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【2020 Application Example】 RPA Robots, Accelerating E-commerce Work Efficiency by 15 Times

Labor-intensive, prone to oversights and errors, low shipping efficiency

A domestic hook-and-loop tape traditional manufacturing transformation and brand management company has expanded new markets and business opportunities through the e-commerce platform model. This requires reliance on substantial labor for product listing, order organizing, inventory management, and shipment tracking. This results in limited product varieties and quantities that can be handled, and manual data entry is often prone to oversights or errors, affecting shipping efficiency and customer satisfaction, which is critical for the competitive advantage of the business in e-commerce.

Internally, many operations rely heavily on repetitive tasks across various computer systems, web pages, emails, etc. Currently listed on 15 e-commerce platforms, updating single e-commerce information alone requires 2-3 months (over 200 items), making rapid expansion difficult; limited by manpower, product information is not detailed enough, leading to doubts in e-commerce reviews, affecting orders and subsequent satisfaction. Presently, orders are only confirmed once a day, leading to an information gap of up to 24 hours. Annually, there are over ten thousand orders to process into shipment orders, typically accumulating for 15-30 days before once grouping deductions from inventory, resulting in always inaccurate stock levels.

Streamlined Client Interface, Accelerating Implementation Efficiency

The mentoring team collaborates with Ruijing Engineering Technology to integrate AI and RPA technologies through a web-based architecture. Robotic Process Automation (RPA) applications are not installed on the local desktop but are stored on a server and accessed only when needed by the user.

This technology, known as Thin Client, provides higher performance and security compared to the Thick Client, which requires downloading applications and data to the local desktop. The Thin Client does not require downloads on the local machine.

RPA collaborative service features include:  Web Scraping: Complex web data collection and arrangement  Email manipulation: Data analysis and disassembly of content and attachments  Web operation: Precise and rapid web operations or filling in specific fields  Application operation: Timed positioning operations of other window applications  Data processing: Data format conversion, decomposition, and reassembly  File Exchange Management: Timed file production, add/delete/modify, FTP upload/download  Database operation: Heterogeneous database data exchange, read or write to a specific DB  Data recognition: Fixed format field data processing; screenshot, snapshot, alphanumeric text parsing and recognition  Scheduling: Can be timed, repeated, cross-process all the above processes  Alert mechanism: Email, Line Notification etc. designated or broadcast notification

Software Robot Technology Solution Execution Architecture

▲Software Robot Technology Solution Execution Architecture

AI software robots enhance the processing speed of orders, inventory management, and purchasing in manufacturing operations, developing automated services to avoid data duplication and input errors, and seamlessly integrating processes across systems operating 24/7. The war room panel facilitates statistical analysis and real-time sales conditions on each e-commerce platform, predicting and optimizing product inventory.

Direct Purchase Order Process Automation Robot

▲Direct Purchase Order Process Automation Robot

E-commerce Information War Room Statistical Analysis Dashboard

▲E-commerce Information War Room Statistical Analysis Dashboard

Software Zero Errors, Reducing Costs by 15% to 90%

面對快速變化又競爭激烈的市場環境,更需要減少重複性、低產值的工作,將人力運用在更高價值的工作上。

Facing a rapidly changing and highly competitive market environment, it is essential to reduce repetitive, low-value tasks, focusing manpower on higher-value work. RPA software robots are 15 times more efficient than indirect staff, also enhancing process quality to near-zero error rate execution quality, offering opportunities to reduce costs by 15% to 90%. Since it doesn't require significant changes to existing workflows, businesses generally do not need to spend substantial manpower on retraining or adapting to new workflows, which contributes to a higher acceptance rate among businesses. Even in software deployment, it only takes about 4-5 weeks to go live.

「Translated content is generated by ChatGPT and is for reference only. Translation date:2024-05-19」

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Realizing the dream of unmanned stores, Magpie Life is building the future of the smartphone industry

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AI Assists the Red Cross for Smarter Emergency Response

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disaster training of the Nantou Red Cross, a 'Natural Disaster Emergency Preparedness Material Dispatch and Supplement Decision System' is establishedreferred to as the Emergency Preparedness Material System。 In material management, inventory data along with immediate supply data are entered into the Emergency Preparedness Material System for comparison and analysis, helping the Nantou Red Cross quickly recognize materials like cookiesdry food, beverages, frozen food, toilet paper, etc, and determining whether they should be 'preparedness' materials or regularly distributed materials Adding to this, information forecasting understands the potential disaster conditions in remote areas, facilitating food delivery, addressing both front-end food wastage and backend practical needs When a natural disaster occurs, it enables faster response and decision-making, completing material deployment, hence increasing the speed of material operation transition20。 AI Emergency Preparedness Material 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【導入案例】防患於未然 麗臺科技研發心臟衰竭AI辨識技術可及早發現病徵
Preventing Problems Before They Arise: Leadtek Research Develops AI Technology for Early Detection of Heart Failure Symptoms

With the increase in the elderly population, the incidence of various chronic diseases is rising daily Among these, heart failure is not only a silent killer it has a very long disease course with a high recurrence rate, leading to increased burden on healthcare personnel However, by using medically certified electrocardiography acoustics devices, coupled with AI predictive assessment of heart failure risk and remote care systems, diagnosis can be aided significantly, helping doctors make accurate diagnoses for subsequent patient medical care or referrals Heart failure has a lengthy course and medical expenditure is five times that of diabetes If you find yourself short of breath even with minimal movement, or if you wake up from sleep needing to sit up to feel comfortable, or if you have symptoms such as swollen lower limbs, anxiety, restlessness, fatigue, or a loss of appetite, be cautious These could be signs of heart failure According to statistics, there are about 60 million people with heart failure worldwide, with 5 million new cases every year In China, nearly 290 million people suffer from cardiovascular diseases, accounting for the second leading cause of death among urban residents around 12 million of these are heart failure patients, accounting for over 59 of cardiac-related deaths The disease course of heart failure is exceptionally long, and both its recurrence and rehospitalization rates are exceedingly high, resulting in medical costs that are twice that of hypertension and five times those of diabetes According to US research statistics, the 30-day mortality rates for patients with myocardial infarction and heart failure are respectively 166 and 111, and the rehospitalization rates within 30 days are 199 and 244 The symptoms of heart failure, because they are similar to those of other diseases such as chronic obstructive pulmonary disease and asthma, have an 185 misdiagnosis rate, which poses a challenging problem for healthcare institutions Leadtek, a major graphics card manufacturer, has been investing in the medical and healthcare sector since 2000 Following two heart attacks in 2011 and 2015 experienced by Chairman Lu Kunshan, Leadtek has focused on health big data, independently developing AI technology for heart failure recognition This AI application reads patients' electrocardiograms and phonocardiograms to perform anomaly detection and model prediction of heart failure risk, enabling early detection of disease symptoms Leadtek independently developed heart failure AI recognition technology to predict medical history and risk Leadtek's independently developed heart failure AI recognition technology has the following three judgment functions 1 Prediction of heart failure history Classifies electrocardiogram and phonocardiogram data into 'with hospitalization history of heart failure' and 'no history of heart failure' 2 Risk prediction of heart failure Provides a predictive risk value of heart failure occurrence based on the electrocardiogram and phonocardiogram data 3 Prediction of heart failure recurrence risk For patients with heart failure, it reads their phonocardiogram and electrocardiogram data, assessing the risk prediction of heart failure recurrence Leadtek states that the application of heart failure AI recognition technology can assist doctors in making more efficient and accurate diagnoses, facilitating subsequent medical treatment or referrals for patients As an instance, in studies of heart failure patients discharged from Taipei Veterans General Hospital, using the EMAT Electromechanical Activation Time index and SDI Systolic Dysfunction Index calculated by the synchronized electrocardiography-acoustic device as treatment guidelines resulted in a higher survival rate compared to those treated based on traditional symptoms This research has also been published in the authoritative international cardiology journal JACC, receiving recognition in the international market System manufacturers can apply heart failure AI recognition technology for other value-added applications Leadtek states that cooperating system manufacturers can choose to build their own heart failure AI risk prediction engine, uploading their system's electrocardiogram and phonocardiogram data to Leadtek's heart failure AI risk prediction engine, which then returns risk prediction values for integration by system manufacturers cooperating manufacturers as a value-added application input Not just for clinical use, the heart failure AI risk prediction engine can also be extended for use at home or in the workplace Additionally, this system can be extended to other applications, including One, hospital outpatient screening Doctors can use the electrocardiogram and phonocardiogram recorder along with the heart failure AI risk prediction model to conduct a 10-second rapid test in outpatient and emergency departments to assess a patient's cardiac history and heart failure risk Two, discharge risk assessment Doctors can use the electrocardiogram and phonocardiogram recorder along with the heart failure AI risk prediction model to assess the heart failure risk during a patient's hospital stay The test data can serve as a pre-discharge risk assessment and prognostic indicator Three, continuous home care Patients can use the electrocardiogram and phonocardiogram recorder, wearable electrocardiogram recorder, and transmit through a home transmission box gateway to measure electrocardiogram and phonocardiogram signals at home and upload them to the amor health cloud platform for heart failure AI risk prediction analysis Patients can manage their health autonomously via an APP, reviewing historical physiological trends disease management nurses can manage member health through the health management backend Web Four, home rehabilitation training Patients can wear a health bracelet to monitor activity, fatigue, circulation, and sleep, autonomously managing their health through the mobile APP and observing the risk of heart failure, engaging in exercise and rehabilitation training to aid in swift recovery The heart failure AI recognition technology system can also be extended to employee home care applications Additionally, in factories or offices, this system can also achieve employee health management goals, with applications including One, workplace safety units Provide employees with wearable electrocardiogram recorders before they start work duties Two, physiological monitoring for business executors While executing business duties or training, employees wear wearable electrocardiogram recorders for fatigue warnings, signaling whether physiological conditions allow continued execution of tasks Task segments can use data transmission boxes or apps to upload physiological monitoring information to the health management platform, assessing the heart failure risk for operations staff, with test data serving as an indicator for enterprise resource human units and public safety Three, workplace physiological monitoring center care The workplace physiological monitoring center can inspect and record employees' historicalphysiological trends through the health cloud platform Four, workplace nursing units Nursing units receiving instructions from the physiological monitoring center can provide health management advice based on employees' physiological trends nursing centers can manage employee health through the health management backend Web Five, employees can wear health bracelets to monitor activity, fatigue, circulation, and sleep, autonomously managing their health and observing the risk of heart failure through the mobile APP, engaging in exercise and rehabilitation training to aid in rapid recovery Workplace application of heart failure cloud care and big data center diagram「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」