:::

【2020 Application Example】 Small and Medium Enterprises AI Competency Evaluation System, Significantly Reducing the Cost of Competency Implementation for Businesses!

IBM's supercomputer Watson can predict when employees are likely to resign, with an accuracy rate of 95%, saving IBM up to $300 million a year in retaining employees. Moreover, through cloud computing services and modernization, IBM has streamlined 30% of personnel costs, allowing the remaining employees to earn higher salaries and engage in more valuable work.

So, in Taiwan, how can we ensure that 'employees who stay can receive higher salaries and perform more valuable work'? The key lies in the 'competence setting' for each position. According to the 'iCAP Competency Development Application Platform' established by the Ministry of Labor's Workforce Development Agency, every position has its main responsibilities, work tasks, behavioral indicators, work outputs, knowledge, skills, and attitudes. Only by establishing 'competency' for each position can enterprises effectively apply this in employee recruitment, education and training, and performance management. Without this, not knowing what employees should do is like groping in the dark, which can pose risks to business operations.

Competency Benchmark Example

▲Competency Benchmark Example

Currently, on the 'iCAP Competency Development Application Platform', there are 872 established competency benchmarks, including 553 items completed by various ministries. This includes 253 items from the Ministry of Labor and 66 items from the Ministry of Education. If companies want to establish their own 'competency benchmarks', they need to search for reference materials on the 'iCAP Competency Development Application Platform'. Suppose a company wants to recruit 'sales' personnel but doesn't know what 'sales personnel' should do; they should first search for 'sales personnel' as shown in the figure below.

Searching for 'sales' on the 'iCAP Competency Development Application Platform'

▲Searching for 'sales' on the 'iCAP Competency Development Application Platform'

You can find that there are 18 types of sales personnel. At this point, the company needs to go through each one, check, read, and organize into the 'competency benchmarks' they need; however, if we search what should be a common position in any company, 'general affairs', the result is unexpectedly zero items.

Searching for 'general affairs' on the 'iCAP Competency Development Application Platform'

▲Searching for 'general affairs' on the 'iCAP Competency Development Application Platform'

As seen above, although the 'iCAP Competency Development Application Platform' established by the Ministry of Labor's Workforce Development Agency can solve some of the 'competency benchmarks' for positions, the division of labor within each company is different, and some positions might not be found on the 'iCAP Competency Development Application Platform'. Secondly, in small and medium enterprises, there are often 'multi-skilled workers', meaning many job responsibilities are on a single employee. For example, in small enterprises with less than 30 people, usually, accounting, general affairs, and HR are handled by the same person. If you want to establish competency benchmarks for this person, you have to search separately for 'accounting', 'general affairs', and 'HR', and then integrate these three types of job competencies, which is often time-consuming and ineffective.

This 'Small and Medium Enterprises AI Competency Evaluation System' aims to let 'people fully utilize their capabilities', by introducing AI to more accurately establish basic competency standards for employees, and to track their competency performance at any time.

Competency models are all generated and adjusted manually, which is time-consuming

A domestic exporter of screws, nuts, fasteners, etc., had all its competency models generated and adjusted manually. The execution process was time-consuming and insufficient to meet company needs due to personnel changes, such as: previously, Qiao Mai Enterprise had specialized 'production control personnel', but after this personnel resigned, this job had to be done by other employees, meaning other employees' competency models needed to be adjusted immediately. Or if the company needed to set up a development department due to future development, but previously no one had relevant experience, not only did they not know how to select from within, but also did not understand how to describe on a recruitment website to find the talent they really wanted.

Besides, the CEO of this company has always been troubled by internal performance management. Due to the lack of precise standards and systems to measure employee performance, the results of each performance assessment did not accurately reflect the true performance of the employees, forming assessment blind spots and unable to identify truly deserving employees. Thus, it is hoped that with the AI competency evaluation system, the necessary competencies for the development department can be immediately clarified, as well as how recruitment and performance appraisals should be conducted, so as to effectively solve the pain of unclear responsibilities and inaccurate assessments within the company. Thus, its benefits are significant!

AI Competency System Establishment X Deep Learning

This 4-month HR field competency system project has a clear execution direction, but the introduction of explanatory models such as Seq2Seq, Deep Keyphrase Generation, Tf-IDF keyword extraction algorithms, and PageRank are new attempts in the HR field. During the process, open-source big data architecture is used for natural language processing to complete Word2Vector and index, and inverted index to establish keyword weight and relevance. Due to the inability to process like image data with continuous numbers, it is necessary to simplify the feature values with related keywords such as skills, knowledge, and job categories. Basic steps are briefly described as follows:

1. Establish a Propagation model using Google's long-used LTR mixed Pointwise recommendation engine (2 months)

2. Establish a Back Propagation model (2 months), adjust the hyperparameters of the loss function

3. Adjust the hyperparameters of the CF model

4. Establish a human-machine collaboration mechanism to obtain more data to feed the Model 5. Repeat the above steps

During the development process of the competency model, Lianhe Trend Co., Ltd. and Weiguang International Information Co., Ltd. held multiple discussions, believing that there are interconnections between competencies. After establishing the knowledge graph, further upload the competency scale to the Neo4j graph database for processing complex relational data structures with excellent performance. Currently, 500 competency scales have been uploaded to the Neo4j relationship analysis platform.

Using python for wor2vector natural language analysis

▲Using python for wor2vector natural language analysis

In addition to describing a position with a tensor after word2vector, finding out the appearance of this position's knowledge graph, according to this knowledge graph, one can understand the relevance between different positions and the similarity performance of their dimensions. Finally, this knowledge graph is used to establish the company's 'competency model' and train it with deep learning.

AI Competency Evaluation System Interface

▲AI Competency Evaluation System Interface

In the future, in addition to establishing their own competency models, companies can also be opened to end-users. Individuals can analyze their own competency performance to understand their possibilities for job change and their market value, as well as identify skills needing enhancement. If companies respond to this knowledge graph, they can develop cross-industry products in the future.

1. Short-term: Analyze the competency scales (iCAP, iPAS) published by the government with natural language and keyword models, and cooperate with unsupervised learning to establish 'Native Competency Base Unit Models'.

2. Medium-term: Tailor-made exclusive competency models for enterprises. Based on the existing 'Native Competency Base Unit Models', experts use supervised learning to train the individual company's 'Distributed Derivative Competency Models'.

3. Long-term: Establish 'Reinforcement Learning' models, incorporating employee career cognition and planning.

Competency model recommendations, comparable to professional human resource consultants

Through the dynamic learning of the competency knowledge graph through unsupervised learning, individual companies' competency models are quickly established. Internal human resources personnel or external professional HR consultants can then use the generated competency models to assess and apply aspects of talent recruitment, competency inventory, performance management, and education and training. The system will automatically suggest competencies to be strengthened according to the company's existing job structure, including related knowledge, skills, and attitudes. Through the continuous introduction and training of data, the system learns the employer's actual view of the model for that profession and feeds back to the cloud competency scale, completing the dynamic learning of the knowledge graph through transfer learning. In the future, it can be comparable to professional HR consultants, thereby rapidly assisting many cross-disciplinary or technologically diverse companies in training employee competencies.

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

Recommend Cases

【解決方案】優式AI智能割草機器人 搶攻高爾夫藍海市場
USRROBOT's AI Lawn Mowing Robot Enters the Blue Ocean of Golf Market

An AI smart lawn mowing robot, resembling a vacuum robot, shuttles back and forth on the 30-hectare golf course lawn for weeding This robot, independently developed and designed by Taiwanese, is equipped with the world's first electronic fencing positioning technology which utilizes high-precision GPS integrated with cloud AI computation to determine the most efficient mowing paths, targeting the lucrative blue ocean market of golf courses This AI lawn mowing robot was developed by USRROBOT, a Taiwanese startup established in 2019 Chao-Cheng Chen, the president of USRROBOT, once served as the executive vice president of one of the top five ODM tech companies in Taiwan, and specializes in software and hardware integration When he served as the chairman of the Service Robot Alliance, he knew that the service robot industry was bound grow rapidly due to declining birth rates and the growingly severe labor shortage New demand - The horticulture market is large and the has rigid demand "To develop the core technology of service robots, we must find rigid demand Looking at European and American countries, there is a shortage of labor, but demand for horticulture has increased, and there has been a long-term shortage of 7-10 of horticultural workers" Under this strong "rigid demand," Chao-Cheng Chen established USRROBOT, and the company's first product is the AI lawn mowing robot In terms of overseas markets, the United States is the world's largest horticulture market, accounting for 30-40 of the global output value It is estimated that there are about 1 million horticulture workers, but they have been experiencing a labor shortage of 7-10 in recent years and have not been able to improve the situation The main reasons for labor shortage are Aging population and gardening is a labor-intensive job, so young people don't want to do it Unlike in Taiwan, European and American countries attach great importance to lawn maintenance and have expressly stipulated in the law that heavy fines will be imposed for failing to mow the lawn Therefore, the AI lawn mowing robot has considerable market development potential The introduction of AI multi-device collaborative mowing sensor technology is expected to reduce the burden of staff maintaining the golf course The AI lawn mowing robot developed by USRROBOT is currently in its second generation Domestic universities and well-known art museums are using the latest model M1, and it is also being used by some world-renowned high-tech companies and well-known universities in the United States The company is currently involved in negotiations for subsequent business cooperation USRROBOT stated that the professional RTK system currently used can reduce the original GPS positioning error from tens of meters to about 2 centimeters, allowing the robot to move accurately outdoors After setting the boundaries, it can be easily operated using the app New application - Implementation in golf courses solves the problem of labor aging and shortage Chao-Cheng Chen further explained that the National Land Surveying and Mapping Center is a RTK service provider RTK provides the error reference map of the positioning point USRROBOT can access the positioning error value of a specific position through 4G Internet access The AI algorithm of USRROBOT reduces the general 10-20 m error of GPS to 2 cm After positioning, USRROBOT then uses six-axis accelerator positioning, gyroscopes, and wheel differential sensing devices for software and hardware integration Only by matching the wheel's movement pattern and the terrain can accurate mowing path planning be achieved The AI lawn mowing robot, which is 62 cm wide, 84 cm long, 46 cm high, and weighs only 25 kg, can set the mowing boundaries in the cloud It can avoid pools and sand pits through settings, using AI algorithms to automatically calculate the optimal path It is able to mow approximately 150 ping of grass in one hour The battery can be used continuously for more than 6 hours The battery life is currently the highest in the world In addition to general gardening companies, with the assistance of the AI project team of the Industrial Development Bureau, Ministry of Economic Affairs, USRROBOT's AI lawn mowing robot has been applied to golf course lawn mowing A well-known golf course located in Taiping District, Taichung City currently has a staff of 5 people who are responsible for the lawn, planting maintenance, and other landscape maintenance of the entire 30-hectare course However, the average age of staff is as high as 55 years old, and the golf course has been unable to recruit new staff members for a long time In view of the aging staff and the shortage of manpower, the golf course hopes to mitigate the impact with AI technology, and is therefore using AI multi-device collaborative mowing sensor technology, in hopes of reducing the burden of staff maintaining the golf course New challenges - Expert systems are needed to overcome difficulties with different grass species "This AI lawn mowing robot has low noise, low pollution, low labor costs, and is waterproof and anti-theft In the lawn mowing process, it can identify and avoid obstacles through ultrasonic sensors while maintaining mowing quality, maintaining aesthetic and consistent grass length" Chao-Cheng Chen went on to say that the most important part about golf courses is that the grass pattern should be beautiful and free from diseases and pests Based on the site survey, golf courses are mainly divided into three major areas green, fairway and rough There is no problem using the current mowing robot to mow the rough area, and it can overcome slopes within 20 degreesThe short grass in the fairway area may only be two centimeters long, and the grass types are also different, so the cutterhead design needs to be modifiedAs for the grass in the green area, the grass must be mowed close to the ground and maintained in a consistent direction because it affects the putting speed Many factors will affect the green index, and this part requires more research and testing The AI lawn mowing robot can identify and avoid obstacles through ultrasonic sensors while maintaining mowing quality The AI smart lawn mowing robot has a built-in camera that can be used to detect the health condition of the lawn Chao-Cheng Chen said that in the future, an expert system will also be introduced for early determination of whether there are diseases, pests in the lawn or whether there is sufficient moisture, and provide lawn health data analysis to customers, so that they can take preventive and response measures sooner to reduce disaster losses Chao-Cheng Chen, who is also a good golfer himself, said that golf has developed well in Taiwan However, due to weather factors, such as rainy and humid climate and typhoons, Taiwan's golf courses have harder soil and more potholes compared with top tier golf courses overseas If AI lawn mowing robots are to be widely introduced into golf courses, there are still many difficulties that must be overcome However, Taiwan's difficult terrain creates a good testing ground Once Taiwan can overcome the many problems and successfully introduce the robot, it will be able to expand to overseas markets and seize new market opportunities in a blue ocean Chao-Cheng Chen, President of USRROBOT nbsp

這是一張圖片。 This is a picture.
AI-Based PCBA Surface Defect Detection Improvements

With the introduction of theAOIAIWith the introduction of the system, we can improve product yield, reduce costs, and from a business perspective, increase customer trust and sales revenue Moreover, AIit has advantages that are difficult to imitate, unlike other equipment that can be bought with money, making it hard for our competitors to catch up with us Our company's current development We are committed toIOTsmart manufacturing our systems already include smart materials systems, environmental humidity control systems, anti-miscarriage systems, smart procurement computation systems, smart inventory systems, solder paste management systems, and production management systems We have asked other manufacturers about the possibility ofAIinspectingPCBAsurface defects, each hoping that we would purchase their equipment, but none were effective upon verification After discussing with IT service providers, we defined it asAOIAIa feasible operational model Tzuhong Technology has invested inAOIAIan inspection plan to checkSMTtext on components, solder joints, polarity, missing partsand usingAIto replace manual learningAOIand define the 'potentially defective' parts, enhancing productivity and reducing misjudgment rates Industry pain points Taiwan faces a severe labor shortage, especially those willing to perform visual inspections are few and typically older, increasing the frequency of missed inspections Thus, the most critical bottleneck in the pursuit of high-quality electronics has become post-production inspections Previous consumer products with undetected anomalies were acceptable within a certain ratio However, in the automotive industry today, undetected defects could lead to fatalities hence, the automotive industry has extremely high quality demands To survive in the automotive supply chain, we must address the issue of undetectable anomalies Moreover, as wages in Taiwan continue to rise, we can only endeavor toAIreplace traditional manpower with technology, otherwise, even if the anomaly leakage problem is resolved, the relatively high labor costs will still prevent competitiveness in this industry Application technology and explanation Initially,Figure 1,PCBUpon emerging,Reflowsystem, it will undergoAOIwill undergo inspection, dividing into 'suspected defective' and good products At this point, the 'suspected defective' portion accounts for20manual review for these20parts, further classifying the 'suspected defective' portion into good and defective products With We aim to leverageAItechnology, to shift from manual re-inspection of these20technology, we aim to replace manual review of 'suspected defective' products withAIand after review, the results still yield 'good' and 'suspected defective' products, but now 'suspected defective' comprises only3thus reducing the workload of Tzuhong's employees from20down to only3In theory, it isAOIIn theory, after inspection, it is further reviewed byAIbut it appears to go throughAOIonly, so we call this technologyA0IAIDetectionFigure 2。 The original AOI inspection process The operator will place the testPCBboard intoAOIthe inspection equipment, outputtingAOI information on defective products, then manually re-inspect one by one to determine if they are defective AOIAI inspection process The operator will place the testPCBboard intoAOIthe inspection equipment, outputtingAOIinformation on defective products after, then proceed byAIfirst performingAOIre-assessment of defective products, outputtingAIinformation on defective products afterward, then manually re-inspect one by one to determine if they are defective Process differences By introducing theAOIAIsystem, not only can we enhance the efficiency and yield of visual inspection personnel, we also have this timeAIexperience in system introduction, we will also incorporateAIthe use of big data into Tzuhong's existing smart manufacturing systems, further enhancing the performance of our smart manufacturing systems and reducing the pressure on employees Difference between pre and post-introduction Promotion strategy 1 Similar field diffusion allSMTmanufacturers face bottlenecks in inspections leading to shipment delays introducing this system can solve the severe labor shortage issue and enhance shipment speed and quality, allowing self-promotion to customers or through equipment dealers to cater to relevant needs 2 Cross-industry expansion plans negotiate withAOImanufacturers to directly integrateAIthe system intoAOItheir systems, enhancing their market competitiveness Profit strategy 1 In collaboration withAOImanufacturers, collect licensing fees 2 Direct sales toSMTthe manufacturing industryAIsystems 3 ProvideSMTmanufacturing industryAOIAIsystem subscription model「Translated content is generated by ChatGPT and is for reference only Translation date:2024-12-09」

這是一張圖片。 This is a picture.
AI Assists the Red Cross for Smarter Emergency Response

More Preparation Less Loss The Taiwan Food Bank Association, a non-profit organization, collects donations daily from wholesalers, retailers, manufacturers, and even kind-hearted individuals across Taiwan They also rescue consumable materials that are about to be discarded, properly allocate and deliver to households in need, aiding local underprivileged populations When natural disasters such as earthquakes, landslides, mudslides, typhoons, floods, and droughts occur in Taiwan, the food bank's resources can be immediately deployed for disaster relief This field verification unit is the Nantou County Red Cross AssociationOne of the food bank locations, hereinafter referred to as the Nantou Red CrossIs responsible for tasks like pre-disaster supplies preparation and disaster relief material distribution, helping the government bear the responsibility of disaster relief and aid In Taiwan, various natural disasters have characteristics of different duration and spatial coverage, wide or narrow With the normalization of extreme weather, the scale and number of disasters are gradually increasing and becoming harder to predict The required amount and type of materials differ by disaster, and they must address the lifestyles of the affected areas, rescue needs, traffic conditions, geographical restrictions, and other factors for varied material allocation, facing numerous challenges Typhoon Kanu severely damaged transportation in Nantou mountain areas Nantou County Red Cross planned the mountainous route Puli gt Fazhi Elementary School gt Qin'ai Village gt Aowanda to deliver supplies Disasters happen repeatedly We need to be prepared at all times Effective disaster preparedness can mitigate the impact, including swift response to material needs in affected areas, aid distribution, and even psychological support, providing added security for life and property of those in disaster zones Lack of Timeliness in Disaster Information To improve the living conditions and address the lack of supplies in remote areas, the Taiwan Food Bank Association has partnered with the Nantou Red Cross and has successively established food bank points in Nantou City, Puli, and Ren'aiLixing, Ruiyan, XinyiWangmei, Tongfu, Shuili, Lugu and Caotun among others9establish food bank locations, providing supplies worth a certain amount per household every month6001000in New Taiwan Dollars However, many challenges still need to be overcome during natural disasters For example, when typhoons, earthquakes, and landslides occur, the information source for disaster relief dispatch systems relies on post-disaster reports The time lag between reporting, response, and execution prevents timely adjustment and distribution of 'disaster relief' supplies based on the needs of affected areas, affecting rescue efficiency due to lack of timely information The 'preparedness' supplies of the Nantou Red Crosssuch as dry food, water, instant noodles, etc,are recorded manually in terms of stock, expiration dates, and distribution,When a disaster occurs, there is a chance that 'preparedness' supplies have expired and cannot become 'disaster relief' supplies It’s also possible that both conditions mentioned above occur simultaneously, leading to a need for more time to reassign 'preparedness' supplies into usable 'disaster relief' materials On the other hand, upon receiving information about shortages in disaster areas, the supplies donated by the public often grossly differ from the actual needs of the disaster zone, leading to an excess of supplies The Process of Material Operations Before and After a Natural Disaster AIAnticipating Natural Disasters Reinforcing the Accuracy of Preparedness Material Dispatch Application API Technology connects to compute the state of the climate, the intensity of disaster rescues, prioritizing the main tasks of the Nantou Red Cross and the needed areas of search and rescue Coordinated with the existing heavy rain and typhoon simulation 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 System Helps Rapidly Adapt Material Distribution Through the field verification of the Nantou Red CrossAIthe system, material management, and related applications are promoted to more emergency response organizations in different areas, while continuously improving the alert functions within the Emergency Preparedness Material System, strengthening the technological foundation for alerts, enhancing prediction accuracySystem immediacy, and optimizing the data collection and analysis process Simultaneously, it can collaborate with government agencies, meteorological departments, or other rescue teams to discuss integrating more data sources, establishing a mechanism to share resources and data promptly, sharing information in real-time, helping more emergency response organizations enhance their disaster response abilities, seizing the golden rescue time 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-12-12」