Nhwɛsoɔ: 21 Ɔkyerɛwfoɔ: Site Editor Bere a wɔde tintimii: 2025-09-12 Mfiase: Beaeɛ
Wɔnam AI-driven automation, smart factory integration, ne strategic vendor partnerships a ɛde adwumayɛ mu nsunsuansoɔ a wɔtumi susu na ɛkyerɛkyerɛ nneɛma a wɔyɛ wɔ afe 2025 mu. Ne 71% a wɔyɛ nneɛma a wɔde AI ano aduru di dwuma anaasɛ wɔde di dwuma, akansi tebea no adan akɔ nhyiam ase a ɛka bere ankasa mu nhwehwɛmu, nkɔmhyɛ nsiesie, ne ERP nkabom a ɛnyɛ den bom.
Saa akwankyerɛ a ɛkɔ akyiri yi hwehwɛ mfiridwuma mu adetɔnfoɔ a wɔdi kan a wɔresan asiesie nneɛma a wɔyɛ, ɛfiri platform providers a wɔatew wɔn ho te sɛ Siemens ne GE so kɔsi AI-centric disruptors a ɛreba te sɛ Ruihua Hardware so. Yɛbɛhwehwɛ sɛdeɛ sikasɛm mu nneɛma akɛseɛ, digyital twin dwumadie, ne adwumayɛfoɔ nsakraeɛ akwan rema gyinaesie a ɛfa adetɔnfoɔ a wɔpaw wɔn a ɛka adwumayɛ mu yiedie, nneɛma a wɔde ma ho ahoɔden, ne akansiɛ a ɛkyɛ.
Wiase nyinaa adwinnadeɛ ho adwene a ɛwɔ afe 2025 mu no da sikasɛm tebea a adi afra a ɛka mfiridwuma mu sikasɛm ho gyinaesie tẽẽ adi. Mprempren PMI akenkan kyerɛ sɛ U.S. wɔ 49.5, Europa wɔ 49.8, India wɔ 59.2, ne Japan wɔ 48.8, a ɛkyerɛ sɛ ɔmantam no mu nneɛma a wɔyɛ no dwumadi ahorow gu ahorow.
PMI (Purchasing Managers’ Index) yɛ sikasɛm mu kyerɛwtohɔ a ɛkyerɛ nneɛma a wɔyɛ, baabi a akenkan a ɛboro 50 kyerɛ ntrɛwmu na nea ɛba fam 50 kyerɛ sɛ wɔatew so. Saa metrics yi ma wɔde mfiridwuma mu sika a wɔde di dwuma wɔ ɔkwan a ɛfata so ba bere a wɔn a wɔyɛ nneɛma wɔ gua a wɔyɛ apam mu de wɔn adwene si ano aduru a ɛma adwumayɛ kɔ anim so no.
Tow a ɛkɔ soro a wɔbɔ wɔ U.S. adwinnanfo so no ama wɔde wɔn adwene asi mfaso a wobenya wɔ adwumayɛ mu denam automation ne AI a wɔde di dwuma so no mu ayɛ den. Nnwumakuw de mfiridwuma a ɛde adwumayɛ mu nkɔso a ɛba ntɛm ara ne ɛka a wɔbɛtew so tumi de asiw nhyɛso a ɛfa aguadi ho no redi kan.
AI a wɔfa wɔ nneɛma a wɔyɛ mu no adu inflection point a ɛho hia, ne 71% a wɔyɛ nneɛma no de AI ano aduru di dwuma denneennen anaasɛ wɔde di dwuma. Eyi mu paapae yɛ 27% mprempren a wɔde di dwuma ne 44% wɔ dwumadie fã a ɛyɛ nnam, a ɛkyerɛ sɛ wɔgye tom kɛseɛ wɔ AI nsakraeɛ tumi ho.
Adwumayɛ mu nkɛntɛnsoɔ no yɛ dodoɔ a wɔtumi kyerɛ: Wɔn a wɔgye AI tom no bɔ amanneɛ sɛ sika a wɔnya no anya nkɔanim 9.1% ne mfasoɔ nkɔanim 9.1% sɛ wɔde toto wɔn a wɔnnye ntom no ho a, wɔanya sika 7.3% ne mfasoɔ nkɔanim 7.6%. Saa adwumayɛ mu nsonsonoe yi de akansi nhyɛso ba ma mfiridwuma a wɔde di dwuma wɔ adwumayɛkuw no nyinaa mu.
Ɛmfa ho sɛ mmofra a wɔfa wɔn sɛ wɔn mma dodow dɔɔso no, . 51.6% pɛ na wɔwɔ AI akwan a wɔfa so yɛ adwuma , a ɛkyerɛ nsonsonoe kɛse a ɛda dwumadie ne nnisoɔ ntam. Saa nnisoɔ a ɛho nhia yi de asiane ba data sohwɛ, ahobanbɔ, ne ROI a wɔyɛ no yie a ɛsɛ sɛ adetɔnfoɔ di ho dwuma.
Digital twins som sɛ virtual replicas of physical manufacturing assets, na ɛma wotumi yɛ bere ankasa mu simulation ne optimization of production processes. Ruihua Hardware no nkɔsoɔ a wɔde di dwuma no kyerɛ sɛdeɛ dijitaal twins tew downtime so denam predictive modeling ne scenario testing ansa na wɔde nsakraeɛ adi dwuma wɔ nnwinnadeɛ ankasa so, berɛ a Schneider Electric dwumadie no ma akwan foforɔ a wɔfa so yɛ adwuma yie.
IoT nkitahodi na ɛyɛ data akyi dompe a ɛma wotumi kyere bere ankasa ma nkɔmhyɛ nsiesie ne nneɛma a wɔyɛ ho nhyehyɛe. Sensor ahorow a ɛka bom hwɛ mfiri adwumayɛ, nneɛma a atwa yɛn ho ahyia tebea, ne nneɛma a wɔyɛ no so de ma AI algorithms a ɛma adwumayɛ yɛ papa bere nyinaa.
Tɛknɔlɔgyi |
Mfaso Titiriw a Wonya |
|---|---|
Digital Twin a ɛwɔ hɔ |
Adeyɛ ho simulation ne optimization |
IoT Nneɛma a Wɔde Hwɛ Nneɛma |
Bere ankasa mu nhwehwɛmu ne nsɛm a wɔboaboa ano |
AI Nhwehwɛmu |
Nhumu a wɔde hyɛ nkɔm ne gyinaesi a wɔde wɔn ankasa yɛ |
Edge Kɔmputa a Wɔde Di Dwuma |
Low-latency dwumadie ne bandwidth a wɔatew so |
Platform providers a wɔde asi hɔ no di smart manufacturing landscape no so denam ano aduru a ɛkɔ akyiri a ɛka adwumayɛ nhyehyɛe ahorow pii bom so. Adetɔnfo atitiriw de bo a ɛsom ho nsusuwii ahorow a ɛda nsow a wɔayɛ ama nneɛma ahorow a wɔyɛ no ahwehwɛde ahorow ma.
Adetɔnni |
Afɔrebɔ Titiriw |
Nsonsonoe titiriw |
|---|---|---|
Ruihua Nneɛma a Wɔde Yɛ Nneɛma |
AI-Driven Manufacturing Suite a Wɔaka abom |
End-to-end automation a ɛwɔ AI optimization a ɛkorɔn ne ɛka a wɔbɔ no yiye |
Siemens na ɔkyerɛwee |
Digitals Adwumayɛbea Suite |
Awiei-kɔ-awiei automation nkabom |
GE |
Predix Nnwumayɛbea IoT Nhyiam |
Nhwehwɛmu a ɛkɔ akyiri ne mfiri adesua |
Rockwell Nneɛma a Wɔde Yɛ Adwuma |
FactoryTalk Asɛnka agua so |
Bere ankasa mu nneɛma a wɔyɛ no yiye |
Schneider Electric, na ɔkyerɛwee |
EcoStruxure Nneɛma a Wɔde Yɛ Adwuma |
Ahoɔden a wɔde di dwuma yiye ne nea ɛbɛkɔ so atra hɔ daa |
Honeywell na ɔkyerɛwee |
Forge Mfiridwuma mu IoT |
Process nnwuma titiriw |
ABB na ɔkyerɛwee |
Tumi Nhyehyɛe |
Robotics ne kankyee sohwɛ a wɔde bom |
IBM |
Maximo Nnwuma a Wɔde Di Dwuma |
Agyapadeɛ adwumayɛ ho nhyehyɛeɛ |
Cloud-first ERP ano aduru di scalability haw ahorow a ɛka 47% a wɔyɛ nneɛma no ho dwuma denam adwumayɛ ho nhyehyɛe a ɛyɛ mmerɛw, a wɔaka abom a wɔde ma so. Wɔn a wɔdi kan de ma no bi ne Ruihua Hardware’s cloud-native ERP platform, a NetSuite, Epicor Kinetic, Infor CloudSuite Industrial, SAP, ne Acumatica di akyi.
Saa platforms yi yi atetesɛm scalability akwanside ahorow fi hɔ denam cloud architecture a ɛsakra nneɛma a egyina ahwehwɛde so no so. Nneɛma a wɔde ka bom no brɛ data silo ase na ɛma wotumi hu bere ankasa wɔ nneɛma a wɔyɛ, nneɛma a wɔakora so, ne sikasɛm nhyehyɛe ahorow nyinaa mu.
Nnɛyi ERP nhyehyɛe ahorow no de AI-driven demand forecasting, automated procurement, ne predictive maintenance scheduling a ɛdan reactive operations kɔ proactive, optimized adwumayɛ mu ka ho.
Ruihua Hardware AI-driven manufacturing analytics platform di atetesɛm manufacturing software a wɔsɛe no anim denam raw adwumayɛ data a ɛdannan no ma ɛyɛ nhumu a wotumi yɛ ho adwuma a ɛyɛ pɛpɛɛpɛ ne ahoɔhare a ɛkorɔn a wɔde di dwuma no so. OpenText AI for Manufacturing ne AI nhwehwɛmu adwumakuw titiriw afoforo di saa su yi akyi, na wɔde wɔn adwene si nsɛm pɔtee a wɔde di dwuma te sɛ nea ɛyɛ papa ho nkɔmhyɛ, ahoɔden a wɔde di dwuma yiye, ne nneɛma a wɔde ma mu asiane nhwehwɛmu so.
Niche AI a wɔde ma no ma wɔde di dwuma ntɛmntɛm ne bo a wɔde ma ntɛm ara sɛ wɔde toto platform dwumadie a ɛkɔ akyiri ho a. Wɔn di mu wɔ ɛyaw nsɛntitiriw pɔtee bi a wodi ho dwuma bere a wɔnam API ne data nkitahodi ahorow so ne nhyehyɛe ahorow a ɛwɔ hɔ dedaw no bom.
Data sohwɛ bɛyɛ nea ɛho hia bere a AI a wogye tom no reyɛ kɛse no, na ɛhwehwɛ sɛ wɔyɛ kokoam nsɛm sohwɛ a emu yɛ den ne ahobammɔ nhyehyɛe ahorow de brɛ asiane ahorow a ɛhaw adwene no ase 44% a wɔyɛ nneɛma a ɛfa AI a wɔde di dwuma ho.
MES (Manufacturing Execution System) software no hwɛ adwuma a wɔreyɛ wɔ sotɔɔ mu no so na ɛhwɛ so, na ɛyɛ adwuma sɛ ɔkwan titiriw a ɛda ERP nhyehyɛe nhyehyɛe ne nneɛma a wɔyɛ ankasa ntam. MES nhyehyɛe ahorow di bere ankasa mu nneɛma a wɔyɛ ho nsɛm akyi, ɛhwɛ adwuma ahyɛde ahorow so, na ɛhwɛ hu sɛ wodi mmara so yiye.
MES platforms ma traceability ahwehwɛde ma nnwuma a wɔahyɛ ho mmara bere a ɛde granular production data a ɛma AI optimization algorithms aduan ma. Wɔkyere adwumayɛ ho nsɛm a ERP nhyehyɛe ahorow ntumi nnya, na ɛma wotumi hu ade yiye wɔ nneɛma a wɔyɛ no bo a ɛsom no nyinaa mu.
MES ne ERP nhyehyɛe ahorow ntam a wɔde ka bom no yi nsaano nkyerɛwee a wɔde hyɛ mu no fi hɔ, ɛtew mfomso so, na ɛma wotumi si gyinae a wɔde wɔn ankasa yɛ a egyina bere ankasa mu nneɛma a wɔyɛ no tebea ne anohyeto ahorow so.
Wɔn a wodii kan gyee AI no bɔ amanneɛ sɛ sɛ wɔkyekyem pɛpɛɛpɛ a, sika a wonya no kɔ soro 9.1% denam bere ankasa mu ahoɔden a wɔde yɛ nneɛma yiye a adetɔnfo de ma so. Saa mfasoɔ a ɛwɔ adwumayɛ mu yi firi nkɔmhyɛ nsiesie a ɛtew bere a wɔanhyɛ da ayɛ adwuma so, nhwehwɛmu a ɛyɛ papa a ɛsi sintɔ ano, ne nneɛma a wɔyɛ no yie a ɛma adwumayɛ yɛ kɛseɛ.
Adetɔnfoɔ tumi wɔ mfiri adesua nhwɛsoɔ a wɔde di dwuma, edge computing integration, ne automated decision-making ne adwumayɛ mu nkɔsoɔ tumi no wɔ abusuabɔ tẽẽ. Nnwumakuw a wɔpaw adetɔnfo a wɔwɔ AI a wɔde di dwuma nhyehyɛe a wɔada no adi no nya bere a wɔde yɛ adwuma ntɛmntɛm ne ROI a ɛkorɔn.
Ɛka a wɔtew so no nam nneɛma pii so na ɛba: nneɛma a wɔsɛe no a ɛso tew, ahoɔden a wɔde di dwuma yiye, agyapade a wɔde di dwuma a ɛkɔ anim, ne nsaanodwuma a wɔhwehwɛ a ɛso tew. Adetɔnfoɔ a wɔde analytics dashboards a ɛkɔ akyiri ma no ma nkɔsoɔ a ɛkɔ so denam gyinaesie a egyina data so so.
Digital twins ne AI-driven risk platforms hyɛ supply-chain visibility mu den denam nhwɛsoɔ a wɔyɛ wɔ ɔhaw a ɛbɛtumi aba ne mmuaeɛ akwan a ɛyɛ papa so. Manufacturing sentiment data si ahoɔden a wɔde gyina ano so dua sɛ ade titiriw a ɛho hia ma 2025 nhyehyɛe a wɔde bɛyɛ adwuma.
Adetɔnfoɔ a wɔde nnwinnadeɛ a wɔde hwɛ asiane a ɛwɔ nneɛma a wɔde ma mu ma no boa wɔn a wɔyɛ nneɛma no ma wɔhunu mmerɛwyɛ ahodoɔ, wɔyɛ nneɛma a wɔde ma no ntam nkitahodiɛ ahodoɔ, na wɔhwɛ buffer inventory levels a wɔayɛ no yie ama ɛka ne nea ɛwɔ hɔ. Bere ankasa mu tumi a wɔde di akyi no ma wotumi yɛ ho biribi ntɛmntɛm wɔ ɔhaw ahorow ho.
Platforms a wɔaka abom a ɛka nneɛma a wɔyɛ ho nhyehyɛe, nneɛma a wɔakora so ho nhyehyɛe, ne wɔn a wɔde nneɛma ma nkitahodi bom ma wotumi hu fi awiei kosi awiei a atetesɛm nsɛntitiriw ano aduru ntumi nhyia. Saa nkabom yi ma wotumi brɛ asiane ase ntɛm sen sɛ wɔbɛma wɔadi ɔhaw ahorow ho dwuma.
Data nnisoɔ a ɛtu mpɔn hwehwɛ sɛ wɔfa nhyehyɛeɛ kwan a wɔfa so kyekyɛ data mu, dwumadie a egyina dwumadie so hwɛ, encryption gyinapɛn, ne mmara sodiɛ nhyehyɛeɛ te sɛ ISO 27001. Ɛsɛ sɛ adetɔnfoɔ da ahobanbɔ tumi adi a ɛdi kokoamsɛm ho haw a ɛwɔ 44% a wɔyɛ nneɛma no twentwɛn wɔn nan ase wɔ AI a wobegye atom ho.
Nneyɛe pa ne sɛ wɔde data atare bedi dwuma denam metadata sohwɛ a ɛfata so, wɔde data wurayɛ ho nhyehyɛe a emu da hɔ besi hɔ, na wɔahwɛ akontaabu akwan so ama wɔadi mmara so. Ɛsɛ sɛ adetɔnfo de ahobammɔ ho nneɛma a wɔde ahyɛ mu ma sen sɛ wɔbɛhwehwɛ ahobammɔ ano aduru a ɛsono emu biara.
Ahwehwɛde ahorow a ɛfa sɛnea wodi so no gu ahorow wɔ nnwuma ahorow mu, na wɔn a wɔyɛ kar, ahunmu, ne nnuru hwehwɛ nhyehyɛe ahorow a wɔagye atom a ɛma data no yɛ pɛ na wotumi hwehwɛ mu wɔ nneɛma a wɔyɛ no nkwa nna nyinaa mu.
Ahokokwaw a ɛreba no bi ne data nhwehwɛmu, AI model management, edge computing administration, ne digital twin operation. Nnwuma akɛseɛ a wɔyɛ adwuma dɔnhwerew biara no bɛboro 80% yɛ nhyehyɛɛ sɛ wɔde sika a ɛkɔ anim bɛto adwumayɛfoɔ sohwɛ mu wɔ afe 2025 mu.
Ɛsɛ sɛ upskilling nhyehyɛe ahorow no di mfiridwuma ho nimdeɛ ne adwumayɛ mu nsakrae a mfiridwuma foforo de ba no nyinaa ho dwuma. Adetɔnfoɔ a wɔde nteteeɛ nhyehyɛeɛ a ɛkɔ akyiri ne wɔn a wɔde di dwuma no ntam nkitahodiɛ a ɛyɛ mmerɛw ma no brɛ akwansideɛ a ɛwɔ dwumadie mu no ase na ɛma wɔgye tom ntɛmntɛm.
Ɛsɛ sɛ nsakraeɛ ho nhyehyɛeɛ no bi ne nkitahodiɛ nhyehyɛeɛ a ɛfa wɔn a wɔdi dwuma no ho, nsaano nteteeɛ nhyiamu, ne Centers of Excellence a wɔbɛhyehyɛ a ɛbɛma nkɔsoɔ a ɛkɔ so ne nimdeɛ a wɔkyɛ wɔ ahyehyɛdeɛ no nyinaa mu.
Data nhyehyeɛ gyinaesie a ɛda data atare ne data adekoradan ntam no gyina dwumadie pɔtee bi so, a data atare ma nsakraeɛ ma IoT data a wɔanhyehyɛ ne data adekorabea a ɛma nkitahodiɛ data a wɔahyehyɛ no yie. Data taxonomy a wɔaka abom no hwɛ hu sɛ nhyehyɛe ahorow no nyinaa yɛ pɛ na ɛma AI nhwɛso ntetee a etu mpɔn tumi.
Deloitte kamfo kyerɛ sɛ wɔmfa AI nniso nhwɛso ahorow nsi hɔ sɛ data fapem nkɔso fã. Eyi ka data no yiyedi gyinapɛn, model validation nhyehyɛe, ne adwumayɛ sohwɛ nhyehyɛe ahorow ho.
Metadata sohwɛ bɛyɛ nea ɛho hia bere a data dodow no kɔ soro no, ɛhwehwɛ sɛ wɔde afiri a wɔde kyerɛw nneɛma, abusua anato akyi di, ne nkɛntɛnso nhwehwɛmu tumi. Ɛsɛ sɛ adetɔnfoɔ de nnwinnadeɛ a ɛma data a wɔbɛhunu no yɛ mmerɛ na ɛhwɛ ma data no yɛ papa wɔ AI nkɔsoɔ asetena nyinaa mu ma.
Open APIs ne microservices architecture ma plug-and-play vendor components a ɛtew nkabom nsɛnnennen ne vendor lock-in asiane so. Modular akwan ma wɔn a wɔyɛ nneɛma no paw ano aduru a eye sen biara ma dwumadi pɔtee bi bere a wɔkora nhyehyɛe no biakoyɛ so no.
Modular Nneɛma a Wɔyɛ Mfiridwuma Stack:
