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【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

【導入案例】維繫遊艇王國美譽 嘉信遊艇導入國內第一套FRP複材超音波智慧檢測
Maintaining the reputation of the “Kingdom of Yachts” - Kha Shing Enterprise introduces the first domestic FRP ultrasonic smart inspection of composite materials

The Kaohsiung-based Kha Shing Enterprise Co, Ltd was established over 40 years ago, and is Taiwan's largest customized yacht company with customers all over America, Europe, Asia, and Australia, earning Taiwan the reputation of the "Kingdom of Yachts" Current FRP hull inspection still relies on traditional methods, such as visual inspection and knocking sounds, which is time-consuming and labor-intensive Kha Shing has applied PAUT array ultrasonic inspection to hull FRP composite materials for the first time, and combined it with AI to interpret ultrasound images, develop complete intelligent solutions, and create emerging markets for inspection companies Kha Shing Enterprise Co, Ltd was formerly Kha Shing Wood Industry Co, Ltd, and was a factory specializing in wood import in Kaohsiung Linhai Industrial Park when it was first established It began to design, manufacture, and sell yachts in 1977 After the second-generation successor of the company, President Kung Chun-Hao entered the company, he made a breakthrough in the previous manufacturing model that relied mainly on the skills of master craftsmen, introduced digital manufacturing to accelerate shipbuilding, and began to make larger yachts, ranking in the top 20 manufacturers worldwide among manufacturers of large yachts over 24 feet It also set a record of delivering 94 yachts within one year, earning Taiwan the reputation of "Kingdom of Yachts" Defect detection ensures yacht quality, using AI to replace humans to achieve higher efficiency Defect detection is very important to ensuring yacht quality At present, the yacht industry still uses very traditional defect detection methods The hull structure is usually made by hand lay-up or the vacuum infusion process, using visual inspection or knocking and the frequency of the sound to determine defects It requires time-consuming manual inspection If there are any defects, they must be reworked and repaired, and a gel coat subsequently sprayed The hull must be constructed in sections to facilitate inspection For large yachts over 24 meters long, construction in sections is very time-consuming and labor-intensive To shorten the time of the yacht manufacturing process, Kha Shing Enterprise will first carry out the gel coating process for the hull, and then perform the hand lay-on process The hull manufacturing process has two types of composite material test specimen structures In terms of 54-foot yacht hulls, the hull contains gel coat, core material, fiber and resin, and the total thickness is about 32cmplusmn01cm, which is twice the total thickness of FRP hull without core material of about 16cmplusmn01cm Defects such as incomplete impregnation of glass fiber or residual air bubbles between glass fiber and resin occasionally occur during the manufacturing process The types of defects include insufficient resin, voids, and delamination Once defects occur, the supply of hull materials will be insufficient and yacht delivery will be delayed Schematic diagram of types of FRP hull In order to solve this problem, Kha Shing Enterprise has engaged in technical cooperated with the metal materials industry and the AI technology industry, combining the ultrasonic inspection expertise of the metal materials industry with AI technologies developed by the AI technology industry in recent years to help solve issues of Kha Shing Enterprise with defect detection The method uses PAUT on the composite material structure of yachts, conducts FRP ultrasonic evaluation to determine the thickness of the yacht hull and material properties, and evaluates the ultrasonic probe frequency applicable to the hull structure based on professional ultrasonic experience After testing, a frequency of 5MHz and a probe width of 45mm can successfully find the location and size of defects in the simulated defect test specimen The three parties jointly found defect detection solutions from array ultrasonic evaluation, AI technology model development, and actual application in yachts The image inspected is an ultrasound image The image displays different colors based on the ultrasonic feedback signal An AI model that automatically identifies defective parts is established through the YOLO algorithm If the amount of abnormal data collected is insufficient for training, the CNN-based Autoencoder algorithm is used to collect normal image data for training and construct an AI model for abnormality detection The object detection YOLO model is trained by inputting image data marked as having defects, while the abnormality detection model is trained by inputting image data without defects Simulated defective specimen corresponding to PAUT results Defect detection by and AI system can shorten the construction period by 15 months and speed up determination by 50 After the development of this AI system is completed, it will be validated on actual 54-foot yachts of Kha Shing Enterprise, and can effectively resolve issues with defects The application of AI technology in ultrasonic inspection for intelligent determination is expected to accelerate determination by approximately 50, and will also shortens the construction period by 15 months, effectively improving the speed and quality of the yacht manufacturing process As Taiwan develops larger and more refined yachts, it will create opportunities for industry optimization and transformation, as well as opportunities for the development of key technologies The application of an AI ultrasonic inspection solution for composite materials is the first of its kind in the yacht industry, and is expected to attract more yacht manufacturers with inspection needs The AI ultrasonic inspection solution for composite materials has three major competitive advantages 1 Professional inspection experience and digital database to facilitate process management and analysis 2 Automatic AI determination and identification quickly identifies defects and provides immediate feedback to process engineers 3 High-efficiency process inspection provides defect repair recommendations, reduces damage rate, and improves the strength and quality of composite materials The application of AI technology can optimize the yacht manufacturing process, reduce manual inspection, create added value through the application of AI in Taiwanrsquos yacht industry, increase international purchase orders, and allow Taiwan yachts to continue to enjoy a good reputation in the world Furthermore, this business model has also spread to fields of application related to composite materials, increasing cross-sector market usage It is estimated to contribute approximately NT14 to NT2 billion in economic benefits to Taiwan's equipment maintenance and non-destructive testing market

這是一張圖片。 This is a picture.
Realizing the dream of unmanned stores, Magpie Life is building the future of the smartphone industry

"The DNA of Magpie Life is not limited to vending machines We believe that vending machines combine technology, access, and humanities to bring us exciting results" This is a sentence on the official website of Magpie Life Let the vending machines bring To live a pleasant life and build a considerate, technological and sustainable future for the smartphone industry is also the original intention of Magpie Life Founded in 2018, Magpie Life launched Taiwan’s first private-brand mobile payment scan code sensor 4 months after its establishment, completing the consumption experience through screen touch The Magpie U1 smart vending machine manages the POS system and gathers data in the background, allowing consumers to synchronize with the world's new retail pace and experience a new retail consumption experience of purchasing convenience, checkout security, visual entertainment, and improved logistics replenishment efficiency Traditional vending machines lack information visibility and AI technology assists in information transparencyThis time, the Magpie smart vending machine is also equipped with AI technology to provide adjustable shelf space , a vending machine equipped with an industrial computer and a large-size touch display screen to achieve the purpose of a store-less store Magpie Life stated that the biggest problem with traditional vending machines is the lack of information visibility To check inventory, replenishment personnel must physically inspect each machine, which is time-consuming and costly When a machine breaks down, it will generally be unable to operate for a long time Most failures go unreported and are not discovered until the next restocking crew arrives to replenish supplies Then you have to wait for a service technician to be scheduled, which can take weeks Traditional vending machines lack real-time interactivity When consumers encounter problems after inserting coins, manufacturers cannot handle them immediately In addition, traditional vending machines are less flexible and cannot adapt to changes in consumer preferences Traditional vending machines have shortcomings such as limited change shopping, single payment tools, limited number of products, and few choices Affected by the COVID-19 epidemic, consumption habits have shifted to contactless methods, causing the unmanned store market to heat up Generally, vending machines can only place relatively simple products such as drinks, food, etc The properties available for sale are limited The patented vending machine developed by Magpie can adjust the shelf space and is equipped with a lifting cargo elevator, which is suitable for various types of goods In addition, the machine is equipped with an industrial computer and a large-size touch display screen, which can meet the needs of advertising support at the same time It is expected to move towards a storeless store According to Magpie Life Observation, the consumer market trend in the past two years is that consumers demand convenient life, food consumption patterns value dining experiencesimple and fast, and are equipped with mobile phone-connected ordering models, and hot drinks and Fresh food delivery is the focus of two major trends The location, items sold, consumption methods and multiple payment methods are the focus of market growth for smart vending machines In terms of convenience, Taiwanese consumers still prefer to purchase vending machine food near stations, airports, schools, and businesses in business districts Various payment methods are also gaining more support from consumers, indicating that in the future, automatic Vending machines can be developed in two directions diversified items and diversified payment methods AI sales forecast technology integrates back-end management to achieve precise marketing purposesDue to the wide variety of products, it is difficult to know the performance of products under different factors such as season, market conditions , promotional activities, etc, it is easy to cause out-of-stock or over-inventory situations Magpie Life has specially developed "AI sales forecasting technology" and integrated it into the back-end management system, hoping to lock in customer purchasing preferences and intentions through data analysis In order to achieve the purpose of precise marketing, make accurate business decisions and effectively allocate limited resources The introduction of AI systems can achieve the three major goals of precise marketing, inventory management and supply chain management This system is a replenishment decision-making aid designed specifically for supply chain managers It uses AI to predict future sales demand, helping companies effectively optimize production capacity, inventory and distribution strategies Its overall system architecture includes1 Data exploratory analysis function Provides automatic value filling, automatic coding and automatic feature screening functions for missing values in the data 2 Modeling function 1 Provides model training functions for two types of prediction problems regression Regression and time series Time Series Forecast nbsp2 Supports Auto ML automatic modeling, and the best model is recommended by the system Integrated models can also be established to improve model accuracy nbsp3 Supports multiple algorithm types Random Forest, XGBoost, GBM and other algorithms nbsp4 Supports a variety of time series models exponential smoothing, ARIMA, ARIMAX, intermittent demand, dynamic multiple regression and other models nbsp5 Supports a variety of model evaluation indicators R, MAE, MSE, RMSE, Deviance, AUC, Lift top 1, Misclassification and other indicators nbsp6 Supports automatic cutting of training data sets and Holdout verification data sets, and can manually adjust the ratio nbsp7 Supports automatic model ensemble learning Stacked Ensemble, balancing function learning Balancing Classes, and Early Stopping nbsp8 Supports the creation of multiple models at the same time The system will allocate resources according to modeling needs, so that modeling, prediction and other tasks have independent computing resources and do not affect each other In the overall server space With an upper limit, computing resources can be used efficiently nbsp9 It has in-memory computing function, which can use large-capacity and high-speed memory to perform calculations to avoid reading and writing a large number of files from the hard disk and improve computing performance 3 Data concatenation function Using API grafting and complete data concatenation automation, there is no need to manually import data, improving user experience 4 Chart analysis function Provides visual charts and basic statistical values for product sales AI data analysis solutions have two major advantages 1 Entrepreneurship machines can be rented and sold at low cost to open unmanned physical stores and cooperate with the chain retail industry Through smart machines, entrepreneurs can rent and sell them at a lower cost than the store rent Cost of running a retail business Two cooperation models, machine sales and leasing, are provided, and the choice is based on the evaluation of the industry 2 Various types of products are put on the shelves Products are sold anytime and anywhere 24 hours a day Up to 60 kinds of diversified products can be put on the shelves Large transparent windows enhance the visibility of products Regular replenishment and tracking of product sales status are available, and product types can be adjusted according to needs Recently, the line between the Internet and the physical world has blurred, the way customers interact has changed significantly, and consumer demand is changing and personalized The retail industry is facing unprecedented challenges and uncertainties, and mastering data has become key AI data analysis solutions can help the retail industry quickly activate large amounts of data, create seamless personalized experiences, optimize the operational value chain and improve efficiency, and strengthen the core competitiveness of enterprises 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」