÀº ¿¹Áö º¸Àü°ú µµ½Ã ¹°·ù ÀüüÀÇ ¿øÈ°ÇÑ Á¶Á¤À» °¡´ÉÇÏ°Ô ÇÕ´Ï´Ù. ±ÔÁ¦ ÇÁ·¹ÀÓ¿öÅ©ÀÌ ÁøÈ­ÇÏ´Â µ¿¾È, ¼ÒÇÁÆ®¿þ¾î´Â ÄÄÇöóÀÌ¾ð½º¿Í ÀûÀÀ¼ºÀ» º¸ÀåÇϰí ÀÚÀ² ¹è¼ÛÀÇ Çõ½Å°ú °æÀï Â÷º°È­ÀÇ ¹éº»À¸·Î ÀÚ¸® ¸Å±èµË´Ï´Ù.

¿¹Ãø ±â°£ µ¿¾È ¼Ò¸Å¾÷ ºÎ¹®ÀÇ °¡Àå ³ôÀº CAGRÀ» ³ªÅ¸³¾ °ÍÀ¸·Î ¿¹»ó

¿¹Ãø ±â°£ µ¿¾È ¼Óµµ¿Í ÆíÀǼº¿¡ ´ëÇÑ ¼ÒºñÀÚÀÇ ±â´ë°¡ ³ô¾ÆÁü¿¡ µû¶ó ¼Ò¸Å ºÎ¹®ÀÌ °¡Àå ³ôÀº ¼ºÀå·üÀ» º¸ÀÏ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù. ºñ¿ë È¿À²ÀûÀÎ ºñÁ¢ÃË½Ä ¹è¼Û ¼Ö·ç¼Ç¿¡ ´ëÇÑ ¼ÒºñÀÚ ¼ö¿ä´Â ÀÚÀ² ÁÖÇà Â÷·® ¹× ½º¸¶Æ® ¶ó¿ìÆÃ ½Ã½ºÅÛÀÇ ±â¼ú Çõ½ÅÀ» ÃËÁøÇϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ±â¼úÀ» ¿È´Ïä³Î Àü·«¿¡ ÅëÇÕÇÔÀ¸·Î½á ¼Ò¸Å¾÷ü´Â µµ½Ã À̵¿¼ºÀ» À籸¼ºÇϰí ÀÌ»êȭź¼Ò ¹èÃâ·®À» ÁÙÀÌ°í °í°´ ¸¸Á·µµ¸¦ ³ôÀ̰í ÀÖ½À´Ï´Ù. ±× ±Ô¸ð¿Í ¿µÇâ·ÂÀº ÀÚÀ²ÀûÀÎ ¹è¼ÛÀ» ½ÃÇè ´Ü°è¿¡¼­ ÁÖ·ù ¹°·ù ÀÎÇÁ¶ó·Î º¯¸ð½ÃŰ´Â µ¥ ¸Å¿ì Áß¿äÇÕ´Ï´Ù.

ÃÖ´ë Á¡À¯À²À» Â÷ÁöÇÏ´Â Áö¿ª :

¿¹Ãø ±â°£ µ¿¾È ¾Æ½Ã¾ÆÅÂÆò¾çÀº ±Þ¼ÓÇÑ µµ½ÃÈ­, ÀüÀÚ»ó°Å·¡ È®´ë, º¸´Ù ½Å¼ÓÇÏ°í ºñ¿ë È¿À²ÀûÀÎ ¹è¼Û¿¡ ´ëÇÑ ¼ÒºñÀÚ ¼ö¿ä Áõ°¡·Î ÃÖ´ë ½ÃÀå Á¡À¯À²À» Â÷ÁöÇÒ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù. Áß±¹, ÀϺ», Àεµ µîÀÇ ±¹°¡µéÀº AI, ·Îº¿ °øÇÐ, ½º¸¶Æ® ¹°·ù¿¡ ¸¹Àº ÅõÀÚ¸¦ Çϰí ÀÖÀ¸¸ç, µµÀÔÀ» ÃËÁøÇϰí ÀÖ½À´Ï´Ù. ÀΰǺñ »ó½Â°ú ±³Åë üÁõÀº ÀÚÀ² ¼Ö·ç¼ÇÀ¸·ÎÀÇ ÀüȯÀ» ´õ¿í °¡¼ÓÈ­Çϰí ÀÖ½À´Ï´Ù. °Ô´Ù°¡ ½º¸¶Æ®½ÃƼ ±¸»ó°ú Áö¼Ó°¡´ÉÇÑ ¿î¼Û ½Ã½ºÅÛ¿¡ ´ëÇÑ Á¤ºÎÀÇ Áö¿øÀº ½ÃÀå È®´ë¸¦ °­È­Çϰí È¿À²¼º°ú °í°´ ¸¸Á·µµ¸¦ ³ôÀ̰í ÀÖ½À´Ï´Ù.

°¡Àå ³ôÀº CAGRÀ» ³ªÅ¸³»´Â Áö¿ª :

¿¹Ãø ±â°£ µ¿¾È ºÏ¹Ì´Â ¼Óµµ, Á¤È®¼º ¹× Áö¼Ó°¡´É¼ºÀ» ³ôÀ̱⠶§¹®¿¡ °¡Àå ³ôÀº CAGRÀ» ³ªÅ¸³¾ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù. ÀüÀÚ»ó°Å·¡ ¼ºÀå°ú ³ëµ¿·Â ºÎÁ·¿¡ ÈûÀÔ¾î ¹«ÀÎ Â÷·®°ú ¹«ÀÎ Ç×°ø±â´Â °í°´ ¸¸Á·µµ¸¦ ³ôÀ̸鼭 ¹è¼Û ½Ã°£°ú ¿î¿µ ºñ¿ëÀ» Àý°¨ÇÕ´Ï´Ù. ÀÌ·¯ÇÑ Çõ½ÅÀº Àüµ¿ Â÷·®ÀÇ ¹èÃâ °¨¼Ò¸¦ Áö¿øÇϰí AI¸¦ Ȱ¿ëÇÑ ·çÆ® °èȹÀ» ÅëÇØ µµ½Ã À̵¿¼ºÀ» ÃÖÀûÈ­ÇÕ´Ï´Ù. µµÀÔÀÌ °¡¼ÓµÊ¿¡ µû¶ó ½ÃÀåÀº ·Îº¿ °øÇаú µ¥ÀÌÅÍ ºÐ¼®¿¡¼­ °í¿ë âÃâÀ» ÃËÁøÇÏ°í ºÏ¹Ì¸¦ ½º¸¶Æ®Çϰí È¿À²ÀûÀÎ ¹°·ùÀÇ ¼¼°è ¸®´õ·Î ÀÚ¸® ¸Å±èÇϰí ÀÖ½À´Ï´Ù.

¹«·á ¸ÂÃã ¼­ºñ½º

ÀÌ º¸°í¼­¸¦ ±¸µ¶ÇÏ´Â °í°´Àº ´ÙÀ½ ¹«·á ¸ÂÃã¼³Á¤ ¿É¼Ç Áß Çϳª¸¦ »ç¿ëÇÒ ¼ö ÀÖ½À´Ï´Ù.

  • ±â¾÷ ÇÁ·ÎÆÄÀÏ
    • Ãß°¡ ½ÃÀå ±â¾÷ÀÇ Á¾ÇÕÀû ÇÁ·ÎÆÄÀϸµ(3°³»ç±îÁö)
    • ÁÖ¿ä ±â¾÷ÀÇ SWOT ºÐ¼®(3°³»ç±îÁö)
  • Áö¿ª ¼¼ºÐÈ­
    • °í°´ÀÇ °ü½É¿¡ ÀÀÇÑ ÁÖ¿ä±¹ ½ÃÀå Ã߰衤¿¹Ãø¡¤CAGR(ÁÖ : Ÿ´ç¼º È®Àο¡ µû¸§)
  • °æÀï º¥Ä¡¸¶Å·
    • Á¦Ç° Æ÷Æ®Æú¸®¿À, Áö¸®Àû Á¸Àç, Àü·«Àû Á¦ÈÞ¿¡ ±â¹ÝÇÑ ÁÖ¿ä ±â¾÷ º¥Ä¡¸¶Å·

¸ñÂ÷

Á¦1Àå ÁÖ¿ä ¿ä¾à

Á¦2Àå ¼­¹®

  • °³¿ä
  • ÀÌÇØ°ü°èÀÚ
  • Á¶»ç ¹üÀ§
  • Á¶»ç ¹æ¹ý
    • µ¥ÀÌÅÍ ¸¶ÀÌ´×
    • µ¥ÀÌÅÍ ºÐ¼®
    • µ¥ÀÌÅÍ °ËÁõ
    • Á¶»ç Á¢±Ù
  • Á¶»ç ÀÚ·á
    • 1Â÷ Á¶»ç ÀÚ·á
    • 2Â÷ Á¶»ç Á¤º¸¿ø
    • ÀüÁ¦Á¶°Ç

Á¦3Àå ½ÃÀå µ¿Ç⠺м®

  • ¼ºÀå ÃËÁø¿äÀÎ
  • ¼ºÀå ¾ïÁ¦¿äÀÎ
  • ±âȸ
  • À§Çù
  • ¿ëµµ ºÐ¼®
  • ÃÖÁ¾ »ç¿ëÀÚ ºÐ¼®
  • ½ÅÈï ½ÃÀå
  • COVID-19ÀÇ ¿µÇâ

Á¦4Àå Porter's Five Forces ºÐ¼®

  • °ø±Þ±â¾÷ÀÇ Çù»ó·Â
  • ±¸¸ÅÀÚÀÇ Çù»ó·Â
  • ´ëüǰÀÇ À§Çù
  • ½Å±Ô Âü°¡¾÷üÀÇ À§Çù
  • °æÀï ±â¾÷ °£ °æÀï °ü°è

Á¦5Àå ¼¼°èÀÇ ÀÚÀ²Çü ¶ó½ºÆ® ¸¶ÀÏ ¹è¼Û ½ÃÀå : Ç÷§Æûº°

  • °øÁß ¹è¼Û µå·Ð
  • Áö»ó ¹è¼Û Â÷·®
  • ÀÚÀ²ÁÖÇà Æ®·° ¹× ¹ê

Á¦6Àå ¼¼°èÀÇ ÀÚÀ²Çü ¶ó½ºÆ® ¸¶ÀÏ ¹è¼Û ½ÃÀå : ¼Ö·ç¼Çº°

  • ¼­ºñ½º
  • ¼ÒÇÁÆ®¿þ¾î
  • Çϵå¿þ¾î

Á¦7Àå ¼¼°èÀÇ ÀÚÀ²Çü ¶ó½ºÆ® ¸¶ÀÏ ¹è¼Û ½ÃÀå : ¹üÀ§º°

  • ´Ü°Å¸®(<20 km)
  • Àå°Å¸®(>20 km)

Á¦8Àå ¼¼°èÀÇ ÀÚÀ²Çü ¶ó½ºÆ® ¸¶ÀÏ ¹è¼Û ½ÃÀå : ¿ëµµº°

  • ½ÄÀ½·áÀÇ ¹è¼Û
  • ¼Ò¸Å ¹× EÄ¿¸Ó½º ¹è¼Û
  • ÇコÄÉ¾î ¹× Á¦¾à ¹è¼Û
  • ¿ìÆí ¹× ¼ÒÆ÷ ¹è¼Û
  • ±âŸ

Á¦9Àå ¼¼°èÀÇ ÀÚÀ²Çü ¶ó½ºÆ® ¸¶ÀÏ ¹è¼Û ½ÃÀå : ÃÖÁ¾ »ç¿ëÀÚº°

  • ¼Ò¸Åü
  • ÀüÀÚ»ó°Å·¡ ±â¾÷
  • ¹°·ùȸ»ç
  • ÀÇ·á Á¦°øÀÚ
  • ±âŸ ÃÖÁ¾ »ç¿ëÀÚ

Á¦10Àå ¼¼°èÀÇ ÀÚÀ²Çü ¶ó½ºÆ® ¸¶ÀÏ ¹è¼Û ½ÃÀå : Áö¿ªº°

  • ºÏ¹Ì
    • ¹Ì±¹
    • ij³ª´Ù
    • ¸ß½ÃÄÚ
  • À¯·´
    • µ¶ÀÏ
    • ¿µ±¹
    • ÀÌÅ»¸®¾Æ
    • ÇÁ¶û½º
    • ½ºÆäÀÎ
    • ±âŸ À¯·´
  • ¾Æ½Ã¾ÆÅÂÆò¾ç
    • ÀϺ»
    • Áß±¹
    • Àεµ
    • È£ÁÖ
    • ´ºÁú·£µå
    • Çѱ¹
    • ±âŸ ¾Æ½Ã¾ÆÅÂÆò¾ç
  • ³²¹Ì
    • ¾Æ¸£ÇîÆ¼³ª
    • ºê¶óÁú
    • Ä¥·¹
    • ±âŸ ³²¹Ì
  • Áßµ¿ ¹× ¾ÆÇÁ¸®Ä«
    • »ç¿ìµð¾Æ¶óºñ¾Æ
    • ¾Æ¶ø¿¡¹Ì¸®Æ®(UAE)
    • īŸ¸£
    • ³²¾ÆÇÁ¸®Ä«
    • ±âŸ Áßµ¿ ¹× ¾ÆÇÁ¸®Ä«

Á¦11Àå ÁÖ¿ä ¹ßÀü

  • °è¾à, ÆÄÆ®³Ê½Ê, Çù¾÷, ÇÕÀÛÅõÀÚ
  • Àμö¿Í ÇÕº´
  • ½ÅÁ¦Ç° ¹ß¸Å
  • »ç¾÷ È®´ë
  • ±âŸ ÁÖ¿ä Àü·«

Á¦12Àå ±â¾÷ ÇÁ·ÎÆÄÀϸµ

  • Starship Technologies
  • Nuro
  • Amazon Robotics
  • JD.com
  • Alibaba Group
  • FedEx
  • UPS Flight Forward
  • Wing
  • Zipline
  • Kiwibot
  • Boxbot
  • Postmates
  • Robby Technologies
  • Marble
  • Refraction AI
  • Eliport
  • Neolix
  • Udelv
  • Gatik AI
  • Caterpillar Inc.
KTH 25.09.10

According to Stratistics MRC, the Global Autonomous Last-Mile Delivery Market is accounted for $25.9 billion in 2025 and is expected to reach $134.6 billion by 2032 growing at a CAGR of 26.5% during the forecast period. Autonomous Last-Mile Delivery refers to the use of self-driving vehicles, drones, or robots to transport goods from a distribution hub or local facility directly to the customer's doorstep without human intervention. This technology leverages advanced navigation systems, artificial intelligence, sensors, and connectivity solutions to ensure timely, efficient, and contactless deliveries. It aims to reduce operational costs, minimize delivery times, and address challenges such as driver shortages and urban congestion. Widely adopted in e-commerce, food, and logistics sectors, autonomous last-mile delivery is reshaping supply chains by offering scalable, sustainable, and customer-centric solutions to meet rising consumer expectations.

Market Dynamics:

Driver:

Rising E-commerce Growth

Surging e-commerce demand is catalyzing rapid innovation in autonomous last-mile delivery, driving efficiency, scalability, and cost reduction. As online retail expands across urban and semi-urban zones, autonomous vehicles and drones offer faster, contactless delivery solutions, reducing human dependency and operational bottlenecks. This shift is accelerating investment in AI-driven logistics, smart routing, and regulatory frameworks, transforming last-mile delivery into a high-growth frontier. The synergy between e-commerce and automation is reshaping consumer expectations and logistics infrastructure worldwide.

Restraint:

Regulatory Barriers

Regulatory barriers significantly hinder the autonomous last-mile delivery market by delaying pilot programs, complicating cross-border operations, and increasing compliance costs. Fragmented policies across regions create uncertainty, discouraging investment and innovation. Restrictions on data usage, vehicle classification, and urban access zones further constrain scalability. These hurdles slow commercialization, limit competitive advantage, and stifle ecosystem growth, impeding the sector's potential to revolutionize logistics efficiency and consumer convenience in densely populated areas.

Opportunity:

Technological Advancements

Technological advancements are revolutionizing the autonomous last-mile delivery market by enhancing efficiency, scalability, and consumer satisfaction. Innovations in AI, robotics, and IoT enable precise navigation, real-time tracking, and adaptive route optimization, reducing delivery times and operational costs. Integration of 5G and edge computing accelerates data processing, while sustainable electric fleets support eco-friendly logistics. These breakthroughs are driving widespread adoption across urban and suburban landscapes, reshaping retail, food, and healthcare delivery with smarter, safer, and more responsive solutions.

Threat:

High Initial Investment

High initial investment significantly hampers the growth of the autonomous last-mile delivery market by deterring small and mid-sized logistics firms from adoption. The steep costs of advanced robotics, AI integration, and regulatory compliance create barriers to entry, slowing scalability and innovation. This financial burden delays ROI, limits pilot deployments, and restricts geographic expansion, especially in emerging markets-ultimately stalling broader commercialization and ecosystem development across the delivery value chain.

Covid-19 Impact

The COVID-19 pandemic accelerated the adoption of autonomous last-mile delivery as contactless logistics became essential. Lockdowns and surging e-commerce demand drove innovation in delivery drones and robots, reducing human exposure risks. Despite initial disruptions, the market saw long-term structural shifts, with increased investment and regulatory support. Autonomous solutions gained traction in grocery and healthcare segments, reshaping consumer expectations and solidifying their role in resilient, future-ready supply chains

The software segment is expected to be the largest during the forecast period

The software segment is expected to account for the largest market share during the forecast period, due to scalability, and real-time optimization. Advanced routing algorithms, fleet management platforms, and AI-powered decision systems enhance delivery speed, reduce operational costs, and improve customer satisfaction. Integration with IoT and cloud infrastructure enables predictive maintenance and seamless coordination across urban logistics. As regulatory frameworks evolve, software ensures compliance and adaptability, positioning it as the backbone of autonomous delivery innovation and competitive differentiation.

The retailers segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the retailers segment is predicted to witness the highest growth rate, due to rising consumer expectations for speed and convenience. Their demand for cost-efficient, contactless delivery solutions is driving innovation in autonomous fleets and smart routing systems. By integrating these technologies into omnichannel strategies, retailers are reshaping urban mobility, reducing carbon footprints, and enhancing customer satisfaction. Their scale and influence are pivotal in transforming autonomous delivery from pilot phase to mainstream logistics infrastructure.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share due to rapid urbanization, expanding e-commerce, and increasing consumer demand for faster and cost-effective deliveries. Countries like China, Japan, and India are investing heavily in AI, robotics, and smart logistics, fostering adoption. Rising labor costs and traffic congestion are further accelerating the shift toward autonomous solutions. Additionally, government support for smart city initiatives and sustainable transport systems strengthens the market's expansion, enhancing efficiency and customer satisfaction.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, as it enhances speed, precision, and sustainability. Driven by e-commerce growth and labor shortages, unmanned vehicles and drones reduce delivery times and operational costs while improving customer satisfaction. These innovations also support emissions reduction through electric fleets and optimize urban mobility using AI-powered route planning. As adoption accelerates, the market fosters job creation in robotics and data analytics, positioning North America as a global leader in smart, efficient logistics.

Key players in the market

Some of the key players profiled in the Autonomous Last-Mile Delivery Market include Starship Technologies, Nuro, Amazon Robotics, JD.com, Alibaba Group, FedEx, UPS Flight Forward, Wing, Zipline, Kiwibot, Boxbot, Postmates, Robby Technologies, Marble, Refraction AI, Eliport, Neolix, Udelv, Gatik AI and Caterpillar Inc.

Key Developments:

In August 2025, Caterpillar has forged a long-term strategic alliance with Hunt Energy to power "always-on" data centers. Beginning in Texas, the multi-year plan aims to roll out up to 1 GW of reliable, independent energy across North America.

In May 2025, SAP and Alibaba Group have forged a strategic pact to accelerate cloud transformation. Alibaba will adopt SAP Cloud ERP Private and a full SAP suite-plus AI tools like Qwen-boosting agility, resilience, and digital scale across Asia.

In March 2024, FedEx and Amazon quietly explored a potential tie-up to handle e-commerce returns via FedEx's retail locations. Though talks fell through, the move signaled shifting tides in parcel delivery as both sought fresh paths amid evolving logistics landscapes.

Platforms Covered:

  • Aerial Delivery Drones
  • Ground Delivery Vehicles
  • Self-Driving Trucks & Vans

Solutions Covered:

  • Services
  • Software
  • Hardware

Ranges Covered:

  • Short Range (<20 km)
  • Long Range (>20 km)

Applications Covered:

  • Food & Beverages Delivery
  • Retail & E-commerce Delivery
  • Healthcare & Pharmaceutical Delivery
  • Postal & Parcel Delivery
  • Other Applications

End Users Covered:

  • Retailers
  • E-commerce Companies
  • Logistics Companies
  • Healthcare Providers
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Autonomous Last-Mile Delivery Market, By Platform

  • 5.1 Introduction
  • 5.2 Aerial Delivery Drones
  • 5.3 Ground Delivery Vehicles
  • 5.4 Self-Driving Trucks & Vans

6 Global Autonomous Last-Mile Delivery Market, By Solution

  • 6.1 Introduction
  • 6.2 Services
  • 6.3 Software
  • 6.4 Hardware

7 Global Autonomous Last-Mile Delivery Market, By Range

  • 7.1 Introduction
  • 7.2 Short Range (<20 km)
  • 7.3 Long Range (>20 km)

8 Global Autonomous Last-Mile Delivery Market, By Application

  • 8.1 Introduction
  • 8.2 Food & Beverages Delivery
  • 8.3 Retail & E-commerce Delivery
  • 8.4 Healthcare & Pharmaceutical Delivery
  • 8.5 Postal & Parcel Delivery
  • 8.6 Other Applications

9 Global Autonomous Last-Mile Delivery Market, By End User

  • 9.1 Introduction
  • 9.2 Retailers
  • 9.3 E-commerce Companies
  • 9.4 Logistics Companies
  • 9.5 Healthcare Providers
  • 9.6 Other End Users

10 Global Autonomous Last-Mile Delivery Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Starship Technologies
  • 12.2 Nuro
  • 12.3 Amazon Robotics
  • 12.4 JD.com
  • 12.5 Alibaba Group
  • 12.6 FedEx
  • 12.7 UPS Flight Forward
  • 12.8 Wing
  • 12.9 Zipline
  • 12.10 Kiwibot
  • 12.11 Boxbot
  • 12.12 Postmates
  • 12.13 Robby Technologies
  • 12.14 Marble
  • 12.15 Refraction AI
  • 12.16 Eliport
  • 12.17 Neolix
  • 12.18 Udelv
  • 12.19 Gatik AI
  • 12.20 Caterpillar Inc.
»ùÇà ¿äû ¸ñ·Ï
0 °ÇÀÇ »óǰÀ» ¼±Åà Áß
¸ñ·Ï º¸±â
Àüü»èÁ¦