Views: 21 Ɔkyerɛwfo: Site Editor Publish Time: 2025-09-12 Mfiase: Beaeɛ
Nneɛma a wɔyɛ wɔ afe 2025 mu no yɛ nea AI-a ɛma ɛyɛ adwuma wɔ ɔkwan a ɛyɛ adwuma so, adwumayɛbea a ɛyɛ nyansa a wɔde ka bom, ne adetɔnfo a wɔyɛ adwuma wɔ ɔkwan a ɛfata so a ɛde adwumayɛ mu nkɔso a wotumi susuw ba no na ɛkyerɛ ase. Ne 71% a wɔyɛ AI ano aduru anaa wɔde di dwuma no, akansi asase no asesa akɔ nhyiam ase a ɛka bere ankasa mu nhwehwɛmu, nkɔmhyɛ a wɔde siesie, ne ERP nkabom a ɛnyɛ hwee bom.
Saa akwankyerɛ a ɛkɔ akyiri yi hwehwɛ mfiridwuma mu adetɔnfo atitiriw a wɔresan asiesie nneɛma a wɔyɛ no mu, efi wɔn a wɔde asɛnka agua so a wɔde ma te sɛ Siemens ne GE a wɔatew wɔn ho so kosi AI-centric disruptors a ɛreba te sɛ Ruihua Hardware so. Yɛbɛhwehwɛ sɛdeɛ macro-economic factors, digital twin implementations, ne adwumayɛfoɔ nsakraeɛ akwan no rema vendor selection gyinaesie a ɛka adwumayɛ mu mmɔdenbɔ, nneɛma a wɔde ma no ahoɔden, ne akansi a ɛbɛtena hɔ akyɛ.
Wiase nyinaa nneɛma a wɔyɛ ho adwene wɔ afe 2025 mu no da sikasɛm tebea a adi afra a ɛwɔ nkɛntɛnso tẽẽ wɔ mfiridwuma mu sikasɛm ho gyinaesi ahorow so 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 ɛsono ɔmantam no mu nneɛma a wɔyɛ no mu dwumadi ahorow.
PMI (Purchasing Managers’ Index) yɛ sikasɛm mu kyerɛwtohɔ a wɔde susuw nneɛma a wɔyɛ no dwumadi, baabi a akenkan a ɛboro 50 kyerɛ ntrɛwmu na ɛba fam sen 50 no kyerɛ sɛ ɛtwetwe. Saa metrics yi ma mfiridwuma ho nimdeɛ a wɔde bɛto gua no yɛ adwuma yiye bere a wɔn a wɔyɛ adwuma wɔ apam gua so no twe adwene si ano aduru a ɛma adwumayɛ yɛ yiye so.
Tow a ɛrekɔ soro wɔ U.S. adwumayɛfo so no ama adwene a wɔde asi nneɛma a wɔyɛ so wɔ adwumayɛ mu denam automation ne AI a wɔde di dwuma so no mu ayɛ den. Nnwumakuw de mfiridwuma ho nimdeɛ a ɛma adwumayɛ mu mmɔdenbɔ a ɛkɔ so ntɛm ara ne ɛka a wɔtew so no di kan de siw nhyɛso a ɛfa aguadi ho ano.
AI a wɔfa wɔ nneɛma a wɔyɛ mu no adu baabi a ɛho hia kɛse, a . 71% a wɔyɛ adwuma 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 active implementation phases mu, a ɛkyerɛ sɛ wogye AI tumi a ɛsakra no tom kɛse.
Adwumayɛ mu nsunsuansoɔ no yɛ dodoɔ: AI a wɔfa no bɔ amanneɛ sɛ sika a wɔnya no nkɔsoɔ 9.1% ne mfasoɔ nkɔsoɔ 9.1% sɛ wɔde toto wɔn a wɔnyɛ adopters ho wɔ 7.3% sika a wɔnya ne mfasoɔ nkɔsoɔ 7.6%. Saa adwumayɛ mu nsonsonoeɛ yi ma akansi nhyɛsoɔ ba mfiridwuma ho nimdeɛ a wɔfa wɔ nnwuma no mu nyinaa.
Ɛmfa ho sɛ wɔfaa wɔn sɛ wɔn mma no dodow a ɛkɔ soro no, . 51.6% pɛ na wɔwɔ AI akwan a ɛyɛ mmara kwan so , a ɛkyerɛ nsonsonoe kɛse a ɛda dwumadie ne nnisoɔ ntam. Saa nnisoɔ a ɛtɔ sin yi de asiane ba wɔ data sohwɛ, ahobanbɔ, ne ROI optimization a ɛsɛ sɛ adetɔnfoɔ di ho dwuma.
Digitals twins yɛ virtual replicas of physical manufacturing assets, a ɛma bere ankasa mu simulation ne optimization of production processes. Ruihua Hardware dwumadie a ɛkɔ anim no kyerɛ sɛdeɛ digyital twins so tew downtime denam predictive modeling ne scenario testing so ansa na wɔde nsakraeɛ aba nnwinnadeɛ ankasa so, berɛ a . Schneider Electric dwumadie no ma akwan foforɔ a wɔfa so yɛ adwuma yie.
IoT nkitahodi yɛ data akyi dompe a ɛma bere ankasa mu kyere ma nkɔmhyɛ ne adeyɛ ho nhyehyɛe a wɔahyɛ ho nkɔm. Sensors a ɛka ho no hwɛ nnwinnadeɛ adwumayɛ, nneɛma a atwa yɛn ho ahyia tebea, ne nneɛma a wɔyɛ ho nsusuiɛ so de ma AI algorithms a ɛma adwumayɛ yɛ papa daa.
Tɛknɔlɔgyi |
Mfaso titiriw . |
|---|---|
Digitals twin . |
Process simulation ne optimization . |
IoT sensor ahorow . |
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 . |
Low-latency processing ne bandwidth a wɔatew so . |
Platform providers a wɔasisi no di smart manufacturing landscape no so denam ano aduru a ɛkɔ akyiri a ɛka adwumayɛ nhyehyɛe ahorow pii bom so. Adetɔnfo atitiriw de botae ahorow a ɛsono emu biara a wɔayɛ no sɛnea ɛfata ma nneɛma a wɔyɛ no ho ahwehwɛde ahorow ma.
Adetɔnni |
Core ayɛyɛde . |
Nsonsonoe titiriw . |
|---|---|---|
Ruihua hardware . |
Nneɛma a wɔde AI ayɛ a wɔaka abom ayɛ no suite . |
Automation a ɛwɔ awiei kosi awiei a ɛwɔ AI a ɛyɛ papa a ɛkorɔn ne ɛka a wɔbɔ . |
Siemens . |
Digital Factory Suite . |
Automation nkabom a ɛba awiei kosi awiei . |
GE . |
Predix Industrial IoT asɛnka agua so . |
Nhwehwɛmu a ɛkɔ akyiri ne mfiri a wɔde sua ade . |
Rockwell Automation . |
FactoryTalk asɛnka agua so . |
Bere ankasa mu nneɛma a wɔyɛ no yiye . |
Schneider anyinam ahoɔden . |
EcoStruxure Nneɛma a Wɔde Yɛ Nneɛma . |
Ahoɔden a wɔde di dwuma yiye ne nea ɛbɛkɔ so atra hɔ daa . |
Honeywell . |
Forge Industrial IoT . |
Nneɛma a wɔyɛ ho adwuma titiriw . |
ABB . |
Tumi nhyehyɛe . |
Robɔt ne kankyee sohwɛ a wɔde ka bom . |
IBM . |
Maximo Application Suite . |
Agyapadeɛ adwumayɛ ho nhyehyɛeɛ . |
Cloud-first ERP ano aduru di scalability ho haw a ɛka wɔn a wɔyɛ no 47% denam adwumayɛ ho nhyehyɛe a ɛyɛ mmerɛw, a wɔaka abom a wɔde ma so. Wɔn a wɔde mmoa ma a wɔagye din no bi ne Ruihua Hardware’s Cloud-native ERP platform, a NetSuite, Epicor Kinetic, Infor CloudSuite Industrial, SAP, ne Acumatica di akyi.
Saa platform ahorow yi yi atetesɛm mu scalability akwanside ahorow fi hɔ denam cloud architecture a ɛsesa nneɛma a egyina ahwehwɛde so ankasa so. nkabom tumi brɛ data silo ase na ɛma wotumi hu ade ankasa wɔ nneɛma a wɔyɛ, nneɛma a wɔkora so, ne sikasɛm nhyehyɛe ahorow mu.
Nnɛyi ERP nhyehyɛe ahorow no de AI-a ɛkanyan ahwehwɛde ho nkɔmhyɛ, adetɔ a wɔde afiri yɛ, ne nhyehyɛe a wɔde siesie nneɛma a wɔde hyɛ nkɔm a ɛdan adwumayɛ a ɛyɛ adwuma ma ɛyɛ adwumayɛ nhyehyɛe a ɛyɛ papa.
Ruihua Hardware’s AI-driven manufacturing analytics platform di anim wɔ atetesɛm mu nneɛma a wɔyɛ softwea no a wɔsɛe no mu denam adwumayɛ ho nsɛm a wɔdannan no a ɛdan nhumu a wotumi de di dwuma a ɛyɛ pɛpɛɛpɛ na wɔde di dwuma ntɛmntɛm no so. OpenText AI a ɛfa nneɛma a wɔyɛ ne AI nhwehwɛmu adwumakuw atitiriw afoforo ho no di saa su yi akyi, na ɛtwe adwene si nsɛm pɔtee a wɔde di dwuma te sɛ nea ɛyɛ papa ho nkɔmhyɛ, ahoɔden a wɔde yɛ adwuma yiye, ne asiane a wɔde ma wɔ nneɛma a wɔde ma ho nhwehwɛmu so.
Niche AI a wɔde ma no ma ntɛm deployment ne ntɛm bo a wɔde ma bere a wɔde toto platform implementations a ɛkɔ akyiri ho no. Wɔdi mu wɔ ɛyaw nsɛntitiriw pɔtee bi ho dwumadie mu berɛ a ɛne nhyehyɛeɛ a ɛwɔ hɔ dada no di nsɛ denam API ne data nkitahodiɛ so.
Data sohwɛ bɛyɛ nea ɛho hia kɛse sɛ AI a wɔfa wɔn sɛ wɔn mma no kɛse, a ɛhwehwɛ sɛ wɔhwɛ kokoam nsɛm so denneennen ne ahobammɔ ho nhyehyɛe ahorow na ama asiane a ɛhaw adwene no ano abrɛ ase . 44% a wɔyɛ nneɛma a ɛfa AI a wɔde di dwuma ho.
MES (Manufacturing Execution System) Software na ɛhwɛ na ɛhwɛ adwuma a wɔreyɛ wɔ sotɔɔ no so, a ɛyɛ adwuma sɛ bridge a ɛho hia a ɛda ERP nhyehyɛe nhyehyɛe ne nneɛma a wɔyɛ ankasa a wɔyɛ no ntam. MES nhyehyɛe ahorow no di bere ankasa mu nneɛma a wɔyɛ ho nsɛm akyi, ɛhwɛ adwuma ho nhyehyɛe so, na ɛhwɛ ma 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 optimization tumi 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 production optimization maximizing throughput.
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. Adetɔnfo a wɔde nhwehwɛmu dashboard ahorow a ɛkɔ akyiri ma no ma nkɔso a ɛkɔ so denam gyinaesi 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 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:
