:::

【2021 Application Example】 Advancing to Smart Logistics 5.0: Hsinchu Logistics Delivers Medical Materials with Ultra-High Efficiency

編輯群2021-10-14
1

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.

Distribution process at Hsinchu Transport before the introduction of AI.

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 has introduced optimized shift planning with AI, creating the most efficient transport route from the starting point to the destination.

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」

Recommend Cases

這是一張圖片。 This is a picture.
Testing Seat Contact Components AI Intelligent Flaw Detection

With rapid development in 5G, AIOT, automotive electronics, and other downstream sectors, the entire supply chain is expected to benefit from this consumer market As product demand momentum gradually increases, increasing production efficiency and reducing operational costs become the most important issues In order to meet the needs of customers for various packaging types, Yingwei Technology has been committed to developing highly customized test seats However, a resulting pain point is the inability to mass-produce and fully automate operations with machines some tasks still rely on manual execution In this project, the probe part of the test seat was outsourced in 2021, and under current and future large-scale demands, work hours, costs, supply, and quality are issues Yingwei faces The company achieves a defect detection rate of 9995, which seems high, but with an average inspector able to inspect 10,000 needles per day, there would still be 5 defective needles On a test seat that is only 3 cm wide with approximately 1,000 needles, just one defective needle could potentially lead to faulty testing at the customer end As the current operational mode relies on manual visual inspection, external factors such as fatigue or oversight of personnel, and subjective judgment by inspectors may lead to the outflow of defective products, which necessitates strict quality control of contact components We once sought to utilize optical inspections Rule-based for controlling the quality of appearances, but the metallic material of the contact components leads to light scattering, background noise interference, background scratches, and material issues that could result in misjudgments Therefore, we decided to look for AI technology service providers to solve our detection difficulties Developments of Dedicated AOI Line Scan Equipment To meet the needs for inspecting thousands to tens of thousands of probes within our company's IC test seats, traditional surface imaging and individual needle imaging would be too slow to achieve rapid inspection and labor-saving goals In response, the service provider proposed a trial with an AOI dedicated line scan module solution Utilizing a width of 63mm on the X-axis for reciprocal scanning of all probes on the test seat, the tests allowed for the simultaneous scanning of 8-9 probes, significantly enhancing the future detection efficiency of AOI machines This project will proceed with the aforementioned innovative Proof of Concept POC, focusing on the development of the line scanning equipment and performing imaging, learning, and training on both normal and abnormal probes provided by our company, with initial AI model training aimed at preliminary approval This project's customized line-scan imaging module Ideal future imaging result illustration A Single AI Technology Solution for MeasurementDetection Needs Unified use of AI DL CNN learning methods, instead of the current Rule-based system which necessitates defining each defect individually, to meet the needs for abrasion measurement and appearance defect detection of malfunctionsforeign objects When the same machine uses both measurement and detection technologies, not only does it increase costs, but it also affects the detection speed Hence, the service provider recommends the use of a line scan device for imaging Its resolution is sufficient for AI to simultaneously determine appearance defects and assess the condition of needle tip abrasion, as detailed below Line scan pixel imaging displaying needle tip abrasion conditions This AI detection technology meets both measurement and inspection needs for Yingwei, not only bringing more benefits to future probe testing but also introducing an innovative axis in AI technology Change the method of human inspection, enhance work efficiency and product quality After combining both hardware line scan and software AI model training approaches, we successfully ventured into new AOI detection applications Following the AI implementation POC, including the development and validation of a customized line scan module and an initial AI model, the plan is to officially develop the AOI machine next year and integrate it into the IC test seat production line Future Prospects Probe manufacturers upstream and downstream IC factory users both have needs for the AOI inspection machine upstream can ensure probe quality before leaving the factory, while downstream users can use this machine to regularly inspect the condition of numerous IC test seats in hand Given the future demands, the AOI machine is poised to have a significant positive impact on the IC testing industry in the foreseeable future 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-12-12」

【導入案例】挺進智慧物流50 新竹物流醫材配送班表超高效率
Advancing to Smart Logistics 5.0: Hsinchu Logistics Delivers Medical Materials with Ultra-High Efficiency

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」

【解決方案】搭上綠能商機 華鉬實業打造全釩液流電池儲能系統設備 長效儲能的最佳選擇
Taking advantage of green energy business opportunities, Hua Molybdenum Industry creates all-vanadium redox flow battery energy storage system equipment, the best choice for long-term energy storage

Green energy is the future trend and will surely lead to huge business opportunities in the future Wind power has been one of the green energy sources that have attracted global attention in recent years It will become an important force in my country's renewable energy and help Taiwan's power generation reach the goal of 20 by 2025 to improve Taiwan's energy independence As the number and power of domestic wind turbines wind turbines increases year by year, it is particularly important to ensure that the power storage equipment achieves safe, long-term performance, is not easily attenuated during charging and discharging, and is sustainable, low-carbon and environmentally friendly At the same time, the wind turbine equipment itself Health inspection, maintenance and repair have also become the focus of wind farm operators In order to meet the needs of wind farm customers, the green energy business unit of Hua Mo Industry has launched long-lasting energy storage all-vanadium redox flow battery electrolyte and wind turbine AI predictive operation and maintenance, providing 100 safety, long-term efficiency and reducing customer initial manufacturing costs cost-effective power energy storage equipment, and through AI predictive operation and maintenance services to help customers reduce power generation costs by 10 and save up to 30 in maintenance and warranty costs Hua Molybdenum Industry was established in 1998 The industry started by refining vanadium, molybdenum and rare metal elements and other products, and used them in high-end steel, professional chemicals and specialty chemicals industries, and vanadium is more like a steel-making Vitamins can increase the effectiveness of steelmaking Among them, vanadium and molybdenum related products are one of the company's main projects The company sees that the all-vanadium redox flow battery, which is 100 vanadium-based, will be a very promising mainstream green energy technology in terms of long-term energy storage in the future, and before 2010 The government has actively invited legal entities such as the Industrial Research Institute to conduct research on related component materials in solid-state batteries and all-vanadium batteries In addition, the Ministry of Economic Affairs expects renewable energy to account for 20 of power generation in 2025 and reach 15GW Based on the above Considering this, Hua Molybdenum Industry decided to devote all its efforts to research and invest in the technological development of self-developed all-vanadium redox flow battery electrolyte in 2017, in order to accelerate the compliance rate of renewable energy in 2025 Hua Molybdenum pointed out that "renewable energy power is relatively unstable, and Taiwan itself lacks lithium resources In lithium battery manufacturing, almost 80-90 of battery cells must rely on foreign procurement, and there is a lack of 100 domestic self-sufficient energy storage Resources and technology "Similarly, how does Taiwan overcome the problem of having no natural vanadium resources To this end, Hua Molybdenum Industry uses original technology to use waste catalysts from petrochemical industries such as CNPC refineries or Taishuo petrochemical processes Up to 10 of the vanadium ion content can be used to extract high-value vanadium resources, thereby producing Taiwan's 100 self-made all-vanadium redox flow battery electrolyte without being affected by resources, effectively achieving resource recycling Since 2017, Hua Molybdenum Industrial has successfully created all-vanadium flow electrolyte technology, and has successfully passed product verification by the Industrial Research Institute, the Nuclear Research Institute and many international manufacturers Taiwan’s power storage energy target is to reach 15GW in 2025 Its power distribution includes 500MW in Taipower’s automatic frequency regulation system, 500MW in E-dReg and 500MW in existing or newly built solar power plants For example, electricity consumption is mainly between 4 pm and 10 pm, which is the peak period for people's daily electricity consumption For this reason, the Energy Administration specifically requires Taipower to strengthen the upgrade of energy storage equipment, which has also driven the market's interest in all-vanadium redox flow batteries Energy storage system equipment is in high demand In addition, Taiwan's current total power reserve construction and contribution has not yet reached 100MW, and the gap from the 2025 target of 15GW of power storage is still more than 15 times Using all-vanadium redox flow batteries to successfully create 100 safe, low-carbon, environmentally friendly and long-lasting energy storage system equipment Compared with the short-term power storage of lithium batteries, the biggest advantage of all-vanadium redox flow batteries is that it is globally recognized as a long-term power reserve It can store energy for a long time up to 12 hours, which means that if it is charged for 12 hours, It can release power for 12 hours Compared with the electricity measurement method of general energy storage systems, which is daily electricity consumption power in kilowatts x time in hours, for all-vanadium redox flow batteries, power and hours are different Special design, the power is also called a stack, which is composed of four materials metal, polymer mold, carbon felt and graphite plate, and the power consumption time is calculated based on the amount of electrolyte in cubes Therefore, when the power electric push x the amount of electrolyte the daily electricity consumption of our all-vanadium redox flow battery for energy storage The product features of the all-vanadium redox flow battery energy storage system equipment include four major features safety, long-term performance, not easy to decay during charging and discharging, and sustainable, low-carbon and environmentally friendly The quality of the all-vanadium flow battery is 100 safe Since the electric energy is stored in the vanadium-containing electrolyte, it can avoid any flammable accidents caused by a fully charged energy storage system In terms of battery life, compared to the short battery life of lithium batteries, all-vanadium redox flow batteries can have a battery life of more than 20-25 years through changes in price Regarding the charge and discharge performance of energy storage, unlike lithium batteries which have a certain number of charge and discharge times 5000-600 times, there is no limit to the number of charge and discharge times of all-vanadium redox flow batteries Regarding zero carbon emissions, which is highly valued globally, unlike lithium batteries which have recycling issues, the electrolyte of the all-vanadium redox flow battery can be used permanently The material components of the stack are environmentally friendly and fully recyclable to create a truly sustainable and low-cost Carbon-friendly energy storage system Onshore wind turbine AI prediction smart operation and maintenance allows customers to reduce power generation costs by 10 and save maintenance and warranty costs by up to 30 Hua Molybdenum Industry not only improves the long-term power storage efficiency of renewable energy customers through all-vanadium redox flow battery energy storage system equipment and helps customers reduce initial purchase costs, but also uses AI smart operation and maintenance empirical calculations for offshore and onshore wind turbines Field demonstrations were drawn on Taipower's onshore wind farm, and we actively accumulated our own technical experience and energy in AI predictive operation and maintenance With the support of the AI HUB project of the Industrial Bureau of the Ministry of Economic Affairs, the cooperation site will focus on the Phase I wind farm of Taipower Corporation and provide smart operation data of wind turbines for more than 6 months for analysis The AI predictive operation and maintenance system for onshore wind turbines uses machine learning The main technology provider comes from ONYX Insight, a subsidiary of British Petroleum BP The company uses AI Hub analysis software technology to analyze the wind turbines faced by Taipower Pain point analysis, including power generation loss of road-based wind turbines and damage prediction of key components of land-based wind turbines such as gearboxes, pitch bearings under abnormal vibration three-dimensional vibration frequency or abnormal temperature, etc output Through this implementation, it can effectively help Taipower reduce power generation costs by 10, increase asset value by 12, and save up to 30 in maintenance and warranty costs In the past three years, ONYX Insight has successfully predicted and operated more than 20,000 offshore or onshore wind turbines around the world, accumulating extremely high AI model accuracy It is believed that the international partnership established with ONYX Insight will effectively guide and accelerate the green energy division of Hua Molybdenum Industry in its goal and layout to become an independent technology service provider for wind turbine AI predictive operation and maintenance Works with partner ONYX insight to provide customers with an AI predictive operation and maintenance system, including wind turbine power generation loss and damage prediction of key wind turbine components Building a solid foundation for domestic wind turbine operation and maintenance, using Taiwan as a base to expand to Southeast Asian wind farms The market output value of offshore wind turbine AI predictive operation and maintenance in Taiwan will exceed NT30 billion in the future, and the energy storage market has an output value of more than 100 billion US dollars globally In the future company vision, Hua Molybdenum Industrial hopes to become An independent technical service provider for vanadium flow battery electrolyte and wind turbine AI predictive operation and maintenance The long-term goal is to establish a local supply chain of vanadium flow battery electrolytes around the world by accumulating abundant technology and performance capital to supply industry needs nearby 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

PopularTags