Initiatives to Develop Digital Human Capital

Explanation

Initiatives to Develop Digital Human Capital
MIYAUCHI Yuuki

1

ISASHI Ryosuke

Note 3) A technology to learn regularity and patterns of datafrom a variety of data including alphanumeric charactersso that computers can determine the current conditionand predict the future.
Note 4) A technology to automatically extract features to be focused on from data and use them for determination of thecurrent condition and future prediction.

Introduction

These days, all industries need to obtain or develop
digital human capitalNote 1). KYB has also systemized internal education and training to develop its own digital
human capital. This report introduces KYB's initiatives
to develop digital human capital.
Note 1) KYB prefers to use the term "human capital" instead of"human resources". This report uses "human capital" accordingly.

1. 1 The Outset of Development of Digital Human CapitalThe outset of the development of digital human capital
was a cross-company activity project in 2016 that applied
data miningNote 2) to analysis of casting failures in thegroup companies. The activities included learning statistics and promoting the use of data mining tools. The
project eventually helped employees understand the necessity of data analysis in the company. Then, anotheractivity was launched in 2018 to develop artificial intelligence (AI) human capital.1) The participants learned theknowledge and technologies necessary for more advanced analysis using machine learning Note 3) or deeplearningNote 4).
In this activity, KYB gathered participants who wereinterested in AI throughout the company and asked engineers who had AI knowledge and technology to give lectures. A regular meeting was held every month to provide
programming courses using e-learning and to carry out
original small tests for participants along with their explanation. Based on the results of their daily learning,participants used various approaches to try to resolve
challenges related to AI implementation given by Administration. The final part of the activity was group work to
resolve actual administration challenges left unsolved in
the company by means of AI. For a challenge related tovibration data of active suspensions for railroad applications, many participants built machine learning models
taking into account cost and even processing speed on
the assumption that the models would be introduced tothe actual system. Thus, the activity yielded marvelous
human capital development. The first term of the activity
in FY2018 was followed by the second term in FY2019.

TAKINO Shinsuke

1. 2 DX Promotion and KYB-IoT Platform
During these activities, the world has been focused on
digital transformation (DX). In Japan, the government
and industry insisted on the necessity of using data anddigital technology. KYB established the Digital Transformation Improvement Dept. in 2019 to accelerate the useof digital technology. Fig. 1 shows KYB's original datautilization stepsNote 5). The first step is digitization thatturns analog information into data. This is followed bydata collection and data saving in databases. The processthus advances toward digitization. It is further assumed
that the process evolves into digital transformation basedon the premise that all business communications andtransactions will be done digitally .

Fig. 1 Data utilization steps

To accelerate the data utilization, KYB constructed itself a KYB-IoT platform on the cloud. The platform supports Steps 3 and 4 of the data utilization process. At theoutset, KYB promoted "defensive DX" mainly in the
production domain.2) Now it has just begun with preparation for "offensive DX" in the product development domain and new business development.3)

Note 2) The process of analyzing large amounts of data using
statistics and other analysis techniques to obtain useful
insights such as hidden patterns of data.

― 38 ―

Note 5) In 2021, some functions had already promoted the useof clouds and AI, but the whole company was still in Step 2.

KAYABA TECHNICAL REVIEW No. 66 APR. 2023

1. 3 Implementation of Digital Human Capital
Development
Along with the construction of the KYB-IoT platform
where employees can flexibly use data with no constraints on time or location, the company is addressing
the development of human capital who can effectively
use the platform. These human capital should be familiar
with business intelligence (BI) toward data-democratization, which is indispensable for the data utilization steps,
and AI that enables more advanced analysis. Besides
these technologies, employees are required to understandthe significance of DX and foster proper IT literacy. They
are given an explanation as to possible business losses
that may arise without the use of data and digital technology and educated to raise their awareness of the necessityof preventing such losses.
In this way, we have established the digital humancapital development program from a broader perspective
covering not only AI but also BI, IT, and clouds. We arealso trying to systemize these activities into a cross-company project through collaboration with Engineering
Planning and the Human Capital Development Center.

2

DX Basics is a lecture course aimed at ensuring that theconcept and understanding of DX take root properly
throughout the company. Trainees learn why the company needs digital technology and why they need to beaware of digital transformation. They also take a lecture
on how to reform the organization to implement DX. Executive Training is conducted with support from Amazon
Web Services, Inc. (AWS)
2. 3 Systemization of Digital Human Capital
Development
Fig. 3 shows the relationship of scale and specialty
among the courses. Centering on the AI and BI humancapital development, the figure indicates DX Basics andother training courses to be provided to employees who
are temporarily sent to the Digital Transformation Improvement Dept. for "studying abroad". As the digital
human capital development is expanded, the map in Fig.
3 will be updated accordingly.

 verview of Digital Human Capital DeO
velopment

2. 1 Definition of Digital Human Capital
Before the start of the digital human capital development, the Digital Transformation Improvement Dept.
preliminarily discussed which digital human capital weshould try to develop with the related functions. As a result, the digital human capital KYB should develop were
defined as "human capital who have both the technicalskill to use digital technology and business transformation skill". This is because, to implement DX, KYB
needs human capital who not only have acquired technical skills by participating in internal and external training
courses but also have business transformation skills withwhich they can continue addressing challenges to achieve
their purpose.
2. 2 Roadmap of Digital Human Capital Development
Fig. 2 shows a roadmap of digital human capital development established in 2020. In addition to IT Education
for improving AI, BI and IT literacy, KYB has started
DX Education to teach the basics of all necessary subjects for DX implementation.

Fig. 3

To establish a scheme or system of digital human capital development, the Digital Transformation Improvement Dept. has worked with the Engineering Planning
Department and Human Capital Development Center
since 2022 to create a skill level chart as shown in Fig. 4.
Its creation is based on our idea that such a chart will
help employees figure out a specific skill level theyshould try to reach next and will appropriately support
their growth as individuals. Besides this chart focusing
on AI and BI, we plan to develop additional skill level
charts for other digital technologies.

Fig. 4
Fig. 2S cale and specialty of digital human capital development

Roadmap of digital human capital development― 39 ―

Digital human capital skill level chart(under discussion)

Initiatives to Develop Digital Human Capital

2. 4 Year-round Digital Human Capital Development Curriculum
Fig. 5 shows the current year-round education curriculum in 2022. The first course of the curriculum is DX
Basics that lectures the necessity of DX and an outline ofdigital technology. Trainees will broadly understand the
purpose of learning about these issues. The curriculum isdesigned to connect this DX Basics to AI and BI humancapital development and further to IT Education as ameans of implementing DX. We will describe AI humancapital development in Chapter 3 and BI human capitaldevelopment in Chapter 4.

Fig. 5

3

IT system with such a machine learning model is recognized as a function involving many issues to be considered. It is impossible for a single role, such as data scientists, to cover all domains. To resolve the issue, data
scientists, data engineers, and machine learning engineers
should technically cooperate with each other throughsmooth mutual communication toward a final common
goal, rather than independently promote their own development work. Then, an education curriculum with
MLOps taken into account was started for all trainees.
This allows them to imagine during their education what
will specifically take place after the introduction of AI/
ML and to build the knowledge and skills necessary forestablishing a sustainable machine learning system.
The lecture materials used in the AI Education Curriculum, except for programming courses, have been prepared by in-house engineers. With these internally prepared materials, the lectures can provide familiar topics
including in-house failure cases.
Note 7) A paradigm that aims to connect the development process of machine learning with the operation process of thesame to achieve continual improvement after the supply ofservices.

FY2022 digital human capital development
education program

AI Human Capital Development

3. 1 AI Human Capital Development Initiative
Currently, KYB has two pillars of AI human capitaldevelopment: AI Education Curriculum and AI Community. The AI Education Curriculum covers a wide rangeof education from basic courses designed to learn knowledge and skills to ensure that no employee is ignorant of
AI/MLNote 6) to practical courses to learn the knowledge
and skills necessary for appropriately operating the machine learning models that have been implemented.
These education courses will be described in detail in
section 3.2. AI Community provides places for employees to openly communicate with each other or to actively
use the knowledge and skills they have obtained througheducation courses for resolving challenges facing thecompany. This will be described in detail in section 3.3.
Note 6) AI services that only need data are differentiated fromML (machine learning) that can build machine learningmodels itself.

3. 2. 2 Track Record
The AI Education Curriculum in 2022 has four courses
including those launched in 2021:(1)AI Basics
AI Basics is an entry-level course of the AI Education
Curriculum and intended to make AI widely popularamong employees. This course has been provided to ensure that no employee is ignorant of AI/ML.
The course consists of lectures by chapter that begin
with data collection, followed by data preprocessing/visualization, feature extraction learning, hyperparameter
Note 8)tuning, and inference, according to the proper machine learning workflow. In the introduction of the lectures, the difference in definition between AI and machine learning is explained. In addition, the lectures
widely cover different specific approaches of machinelearning and the basic theory of deep learning. Materials
used in the lectures include not only simple explanationsof individual technologies but also actual cases in KYB,
for example, what employees doubted in the past AI hu-

3. 2 AI Education Curriculum
3. 2. 1 Background
The AI human capital development initiative launchedin 2018 has yielded certain results so far. To further expand the AI human capital and further increase the practical cases contributing to the company's business, KYB
decided to introduce MLOpsNote 7), which had gained recent attention, to the education system and provide opportunities for employees to learn the operation of machine learning models. This decision was made with thebackground that, although a constructed machine learning model increases in value only if it yields results to
end users as expected, the building and operation of an― 40 ―

Fig. 6

Education materials for AI Basics

KAYABA TECHNICAL REVIEW No. 66 APR. 2023

man capital development and what they were confused
about during implementation. Thus, these materials havebeen designed to be suitable for the current skill level ofthe company's employees. Some of the textbooks provided in the lectures are shown in Fig. 6.
Note 8 ) Parameters that are not automatically learned by machine learning algorithms. Hyperparameters are generally
decided based on the developers' experience and/or optimization methods used.

(2)Programming Basics
The Programming Basics course provides a place fortrainees to output the knowledge they obtained in the AI
Basic course. Trainees who finished the AI Basic course
can implement machine learning through programming.
Based on what they learned in AI Basics, they try to do
programming to build a machine learning model. This
course does not involve a dedicated lecturer teaching in aroom like hands-on training, although it does start at a
specified date and time. Instead, individual trainees access webinars that have been prepared in advance andlearn online. Instructors will only respond to trainees
who have a question. Trainees can look into what they
are not sure about at their own pace and receive support
from instructors whenever they like to resolve the problem right away. The use of this online learning is intended to improve the practical skill of trainees.
(3) AWS Basics
KYB widely uses AWSs as a base to implement machine learning, including the KYB-IoT platform. Trainees who have finished the education curriculum are thus
required to smoothly get ready for the development environment provided for AWSs. In addition to this background, the AWS Basics course is intended to explain thebasic knowledge and techniques about AWS to trainees
before they attend the following AI Practice and AI
Hackathon courses.
It is very important to learn the necessity of using thecloud environment to implement machine learning and
MLOps. If a machine learning system is built in an existing on-premises environment, some technical liabilities
would be generated in future. This course presents suchliabilities and explains the advantages of the cloud environment where data scientists and machine learning engineers can concentrate on essential tasks including development. Like the Programming Basics course, this course
has been designed to allow trainees to freely access webinars within a specified course period and to allow instructors to only respond to trainees who have a question.
Fig. 7 shows some of the materials provided in thecourse.
(4) AI Practice
AI Practice is a hands-on training course where trainees can build machine learning models in a cloud environment based on the knowledge obtained in the AWS
Basic course and then implement the operation of thesemodels. This course was internally developed in 2022.
At the beginning of this hands-on course, trainees aresupposed to carry out implementation of machine learning models in an assumed existing on-premises environ-Fig. 7

Education materials for AWS Basics

ment. They not only simply implement machine learningmodels but also learn effective visualization methods
during preprocessing as well as precautions for dividing
data sets. The course presents case examples of implementation that can be applied by trainees to development
work immediately after they finish the course. Next, they
are supposed to use Amazon SageMaker Note 9) to implement machine learning models in an assumed cloud environment. We have tried to prepare materials for thiscourse to present an overview of the implementation
range as needed, with which trainees can clearly identify
the functions they have implemented at the moment.

Note 9 ) A cloud service that provides an implementation environment for rapid development and deployment of machine learning models.

(5) AI Hackathon
AI Hackathon is an event for trainees to output inteams the knowledge and skills they have obtained
through the year-round education curriculum. Trainee
teams try to solve hypothetical challenges presented by
Administration by using machine learning. This activity
involves not only implementation of machine learningmodels but also the design of peripheral machine learning systems by taking into account the operation system
of machine learning models and the Well-Architected
FrameworkNote 10) proposed by AWS.
During the activity period, an "office hour" for eachteam is available once a week so that the team can consult with AWS Solution ArchitectsNote 11) about any questions about implementation of the design or machinelearning under discussion in the team . Individual members of each team are given a specific role and required
to show check items they should achieve. This check-out
system is used to quantitatively evaluate how close theteam members are to the goal set by Administration after
the completion of the Hackathon. In the Hackathon presentation attended by management and AWS, individual
teams report the results of implementation of machinelearning models and the unique thoughts they put inwhen designing the machine learning system. Photo 1
shows how the AI Hackathon presentation was held. Fig.

― 41 ―

Initiatives to Develop Digital Human Capital

8 shows some of the materials available in the presentation.

Photo 1AI Hackathon presentationcompany, with the aim of further expanding our AI human capital.
3. 3. 2 Track Record
Since its establishment in 2021, human capital withadvanced AI/ML knowledge and skills have gatheredfrom various domains including product development
and production engineering to be members of AI Community.
(1) Information sharing and promotion support
Discussions among AI Community members using
chatting tools are opened within the company. Examples
in the past include introduction of AI/ML development
themes addressed by Community members, troubleshooting support for other members based on member's own
experience in development, and deployment of manuals
for building a PythonNote 12) environment for easier implementation of machine learning. AI Community also plays
the role of supporting the promotion of latest technologies, including verification of pilot versions.
Note 12) A general-purpose programming language that is thede facto standard for AI development.

Fig. 8Hackathon presentation materials

Note 1 0) Major concepts and design principles for designingand executing solutions on the cloud proposed by AWS.
Note 11) A professional who provides appropriate solutions forcustomer needs including cloud services.

3. 3 AI Community
3. 3. 1 Background and Purpose
Independent AI development by AI human capital
within their own department would be inefficient fromthe viewpoints of proliferation of technology and formation of culture. This might generate barriers to immediate
contribution to business. the Digital Transformation Improvement Dept. thought that a medium or a continual
hub for all employees to communicate with each otherbeyond their functions or titles was needed. Then, we decided to plan and operate a place for employees with acommon passion to openly share technologies by discussing what they were concerned about and introducing
the latest technical information and newly publishedbooks to each other. This is AI Community. An initiative
to use your own knowledge and skills to resolve concrete
challenges that was launched in 2022. We thought that AI
Community would become a group contributing to business, thereby enabling the members to continue challenging new things all the time. We will disseminate the success stories of AI Community both inside and outside the(2) Group work
The group work program, which was launched in
2022, is intended to contribute to the company by utilizing the AI/ML knowledge and skills owned by the Community members. The program solicits concrete challenges faced by individual members during their daily
development work. Other members then participate to resolve selected challenges through mutual cooperation.
The following introduces some of these activities:[1] Research on a new analysis approach where structural
analysis with FEM has been reduced for machinelearning
CAE Improvement Dept. has been discussing integrating a detailed FEM (finite element method) based modelof laminated leaf valves into the SA analysis system toachieve high-accuracy prediction of the performance
characteristics of shock absorbers (hereinafter "SA").
FEM can take into account detailed physical phenomenathat cannot be covered by conventional theoretical calculations, such as the fastening axial force and the partial
contact of leaf valves. However, it takes a long time tocomplete the FEM calculation. Then, the department has
improved the prediction accuracy without increasing the
calculation time by reducing the order of the FEM-basedlaminated leaf valve model to a machine learning modeland integrating it into the SA analysis system. This technology has been highly rated both inside and outside thecompany.
AI Community implemented and verified various machine learning models proposed by its members, trying toenhance the prediction accuracy. This activity eventually
helped substantially improve the accuracy of predicting
the SA performance characteristics.
[2] Improving the product stamp inspection performancethrough deep learning
The Production Technology R&D Center is promoting
the automation of appearance inspection in the SA mass
production system through deep learning. The current ac-― 42 ―KAYABA TECHNICAL REVIEW No. 66 APR. 2023

curacy of identifying stamped characters on products using deep learning is 99.92%. However, the current deeplearning method has not been adequately verified throughcomparison with other similar methods.
To achieve an even higher accuracy of the stamping
identification, AI Community has implemented and verified various methods other than the one that was alreadyscheduled to be introduced into the mass production system. Unfortunately, none of them was found to be superior to the current candidate. However, many of them were
the first ever methods implemented by the members who
participated in the activity including YOLONote 13). Community members could distinctly feel their own technicalgrowth through the group work.
Note 13) One of the physical object detection algorithms thatcan recognize the contour of an object at a high speedwith high accuracy.

3. 4 AI Human Capital Development Challenges
and Their Solutions
This section describes challenges related to the AI education curriculum and AI Community as well as solutions
to these challenges.
3. 4. 1 AI Human Capital Development Challenges(1) Follow-up support of the curriculum
Currently, continual follow-up support to employees
who have finished the education curriculum is not available. These employees may fail to maintain the skills
they learned if they are not involved in AI/ML in theirdevelopment work.
(2) Group work activity period
The term of a single course of group work activity of
AI Community is as short as three months. In many cases
members failed to fundamentally resolve challenges.
(3) Evaluation with objective measures
No specific measures have been established to evaluate the education curriculum or AI Community. What results the total human capital development initiatives havebrought about is unclear.
3. 4. 2 Solutions to Challenges
To resolve these challenges, we reviewed the total activities and discussed the following solutions:(1) Encouraging trainees to take part in AI Community
Among trainees who have finished the education curriculum, some do not have an opportunity of getting involved in development work despite being interested in
AL/ML. It is effective to encourage such employees toactively take part in AI Community. Once involved in theCommunity, they can receive continual follow-up support through group work after finishing the curriculum. It
is also expected they will maintain the skills they havelearned.
(2) Reviewing the group work period
The group work activity period of AI Community
should be changed to one year so that members can try tofundamentally resolve challenges. Challenges to be selected should be quantitatively evaluated for business
impact, necessary resources, and other factors using theirown methods. Then, themes that are believed to be mosteffective should be selected.
(3) Strengthening the group work scheme
With an eye toward operation as a machine learning
system, the group work scheme should use AWSs as its
development environment. Group work should be promoted with technical support from AWS. The results ofgroup work activities should be evaluated by determining
whether AI/ML challenges have been resolved by Community members (trainees of AI Education).

4

BI Human Capital Development

4. 1 BI Human Capital Development Initiative
Like the AI human capital development initiative,
KYB's BI human capital development initiative has twopillars: BI Education Curriculum and BI Community.
4. 1. 1 BI Education Curriculum
The BI Education Curriculum provides employeeswith training courses to build important skills for datautilization, including data preprocessing, visualization,
and analysis while practically using TableauNote 14), which
has been introduced into the company as a standard BI
tool.
Note 14) A BI tool provided by Salesforce Japan Co., Ltd.

4. 1. 2 BI Community
BI Community is an in-house community consisting oftrainees who are taking or have finished the education
curriculum as well as those who are interested in inhouse data utilization. Community members share theirknowhow, case examples both inside and outside thecompany, and BI-related information.
4. 2 BI Education Curriculum
4. 2. 1 Background
BI originally refers to the process of "collecting data,
putting it together and accumulating it in a location, analyzing it according to a purpose, and visualizing it into auser-friendly format". However, the BI Basics course
provided in 2020 mainly focused on how to use tools.
The course might have misled its trainees to believe that
BI is just data visualization using tools. Moreover, thecourse was rather focused on attracting new users, failingto provide an adequate approach for trainees to "self-propelling" the data utilization themselves.
After learning this lesson, we modified the curriculumfor 2022 so that trainees can learn step-by-step according
to their interest in BI.
4. 2. 2 Track Record
The BI education curriculum in 2022 has three courses: BI Overview, BI Practice (Entry-level), and BI Practice (Advanced-level). Firstly, the following describes thebreakdown of trainees in different courses of the curriculum in 2022.

― 43 ―BI Overview
BI Practice(Entry-level)
BI Practice(Advanced-level)

Fig. 9Trainees of BI education by fiscal year

Initiatives to Develop Digital Human CapitalFig. 9 shows the number of trainees of the BI education curriculum in these fiscal years. The accumulated total number of trainees is 337. In 2022, 160 employees
took the courses. The majority were employees from theEngineering Division, AC Operations, or Production Division. These functions are those involved in data analysis in their research and development or improvement activity. Still, participation in the curriculum has been
gradually expanded to include affiliated companies andmanagement divisions. We have found more and more
employees from different functions being interested in BI
education.
Now let us explain the details of these BI courses:(1) BI Overview
To provide an opportunity for employees to be interested in BI, it is essential to make them understand thecurrent situation in which we are surrounded by a dataabundant environment and for them to have data literacyNote 15) for implementing BI to begin with. Then, this BI
Overview course was set up for all employees to deepen
their understanding of the preconditions for the use of BI,such as the definition of BI and data literacy with support
from Salesforce Japan Co., Ltd. (hereinafter "Salesforce
Japan").
Data literacy involves analyzing data, which can be
implemented by Tableau. Lectures of this course cover
data analysis. Lecturers present trainees with familiar
data examples such as weather reports and sales data, sothat they can learn, for example, why data visualization
is effective for the analysis. The latter half of the lectures
includes a recreation program for trainees to answer anentertainment quiz by operating screens created on Tableau. The program makes trainees easily understand what
they can do with Tableau, helping them to proceed to thenext step, namely, the BI Practice (Entry-level) course.
(2) BI Practice (Entry-level)
In the BI Practice (Entry-level) course, trainees can receive hands-on practice of basic operation and data classification of Tableau, including simple plotting such as
drawing line and bar graphs, data filtering, and learning
differences between continuous and non-continuous data.
Each section of the course has exercises. Two dedicated assistant instructors support a group of five or six
trainees and respond to their questions. Assistant instructors consist of volunteers from BI Community so thatthese individual instructors can also improve their understanding of BI.
The course uses lecture materials that have been prepared by Salesforce Japan. These materials are brushed
up reflecting feedback from trainees and lecturers.
Some of the results of trainees are shown in the following figures. Note that these results are in the preparation stage before being put in service. They were temporarily created to verify practicality in the case of using inhouse actual data. Fig. 10 shows the dashboard ofcommunication terminals for oil status, vibration, andtemperature sensors along with their battery status. Fig.
11 shows the dashboard for changes in patent applications of different countries that has been developed by

Fig. 10

Layout of sensor communication terminals andtheir battery status

Fig. 11

Analysis of changes in patent applications ofdifferent countriesNote16)

the Intellectual Property Department.
(3) BI Practice (Advanced-level)
The most likely difficulty for users in performing datacollection, analysis, and visualization may be the development of a data environment. Particularly when users
have just started using Tableau, they often handle existing data. That is' why the BI Practice (Advanced-level)
course is focused on how to organize existing data. We
designed the course to include data splitting and data
coupling for linking two or more data sets using Tableau
DesktopNote 17) and Tableau PrepNote 18) as well as data preprocessing such as name-based aggregation by correcting
orthographic variants.
Using a data set consisting of several files, the lecturers explain in a storytelling style how to organize the data
set by identifying any inconsistent parts of the data. All
materials used in the lectures were internally prepared.
We believe that trainees can vividly experience theprocess of data processing, which is important for proper
visualization and analysis of BI data. We have received
feedback from trainees that they have successfully resolved remaining issues.

― 44 ―

Note 15) The generic name for the capability to read, analyze,
use, and describe data.
Note 16) Concrete numbers and names have been blurred.
Note 17) A specially designed tool for data analysis and visualization. One of the Tableau products.
Note 18) A specially designed tool for data pretreatment. One of
the Tableau products.

KAYABA TECHNICAL REVIEW No. 66 APR. 2023

4. 3 BI Community
4. 3. 1 Background
It may be difficult for trainees alone to identify challenges or organize data of their own function. Then, across-function community was established to support
them to achieve "self-propelling". This BI Community
mainly consists of trainees of the BI Education curriculum. Community members can freely consult with eachother about their concerns over how to use Tableau ordata-related issues.
4. 3. 2 Current Challenge
BI Community has 177 members consisting of 101
CreatorsNote 19) and 76 ViewersNote 20). However, active users of Tableau in the last three weeks are not more than
50% of licensed users.

⒜Fig. 12Creator⒝ Viewer

Ratio of active users to licensed users

Thus, the Community activity has not been vitalized asplanned. We recognize that a data-driven culture has not
yet been successfully developed.
Note 19) A type of license that authorizes the use of Tableau
products. Developer license.
Note 20) A type of license that authorizes the access to screensthat have been created.

4. 3. 3 Cause Analysis
We analyzed the behavior of users to figure out thecause of the challenge. The analysis has revealed the following:
About Creators[1] 1 6 Creators make their own results open to thecommunity.
[2] Creators do access the results [1] (within thesame function).
[3] Creators do use Tableau but have not yielded
many results.
About Viewers[4] Viewers access the screens that have been madeopen to the community by the Digital Transformation Improvement Dept.
- Equipment predictive maintenance support system- Equipment data collection/analysis system (MES
service) 4)[1] and [2] imply that Creators have started to proactively act in the functions. According to [3], however,they have not yet reached the stage of creating screens to
be made open to the community. They may face somechallenge. Otherwise, they may have completed analysis,
but just produced results that are locally accessed by alimited number of users.

On the other hand, Viewers usually use Tableau to access screens they want to view. It can be estimated from
[1] and [2] that Viewers are likely to use Tableau if dataor screens that relate to themselves are available. In otherwords, the screens made open to the community may not
be those wanted to view by the Viewers who do not use
Tableau.
From the above, it can be concluded that a support
system for the user group applicable to [3], which is abottleneck, must be developed.
4. 3. 4 Solutions
To resolve the challenge, the operation of the education curriculum should be revised to focus on user support. We discussed the following solutions:(1) Providing on-demand learning contents
The live lectures, which have been prepared and heldby members from the Digital Transformation Improvement Dept. on an as-needed basis, will be changed to avideo streaming service, ensuring the support man-hours.
With this service, trainees can take on-line lectures whenever they like. It can be expected that the number of inhouse users will increase.
(2) Creating a skill map
Along with the video streaming service describedabove in (1), a skill map will be created and made available. The skill map should be linked to the existing lectures so that trainees can obtain visualizations of theirown skill set and maintain their motivation for self-learning.

5

Future Prospects

Since its launch of full-scale initiatives to develop digital human capital in 2021, KYB has promoted the development of an education curriculum and the operation ofcommunities. Through these activities, the company has
enjoyed an increase in AI and BI human capital and newcases of data utilization that have never been seen before.
We will continue considering measures to increase thein-house digital human capital. For AI human capital development, we will build a development platform where
even more AI human capital, ranging from beginners tomasters, can flexibly use AI. We would also like to generate more cases of establishing an MLOps environment
for AI's contribution to business. To do so, it is necessary
to establish a cloud environment operation rule for wider
deployment throughout the company. Besides this, it will
be even more important to carry out DX promotion to
expand the KYB-IoT platform appropriately according to
the scale of function or project. For BI human capital development, we would like to facilitate data linkage
among the business functions centered on the improvement of data analysis skills of individuals. To achievethis, we should discuss the company's general data governance, including how data should be, how data should
be available, where data should be compiled, and who is
responsible for data management, to provide data to users in an easier-to-use way.

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Initiatives to Develop Digital Human Capital

6

In Closing

This report has introduced KYB's initiatives to develop
digital human capital. The true purpose of education isachieved only if employees apply the knowledge andskills they have learned to their daily development work
contributing to business. Toward the implementation ofDX, we would like to continue to generate more cases of
in-house data utilization and expand the education curriculum needed by the company being aware of world
trends.
Finally, we would like to take this opportunity to sincerely thank all those concerned in the related functions
who extended great support and cooperation to the promotion of the initiatives.

3) OHUCHIDA, MIYAUCHI, SAGEHASHI: "Use of AI Technology in Development of SA Elements", KYB Technical Review
No.66 (April 2023)
4) YUBUKI, GABURIELLA: "Building the Foundations for MES
Services in the New Era", KYB Technical Review No.64 (April
2022)

References
1) NAITO: "AI x IoT Initiatives in the Production Technology Domain at KYB", KYB Technical Review No.60 (April 2020)
2) FURUKAWA, ISASHI: "Development of an Equipment Predictive Maintenance System", KYB Technical Review No.63 (October 2021)

AuthorMIYAUCHI Yuuki

ISASHI Ryosuke

Joined the company in 2017.
DX Improvement Dept., EngineeringDiv.
Engaged in system developmentusing AI and promotion of AI humancapital development.

Joined the company in 2017.
DX Improvement Dept., EngineeringDiv.
Engaged in building of data visualization/analysis foundations andpromotion of BI human capitaldevelopment.

TAKINO Shinsuke
Joined the company in 2006.
DX Improvement Dept., EngineeringDiv.
Was engaged in promotion of digital
human capital development.

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