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

編輯群2020-03-30

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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【解決方案】佐翼科技無人機導入高爾夫球場域 可節省一半人力
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"The so-called multispectral is to direct light with different wavelengths on the turf, and the reflected images are collected for analysis" Droxo Tech CEO Liu continued to explain that each plant absorbs light with different wavelengths, so multispectral imaging can determine the growth status of grass species At the same time, combined with AI image recognition, the distribution of pests and diseases can be accurately detected, and the amount of pesticide used is determined on this basis Cross-domain collaboration to build a multi-source turf image databasenbsp Using AI multispectral image recognition technology, Droxo Tech will collect visible light, multispectral, thermal images, and hyperspectral images to establish a multi-source turf image database to fully understand the growth cycle of Bermuda grass Droxo Tech has accumulated rich experience in agricultural AI drone pesticide spraying , but there are still many problems that need to be overcome to implement AI solutions in golf courses For example, it is necessary to establish a new pesticide spraying model and test flight methods, especially the application of multispectral image recognition PoC is not difficult, but actual implementation requires more test evidence, repeated inferences, and collaboration with plant experts This part must rely on the cross-domain integration of legal entities such as the Institute for Information Technology III, gathering more fields for verification, and creating a paradigm before it can be more widely adopted by golf courses There are not many international cases on the application of AI drones in golf courses During the verification process, it is not yet known whether it can be quickly copied to the next golf course However, Droxo Tech CEO Liu believes that through cross-domain collaboration, clearly defining the problems and listing them one by one, supply and demand parties can reach a consensus, propose solutions to each problem, and seek cooperation with internal and external resources Only then will we be able to gradually achieve the goal of making golf courses smarter and smoothly assist the industry with transformation Zuoyi Technology's CEO, Liu Junlin 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

【導入案例】海量數位工程AOI機器智能手臂檢測系統 大幅提高瑕疵檢測精準度
Massive Digital Engineering AOI Intelligent Robotic Arm Inspection System Significantly Improves Defect Detection Accuracy

Taiwan is known as a manufacturing powerhouse, yet quality defect detection has always been a chronic sore point in production lines While AOI equipment is available to assist, most use fixed machinery which are limited by angles, resulting in less precise diagnostics and high false positive rates Massive Digital Engineering introduced an AOI intelligent robotic arm detection system that effectively reduces false positives and increases the accuracy of defect detection Generally, the yield rate of products affects the costs for enterprises and the return rate for customers The quality defect detection process in the manufacturing industry often necessitates a substantial amount of quality inspection labor Although there is AOI equipment to assist, these tools are mostly fixed detection machines Fixed cameras are easily limited by angles, resulting in less precise diagnostics and high false positive rates Thus, personnel need to re-screen and inspect afterwards, often manually visual inspection misses defects on average about 5, and can be as high as 20 Three major pain points in manufacturing quality detection Robotic Arm AOI with dynamic multi-angle inspection helps to solve these issues According to the practical understanding by Massive Digital Engineering, there are three main pain points in detecting product quality within the manufacturing industry Pain point one, manual inspection of product quality is prone to errors Currently, the manufacturing industry largely relies on human labor to inspect product appearance, but human judgment often entails errors, such as surface scratches, color differences, solder appearance, etc The error rate in defect judgment is high, and can only be inspected at the finished product stage, often leading to whole batch rejections and high costs in labor and production Pain point two, inability to quantify and record data from quality inspections Traditional manual inspections do not maintain inspection data, which makes it difficult to assign responsibility when quality disputes occur Moreover, high-end contract manufacturing orders from overseas brands often require traceability and corresponding defect records, which traditional human inspection methods struggle to meet Pain point three, limitations of traditional AOI visual inspection systems Current manufacturing uses AOI visual inspection systems, which due to the limitations of visual software technology, employ fixed cameras, fixed lighting, and single-angle operations This method may handle flat or linear-shaped products like rectangular or square items at a single inspection point However, it is more challenging to implement for products with complex shapes eg, irregular automotive parts, requiring multi-point and multi-degree inspections Massive Digital Engineering developed an AOI intelligent robotic arm detection system, effectively improving the accuracy of defect detection To address the pain points in quality inspection in manufacturing, Massive Digital Engineering initiated the concept of developing a multi-angle, movable inspection device, starting with the combination of two representative technologies in factory automation - robotic arms and machine vision By integrating robotic arms with AOI for dynamic multi-angle AI real-time quality inspection, the limitations of fixed inspection systems are addressed, and visual inspection techniques are enhanced by leveraging artificial intelligence, further elevating the sampling of images from flat to multi-dimensional and multi-angular Selected the automotive industry as the real-world testing ground to quickly respond to customer needs The AOI intelligent robotic arm detection system, utilizing AI technology including unsupervised learning, supervised learning, and semi-supervised learning, allows operators to use unsupervised deep learning techniques to learn about good products even when initial samples are incomplete or there are no defective samples, applying it in the visual inspection of automatic welding of car trusses This can solve issues of limited angles with fixed machinery before implementation, less precise diagnostics, and high false positive rates Automotive components are high in unit price and demand a stricter defect detection accuracy In industries that have adopted AI services, the automotive manufacturing sector was chosen as the real-world testing ground Massive Digital Engineering states that the automotive industry mainly consists of related component manufacturers and components typically have a higher unit price, hence requiring more in terms of quality inspection and yield rates, and demanding stricter accuracy Therefore, the automotive sector was chosen as the area for introduction By using a robotic arm combined with AI for dynamic multi-angle AOI visual real-time quality inspection, not only can the defect quality error rate of automotive components be improved, but the fixed-point AOI optical inspection can be enhanced to meet the measurement needs of most industries and finally, establishing a third-party system platform to build an integrated monitoring system platform, enabling immediate response and action when issues arise This system allows for recording and storing important data of products leaving the factory, serving as a basis for future digital production lines and virtual production At the same time, in the event of defects, it can immediately connect to Massive's MES monitoring system, quickly responding to the relevant manufacturing decision-making department, subsequently utilizing ERP systems for project management and reviews, effectively improving production efficiency and reducing production costs Helps to reduce communication costs and aims to become an industry standard In terms of industry integration, it provides a foundational standard for data continuity among upstream and downstream businesses, reducing communication costs within the supply chain Through certification of the contract manufacturers and brand owners, there is a chance to become the industry standard configuration Through the data database established by this project, operators can further optimize their supply chain management solutions using big data analysis Data Analysis, based on data, establish forecast planning, and utilizing technology to link upstream and downstream data of the supply chain, accurately controlling product quality In the future, when interfacing with European, American, and Japanese markets, which demand highly fine-tuned orders, operators can respond and integrate the industry supply chain Supply Chain more swiftly Ultimately, through the benchmark demonstration industry's field verification, such as with the automotive component manufacturing industry used as the benchmark demonstration field, by implementing the robotic arm combined with AI for dynamic multi-angle AOI visual real-time quality inspection system project, the supply chain connection between automotive contract manufacturers and OEMs can be optimized, becoming the industry standard Further seeking more AI teams to join the cross-industry development on the field collaboration platform, driving the overall ecosystem combining AI innovation with field application Self-driving vehicle developed by Massive Digital Engineering「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

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