Susie Jones
Hírek és frissítések • 3 min olvasni

A mesterséges intelligencia és annak hatása a fuvarozási ágazat fenntarthatóságára

Létrehozva: 29. 08. 2024

Frissítve: 29. 08. 2024

A mesterséges intelligencia (AI) kifejezés az elmúlt években a modern szókincs részévé vált. A megértés hiánya és a tévhitek miatt gyakran negatívan ítélik meg. A fenntartások ellenére a mesterséges intelligencia pozitívan befolyásolhatja a fenntarthatóságot, a munkafolyamatokat és a gazdaságot. A közlekedési ágazat által termelt CO2-kibocsátás több mint 25% esetében a mesterséges intelligencia pozitív változást hozhat a fenntartható jövő érdekében?

Mi az AI?

Az Oxford Languages szerint a mesterséges intelligencia:

"olyan számítógépes rendszerek elmélete és fejlesztése, amelyek képesek olyan feladatok elvégzésére, amelyek általában emberi intelligenciát igényelnek, mint például a vizuális érzékelés, beszédfelismerés, döntéshozatal és nyelvek közötti fordítás. "

Fenntarthatóság a fuvarozási ágazatban

Az iparág kritikus válaszúthoz érkezett a megnövekedett igények és a változó szállítási típusok miatt - az online vásárlás az egyik leggyorsabban növekvő igény. Miközben a megnövekedett kereslet előnyös lehet a szállítmányozási ágazat számára, a bolygóra nézve létfontosságú környezeti dilemmával kell szembenéznie.

Az iparág a következő módon hat a környezetre:

  • Zajszennyezés: A teherautók nagy számban hozzájárulhatnak a zajszennyezéshez. A zajszennyezés megzavarja a városi és vidéki területeket, és gyakran kihat a lakosok és az élővilág jólétére. A magas zajszintnek való kitettség stresszhez és szív- és érrendszeri problémákhoz vezethet.

  • Szén-dioxid-kibocsátás: A teherautók hagyományos üzemanyagai CO2-t bocsátanak ki a légkörbe. A teljes globális közlekedési kibocsátás 29%-a a teherautózásból származik.

  • Levegőminőség: Mindkettő rontja a levegő minőségét, károsítja a környezetet, és az embereknél légzési problémákhoz vezet.

Hogyan javíthatja az AI a fenntarthatóságot

  • Útvonaltervezés: A nem megfelelő útvonaltervezés pénzbe, időbe és erőforrásokba kerülhet a flottáknak. A mesterséges intelligencia képes pontosan megjósolni az igényeket és a követelményeket - a legfenntarthatóbb útvonalat ajánlva. Algoritmusai képesek elemezni a forgalmi mintákat, az időjárási körülményeket és az üzemanyag-hatékonyságot a szállítási útvonalak optimalizálása érdekében. Ez a folyamat pénzt takarít meg, és hozzájárul a szén-dioxid-kibocsátás csökkentéséhez.

  • A fogyasztói kereslet előrejelzése: A termékeket gyakran elszállítják a fogyasztónak, de nem igénylik, ami erőforrás-pazarláshoz és jelentős környezeti hatáshoz vezet. Az AI gépi tanulás és a prediktív elemzés segíteni fogja a gyártókat a kereslet előrejelzésében, a kínálat racionalizálásában és a gyártási folyamatok optimalizálásában. Az áruk kézbesítésével és át nem vételezésével kapcsolatos érdemi meglátások révén a mesterséges intelligencia tájékoztathatja a logisztikai vállalatokat arról, hogy milyen árukat kell szállítaniuk - így a környezeti problémákat már a kezdetektől fogva kezelhetik.

  • Vezetőfelügyelet: A mesterséges intelligencia algoritmusok elemzik a járművezetők viselkedését, például a gyorshajtást, a durva fékezést és az üresjáratot. Ennek alapján a mesterséges intelligencia útmutatást és ösztönzést nyújthat az üzemanyag-takarékosabb vezetéshez.

  • Energiacsökkentés: A mesterséges intelligencia segíthet a flottatársaságoknak az ellátási láncok energiagazdálkodásában. Képes azonosítani a nem hatékony területeket, és optimalizálási stratégiákat kínálni - beleértve az intelligens mérők, érzékelők és egyéb eszközök adatainak elemzését annak meghatározására, hogy mi használ túl sok energiát.

Átveszi az AI a logisztikai iparágat?

Bár a mesterséges intelligencia alkalmazása szerepet fog játszani a logisztikai iparban, nem valószínű, hogy teljesen átveszi az uralmat. Az AI-algoritmusok nem tudnak számolni a spontán eseményekkel és kivételekkel - ezért az emberi részvétel továbbra is döntő szerepet fog játszani. A fuvarozási ágazatban továbbra is lesznek karrierlehetőségek, de jelentősen másképp fognak kinézni.

Hogyan csökkentheti még az iparág a szén-dioxid-kibocsátását?

Az iparág a következőket teheti meg:

  • Alacsony kibocsátású járművek: Az elektromos és hibrid tehergépjárművek csökkenthetik a szén-dioxid-kibocsátást. Mindkettő alkalmas rövid és hosszú távú szállításra.

  • Alternatív üzemanyagok: A 2050-es nettó nullára való csökkentés közeledtével az energiaszabályozás a tisztább alternatívák felé tolódik. A hidrogénezett növényi olaj (HVO) azonnal és jelentősen csökkentheti a kibocsátást. A [Certas Energy HVO] (https://certasenergy.co.uk/my-business/products/hvo/hvo-faq-fuel-guide/) áll a tisztább alternatívára való áttérés mögött - segítve a vállalkozásokat fenntarthatósági céljaik elérésében és a nettó zéró jövőjük felé tett érdemi lépések megtételében. A HVO a következő előnyöket biztosítja:

  • Az üvegházhatást okozó gázolajkibocsátás azonnali, akár 90%-os csökkenése a hagyományos dízelhez képest a termék teljes életciklusa során.

  • Kevesebb nitrogén-oxid, mint a hagyományos dízel

  • Kevesebb részecske, mint a hagyományos dízelüzeműeké

  • Biológiailag könnyen lebomló

  • Hosszú, akár 10 évig tartó eltarthatóság

  • Gyakorlatilag FAME-mentes

  • Alternatív dízel üzemanyag - nincs szükség motor vagy infrastruktúra módosítására.

  • Járműhatékonyság: Az üzemanyag-hatékony motorok, a csökkentett üresjárati idő és a rendszeres karbantartás javíthatja a hatékonyságot.

  • Intelligens csomagolás: Ez csökkenti a hulladék mennyiségét és a költségeket. Emellett a könnyebb csomagolás javítja a hatékonyságot, ami alacsonyabb szén-dioxid-kibocsátást eredményez.

  • Megújuló energia: A megújuló energiaforrások, például a nap- vagy szélenergia használata a működésben jelentősen csökkentheti a szén-dioxid-kibocsátást.

  • Csökkentse a kitérő kilométereket: A SNAP Account lehetővé teszi a flottakezelők számára, hogy csökkentsék a kitérő kilométereket - a SNAP Account ügyfelei számára elérhető több mint 600 szolgáltató partnerrel biztosan lesz egy megálló az útvonalán.

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