Special Cases

【解決方案】連聯合國都買單 悠由數據應用運用農業數據搶攻全球商機
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2022.3
【2022 Application Example】 Even the United Nations is on board! Yoyo Data Application captures global business opportunities with agricultural data

Nearly 2,000 days in the fields have made Yoyo Data Application a top player in Taiwan’s agricultural data sector Their comprehensive grasp of crop yields, production periods, and prices has enabled them to collaborate with the United Nations The service area for agricultural land skyrocketed from 24 hectares to over 6,000 hectares in less than three years—a 250-fold increase For Wu Junxiao, founder and CEO of Yoyo Data Application, aligning with global environmental trends and becoming a data company at the intersection of climate technology and the green economy to serve the global market is his ultimate entrepreneurial goal Wu Junxiao, originally an engineer, joined the Industrial Technology Research Institute in 2010, where he honed his profound technical and data science analytic skills 'At that time, I was working in data analysis engineering, and almost all data-related materials would be directed to me Additionally, I worked on indoor cultivation boxes, planting vegetables and mushrooms, hence planting the seed of entrepreneurship by integrating agriculture with data analysis,' Wu recalls Since 2016, Wu Junxiao has been frequently visiting farms to 'embed' himself among farmers and agricultural researchers, chatting and sharing information systematically, which quickly established his agricultural know-how Solid data analysis capabilities have even convinced the United Nations In 2017, he left the Institute to start his own business and founded Yoyo Data Application in 2019 Today, many agricultural businesses are his clients, with service areas rapidly climbing from 24 hectares to over 6,000 hectares, expected to surpass 7,000 hectares in 2022 His clientele includes markets in Japan, Central America, and even entities under the United Nations like the World Farmers Organization, which utilizes the 'Yoyo Crop Algorithm System' supported by Yoyo Data How exactly does Yoyo Data Application manage to impress even UN agencies The 'Yoyo Crop Algorithm System' developed by Yoyo Data Application accurately predicts the production period, yield, and prices Firstly, due to Wu Junxiao's precise mastery over agricultural data, Yoyo Data Application's clients don't necessarily need sensors or other hardware devices 'Sensors are expensive and if you buy cheap devices, you just collect a lot of noise or flawed data, which is useless,' Wu explains He continues, 'Collecting data doesn't necessarily require sensors our data solutions can solve problems more directly and effectively' For instance, one of Yoyo Data Application's products, the Yoyo Money Report Agri-price Linebot, developed in collaboration with LINE in 2020, gathers data on origin, wholesale, and terminal prices spanning over 10 years, driven by Yoyo Data’s proprietary AI algorithms This enables the system to autonomously learn about agricultural product trading prices, using big data and AI to perform price prediction analysis, thereby helping buyers reduce transaction risks and expanding the data application to the entire agricultural supply chain Regarding banana prices, the accuracy of price predictions increased from the original 70 to 998 Wu Junxiao notes that both buyers and farmers are very sensitive to prices Now, through the Yoyo Money Report service, both buyers and farmers can precisely understand the fluctuations in agricultural product prices Yoyo Data can also provide customers with optimal decision-making advice based on predictive models for crop growth, yield, and price estimations Currently, price predictions cover 28 types of crops Precise estimates of production periods and price fluctuations allow Yoyo Data to provide differentiated services based on data analysis The 'Yoyo Crop Algorithm System' provided by Yoyo Data Application incorporates a 'Parameter Bank', usually collecting 200-300 parameters, not just straightforward data like temperature and humidity, but also data divided according to the physiological characteristics of the crops Through effective dynamic data algorithms, it can accurately calculate when crops will flower and when they can be harvested, what the yield will be, and so forth For instance, the prediction accuracy of the broccoli production period is 0-4 days, with the flowering period predicted this year to be precisely 0 days, perfectly matching the actual flowering time in the field In these dynamic calculations, a 7-day range is considered reasonable, and the average error value of Yoyo Data's predictions typically ranges from 2-4 days, with most crop production period accuracies above 80 Through effective dynamic data algorithms, over 120 global crops can have their production periods and yields accurately estimated Using these effective dynamic data algorithms can set estimates for production quantities, helping adjust at the production end Yoyo Data Application's clientele primarily includes exporters of fruit crops like pineapples, bananas, guavas, mangos, pomelos, sugar apples, Taiwan's agricultural production is highly homogenized, often leading to a rush to plant the same crops and resulting in price crashes Yoyo Data Application helps clients differentiate their offerings Thus, Wu Junxiao positions his company as a boutique digital consultant, carefully selecting clients for quality over quantity He notes that Taiwanese agricultural clients focus on how to improve yield rates, even categorizing yield rates by quality, aiming for high-quality, specialized export markets whereas international clients prioritize maximizing per-unit yields, showing different operational approaches in domestic and international markets In addition to agricultural fruit, Yoyo Data Application has also extended its services to the fisheries sector, including species like milkfish, sea bass, and white shrimp, all using the same system to establish various parameters related to the growth of fish and shrimp, such as when to feed and when to harvest, and the anticipated yield, timing, and prices Yoyo Data Application harnesses the power of data to create miracles in smart agriculture In response to the company's rapid development, Yoyo Data Application introduced venture capital funds in 2021 to expand its staff and promote its business Wu Junxiao states that in response to the global trend towards net zero carbon emissions by 2050, he plans to help clients plant carbon in the soil, effectively retaining carbon in the land while also connecting clients to carbon trading platforms, creating environmental business opportunities together Wu Junxiao says that from the start of his entrepreneurial journey, he positioned the company as a global entity, thus continuous international collaborations are planned As a data company serving a global clientele and focused on climate technology and the green economy, this represents Wu’s expectations for himself and his company's long-term goals Yoyo Data Application founder and CEO Wu Junxiao「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

2022-03-14
【2021 Application Example】 Savior of Wastewater Treatment: Combining Big Data and AI Technology Opens Another Horizon in the Environmental Industry

As water resources deplete and environmental protection needs increase, wastewater treatment plants have increasingly adopted AI technology to assist in monitoring and warning systems Zhongxin行's integration of big data and AI technology has opened up new possibilities in the environmental industry In the future, besides boosting the technological momentum of the wastewater treatment industry, it can also be promoted to other industries to foster technological and economic development Founded in year 1980 as Zhongxin Engineering later renamed to Zhongxin行 Company Limited, it is one of the largest and most technically equipped environmental companies in the domestic operation and maintenance field Zhongxin行's achievements in the operation and maintenance of sewer systems span across Taiwan, including science parks, industrial zones, international airports, schools, collective housing, national parks, and factories Introduction of AI systems in wastewater plants Precisely reduces medication addition times and lowers the risk of penalties for water quality violations At the wastewater treatment plant in Hsinchu Science Park, Zhongxin行 introduced the 'AOMBR Carbon Source and Aeration Intelligent Enhancement Control System Development,' which accurately predicts air volume control and reduces medication times, thus lowering the risk of hefty fines Zhongxin行 points out that with the vigorous development of advanced industries and increasingly strict effluent standards, a slight misalignment in equipment control can lead to major discrepancies in water quality In recent years, many wastewater treatment facilities have incorporated automatic control functions, yet onsite conditions often deviate slightly from theoretical expectations, causing situations where good treatment technologies must continuously adapt and adjust to achieve effective effluent water quality control 'The better the quality of the effluent, the greater the pressure on the operators This is the biggest pain point for Zhongxin行,' said a senior manager candidly Regular water quality testing and equipment maintenance ensure that effluent water stays below legal standards This means that operators need to be on top of equipment and water quality conditions daily If there are sudden anomalies in influent water quality or equipment malfunctions, linked issues can lead to pollution Therefore, besides performing regular maintenance and testing, it is critical to constantly monitor the dashboard to ensure system stability, consuming both manpower and mental energy Zhongxin行's on-site operators work 24-hour shifts, constantly monitoring effluent water quality Combined with laboratory water testing and analysis, if the wastewater treatment values do not meet requirements, they face both administrative and contractual fines from environmental agencies and granting authorities, which also create significant psychological pressure on the employees Over the years, Zhongxin行 has built up a vast database of water quality information and invaluable experience passed down among employees, allowing a comprehensive understanding of the entire system's operational characteristics Moreover, by analyzing equipment or water quality data for key signals, problems in the treatment units can be pinpointed If AI technology could be adopted to replace manual inspections of wastewater sources and generate pre-warning signals for systematic assessment, it would significantly alleviate the pressure on staff Response time reduced from 8 hours to 4 hours, saving half the time By implementing 'AOMBR Carbon Source and Aeration Intelligent Enhancement Control System Development,' Zhongxin行 utilizes accumulated wastewater data along with verbal recounts of operator experiences on-site With the support of AI technology and environmental engineering principles, key parameters in the biological treatment unit such as carbon source dosages and aeration can be effectively controlled Through the AI transformation of wastewater treatment, a balance is achieved among pollutant removal, microbial growth, equipment energy conservation, and operation economization, achieving rationalized control parameters Carbon source and aeration parameter adjustment steps range from data collection, model training to prediction verification In the long run, incorporating historical data calculations, AI can operate within known boundary conditions, not only recording past water quality and equipment operational characteristics far more accurately, but also developing predictive models to find optimal solutions that offer the best results in terms of chemical use, energy saving, reduced greenhouse gas emissions, and pollutant removal According to Zhongxin行's estimates, originally due to human parameter adjustments leading to errors, controlling response time would take about 8 hours With the introduction of AI technology, not only can measurement errors be reduced, but also the control response time can be shortened to 4 hours, saving around half the time This enhancement increases the turnover rate of personnel and effectively reduces the risks of penalties due to operator errors and thus markedly reducing the pressure on employees Dashboard digital display panel illustration「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

2021-10-11
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【2021 Application Example】 HRT Technology Improves Production Efficiency by 20% Through AOI Detection of Defects in VCSEL Packaging

In 2017, the launch of the iPhone X made 3D sensor technology used in Face ID highly popular, which drove the development of VCSEL, a core component in the 3D sensor module In the detection of defects in incoming packaged VCSEL, the use of AI inference models can solve the industry's issue with low yield and improve reliability to 95 VCSEL technology currently can be used in many applications and various end consumer markets, including robots, mobile devices, surveillance, drones, and ARVR VCSELs are a good solution in applications that require high-speed modulation capabilities, such as cameras and biometrics VCSEL technology has a wide range ofnbsp applications, including in drones Pictured Zoyi Technology's Agricultural Drone VCSEL technology has a wide range of applications, AI technology assists in defect detection HRT Technology stated that the packaged VCSEL market is also facing strong price competition from competitors, and needs to further reduce costs and enhance product competitiveness One of the key problems is the replacement of glass lens with epoxy resin lens The production of traditional glass lenses has high yield, but the cost is higher than that of epoxy resin lenses Due to the cutting process of epoxy resin, the side wall of cutting lines can easily have rough edges, causing it to be oversized The release of stress caused by heat during the mounting process will directly cause the optical lens to break HRT Technology pointed out that the incoming inspection of VCSEL epoxy resin lenses is very important Under the constraints of packaging space, the space for fitting the package and optical lens is limited Moreover, the optical lenses will be confined to a metal frame If the dimensional tolerances are properly controlled, stress release due to heat during mounting can easily cause the optical lens to break, resulting in a yield loss of up to 10 in the VCSEL package reliability verification, resulting in an increase in production costs In order to solve the problems above, HRT Technology hopes to use AI to monitor the size and appearance defects of epoxy resin components in the VCSEL epoxy resin lens incoming stage, verifying whether their dimensions meet specifications, whether the cutting edges are smooth, and whether there are any defects in their appearance Since traditional incoming material inspection requires a rough visual inspection by humans to distinguish the quality The problem of image collection needs to be solved first to successfully collect image data Therefore, HRT Technology first developed an Automated Optical Inspection AOI device, which includes X, Y, Z three-axis motion, high-resolution cameras, and related control software to automatically record images After collecting the image data, opencv aligns the test image and a normal image to determine differences between the two images, and then pixel mapping is used to compare the pixel area to complete initial screening Manual labeling is carried out according to the image classification above, including samples that are normal, have defects in appearance, or have different shape characteristics, and then algorithm training and verification is carried out Residual neural network ResNet or other related algorithms are used for deep learning to identify the quality of lenses Implementation of AOI inspection improves production efficiency by 20 and above Comparing the differences before and after the implementation of AI image inspection, the incoming VCSEL lens inspection before implementation only involved manual inspection of the appearance The lens is packaged on the VCSEL package that has completed die bonding After passing the general light up test, the final reliability test high temperature reflow is performed Failed samples go into the rework process However, after the implementation of AOI inspection, it can screen defective lenses sooner and reduce the cost of subsequent materials input, it can also reduce the need for rework due to failure, improving yield to 95 and above in the reliability verification This is expected to help companies reduce production costs by 10 and increase production efficiency by 20 and above The difference before and after implementing AI image detection HRT Technology pointed out that this technology is an AI application developed based on tiny images It uses deep learning algorithms to identify defects in the images The trained network automatically classifies image data to predetermined categories Defect categories can be determined through reference images, so cumbersome programming is not required In the industrial machine vision environment, deep learning is mainly used for classification tasks in applications, such as inspection of industrial products or identification of parts In the future, with the development of IoT wearable devices and the trend of energy saving, the size of optoelectronic components will continue to shrink This technology can be applied to the detection of defects in the appearance of other tiny optoelectronic components in the future

2021-12-05
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【2020 Application Example】 AI Detection System Using Deep Learning, Detecting Irregular Polyhedral Defects in Just 0.5 Seconds!

Traditional manufacturing industries rely on manual visual inspection of products, lacking stability in quality yieldFor products made by traditional manufacturing industries, 'quality yield performance' is a critical issue and a decisive factor for customer business requirements Although many AOI vision inspection systems have been introduced in recent years, there are still numerous limitations that cannot be overcome when automating these inspection systemsFor example, the production of small quantities of diverse products, the inability to standardize irregular polygonal product dimensions, and the halo effect on glass or metal products from different lighting angles make it difficult to assist product yield filtering through AOI vision inspection, thus many traditional manufacturing industries still use manual visual inspection on their production linesManual inspection is labor-intensive and time-consuming, with expensive solutions from abroadA domestic model creation company often needs to manufacture products that are customized and diverse Although it uses imported high-grade mold equipment, product appearance quality testing is still largely done by manual visual inspection Testing standards vary by employee, and to adequately inspect the appearance of each product, the time each person spends cannot be easily controlled Often the same product needs to be examined repeatedly to meet quality standards, which is very labor-intensive and time-consuming and also sensitive to external environmental influencesAlthough the model company had evaluated adopting foreign AOI vision inspection equipment, a single set of equipment is expensive and only capable of inspecting certain types of product parameters, and lacks a learning feature to achieve diversified inspection goals, thus passive maintenance of the original plan is still necessaryCustomized solution significantly improves inspection efficiency and saves labor costsTo reduce the misjudgment rate of manual operations and operational costs, thus enhancing the competitiveness of the company's products, the model company sought assistance from 500HU Tech Ltd, hoping through customized service to leverage AI Deep Learning technology to improve the shortcomings of traditional AOI vision inspection systems, expanding the range of products that usable vision inspection systems can handle, and more accurately enhancing the accuracy of vision-inspected productsWith the support of the AI Innovation Research Center at National Central University, and based on the definition of five defect conditions provided by the model company, such as scratches, lint, white spots, damage cracks, and uneven baking paint, the initial step involved gathering a training dataset and manually replicating defect conditions on other parts and angles of the product, then using a program to generate defect images under different angles and lighting changes, followed by marking defectsThen, using software methods for training sets required by different algorithms, such as VGG, RestNet, Inception, DenseNet, Xception, SqueezeNet, target migration learning, classification problem Faster_Rcnn, SSD, Yolo, Mask_Rcnn, and other object recognition algorithms, after comprehensive consideration of accuracy and speed, SSD was chosen as the main core testing and inspection algorithmThen, the format of the training set required by the selected algorithm was produced, used as the comparative model then, using different AI frameworks, such as tensorflow, keras, practical verification tests were conducted, and verification test reports were produced Ultimately, optimal application parameters were adjusted for each product inspection, ensuring an average inspection accuracy rate of 95, with the inspection time reduced from 5 seconds to an average of 05 secondsOriginally, the model company's production process involved manual inspection followed by stamping a QC stamp on batches or sorting out defective products After introduction of this inspection system, the original process was maintained, but it sped up the manual judgment time, and during the process, recording for archival purposes took place, with defective items highlighted in red and recorded as photos, thus categorized into a 'defective-to-be-inspected' section Manual inspection would then determine if the product was qualified to move to the next inspection, significantly enhancing inspection efficiency and saving labor costsLow-cost, high-efficiency new AI inspection optionAs the technology of visual inspection by machines replaces human labor, it plays an increasingly vital role in the production of small, diverse orders, urgent orders, and situations where there is a labor shortage In contrast to expensive foreign inspection solutions, domestic providers can offer relatively cheap and customized solutions whether in terms of purchase costs or inspection efficiency, they are attracting more businesses ready to try, effectively enhancing the quality yield of manufacturers and thereby increasing competitiveness「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

2020-07-29
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Records of Application Example

【導入案例】無人智慧販賣機 黑沃咖啡一分鐘打造精品咖啡
【2021 Application Example】 Unmanned Intelligent Vending Machines - Black Wo Coffee Creates Boutique Coffee in a Minute

Technology also carries the aroma of coffee Situated on Gaogong Road in the Southern District of Taichung, the original Black Wo Coffee store covers a space of 28 ping, filled with the scent of coffee mixed with cultural creativity and technology Since its establishment in October 2016, Black Wo Coffee has expanded to 7 directly managed stores and 28 franchise stores across Taiwan Among the 15,000 coffee sellers nationwide, Black Wo Coffee has risen uniquely through the use of AI technology to create an unmanned intelligent vending machine that brews exquisite and aromatic coffee in just one minute Black Wo Coffee's physical store creates a culturally creative and fashionable atmosphere Image Black Wo Coffee official website According to the International Coffee Organization ICO, Taiwanese people consume 285 billion cups of coffee annually, with the market size exceeding 70 billion NT dollars Ambitiously, as per Starbucks' survey, by 2018 the overall Taiwanese coffee market reached 72 billion NT dollars and rose to 90 billion by 2020 Over the last five years, the Taiwanese coffee market has expanded annually by about 20, showing remarkable growth potential Coffee demand presents incredible business opportunities, growing at a rate of 20 annually With coffee now being a symbol of fashionable consumption in Taiwan, aside from first-tier coffee shops like Starbucks and Louisa, there are convenience stores like 7-11 and FamilyMart, and numerous boutique coffee houses scattered through the streets and alleys How to capture consumer attention and stand out in the 'red ocean' of the coffee market requires flexibility and creativity, understanding consumer needs and tastes, which are also essential for cultivating brand loyalty Beyond physical storefronts, Black Wo Coffee is also actively developing digital channels Its ecommerce platform includes the official website, PChome, momo, and group-buying hosts, providing multiple channels and ensuring steady growth in performance Even so, the founder of Black Wo Coffee, Lin Pei-ni, continually seeks innovation Due to the passive and scattered situation with franchise stores in the first three years, it was difficult to actively grasp market trends and the company noticed a certain lag in communicating with consumers and keeping up with brand dynamics, making it challenging to cultivate loyal brand advocates Artisan boutique coffee is deeply beloved by consumers Image Black Wo Coffee official website Through the AI Eagle Eye System, market intelligence costs are significantly reduced To address the dual challenges of not being able to quickly capture market trends and high market research costs, Black Wo Coffee introduced the AI Eagle Eye System in 2020 to scout market intelligence By comprehensively crawling articles from social websites, news platforms, and forums, automatically tagging, and suitably filtering, this system scanned 4,858 articles using 24,290 keywords, enabling precise insights into consumer preferences at minimal costs At the same time, after launching new products, not only can franchise stores be notified promptly, but the acceptance level of consumers can also be assessed through social platforms It serves as a reference for whether to promote aggressively Through the collection of data and analysis by AI algorithms, consumer-preferred flavors are selected, reducing the risks associated with new launches and increasing the success rate of new products Therefore, in 2021, Black Wo Coffee boldly explored new markets by introducing the world's first AIoT smart coffee innovative concept in collaboration with Pxmart for the first 'Intelligent Supermarket', integrating Black Wo Coffee to create an unmanned intelligent hand-drip coffee machine for consumers to enjoy a unique flavor experience Insight into consumer tastes leads to the creation of AIoT Unmanned Intelligent Vending Machines Taiwan's first Pxmart 'Intelligent Supermarket' in Neihu, Taipei introduces the world's first AIoT smart coffee concept store, able to interact with the AI smart coffee vending machine, AI hand-washing coffee machine, and AI vacuum cold brew machine through a mobile app, meeting three different coffee technology experiences in one place The self-service area features the only unmanned intelligent coffee vending machine in Taiwan that uses chilled milk to make milk foam, selecting Black Wo's 5A grade milk, and completing the payment, grinding, and brewing all within one minute The first Pxmart 'Intelligent Supermarket' was established on Ruiguang Road in Neihu District, Taipei Image Pxmart FB fan page The Pxmart Intelligent Supermarket features an AI smart coffee vending machine, which is operated using an app to enjoy aromatic coffee Image Pxmart FB fan page Now, with the addition of AI technology elements, drinking coffee is not just about having coffee it also brings more brand-new tech experiences and conveniences to consumers「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

【導入案例】屈臣氏導入insider AI 技術平台 加強客戶體驗提升轉換率
【2021 Application Example】 Watsons Introduces Insider AI Technology Platform to Strengthen Customer Experience and Enhance Conversion Rates

Watsons Taiwan, holding the leading position in physical chain drugstores in Taiwan, has continued to expand its digital transformation Since establishing Watsons' online store in 2014, apart from actively developing the e-commerce market, the company has significantly enhanced the online and offline OO omni-channel consumer experience by integrating Insider AI technology This integration utilizes extensive in-store sales data, consumer behavior analytics, and AI-driven personalized recommendations delivered at optimal times to increase conversion rates OO Online Plus Offline Boosts Customer Conversion Rate, Driving Business Growth Watsons Group, a global retail giant, has been deeply rooted in Taiwan for the past 30 years specializing in retail, store operation SOPs, and retail supply chain optimizations However, managing an e-commerce platform only began a few years ago Unlike the commonly discussed 'O2O' online to offline in retail, Watsons adopts 'OO', which is offline plus online Currently, about 20 of customers who order at Watsons' online store choose to pick up their goods at physical stores Proper service at these stores acts as a catalyst for converting online-originated customers into additional in-store revenues According to statistics, Watsons has nearly 6 million members with a substantial volume of transactions in physical retail outlets However, with over 12 million active app users and nearly 3 million app downloads, the level of member activation is still lacking By utilizing AI technology for data integration, such as providing optimized product recommendations through AI, Watsons could significantly enhance its customer conversion rate from offline to online consumption or guide online customers to in-store purchases, thereby driving business growth Homepage Personalized Recommendation Module Recommended for You Originally, Watsons used the e-commerce solution Hybris from the global system integrator SAP, which was more geared towards simple display and sales, lacking sufficient technical resources to handle enhancing the consumer experience Insider is a marketing technology martech company with offices in 25 cities globally, including a professional consultancy team in Taiwan that provides localized digital solutions Committed to optimizing digital marketing effectiveness with technology, Insider helps brands drive digital growth and is a partner to many domestic and global enterprises including Watsons, Carrefour, IKEA, Lenovo, Adidas, Sinyi Realty, and Singapore Airlines Insider has shown outstanding performance in improving customer conversion rates, repurchase rates, and advertising ROI through AI technology Watsons introduced Insider's AI algorithms primarily for enhancing customer experience, using AI's personalized and integrated marketing modules to elevate the customer interaction and improve e-commerce conversion rates Additionally, AI functionalities search for the right customers, expanding new customer groups and providing a superior shopping experience Page-specific Discount Code Copy Feature Recommended Based on Customer Behavior Insider has developed various technological modules that can be applied in different customer scenarios to enhance conversion rates Currently, Watsons' e-commerce websiteAPP utilizes different Insider modules, with some parts also tailored based on Watsons' unique attributes such as necessities repurchase, app navigation, and scratch card discounts, designing conversion kits or personalized recommendation modules for specific customer situations within Watsons Introduction of WebAPP Personalized Recommendation and Conversion Module Kits Effectively Increases Conversion Rates by 10 Watsons has already introduced the first four of the planned modules, with a full rollout of all five modules expected by 2021, aiming to enhance both online and offline cross-sales and thereby comprehensively improve Watsons’ overall e-commerce and retail performance 1 Web RecommendationConversion Suit 2 App RecommendationConversion Suit 3 InStory for eCommerce 4 Mobile App Template Store 5 Insider Architect Watsons has currently implemented the AT module, with completion expected by the end of 2021 Since partnering with Insider in 2020, Watsons has introduced WebAPP personalized recommendation and conversion module kits, effectively increasing transaction conversion rates by an average of over 10, with ROAS Return on Ad Spend averaging over 10 Watsons also hopes to integrate POS sales records into Insider's CDP Customer Data Platform to achieve a more optimized OO interaction mechanism and complete an all-channel consumer experience By combining Insider's AI technology, Watsons' self-operated official website, supplemented by extensive in-store sales data and member consumer behaviors, along with AI's personalized recommendations delivered at optimal points, the technology will significantly boost consumer transactions online and interactive opportunities in-store Utilizing new technologies in the competitive e-commerce sector allows Watsons to maintain a unique leadership position in the beautyhealth category in the consumers' minds「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

【導入案例】光學產業AOI導入AI大躍進 徹底解決鏡片瑕疵檢測痛點
【2021 Application Example】 Optical industry AOI imports AI Great Leap Forward to completely solve the pain points of lens defect detection

The stay-at-home economy such as smartphones and remote working is booming, and the information and communication industry is booming, driving the optical industry to flourish However, the defect detection of optical lenses is mostly carried out by human eyes, which is not only time-consuming and labor-intensive, but also limited by the fact that human eyes are prone to fatigue The misjudgment rate is also a lingering pain point for the optical industry Benefiting from the evolution of AI technology, Shangyang Optics introduced diffraction optical technology for shooting, used the images captured by the system as the data source, introduced AI model training, and integrated the camera system and image recognition into a production line workstation, greatly improving defect identification The rate is as high as over 90 Taiwan’s optical production value accounts for 10 of the world’s, and the application range of precision optics is expanding day by day The optical industry is a mainstream product in consumer electronics Even though Taiwan was affected by the Sino-US trade dispute in 2019, the output value of optoelectronics still reached US463 billion, accounting for 10 of the world's total Among them, the "precision optics" segment accounts for NT87 billion approximately US29 billion in output value In view of the increase in the number of smartphone lenses, precision optics still maintains a sustained growth of 4 compared to the decline in other fields Since Sharp launched the world's first camera phone equipped with a rear 110,000-pixel lens in 2000, end consumers' requirements for smartphone camera performance have continued to increase, and with the wave of 5G high-speed Internet The advent of the technology has led to the activation of application markets such as augmented reality AR or virtual reality VR The innovation and application of its technology have added a lot of momentum to the optical industry, and the application fields have extended from smartphones to popularization to the mass consumer markets such as automobiles and home entertainment Optical lenses are inseparable from the economic development of "precision optics" As semiconductor technology continues to mature and network speeds continue to increase, optical lenses are used not only in smartphones, tablets, traditional cameras, projectors, In the field of people's livelihood vehicles, the demand for engineering visual inspection and security applications in high-precision manufacturing processes continues to grow rapidly Optical lens defect detection is mostly done manually "Optical lenses" are essential components of the overall optical-mechanical system The lens finish inspection after incoming materials and before shipment not only affects the overall production line efficiency development, but also has an impact on the quality commitment of end customers that cannot be underestimated For a long time, the optical industry has mostly used human eye detection for defect inspection As production volume continues to increase, not only labor costs continue to rise As inspectors age, their eyesight gradually declines, and the misjudgment rate increases every year In addition, manpower recruitment has been difficult in recent years Even if they are lucky enough to be recruited, it is not easy to develop the inspection technology, and the training time is lengthy, making it impossible to respond to the production line manpower needs in a timely manner Introducing diffraction optical technology and AI training model to improve defect recognition rate to more than 90 The current market is flooded with a large number of automated optical inspection systems, and there are many substantial cases of lens defects However, after years of market exploration and evaluation by Shangyang Optics, this system still cannot solve the current manual inspection problem The main reason is that the appearance of the optical lens is curved and transparent, and it is not easy to photograph various defects, and once the defects are around There is interference from other stray lights, making judgment more difficult Moreover, different types of lenses need to be individually rotated and lit and adjusted according to the defect status before entering the judgment stage The labor consumption ratio is still high, which is not in line with the efficiency and cost Through this, through the matchmaking of the AI project execution team of the Industrial Bureau of the Ministry of Economic Affairs, Xiaoma Optics assisted Shangyang Optoelectronics in establishing an effective defect photography system Pony Optics provides guidance on precision diffraction optics Based on the characteristics of "light" fluctuations, lens defects can be obtained through a unified lens shooting method Current photography systems on the market mostly use geometric optics Geometric optics uses linear light and is not easy to capture defects such as missing coatings, tiny scratches, and liquid dirt The cooperation plan introduces diffraction optical technology for shooting Through precise imaging from all angles, it can achieve higher contrast and better noise reduction than ordinary geometric optical elements, so as to obtain the necessary defective images Schematic diagram of optical lens scratches and defects In order to improve the more detailed defect detection and recognition rate in this case, Shangyang Optics used the image captured by the system as the data source, imported AI model training, and integrated the camera system and image recognition into a production line workstation, which not only improved the defect recognition rate Reaching more than 90, it is more conducive to the subsequent development of automated production lines The AI model training for this cooperation project is provided by Yirui Technology Currently, most manufacturers have introduced AOI systems for production line defect inspection Most of them use OCR optical character recognition, which refers to the analysis and recognition processing of image files of text data , the process of obtaining text and layout information technology needs to be 100 accurate, and there is no room for error, resulting in accidental killings often occurring After adding the AI training model, the optical lens defect recognition rate is greatly improved AIAOI solves the two major pain points of insufficient manpower and high misjudgment rate This time, Yirui Technology and Xiaoma Optics cooperated to install Yirui's AI system in the optical inspection instruments developed by Xiaoma Optics, adding AI algorithms to the optical detection of defects, and training based on the data and needs provided by customers AI model identification can greatly improve the accuracy of identification of defects, improve yield rate, and increase production line efficiency Through the tripartite cooperation between Shangyang Optics, Xiaoma Optics and Yirui Technology, the optical industry AOI is introduced into AI, hoping to completely solve the pain points of industrial lens defect detection Since setting up the production line in 2019, Shangyang Optics hopes to introduce a smart production model In view of the continuous growth of the company's operations and the continuous improvement of production volume, through the introduction and expansion of this achievement, the demand for manpower will be significantly reduced, and the impact of production scheduling can be reduced due to the high accuracy of the discrimination rate index, thereby improving production efficiency Shangyang Optics stated that as the development results are implemented, it will lead the technology to be promoted to upstream and downstream players in the optical industry, such as upstream optical lens raw material suppliers to downstream finished product applications, including immersive gaming equipment and related curved glass products , people's livelihood vehicle and security camera devices, etc 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

【導入案例】「以AI補足傳統產業經驗傳承的斷層」塑料再生製程之產量預測分析
【2021 Application Example】 AI Complements the Disruption of Traditional Industry Experience: Production Forecast Analysis in Plastic Recycling Process

As the number of veteran craftsmen in traditional industries diminishes In Taiwan, SMEs have always played a central role in Taiwan's industry, accompanying Taiwan through various 'economic miracles' periods But as time progresses, the old masters gradually became elderly craftsmen Coupled with the phenomenon of fewer children and changes in the overall industrial structure, fewer and fewer of the new generation are willing to enter traditional industries Now, it can be observed that the main combination on most SMEs' operational fields is formed by 'elder craftsmen' together with 'foreign workers' These experienced craftsmen, who act as living dictionaries of field experience, suffer from a lack of successors to continue the tradition, leading to a growing difficulty in sustaining on-site experience transfer in traditional industries The limits of traditional hands-on process optimization are in sight Located in Tainan Baoan Industrial District, 'Tangxian Company' was established in 1972, initially manufacturing high-quality weaving equipment It possesses the capability to manufacture machinery, and in recent years it has actively developed environmentally friendly plastic recycling equipment in response to international green energy, recycling, and environmental protection demands Ultimately, they have successfully developed low energy consumption, low waste, high purity, and high output recycling granulators with a sleek and efficient machine design supplemented by advanced intelligent control technology Tangxian Company's self-developed plastic recycling granulator equipment However, in the production process of plastic recycling, when faced with hundreds of material types and dozens of process temperatures, speed settings, what is faced is thousands of possible parameter combinations Previously, the adjustment of various production process conditions was reliant on the on-site staff the experience of the craftsmen Thus, during the transition of production of different incoming materials such as PET, PP, PE, a significant amount of raw materials would be wasted during the trial phase The professional information gap in traditional industries Tangxian Company recognizes the importance of data In the past, although process parameters were recorded, due to a lack of data capabilities at the time, it was primarily in paper form, manually written down by the operating staff, accumulating a large amount of paper data However, this also meant a lack of scientifically accurate and detailed information available for real-time reference and adjustment Process parameters logbook, records the state of about a dozen machines and production figures hourly In quality control as well, due to a lack of control over the quality of output and monitoring and feedback mechanisms for unit time production, it becomes difficult to predict the profit conditions of each batch Production management can only estimate and average cost and productivity changes over the process from the outcomes, without being able to objectively and timely restore the production conditions to reasonableness or make clearer adjustments when facing quality abnormalities Site reality left image shows recycled scraps right image shows pellet production Taiwanese manufacturers possess strong machinery manufacturing capabilities, and many modern machines now have data capabilities, recording real-time status and information via IoT But is the infrastructure of the factory's on-site and information systems ready yet When the Old Master Meets AI With government referral, Tangxian Company partnered with a Taiwanese data science company, working together to integrate AI services and optimize internal processes using AI They started with a medium-sized plastic recycling production line within the factory as a trial field After establishing a successful benchmark, this model was expanded to larger plastic recycling machinery within the factory to continue verification and application Initially, both parties converted the past handwritten paper data into digital format using OCR supplemented by manual correction Tangxian Company also worked with the supplier of the human-machine interface of the machinery to integrate the control panel and parameter data into the factory's database, allowing real-time monitoring of machine status and eliminating the complexities and potential errors of manual transcription Panel of plastic recycling granulation machine, showing current process temperatures, speeds, and power usage Meanwhile, the Taiwanese data science company further modeled dozens of parameter data through AI, conducting scenario analysis to simulate various production possibilities under environmental parameters and material inputs, identifying key characteristic parameters and providing parameter adjustment recommendations to decrease costs during the trial phase Applying data analysis to traditional industry machinery processes After the old master receives the raw materials, they only need to enter the relevant material characteristic parameters, and the system automatically generates recommended process parameters After small adjustments by the old master, they proceed with the trial production of the material, effectively reducing the waste of materials, water, electricity, and manpower caused by incorrect attempts Moreover, Tangxian Company has proactively deployed the concept of 'production pedigree' in the plastic recycling process, allowing the batch's raw materials and process parameters to be accessed by scanning a QRCode Production and sales pedigree of plastic recycling pelletizing Taiwan's SMEs have strong machinery capabilities, just waiting for the 'east wind' of data From industrial revolutions 20 to 30, even 40, many Taiwanese SMEs face challenges in transitions not just in upgrading machinery, but after investing in modern equipment and generating data, they do not know how to utilize it effectively It is impractical for these manufacturers to develop a specialized data analysis department on their own meanwhile, Taiwan also has many innovative teams with strong software capabilities in AI and data analysis, possessing the technology but lacking the field and data Therefore, if the traditional industries of Taiwan could be fully integrated with the innovative teams in AI and data analysis, it would not only address the current challenges of manpower and experience transfer faced by traditional industries but also advance Taiwan's development and application of AI significantly「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

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【2021 Application Example】 Factory Helper Chatbot Reduces Machine Downtime to One Day

Jinfeng Machinery Industry, the fourth largest punch press manufacturer globally, has developed an app that connects with LINE, WeChat, and IM emergency communication software Regardless of the number of machines, integrated through a single platform, the production and equipment status can be monitored in real time via mobile phones and tabletsEstablished nearly seventy years ago, Jinfeng Machinery is one of the unsung heroes behind Taiwan's early 'living room as factories' approach, with household subcontracting tasks such as spoon and button metal pressing handled by Jinfeng's machines With the advent of Industry 40 technologies, this 'hidden company' based under Bagua Mountain in Changhua had to adopt AI robots to swiftly address malfunctions and reduce wait timesReal-time monitoring AI robots have become essential assistants on the factory lineGM of Jinfeng Machinery Industry, Tseng Sheng-ming famously said 'Always consider the next step for our customers' With an annual revenue of over 75 billion New Taiwan Dollars, a single day of factory downtime equates to a loss of over 20 million dollars At the forefront of Industry 40, Jinfeng uses various sensors to remotely monitor the operational status of machines and record data Through network-connected gateways that integrate peripheral equipment, monitoring data is transmitted to databases to quickly detect and reduce the risk of downtime Cloud-based, 247, 365-day repair registration and constant monitoring are aimed at achieving the goal of an unmanned factory金豐機器工業總經理曾盛明的名言是:「永遠為客戶設想下一步」,年營業額逾新台幣75 億元的金豐,工廠停工一天等於損失2,000 多萬元,走在工業 40 的浪頭上,金豐透過各式感測器遠程掌握機台運作狀態並記錄數據,運用網路連接閘道器整合周邊設備,將監測數據傳送至數據庫,快速檢知降低停機風險,雲端線上全年365 天、每天24 小時報修等隨時監控,以實現無人化工廠的目標。To speed up the resolution of machine malfunctions, Jinfeng Machinery Industry introduced a customer service chatbot developed by Asia-Pacific Smart Machine Company, featuring multi-round dialogue capabilities Combined with a knowledge graph in the punching field, operators simply need to inquire through the proxy robot to quickly obtain troubleshooting solutions and repair quotes, eliminating the need to wait for Jinfeng technicians to handle issues on-site This approach has reduced downtime to within one day, cutting the time spent on factory malfunction resolutions by up to 50Accelerated security screening processes can significantly save up to 30 of manpowerBy applying AI technology for machine understanding, Asia-Pacific Smart Machine facilitates immediate and accurate problem classification through inquiries by customers and front-line staff Online responses to operational issues and needs are synchronized, scheduling repair personnel and materials to quickly resolve faults and effectively reduce downtime losses In the field of tool machines, Open Talk can integrate with Industry 40 tool machines for machine control and real-time data queries Engineers no longer need to use smartphones or tablets they can simply use voice commands to control machines and make inquiries through installed speakers or robots, promptly notifying maintenance when issues arise, keeping repair time within one day Moreover, the technology provided by Asia-Pacific Smart allows for automatic detection of which production line is problematic, type of issue, and management of the situation, speeding up the repair process and potentially saving up to 30 of manpower「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

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【2020 Application Example】 E-commerce Direct Purchase Order Parsing Automation Robot Solves Inventory Issue

Guo Fang Enterprise, the largest professional Velcro factory in Taiwan, produces Velcro, commonly known as hook-and-loop fasteners World-leading medical equipment suppliers like DJO and the zipper-originated company YKK are among its clients Guo Fang has gained the trust of major manufacturers like YKK mainly by implementing intelligent manufacturing, allowing effective inventory management with the introduction of an e-commerce direct purchase order parsing automation robot, thus solving all inventory problemsGuo Fang Enterprise, a leading Velcro hook-and-loop fasteners manufacturer, was established in 1984 Initially, it had 30 employees, and now it employs over 330 people across Taiwan and VietnamGuo Fang Enterprise offers a complete service from raw textile mills, weaving mills, dyeing and finishing mills, to setting mills Through tensile and color testing, professional computer analysis is used to select pigment combinations and ratios, providing stable product quality, effectively differentiating in the market, and establishing a leading position in the high-quality Velcro market, selling to over 60 countries across five continents Top global medical equipment suppliers like DJO and international giants like YKK are among Guo Fang's clientsCurrently, up to 15 e-commerce platforms rely heavily on manual labor for order sorting, inventory management, and shipping tracking, rendering human resources ineffective in product and market development Although additional temporary workers are employed, updating a single e-commerce platform's information requires working until the following February, making it difficult to respond quickly to market demands Limited by human resources, product information details are insufficient, causing difficulties in improving product ratings on platforms like AmazonIntroducing AI Robots to Fully Control Product Inventory InformationThe team at the Information Management Agency, addressing the aforementioned issues, provided an e-commerce direct purchase order parsing automation robot for trial Based on the new product information provided by Guo Fang, it automatically lists products on e-commerce platforms and periodically checks ordersGuo Fang's ability to gain trust from major manufacturers like YKK is primarily due to the introduction of intelligent manufacturing The manufacturing process variables such as temperature, humidity, and speed are quantified into data, which not only allows for efficiency improvement and reduced wastage after accumulating a large amount of production data but also enables small-scale diversified production Even orders for less popular items can be acceptedDue to the characteristics of small-batch diversity, Guo Fang Enterprise has to process over 4,000 orders annually into shipping documents Usually, it takes about 15-30 days to issue documents and deduct inventory, resulting in always inaccurate inventory records Therefore, the team at the Information Management Agency has utilized an AI software robot solution to develop a POS inventory management automation robot application Upon order placement, no manual dispatch is needed for issuing it automatically connects to the POS to deduct inventory, instantly synchronizing inventory amounts in the POS system across all platforms, ensuring the reliability of product inventory information「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

【導入案例】AI銀髮照護智慧平台
【2020 Application Example】 AI Silver Care Smart Platform

As Taiwan's elderly population gradually grows, more and more people require long-term care, but the supply side is never enough to support such a huge demand In the past, a total of 110,000 caregivers were trained, but currently only about 20,000 are actually engaged in care work According to estimates from the Ministry of Health and Welfare, long-term care 20 will require more than 30,000 care workers, indicating that there is still a large manpower gap to be filled In addition, the turnover rate of nursing staff is also extremely high, which makes the situation even worse This dilemma has caused the elderly who should have received proper differentiated care to be unable to be properly taken care of In addition, it has also caused institutional operators to spend huge time costs on education and training, thus reducing the quality of care AI Silver Care Smart Platform 1 Basic hospital management basic settings, equipment settings, hospital authority role settings, staff management, face recognition, resident role management, fall risk assessment, and bedsore risk assessment 2 Bed management bed management and bed status 3 Resident management resident information basic information, bed records, face recognition forms, resident case closure information 4 Message record face recognition record, fall message record, electronic fence message record and blood glucose machine remote measurement result record On the upper right side are matters that need to be reminded of residents, such as quarterly assessments, new residents within 72 hours, care plans, rehabilitation plans, treatment plans and nutritional assessment plan personnel The lower right is the resident search list and the newly added new resident block The right is the assessment service plan reminder Click to check which residents need to arrange time for the plan Machine and equipment settings If new machines and equipment in the hospital need to be added, such as face recognition lenses, after clicking on the new device in the upper right corner, the corresponding device ID, field name, IP location, and device type can be set After entering the status, account and password, the connection settings between the machine and the corresponding field can be completed Machine and Equipment Settings Permission settings Add a permission button in the upper right corner After clicking it, you can add a permission role and check the CHECKBOX corresponding to each major function This function corresponds to hospital staff management, and you can create new hospital staff corresponding to permission roles , in this way, the member's login account password will have member-exclusive functions appear in the left menu, achieving the purpose of authority control personnel Permission settings Bed Management After clicking the Add Bed button, you can enter the corresponding field name such as which building, regional classification name, dormitory name A01 and bed number 0106 fields, all beds in the hospital After the construction is completed, the beds will be available for residents to choose from Bed Management Bed status You can check whether the current bed is corresponding to the resident If it is corresponding, you can also use the hospital bed to query the corresponding resident information After clicking on the bed history record query, you can query the historical information of all beds occupied Bed status Resident information list When a new resident moves in, he or she can click the Add Resident button on the homepage to enter this page After clicking the Add button, it will be divided into four major categories basic information, emergency contact person, personal living conditions, and imported finances to fill it in After completion, press the save button to return to the resident list Find the newly added resident and click on the case medical record function In addition to the basic information above, there are four items of information that need to be completed for individual residents, such as resident photos and attachments Information, meeting minutes and evaluation records You can upload three photos of residents, which can be used for face recognition and homepage profile pictures The documents to be attached include a copy of the ID card, a household register or a copy of the household registration, a family tree, an ecological map, a low- and middle-income certificate, a disability handbook, a subsidy letter, photos of financial items, and other items Minutes of the meeting are taken to assess the completion of the service plan items to be carried out The evaluation form is to understand the residents in more detail The information and analysis items need to be filled in The system will draw conclusions based on the item analysis and provide the nurse with reference for the care plan Basic information on residents Information attached to the Resident Inspection Import Case AI Silver Care Smart Platform Residential Inspection Attached Information Fall assessment for new residents One of the items in the assessment form is fall risk factor assessment Fill in the questions in the field below, and the system will give a score to determine whether there is a risk assessment judgment This is the current organization's early assessment of fall risk Prevention mechanism Service plan generated 1 Service plan generation 2 Smart reminder function There is a reminder function in the lower right block of the homepage For each resident every month or quarter, after calculation by the system, it will automatically remind nursing or social workers to fill in the form and complete the work required by the resident Smart Reminder Entrance Click on the check-in assessment link to enter the list of residents who need to fill in the information Agency staff then fill in the information according to their nursing or social worker status After completion, the reminder for the residents will disappear and the reminder message will appear again next month Remind evaluation service records The system will also automatically remind you to evaluate the service records every week After the caregivers complete the care plan, they must make a relevant record sheet every week to check whether each service is consistent Smart evaluation function After selecting the residents to be queried, click the evaluation function to enter the evaluation query list Evaluation Query List Import Case AI Silver Care Smart Platform Evaluation Record Click on the evaluation plan query to retrieve all previously recorded data from the system for evaluation use The query records filled in each form will be displayed on the following page in sequence according to the sub-functions Since the AI function of fall and pressure ulcer risk assessment is based on 11 physiological data, the service can be spread to the elderly outside long-term care residential institutions, such as the elderly in day care services and the elderly in home services By It is expected that next year it will be extended to the elderly in day care institutions and the elderly in need of home services 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

【導入案例】「Lawsnote法遵系統」,透過AI技術將法遵CRA流程自動化,提升企業法遵效率。
【2020 Application Example】 The Lawsnote compliance system uses AI technology to automate the compliance risk assessment (CRA) process and improve the compliance efficiency of companies

Trends in financial regulation As the world pays greater attention to regulatory supervision, various fields are facing increasing compliance costs If we were to ask what was the fastest growing field in 2020 I believe many people will think it is regulation The trend of strict supervision has been the most severe in the financial industry Supervisory agencies In Taiwan, including the Financial Supervisory Commission, have imposed growingly strict supervision requirements on the financial industry, as well as heavier fines In response to these supervisory measures, the financial industry gradually implemented new internal control and internal audit systems for compliance a few years ago, such as the assessment of regulatory risks, business units appointing compliance managers as the first line of defense, and compliance self-assessment system Current manual compliance process of compliance personnel However, there is a plethora of financial-related laws and regulations, and business units have a large number of complex business manuals Therefore, many compliance personnel of financial institutions must spend a lot of time on tedious and highly repetitive comparisons of internal and external regulations, in order to help companies avoid risks or fines due to not proposing response measures in their internal regulations when laws are amended Compliance personnel spend a lot of time dealing with regulatory changes Lawsnote Compliance System Solution As Taiwan's leading legal technology solutions provider, Lawsnote received many requests from corporate customers for a compliance system, and began to look into solutions for applying AI to compliance systems It thus developed the Lawsnote RegTech compliance system for compliance personnel to automate parts of their work process, reducing the tedious and repetitive work of compliance personnel Lawsnote automates regulatory changes and internal regulatory adjustments A Regulatory database, search, and notification of regulatory changes As the basis of the RegTech system, the compliance process is triggered by "regulations", so it is necessary to have a "complete" and "real-time" regulatory database and regulatory update mechanism for specific fields However, regulations are not limited to "laws" enacted by the Legislative Yuan, but also include "administrative rules" and "legal orders" enacted by administrative agencies authorized by law, as well as "administrative interpretations" used to interpret regulations These are all considered regulations that the compliance system must comply with There is currently no unified data source for these regulatory data Except for the Laws amp Regulations Database of the Republic of China Taiwan, many regulations are scattered on independent webpages for regulations on the websites of different agencies, organizations, or associations, making the cost of collecting complete regulations very high Since laws and regulations will be amended, new administrative letters and interpretations will be issued or old ones abolished, updating regulatory changes is also a big problem Even if complete laws and regulations are collected once, failure to continuously monitor changes in laws, regulations, and administrative letters and interpretations will also create a gap in compliance As a professional legal search engine, Lawsnote has a complete database of regulations and interpretations, and can send notices of regulatory changes required by various fields in response to the needs of compliance systems B1 Internal regulations database and search Internal management by companies through regulations are called "internal regulations" General types of internal regulations include company internal regulations, standard operating procedures SOPs, and business instruction manuals Depending on the intensity of industry-specific supervision, the number and density of company internal regulations will vary depending on the industry In industries with high supervisory density, the number of internal regulations sometimes reaches thousands or even tens of thousands With such a huge number of internal regulations, paper or simple filing systems can no longer meet the internal needs of enterprises The process of searching for and complying with internal regulations might require a significant amount of time and personnel costs if an internal regulations database and search engine are not establish Lawsnote has Taiwan's most powerful legal information search engine, patent search technology, and uses AI to optimize its sorting algorithm It can establish an "internal regulations database"for internal data, such as company internal regulations, SOPs, and instruction manuals, and applies search engine technology to the internal regulations database, achieving a fast, complete, and easy-to-use internal regulations database and search engine B2 ltRegulations ndash Internal Regulationsgt Article-to-Article Linking Mechanism When regulations are revised, the company's internal regulations must also be inspected and adjusted accordingly The company's internal regulation inspection procedures may be initiated by compliance personnel based on regulatory amendments, or may be initiated by the compliance officer of the business unit the first line of defense, and then reviewed by compliance personnel the second line of defense However, regardless of which unit initiates it, the difficulty lies in finding the article of internal regulations that correspond to the amended article of the law to determine whether amendments are necessary Due to the large number of internal regulations, complicated terms, and the different forms of business involved, if internal regulations must be reviewed every time laws and regulations are revised, it will consume a huge amount of time Therefore, compliance personnel usually rely heavily on experience and aim to minimize risk within limited time Moreover, due to the way internal regulations are written, often using different methods to significantly rewrite and break down laws and regulations, making comparison very difficult for programs If the existing program is used to compare internal and external regulations, many internal regulations cannot be effectively determined After research and testing, Lawsnote designed 3 AI algorithms and 4 rule-base algorithms for cross-comparison, which can establish article-to-article links between thousands of regulations and company internal regulations, helping compliance personnel to immediately determine the necessity of revisions to internal regulations when regulations are revised, significantly saving review time and reducing compliance and internal control risks C Internal control and internal audit self-assessment process for compliance In order to ensure that the compliance officer of business units properly carry out the compliance process, some companies will implement mechanisms such as compliance self-assessment and compliance education, and require the compliance officer of business units to conduct self-assessment of internal control and internal audit processes and review existing risks Compliance personnel or auditors must summarize self-assessment results, or prepare a risk matrix to monitor compliance risks and track vulnerabilities The Lawsnote RegTech compliance system supports expanded workflow solutions, which can extend the workflow to the compliance self-assessment process, customize the integration of the current system and compliance system, and merge the organizational structure and SSO permission control mechanism to create a one-stop compliance system Three core modules of the Lawsnote compliance system Incorporates foreign regulations and is the number one compliance tool for companies Lawsnote will continue to optimize regulatory text parsing and identification technology In addition, we will also develop other legal technology application tools and become the number one compliance tool for enterprises with all-inclusive services In addition to domestic regulations, Lawsnote will also incorporate foreign regulations into the system, so that multinational companies in Taiwan can access information on domestic and foreign regulations Lawsnote has always focused on AI applications, data mining, algorithm design, search engines, and workflow optimization in the legal field, and is committed to saving the time of legal professionals through technology

【導入案例】「AI冷鏈運輸斷鏈預警系統」,降低冷藏產品失溫比例、提升產品價值
【2020 Application Example】 AI Cold Chain Transportation Breakage Warning System - Reducing the Proportion of Temperature Loss in Chilled Products and Enhancing Product Value!

Direct delivery of fresh vegetables and fruits from Shangqing, temperature control is key Preserving the freshness of vegetables and fruits is one of the crucial aspects of the agricultural production and sales model Enhancing the efficiency of fresh preservation and integrating cold chain transportation management are critical issues that agriculture businesses need to address In Taiwan, agricultural lands are small and scattered hence, entering the cold chain transportation immediately after harvesting and strict temperature control are essential for maintaining freshness Instances of temperature loss and cold chain transportation breakdowns are increasingly evident The vegetables supplied by the vendor have been highly favored in the market recently, achieving record sales in chain supermarkets and making efforts to enter higher-end consumer markets Recent acquisitions of fresh vegetable supply channels from McDonald's, Costco, and Taiwan Plastic's Steakhouse highlight the need for previously unnoticed issues in the company's own cold chain transportation system to be addressed and enhanced for storage and transportation efficacy Incorporating more IoT and AI architecture and functionalities This 'AI Cold Chain Transportation Breakage Warning System' uses IoT and AI technology to help vegetable suppliers analyze their cold chain systems, particularly focusing on personnel management and resource wastage or damage to fresh products due to improper decisions by personnel By using the Beacon system, AI analyzes the movement paths of chilled goods within and outside the company, personnel needs management, and data analytics It considers neural network learning elements like 'movement paths of chilled goods after storage', 'personnel involvement', and 'product quality at sale' By learning through AI, the system solves and enhances 'internal personnel merchandise quality', 'external chilled vehicle service quality', and establishes 'product quality monitoring and warning' functionalities, achieving comprehensive beneficial effects IoT sensor data collection Based on different needs of each refrigerated space of the vegetable supplier, temperature or humidity abnormality alarms are set When an anomaly occurs, the authorized person's app notifies with a push notification and informs the SOP For more critical issues, an SMS push service is available to notify surveillance personnel not equipped with the app to handle urgent procedures at once, minimizing loss Temperature and humidity sensors placed in refrigerated spaces Refrigerated storage monitoring system APP screen 為確保生鮮蔬果運送過程中溫度未被破壞,也確保進出冷藏室時間差以保證產品品質,並確保商品於正確時間送達正確地點,「Beacon溫度、濕度監測系統」能依據現場條件自動調整Beacon訊號發送間隔時間(自5秒鐘至5分鐘),且電力能維持至少1年,而溫度、濕度蒐集設備則可應用到非AI功能之冷鏈追蹤記錄系統,並藉手機APP便能獨立偵測、蒐集並進行運輸過程冷鏈溫濕度追蹤,著實大大提升運送過程控管的便利性 Beacon溫度偵測設備安裝 Beacon溫度偵測設備安裝 運送行為資料蒐集 此次合作的蔬果供應商其冷鏈監測項目,包含:位於集貨廠內之真空高速降溫冷卻機(可將貨品快速降溫至0~3)及12個冷藏庫、理貨場的堆高機工作環境溫度大約20~25,停留時間不超過20分鐘,運送車輛上車前車輛裝載空間溫度約0等,這些條件理論上都可符合整體冷鏈需求,但實際運作上卻出現相當多狀況。 此次合作除落實冷鏈運輸及管理細節,同時確保產品運送品質,萬一在運送過程品質發生問題,也能在第一時間透過系統得知貨品狀況,若「貨品已經損壞」則立即退回不要出貨給客戶,若是「成為高風險貨品」(可能保鮮期變短,則立即做成便當或特價促銷處理),若是「安全抵達」則可以追蹤整體運輸溫度變化及批次貨品品質確認,同時對於送錯目的地貨品之狀況也能夠立即追蹤處理,避免交易糾紛,有效降低冷藏產品的失溫耗損比例 Beacon訊號偵測設備安裝 AI建模進行冷鏈風險分析評估 導入AI建模分析後之成果可有效監視每一批冷鏈商品運送過程之品質,同時提供合作企業最真實的冷鏈品質回饋,管理階層對於每日大量之儲存、運輸貨品一目瞭然,同時,系統在人員還沒得知產品因為溫度變化而導致品質改變前,便可立即主動示警,有效減少商品損壞可能。 系統管理後台介面 導入AI及物聯網能量後,大幅提升90以上附加價值 一、冷藏商品失溫損壞比例降低62 以蔬果供應商108年3至6月之牛番茄產品損壞率21做為產品損壞之依據,本計畫系統建立後,冷藏商品因溫度變化品質受損之數量,較安裝AI冷鏈監測系統後之108年7至10月牛番茄商品損傷比例可降低至87。 二、提升產品價值30 以蔬果供應商108年3-6月之牛番茄產品銷售額12,464,175元做為提升產品價值之依據,以物聯網加值AI功能後之冷鏈管理系統價值,較只使用溫度記錄裝置管理系統之價值,108年7至10月牛番茄產品銷售額提升率可達30。 蔬果供應商導入AI冷鏈運輸斷鏈預警系統,展開智慧運輸新篇章 蔬果供應商導入AI冷鏈運輸斷鏈預警系統,可降低冷藏商品失溫損壞比例並提升產品價值,更可利用自動預警過期機制,智慧化記錄空間溫度變化並精準監測物品存放位置。未來在冷鏈營運上,將佈建全新冷鏈服務通路,並多方應用冷鏈品質追蹤管理技術,建立智慧運輸的新篇章「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

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