After incorporating AI technology, traditional logistics companies have seen significant improvements in transportation efficiency and reductions in transportation costs, especially in the transfer of medical materials which involves timely service and rights of hospitals and patients The implementation of intelligent logistics can save medical material businesses the cost of constructing GDP warehouses and other expenses up to millions A major domestic logistics leader, Hsinchu Transport HCT, owns a fleet of 3,500 vehicles and a storage area of 60,000 square meters, providing customized logistics solutions including logistics, commerce, finance, information, distribution, storage, and processing The company handles up to 580,000 parcels per day, with a maximum capacity reaching 900,000 parcels, making the enhancement of transshipment efficiency crucial for HCT Medical materials transportation at hospitals need optimization of current operational processes and enhancements in systematization and intelligence Especially the transportation of hospital medical materials, which encounters various challenges Medical materials suppliers need to cater to varying customer product demands, temperature requirements, and delivery times through multiple logistics providers This highly depends on the experience and careful control of operations staff Whether it is the product shipment or actual logistics process, each step must be interconnected Any human errors can impact the service timing and rights of the hospitals and patients Thus, all concerned businesses, along with the government and hospitals, are working to optimize current operational processes and elevate the level of systematization, automation, and intelligence to minimize service errors and cost losses HCT's distribution process prior to AI implementation Currently, with the government's push for standardized platform operations on the demand side of hospitals, supply-side businesses collaborate through data coordination to improve the accuracy and efficiency of product shipments, enhancing operational quality and management benefits at the demand side At the same time, some businesses are also investing in the standardization and systematization of internal operational processes, thus enhancing operational efficiency and quality In the freight logistics sector, logistics companies' warehouse staff need to expend labor to control different logistics shipment operations If they often receive emergency task notifications for shipments to medical facilities, they usually depend on small regional logistics providers to provide customized delivery services Although this improves delivery times, it does not allow for integrated informational services The new GDP regulations for medical materials require suppliers to undergo GDP compliance certification Therefore, Hsinchu Transport, assisted by the Ministry of Economic Affairs' AI coaching program, not only extends existing logistics services compliant with GDP regulations but will also use data integration and optimized AI technologies to help medical material businesses streamline and improve their logistics operations Complex logistics issues are solved using the Simulated Annealing SA algorithm To meet the 'Good Distribution Practices for Medical Devices,' Hsinchu Transport is not only actively introducing new logistics vehicles but will also implement artificial intelligence-based mathematical optimization technologies to assist in intelligent scheduling at nationwide business points and transshipment stations They aim to optimize the routing of medical materials between business points or regions thereby enhancing efficiency in the distribution process Currently, during the transshipment process of medical materials at Hsinchu Transport, detachable tractor heads and containers are used Each business point and transshipment station differ in location design and staffing, impacting the throughput per unit of time Furthermore, daily cargo conditions size, destination vary, and due to these fluctuating and distinct demands, the deployment of tractor heads and containers changes accordingly Under these circumstances, Hsinchu Transport relies on past experiences to schedule departures at each satellite depot and adjusts daily according to the cargo needs Due to the reliance on empirical scheduling, it is often difficult to consider all variables and considerations, leaving room for improvement in the current departure schedules The cargo delivery planning inherently constitutes an NP-Hard problem, difficult to solve with traditional analytical methods Hsinchu Transport, in collaboration with Singular Infinity, utilizes the Simulated Annealing SA algorithm to find solutions The new logistic service introduced by Hsinchu Transport is 'GDP Container Shift Planning' This planning involves estimating future volumes of medical materials between stations and scheduling container truck shifts accordingly, ensuring timely and quality delivery of medical materials while maximizing operational benefits and reducing travel distances Hsinchu Transport introduces AI-optimized shift planning, constructing the most efficient route from its origin to destination Hsinchu Transport introduces 'Optimized Shift Planning' service, reducing transportation costs by 5 The introduction method involves using cloud software services Hsinchu Transport regularly inputs 'Interchange Item Tables' from station to station into the 'Optimized Shift Planning' service After setting the algorithm parameters, a GDP container shift schedule is generated At the same time, developing a Hsinchu Transport medical material scheduling system allows Hsinchu Transport's medical transport units to compile suitable schedules through the Interchange Item Tables Under the same level of service, it's estimated that this can reduce transportation costs by 5, saving medical material businesses millions in construction costs for GDP warehouses and distribution Due to its requirements for sanitation, temperature, and its fragility, the transportation and transshipment of medical materials should be minimized to reduce exposure and risk However, logistics efficiency and costs must still be considered AI designs the most efficient route for each cargo from its origin to destination, effectively completing daily transportation tasks In response to the future high development demand of industrial logistics, distribution and transshipment AI optimization will be a key issue Through this project, a dedicated project promotion organization will be established, staffed with AI technology, IT, and process domain talents After accumulating implementation experience, the application of AI will gradually expand, comprehensively optimizing and transforming Hsinchu Transport's operational system, and partnering with AIOT and various AI domain partners to accelerate and expand the achievement of benefits「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

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」

How to integrate consumer data from various group companies to create advertising synergy and enhance the conversion rate of e-commerce guided orders is probably what every cross-industry business owner dreams of No problem, this can be achieved gradually through AI Eastern Home Shopping is affiliated with the Eastern International Group, which includes East International, Eastern News Cloud, Eastern Insurance Representatives, Eastern Natural Beauty, Eastern Global Marketing, Eastern Pet Cloud, HerEastern, Focus Media, Hong Kong Strawberry Net, and Bear Mom's Vegetable Market, among other companies With cross-industry and cross-domain relationships within the group, and independent operations of membership systems in each unit, consumer data could not be exchanged within the group, making it difficult to uphold Eastern Group's promise to 'place customers in a godly position' Eastern Group’s companies cover a wide range of industries, with large and scattered member databases The Eastern Group boasts significant member traffic and has applied AI news recommendation algorithms and other related technologies across various venues However, the independence of member systems in each unit of the Eastern Group prevents the exchange of consumer data within the group, lacking a comprehensive basis for consumer behavior analysis This results in the inability to enhance the precision of personalized services and marketing strategies When analyzing the challenges and trends of the current retail market, Eastern Group remarked that in response to changing consumer demands, non-traditional business models are emerging, leading to the fragmentation of retail Various emerging business models provide services or products catering to their niche markets, leading consumers to rely less on traditional retail models Retail fragmentation, becoming more apparent in emerging countries, rapidly develops new forms of retail such as high-growth flash sale eCommerce, which threatens traditional B2B2C eCommerce platforms These emerging business models quickly divide traditional retail spaces and could revolutionize existing market rules The retail market is expected to continue evolving towards segmentation The rapid integration of AI applications in the new retail industry to meet highly competitive markets Under the trend of merging physical and digital realms, the line between offline retailers and online e-commerce is increasingly blurred Offline retailers are setting up brand official websites and developing brand apps, investing in e-commerce platforms, while e-commerce operators are starting to established offline physical experience stores, enhancing touchpoints with customers Both are exploring consumer data profiles through offline-online integration, based on AI technologies like machine learning, deep learning, computer vision, language processing, mobility control, and decision-making technologies to actively integrate intelligent retail AI applications, shaping the new retail industry Additionally, Google Chrome claimed in 2021 that it would disable 3rd party cookie functionality within two years, causing retail companies to lose the ability to track personalization via Cookies and understand user behavior across different times, locations, and ads This will prevent cross-device, cross-platform tracking, forcing companies to transform and face big challenges in traffic advertising sales Therefore, the Eastern Group decided to implement the 'OneID AI Traffic Monetization Service Validation Plan', establishing an exclusive data alliance for the Eastern Group, using 'Unified ID' for cross-industry, cross-service data exchange Transforming from collecting personalized data of related companies to analyzing common behavioral characteristics of consumers across industries, segmenting them to obtain users with similar behaviors, and providing interesting content Additionally, utilizing first-party data and AI technologies to improve ad click-through rates, enhancing the advertising value and e-commerce guided order conversion rates This AI technology project is co-developed by Eastern and ASUS computers, encompassing major development tasks such as project planning, system architecture design, system environment setup, algorithm development, algorithm model validation, and system verification The employed technologies include a big data parallel processing framework, natural language processing, user recommendation embedding systems, similarity search, search engine indexing, and click rate prediction This project aims to develop a comprehensive data collection, processing, and integration platform 'Data Middleware', collecting various data sources, focusing on users as the basic unit, forming structured data tables, and calculating user tags for precise characterization of each user Subsequently, this data is utilized for precise AI advertisement placements Eastern Data Middleware structure diagram Eastern Home Shopping introduces OneID AI Traffic Monetization Service, predicting cost-effectiveness to be up to 2 times Eastern stated that this project primarily applies 'user behavior data' and 'AI technology', with user behavior data provided by the Eastern Group and AI technology being co-developed by company and ASUS teams, covering systems such as AD Serve, precise audience estimation system, AI automatic optimization system, advertising efficacy system, and user profiling system The customer data and traffic of AI technology co-developed with ASUS remain independent and not interconnected According to estimates, this development project's total cost-effectiveness could reach 200, expected to precisely capture the user's digital trajectories, behavior, and profiles, potentially resulting in significant growth in customer lifetime value LTV, effectively integrating Eastern's online and offline services, enhancing membership service content, and substantially increasing corporate value In the future, as the Eastern Group continues to expand into international markets, it currently targets Mainland China as the primary promotion market, extending the entire service module with Eastern Global’s operational model to the global Chinese market while ensuring compliance with GDPA, merging it with Strawberry Net to provide Eastern's new retail services with the advantages of big data and AI globally Eastern Group will expand its services and technology to the global market through Strawberry Net「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」