This article seeks to clarify the benefits of data-driven plant optimization using, Overall Equipment Effectiveness (OEE) , explain the data cycle as it applies to plant optimization and the key areas of industry that can be improved through implementation.
The 21st Century has seen major changes in the way industrial processes and machinery are analyzed and optimized. Technological advances have heralded a new age of innovation, often referred to as the 4th Industrial Revolution or Industry 4.0. Wireless interconnectivity of machinery and devices (Internet of Things or IoT) has allowed for more efficient collection and monitoring of data. Modern industrial processes can now be scrutinized, analyzed and adapted more than ever. To stay profitable, companies are under more and more pressure to reach tough industrial KPIs.
But what does this mean in practice for businesses that implement advanced data systems and optimization processes?
What is OEE?
OEE stands for Overall Equipment Effectiveness (OEE). It is a lean manufacturing term, originally introduced by the Japanese. Before moving on to the practicalities and benefits of data-driven plant performance optimization, it’s important to grasp the concept of OEE and understand its impact on modern lean manufacturing and industrial processes.
OEE is a measure of how effectively industrial equipment and plant performs. This is done by calculating a percentage, with 100% representing perfectly optimised plant performance. It is measured in three areas – quality, performance and availability. A process that scores 100% in all three areas would show good quality output, as fast as possible, with zero machine downtime.
OEE can also be linked to the losses caused by less than 100% optimization, making it a powerful way to reduce costs and improve profits. OEE involves collecting a lot of data from all plant and machinery operations, analyzing it thoroughly and gaining insights to improve performance.
The main benefits of OEE and data-driven plant performance optimization
The obvious benefit is that plant and asset managers will have greater insight into the operation of plant and machinery, allowing them to make better-informed decisions. They can quickly pinpoint plant that is underperforming and addresses the problems quickly. Companies that introduce sophisticated Human-Machine Interfaces (HMI) will gain a serious edge over those that don’t, as the enhanced control system will increase productivity dramatically.
Increased automation is also a major benefit. For example, a spare part that frequently needs replacing can be monitored closely for wear by a sensor. The system can be set up so that when the part is at a predetermined level of wear, a replacement is automatically ordered, therefore eliminating machine downtime while waiting for delivery of parts and avoiding plant failure.
Another major benefit is that modern optimization systems are capable of automatically adjusting production volume according to demand or manufacturing material prices. Previously, this would have involved lengthy research and calculation.
In fact, all areas of industry will benefit from the increased use of data in terms of increased efficiency and productivity. When combined with machine learning algorithms, data output can be used to allow industrial systems to effectively streamline themselves. Targeted aggregation and in-depth analysis and simulation of data can be fully automated for cost savings and productivity increases.
The four stages of data-driven plant optimization
A typical data cycle for plant optimization is as follows:
- Record and monitor data – Real-time recording of data such as operation speed, wear and tear, motor usage, energy usage, etc. is carried out using IoT sensors.
- Upload of data – All data is wirelessly uploaded to a cloud storage system often referred to as the industrial cloud.
- Analysis of data – The data is automatically fed into simulation software or a virtual plant model and data insights generated.
- Reports and action – Reports are automatically produced and can be sent remotely to the relevant people. In some cases, automatic action will be taken such as ordering replacement parts.
The key areas of data-driven plant performance optimization
The following areas can be optimized through the efficient use of data collection and analysis across various sites:
In today’s environmentally conscious world, businesses are under pressure to use more sustainable practices and reduce energy consumption. The reduction of energy usage will also result in considerable cost savings over the long term.
Plant performance data systems can be implemented that monitor and target energy usage. For instance, the current being used by an electric motor can be carefully analyzed during operation, meaning that the electrical input can be reduced during periods of low usage.
Using data analytics to improve process efficiency will help companies to maintain a competitive edge. As industrial processes become more complex and technical, using targeted data will help to keep them manageable and streamlined.
For instance, manufacturing companies may use multiple machines and plant operations throughout product fabrication. Data analytics can identify any stages that are slowing production down, and alternative workflows or tweaks to existing ones can be implemented to speed things up.
Control loops are a major area for potential improvement in most plant processes. Research has shown that around 50% of control loops require fine-tuning, 25% are ineffective and 25% show declining performance over time. Approximately 33% of control loops are still manually controlled and 25% use commissioning parameters which are no longer applicable. Often, a control or systems engineer will singlehandedly manage dozens or even hundreds of control loops, making it impossible to achieve optimal performance. Data systems can help to alleviate all these control loop problems.
An effective process data system will:
- Analyze control states and provide reports and KPI calculations
- Provide hierarchical plant detail and overviews
- Generate data-driven insights and optimization suggestions
- Highlight maintenance issues or automatically take preventative actions
- Create control loop reports and in some cases suggest improvements
Plant and Machinery Usage
As mentioned earlier, plant and machinery can be fitted with multiple wireless sensors to measure wear and tear. This means early intervention can be made before the machinery fails, creating a pro-active approach to maintenance, rather than a reactive one.
Well-implemented plant data analysis can minimize machine downtime and eliminate complete failure or serious damage.
Another key area that can be easily overlooked but is of critical importance in today’s world is cybersecurity. Experts warn that industrial cyber attacks are becoming more common and are likely to increase in the coming years.
Cyber attacks can take the form of stealing intellectual property or sabotage of operations. With global terrorism on the increase, large-scale industrial attacks designed to cause chaos will become more common. With effective data analysis and in-built security systems, any operational anomalies will be quickly flagged up, negating the risk of serious damage.
The future of data-driven plant performance optimization
As data collection and analysis systems and simulation software become more intelligent, the scope for predictive performance and automated responses will improve data-driven plant performance.
For instance, before integrating a new piece of machinery into a complex system, existing data can be coupled with advanced simulation modelling software and specification data from the manufacturer of the machinery to predict the effect it will have on production. The software can then suggest changes to the workflow or process without the need for an in-depth engineering study.
We are entering an exciting new phase in plant and machinery optimization, with data analytics and smart systems at the forefront of delivering efficiency and cost savings. Businesses that embrace these new technologies and methodologies will stay ahead and maintain a competitive edge in this fast-changing industrial landscape.
Makala hii inatafuta kufafanua faida za uboreshaji wa kiwanda unaoendeshwa na data kwa kutumia Ufanisi wa Jumla wa Vifaa (OEE), kuelezea mzunguko wa data kama inatumika kwa uboreshaji wa kiwanda na maeneo muhimu ya tasnia ambayo yanaweza kuboreshwa kupitia utekelezaji. Karne ya 21 imeona mabadiliko makubwa katika njia ya michakato ya viwanda na mashine kuchambuliwa na optimized. Maendeleo ya kiteknolojia yametangaza enzi mpya ya uvumbuzi, mara nyingi inajulikana kama Mapinduzi ya Viwanda ya 4 au Viwanda 4.0. Uunganisho wa wireless wa mashine na vifaa (Internet ya Vitu au IoT) imeruhusu ukusanyaji na ufuatiliaji bora wa data. Utaratibu wa kisasa wa viwanda sasa unaweza kuchunguzwa, kuchambuliwa na kurekebishwa zaidi kuliko hapo awali. Ili kuendelea kuwa na faida, makampuni ni chini ya shinikizo zaidi na zaidi kufikia ngumu viwanda KPIs. Lakini hii inamaanisha nini katika mazoezi kwa biashara ambazo kutekeleza mifumo ya data ya juu na michakato ya uboreshaji? OE ni nini? OEE ni kifupi cha Overall Equipment Efficiency (OEE). Ni neno la utengenezaji laini, ambalo lilitolewa na Wajapani. Kabla ya kuhamia kwa vitendo na faida za data-kuendeshwa kiwanda utendaji optimization, ni muhimu kuelewa dhana ya OEE na kuelewa athari zake juu ya kisasa lean viwanda na michakato ya viwanda. OEE ni kipimo cha jinsi vifaa vya viwanda na mitambo hufanya kazi. Hii ni kufanyika kwa kuhesabu asilimia, na 100% inawakilisha utendaji kikamilifu optimized kiwanda. Ni kipimo katika maeneo matatu <unk> ubora, utendaji na upatikanaji. Utaratibu ambao unapata alama ya 100% katika maeneo yote matatu utaonyesha matokeo mazuri ya ubora, haraka iwezekanavyo, na wakati wa mashine ya sifuri. OEE pia inaweza kuhusishwa na hasara zinazosababishwa na chini ya 100% optimization, na kuifanya kuwa njia yenye nguvu ya kupunguza gharama na kuboresha faida. OEE inahusisha kukusanya data nyingi kutoka kwa shughuli zote za kiwanda na mashine, kuichambua kwa undani na kupata ufahamu ili kuboresha utendaji. Faida kuu ya OEE na Data-Driven Plant Performance Optimization: Faida ya wazi ni kwamba wasimamizi wa kiwanda na mali watakuwa na ufahamu mkubwa juu ya utendaji wa kiwanda na mashine, na kuwawezesha kufanya maamuzi bora ya habari. Wanaweza kutambua haraka mmea ambao una utendaji duni na kushughulikia matatizo haraka. Kampuni ambazo kuanzisha sophisticated Human-Machine Interfaces (HMI) kupata makali kubwa juu ya wale ambao hawana, kama mfumo wa kudhibiti kuboreshwa itaongeza uzalishaji kwa kiasi kikubwa. Kuongezeka kwa automatisering pia ni faida kubwa. Kwa mfano, sehemu ya ziada ambayo mara nyingi inahitaji kubadilishwa inaweza kufuatiliwa kwa ukaribu kwa ajili ya kuvaa na sensor. Mfumo unaweza kuanzishwa ili wakati sehemu iko katika kiwango cha kuharibika, kubadilisha ni moja kwa moja ili, hivyo kuondoa mashine downtime wakati kusubiri kwa ajili ya utoaji wa sehemu na kuepuka kushindwa kiwanda. Faida nyingine kubwa ni kwamba mifumo ya kisasa ya uboreshaji ni uwezo wa kurekebisha moja kwa moja kiasi cha uzalishaji kulingana na mahitaji au bei ya vifaa vya utengenezaji. Hapo awali, hilo lingehusisha utafiti na hesabu ndefu. Kwa kweli, maeneo yote ya viwanda yatanufaika na kuongezeka kwa matumizi ya data katika suala la kuongeza ufanisi na uzalishaji. Wakati pamoja na mashine kujifunza algorithms, pato data inaweza kutumika kuruhusu mifumo ya viwanda kwa ufanisi streamline wenyewe. Targeted aggregation na kina uchambuzi na simulation ya data inaweza kuwa kikamilifu automatiska kwa ajili ya kuokoa gharama na ongezeko la uzalishaji. Hatua nne za data-kuendeshwa kiwanda optimization mzunguko wa kawaida wa data kwa ajili ya kiwanda optimization ni kama ifuatavyo: - Rekodi na kufuatilia data <unk> Real-wakati rekodi ya data kama vile kasi ya operesheni, kuvaa na machozi, matumizi ya motor, matumizi ya nishati, nk. Hii ni kwa sababu ya matumizi ya vifaa vya IoT, sensorer na vifaa vya vifaa vya IoT. - Upakuaji wa data <unk> Takwimu zote ni wirelessly kupakuliwa kwa mfumo wa kuhifadhi wingu mara nyingi inajulikana kama wingu viwanda. - Uchambuzi wa data <unk> Data ni moja kwa moja kulishwa katika programu simulation au mfano virtual kiwanda na data ufahamu yanayotokana. - Ripoti na hatua <unk> Ripoti ni moja kwa moja zinazozalishwa na inaweza kutumwa kwa mbali kwa watu husika. Katika baadhi ya kesi, hatua moja kwa moja itachukuliwa kama vile kuagiza sehemu za kubadilisha. Sehemu za msingi za optimization ya utendaji wa kiwanda inayoendeshwa na data: Sehemu zifuatazo zinaweza kuboreshwa kupitia matumizi bora ya ukusanyaji wa data na uchambuzi katika maeneo mbalimbali: Katika ulimwengu wa leo wa mazingira, biashara ziko chini ya shinikizo la kutumia mazoea ya kudumu zaidi na kupunguza matumizi ya nishati. Kupunguzwa kwa matumizi ya nishati pia kutasababisha akiba kubwa ya gharama kwa muda mrefu. Mifumo ya data ya utendaji wa kiwanda inaweza kutekelezwa ambayo inasimamia na kulenga matumizi ya nishati. Kwa mfano, umeme unaotumiwa na injini ya umeme unaweza kuchambuliwa kwa uangalifu wakati wa kazi, ikimaanisha kwamba pembejeo ya umeme inaweza kupunguzwa wakati wa matumizi ya chini. Kutumia uchambuzi wa data ili kuboresha ufanisi wa mchakato itasaidia makampuni kudumisha makali ya ushindani. Kama michakato ya viwanda kuwa zaidi tata na kiufundi, kutumia data lengo itasaidia kuweka yao manageable na streamlined. Kwa mfano, makampuni ya utengenezaji yanaweza kutumia mashine nyingi na shughuli za mmea katika utengenezaji wa bidhaa. Uchambuzi wa data unaweza kutambua hatua yoyote ambayo hupunguza uzalishaji, na mtiririko wa kazi mbadala au tweaks kwa zilizopo zinaweza kutekelezwa ili kuharakisha mambo. Mzunguko wa kudhibiti ni eneo kuu kwa ajili ya uboreshaji wa uwezekano katika michakato mingi ya mmea. Utafiti umeonyesha kuwa karibu 50% ya mzunguko wa kudhibiti unahitaji kusawazisha, 25% ni isiyofaa na 25% inaonyesha kupungua kwa utendaji kwa muda. Karibu asilimia 33 ya mzunguko wa kudhibiti bado unadhibitiwa kwa mkono na asilimia 25 hutumia vigezo vya kuanzisha ambavyo havitumiki tena. Mara nyingi, mhandisi wa udhibiti au mifumo ataweza kusimamia kwa mikono yake mwenyewe makumi au hata mamia ya vifungo vya udhibiti, na kufanya iwe vigumu kufikia utendaji bora. Mifumo ya data inaweza kusaidia kupunguza matatizo haya yote ya mzunguko wa kudhibiti. Mfumo wa data ya mchakato unaoweza kutekelezwa utachambua hali za udhibiti na kutoa ripoti na hesabu za KPI, kutoa maelezo ya kiwanda na muhtasari wa hierarchical, kuzalisha maoni ya data na mapendekezo ya uboreshaji, kuonyesha masuala ya matengenezo au kuchukua hatua za kuzuia moja kwa moja, kuunda ripoti za mzunguko wa udhibiti na katika hali fulani kupendekeza maboresho. Hii ina maana ya kuingilia mapema inaweza kufanywa kabla ya mashine kushindwa, kuunda mbinu ya proactive kwa ajili ya matengenezo, badala ya moja reactive. Uchambuzi wa data ya kiwanda uliotengenezwa vizuri unaweza kupunguza wakati wa mashine na kuondoa kushindwa kabisa au uharibifu mkubwa. Eneo jingine muhimu ambalo linaweza kupuuzwa kwa urahisi lakini ni muhimu sana katika ulimwengu wa leo ni usalama wa mtandao. Wataalamu wa teknolojia wanasema kuwa mashambulizi ya kibiashara yanaongezeka na huenda yakaongezeka katika miaka ijayo. Mashambulizi ya kiber inaweza kuchukua fomu ya kuiba mali ya akili au uharibifu wa shughuli. Kwa kuwa ugaidi ulimwenguni pote unaongezeka, mashambulizi makubwa ya viwanda yaliyokusudiwa kusababisha machafuko yatakuwa ya kawaida zaidi. Kwa uchambuzi wa data na mifumo ya usalama iliyojengwa, kasoro yoyote ya uendeshaji itaonyeshwa haraka, na kuondoa hatari ya uharibifu mkubwa. Wakati ujao wa data-kuendeshwa kiwanda utendaji optimization: kama data ukusanyaji na uchambuzi mifumo na simulation programu kuwa zaidi akili, uwezo wa utabiri utendaji na majibu automatiska kuboresha data-kuendeshwa kiwanda utendaji. Kwa mfano, kabla ya kuunganisha kipande kipya cha mashine katika mfumo tata, data zilizopo zinaweza kuunganishwa na programu ya hali ya juu ya simulation na data ya vipimo kutoka kwa mtengenezaji wa mashine ili kutabiri athari itakayokuwa nayo kwa uzalishaji. Programu inaweza kisha kupendekeza mabadiliko ya mtiririko wa kazi au mchakato bila haja ya utafiti wa kina wa uhandisi. Tunaingia katika hatua mpya ya kuvutia katika optimization ya kiwanda na mashine, na uchambuzi wa data na mifumo ya smart katika mstari wa mbele wa kutoa ufanisi na kuokoa gharama. Biashara ambazo zinakubali teknolojia na mbinu hizi mpya zitabaki mbele na kudumisha faida ya ushindani katika mazingira haya ya viwanda yanayobadilika haraka.