一般社団法人インダストリアル・バリューチェーン・イニシアティブ

Data Ownership Studies through Field Examples:(1)Exchange of Work Progress Data in Made-toorder Manufacturing(2) Judging the quality of high-speed precisionpress work
(3) Carbon traceability starting from castings

Advanced Study Groupon Data Sovereignty (ASG-22)
IVI

July 31st, 2024
1

Table of Contents
1. Introduction: About this document
2. Exchange of Work Progress Data in Made-toorder manufacturing
3. Judging the quality of high-speed precision press
work
4. Carbon traceability starting from castings
5. Acknowledgements
6. Authors and Advanced Study Group Members(C) 2024. Industrial Value Chain Initiative2

1. Introduction: About this document
This document presents the discussions and deliberations of the Data
Sovereignty ASG, Advanced Study Group, regarding data ownership anddescription in specific examples.
The importance of data utilization in manufacturing is widely recognized,
and government measures such as Connected Industries are being
promoted, but the lack of accumulation of data contracting practices has
been identified as an issue.
In response, IVI organized the Data Ownership Committee in 2017 andbegan activities aimed at protecting the rights of shop floor data (IVI
named it “deep data”) in the IoT era. This committee was reorganized into
the Data Ownership ASG in 2018, which later became the Data Sovereignty
ASG in 2021 to develop and continue its activities.
In order to realize higher value in connected manufacturing in the IoT era,
it is important to be able to negotiate, judge, and realize ownership ofdeep data appropriately according to the business environment of eachcompany. This document is the result of discussions and examinations
conducted by the parties concerned, and is expected to contribute to the
expansion of recognition of data ownership, activation of discussions, andbetter realization of data ownership.
(C) 2024. Industrial Value Chain Initiative3

1. Introduction: About this document
To promote the utilization of deep data while protecting the rights of deepdata, IVI has conducted a hearing survey on the current status and issuesof data ownership and solutions for data transfers between companies, andhas published it as a white paper※.
※ IVI Data Ownership ASG “current status and issues of data ownership and solutions for”; Sept. 2019https://iv-i.org/downloads/whitepaper_asg/ (in Japanese only)

In addition to the above efforts, examples of data ownership studies in
specific cases are considered to be helpful for implementation by those incharge of the field. Therefore, this document presents the contents
discussed in the Data Sovereignty ASG, together with its description.
We hope that this document will be used as a reference for promoting
understanding of the concept of data ownership in the field, sharing
understanding with legal and intellectual property departments andinformation-related personnel, and considering and negotiating data
ownership and its implementation in data trading platforms whencompanies actually decide to exchange data in the field. We hope you willfind this document useful. Please note that this document is a summary ofthe discussions at the Data Sovereignty ASG and we are not responsible forany actions taken by users using this document.
(C) 2024. Industrial Value Chain Initiative4

2. Exchange of Work Progress Data in Made-to-order
Manufacturing
This section describes the results of the following two IVI‘s smart
manufacturing business scenario working group projects:✓ 6E02 "How to Connect for Mass Customization" and✓ 7E03 "Affordable data collaboration for small and medium-sized
manufacturing companies".
Reference:
IVI Open Symposium 2021 -Springhttps://iv-i.org/en/2021/02/28/english-announcement-of-ivi-open-symposium-2021-spring/
IVI Open Symposium 2022 -Springhttps://iv-i.org/en/2022/02/23/english-information-on-ivi-open-symposium-2022-spring/(C) 2024. Industrial Value Chain Initiative5

2.1 Target Situation
Target Sites：✓The target companies are Manufacturer A, a make-to-order manufacturing
company, and its component suppliers.
✓The supply chain is multi-tiered.
✓In some cases, Manufacturer A provides free-issue components /assemblies to its component supplier.
✓The manufactured product is a custom product with a delivery time ofmore than one year.
✓Forecasts are made with standard lead times, but there are no precise
estimates at the beginning.
✓Once a plan is established, each company makes adjustments torefine the plan.
✓It is not simply a matter of making products as quickly as possible (JustIn-Time:JIT may be preferred to overcome storage space issues).
✓The situation may change during the production process due to changes indelivery dates from final customer, tight supply of materials, and so on.

(C) 2024. Industrial Value Chain Initiative6

2.1 Current Issue✓ Manufacturing progress information between companies and factories is not obtained in a timely
manner with good accuracy.

Chart of exchange; As-Is

Procure necessary
materials andmanage progressclient side
representative
I want you to make therequest with thecorrect drawings. Not
the old drawings thatare used all the time.

I would like to be
notified of any changes
in delivery dates or
specifications as
appropriate.

Order quantity

Requirement
specification

There is information thatcannot be exchanged or isdifficult to exchange.

I need aquick answer.
Delivery date;client-side buffer

Number ofcompletions
Finished product

Delivery date

Contract
information

Delivery date;delay risk

Delivery date;
supplier-side buffer

Order data

Sales representativeon the supplier side
I need you todemand realistic
deadlines.

I have to use
different
systems.

Production management personnel
on the supplier side
Work progressinterim inventory
or materials

Number of interim
inventory or materials(C) 2024. Industrial Value Chain Initiative7

2.2 Exchange Data to overcome issues
Purpose of data exchange:✓Delivery date confirmation, i.e. which parts are to be shipped,
when and how many, in a timely and accurate manner. Supplierwants to know which parts are to be wanted, when and how many,in a timely and accurate manner.
✓Adjust production priorities.
✓Facilitate JIT supply of finished products, parts/materials and freeissued parts.
Data to be exchanged:✓Requirements, progress data and inventory data
✓Consider exchanging data between two companies for
simplification.

(C) 2024. Industrial Value Chain Initiative8

2.2 Examples of data to be exchanged
Client company A

Supplier company B

Order data

person incharge ofordering

Number of order: 12345678-01-01
Item ID: ABCD-E1
Lot size: 1
Manufacturing process: “Material loaded, Start ofproduction, Production complete, Shipped out“
Delivery date: 2022/02/28 00:00:00
Order date: 2022/02/18 00:00:00

Sales
representative

Work Progress data
Order number: 12345678-01-01
Part number: ABCD-E1
Process number: 1
Process: ”Material loaded”
Process status: ”in preparation”
Planned quantity: 1
Actual quantity: 0
Planned delivery date: 01/22/2022 00:00:00
Actual delivery date: 01/22/2022 00:00:00
Requested delivery date: 02/28/2022 00:00:00

Production
management
personnel(C) 2024. Industrial Value Chain Initiative9

2.3 Considering data ownership
1. The data shall be referenced only by the two companies thattransfer the data (i.e., the received data shall not be provided toany other company). Since there are advantages or
disadvantages in narrowing or widening the reference range, thisstudy assumes a narrower case in order to reduce concerns* suchas use for other purposes in data sharing.
➢ If the scope of reference is narrow, the company's data will bereferenced only by those with whom it has a direct order-receipt
relationship, thereby reducing the risk that the company's orderstatus or production capacity will be guessed.
➢ If the reference range is wide, it is easier to infer the cause of
progress delays and to adjust production plans.
2. Immediate transmission of updated data shall not be committed.
3. The scope data exchange shall be determined by the twocompanies that exchange data.
* IVI data ownership ASG “Data Ownership Status, Challenges and Solutions “, Sept.
2019
https://iv-i.org/downloads/whitepaper_asg/(C) 2024. Industrial Value Chain Initiative10

2.3 Considering data ownership
Client company A
Product A001

Part A002

Supplier company B
Part A002

Part A003

Part B001
Part B002

Supplier company D

Material company C
Material C001

Client company A;
supplied part
Part A004

Part A003

Part D001

Person in charge of ordering at company A does:➢ enter order information of part A002 and A003
➢ receive and refer to the order progress of parts A002 and A003 from Company B and Company
D, respectively.
For example, if the process "material arrival" for part A002 is not completed beyond the scheduled
time, we can guess that "material arrival for part A002 is not in progress“; we can not guess
whether the arrival of parts B001 or B002 is in progress.

Sales representative and production management personnel at company B does:➢ refer to the order information for part A002.
➢ enter the order progress for part A002.
If Company A changes its delivery request for A002 due to a change in the delivery date ofproduct A001 or a delay in the progress of part A003, Company B can also adjust its priority for
A002 production.
It is not possible to refer to the order progress of Company D; it is impossible to even know that
Company A has placed orders with Company D. The reverse is also true.
(C) 2024. Industrial Value Chain Initiative11

3. Judging the quality of high-speed precision press
workThis section describes the results of the following three IVI‘s smart
manufacturing business scenario working group projects:✓ 6E03-2 "Defect Detection of High Speed Press Machine by AE Sensor",✓ 7B02 “Implementing Sensors and Image AI for Predictive Maintenance",and✓ 7E01 "Inter-company collaboration through AI data distribution platform".
Reference:
IVI Open Symposium 2021 -Springhttps://iv-i.org/en/2021/02/28/english-announcement-of-ivi-open-symposium-2021-spring/
IVI Open Symposium 2022 -Springhttps://iv-i.org/en/2022/02/23/english-information-on-ivi-open-symposium-2022-spring/(C) 2024. Industrial Value Chain Initiative12

3.1 Target Situation■Target Sites：✓The target companies are Company A, a manufacturer of high-speed
precision press work, and Company B, an AI vendor.
✓Company A also manufactures its own molds and has excellent
expertise in precision machining.
✓Company A conducts off-line visual inspections.
✓Large quantities of defective products may be generated, for example,
in the event of a mold malfunction.
✓By installing sensors on presses and using AI to perform online good/failjudgment, the company A aims to curb the number of defective products
generated.
✓The permission of the ordering company can be obtained to send (nonimage) sensor data outside the company (to automate the good/failjudgment).
✓In the future, company A aims to provide the molds and the AI toneighboring companies in the same industry, so that they can jointly
accept large-scale orders.
(C) 2024. Industrial Value Chain Initiative13

3.1 Target Situation■Current Issues:✓Large quantities of defective products may be produced.
✓Since sensor data is acquired at high speed and in large volumes, it is noteasy to provide it to Company B.
✓When providing sensor data to Company B, it is not easy to make themcomply with the agreement, such as the authority to use the data.
✓When receiving the AI model from Company B, it is not easy to prove
compliance with the agreement, such as authority to use.
■ Our goals:
As shown on the next page, we aim to develop an AI model using thefollowing procedure and embed it in an edge controller to enable online
good/fail judgment.
➢As shown in (1) through (5), Company A provides sensor data to
Company B for AI model development.
➢Company B will provide AI model development, speed and accuracy values
to Company A as shown in (6)-(9).
➢As shown in (10)-(17), the AI model is provided by Company B toCompany A and incorporated into the edge controller.
(C) 2024. Industrial Value Chain Initiative 14

3.1 Target Situation(17) Alarm notification(1) Data collection(2) Data conversion(3) Data storage(4) Drafting of transaction
agreements(2) Data conversion(3) Data storage

Dropbox(6) Data analysis(16) Abnormal
detection(5) Approval of transaction
agreement(6) Data analysis(7) AI model generation(8) AI model catalog release

Edge controller(15) AI model
implementation(9) AI model catalog browse

AI modelcatalog(8) AI model catalog release(14) AI model download(1) Data collection

AI modelAI modelAI model(7) AI model
generation• AE sensor• High speed press• Various IoT
machine• Closed forging pressmachine with intelligent
molds(4) Drafting oftransaction agreements

Cooperation
terminal(5) Approval oftransaction agreement

Cooperation
manager(11) AI model
request receipt(10) AI model
request submission

AI model URL

Manufacturer; Data provider(11) AI model request receipt(12) AI model URL submission
連携ターミナル(12) AI model URL
Submission(13) AI model URL receipt
Person inchargeAI model URL

CIOF(9) AI model catalog browse(10) AI model request
submission

Person inchargeAI model URLAI Vendor; Data provider(13) AI model URL receipt(14) AI model download(15) AI model implementation(16) Abnormal
detection(17) Alarm
notification(C) 2024. Industrial Value Chain Initiative15

3.2 Data to be exchanged✓Data stored in step (3) is stored in storage in the cloud in advance.
✓It will be made accessible by company B; the access URL is sent tocompany B, after an agreement on AI model development is madebetween Company A and Company B as an agreement in thetransaction contract.
✓It includes information on the type of sensor, in addition to thetime-series values for each sensor,✓During the development of the AI model, Company B will also ask
Company A questions about the data and check for data defects.
✓The AI model catalog provided in (8) includes the accuracy
achieved with the above data and the time required to make adecision.
✓AI model development may be iterative, with an AI model catalog
created for each version.

(C) 2024. Industrial Value Chain Initiative16

3.3 Considering data ownership
Data provided by Company A to Company B will be used only for AI
model development, and will be discarded after development iscomplete, including cleansing data.
If the AI model catalog provided by Company B to Company A is
copyrighted, the copyright belongs to Company B.
If Company B makes an invention in the development of an AI
model, e.g., a cleansing device, the rights to the invention belong toCompany B.
If Company B invents an invention in the development of an AI
model, for example, if it invents a method of data cleansing, theright to the invention belongs to Company B.
Company B will provide Company A with the developed AI modeland its catalog, but not the cleansed data.
Company A does not reverse engineer AI models.

(C) 2024. Industrial Value Chain Initiative17

4. Carbon traceability starting from castings
This section describes the results of the following IVI‘s smart manufacturingbusiness scenario working group project:✓ 7E02 “Monetization model for inter-company data distribution".
Reference:
IVI Open Symposium 2022 -Springhttps://iv-i.org/en/2022/02/23/english-information-on-ivi-open-symposium-2022-spring/(C) 2024. Industrial Value Chain Initiative18

4.1 Target Situation■Target Sites：✓The target companies are foundries of metalworking components
suppliers and finished product manufacturers.
✓In the casting process, sand is kneaded to make a frame andmolten iron is poured into it.
✓IoT data, such as power consumption of equipment, as well aswork performance and production information, are available.
✓The service provider's CO2 emissions calculation function can beutilized.
✓The goal is for suppliers to be able to calculate and share the
manufacturing carbon footprint of products, CFP, by component
with the finished products manufacturer.
✓In the future, the company will work on data valorization, such as
supply chain CFP optimization simulations.

(C) 2024. Industrial Value Chain Initiative19

4.1 Target Situation■Current Issues:✓It is difficult to calculate and share manufacturing CFP bycomponent with finished products manufacturers.
Inter-company collaboration has
not been digitized.

Infrastructure

インフラ
Need to address environmental
issues and regulations such ascarbon neutrality.

Regulations would promoteinter-company collaboration, e.g.
carbon neutrality, etc.
A mechanism is needed toaddress common corporate
issues such as regulatorycompliance.
social conditions

It is difficult to take test results
to the factory and provide themto other companies.

Information
management

情報管理

Criteria are needed to determine
if data can be provided.
A win-win overall optimization
scenario is needed, including
data providers.

Monetize

マネタイズ

It is necessary to assure that
actual data is being obtained
from the equipment.

New Value Direction(C) 2024. Industrial Value Chain Initiative20

4.1 Target Situation■ Our immediate goals：
Suppliers will be able to calculate manufacturing CFP at component suppliers and to be able toturn the improvement loop.
■Our future goals：
Combining the CO2 calculation function and data distribution through CIOF, manufacturers andsuppliers will realize the great social value of preventing global warming through their own
improvement loops toward carbon neutrality and the coordination of the entire supply chain.

Data valorization service platform from provider
Supply chain CFP optimization simulation

Target site
(Supplier's own improvement loop

Ma teri al CO2
convers ion ta ble

Energy CO2
conversion
table

Energy CO2
convers ion ta ble

CO2 convers ion
va l ues
by ma terial

CO2 convers ion
va l ues by component
ma nufacturing

Temperature,
power

Inter company SCM-BOM(CO2)Energy CO2
conversion
table

CO2 conversion
values bylogistics

Distance,
weightCO2 conversion
values by productmanufacturing

Temperature,power

Connected ndustries
Open Framework
Material
Disbursement
Results

Temperature,
power

Components
manufacturing
recordMaterial
dispatchorder CFP

Distance,
weightComponents
manufacturing
order CFP

Components
shipment
order CFP

Component supplier• Change ofpurchasedmaterials• Guidance forsuppliers

Purchased CFP
by component

Components
logistics
order CFP

Components
manufacturing

CFP analysis of
manufacturing
by component

Components
storage

Logistics

Products
Manufacturing
recordProducts
Manufacturing
order CFP

Products
Shipment
order CFP

Finished products
manufacturer
CFP analysis bycomponents purchased

CFP analysis of logistics• Manufacturing
technology improvement• Switching to newequipment• Production areaswitching, etc.

Temperature,
power

Components
dispatch order
CFP

3PL + Logistics
Provider

CFP analysis bymaterial purchasedMaterials
storage

Components
logistics record• Change of Logistics Provider• Review of logistics modes,• including use of EV transport vehicles• Change ofpurchased

CFP analysis by
manufacturing
by product• Manufacturing
technology improvement• Switching to new
Finished
Productsequipment• Production area
products
storageswitching, etc.
manufacturing(C) 2024. Industrial
Value Chain Initiative 21

Components • components
Guidance forsuppliers
storage4.1 Target Situation■Target process：
The target process is shown in the figure below.
Target process
対象工程
溶解工程
高周波誘導炉Molding process at
automatic molding
machine

造形工程
自動造形機

Frame

鋳
物
砂

casting
sand

Water

水

混錬工程
Kneading
process at
kneading machine
混錬機

注
湯

型

解
枠

Removing
frame

Finishing;
仕上sand removal /砂除去／バリ取り
deburring

加工
工程

Machiningprocess

Target

対象工程
process

製
品

塗装
工程

Coating
process

対象工程

Target process;CO2 emissions are

not calculated×
CO2排出量組立
工程

Assembly
process

Component

Sand

砂

Pouring
meltingiron

溶
湯Component

Melting process at
high-frequency
induction furnaces

Moltenmetal

Materials

材
料

製
品

対象外
Excludedprocess
工程(C) 2024. Industrial Value Chain Initiative22

4.2 Data to be exchanged

High-frequency
induction furnaces

Kneading machine

Automatic molding
machine(1) Melting process result
recording(2) Power consumption at
kneading process(3) Manufacturing record

Operation result
recordingPreparing monthly
report of melting(1) Melting process
result recording(2) Power
consumption at
kneading process

Measuring electricity(3) Production
recordProduction record(4) Conversion to CO2emissions(6) Reporting(5) Visualization of CO2
emissions(C) 2024. Industrial Value Chain Initiative23

4.2 Data to be exchanged
Company A; SupplierCompany B; Service provider

工場管理システム

CO2 emissions calculation
CO2排出量算出

Factory management system
Melting
process
溶解工程
result
作業実績
registration
登録アプリ
application

エッジデータ受信用
Controller
forreceivingedge data
コントローラー
Production
生産管理
control
Cooperation
連携
terminal
ターミナル

Daily
report
溶解工程
of melting
作業日報
process

CO2
emissions
CO2排出量calculation

算出

DB

Power
monitor
電力モニタ

Viewerビューワ

Cooperation
連携
terminal

ターミナル

Controller
for edge
エッジデータ送信用
dataコントローラー
transmission

Reference(C) 2024. Industrial Value Chain Initiative24

4.3 Considering data ownership
Supplier A shares (1) melting process result recording, (2) power
consumption at kneading process, and (3) automatic molding machine production
record with service provider B.
Regarding high-frequency induction furnaces and kneading machines, Company A
also share with Company B number, (0) information about power supply, otherspecification information and the electricity used in the factory, e.g., whether it isrenewable electricity or not.
Service provider B's CO2 emissions calculation function performs a proportional
calculation based on (1) melting process result recording to determine the power
used per component manufacturing order, uses (3) automatic molding machine
production record to prorate (2) power consumption at kneading process for eachcomponents manufacturing order, and then calculates CFP for each component
manufacturing order using a energy CO2 conversion table to calculate CO2 emissions
from energy consumption such as electricity and gas.
Suppose that these proration calculation programs and conversion tables were notprovided by Company A or custom developed based on individual requirements, butwere originally developed and owned by Company B.
The CFP for each components manufacturing order is stored on the system managed
by Company B, and Company A refers to it through a viewer program on a Web
browser. Company A can also download the CFP data.
Using this CFP, Company A turns an improvement loop to reduce CO2 emissions.
(C) 2024. Industrial Value Chain Initiative25

4.3 Considering data ownership
From the data provided by supplier A to service provider B, it is theoretically possible
to calculate and infer capacity information, operation information, progress
information, defect rate information, manufacturing management methods, anddesign information, in addition to CO2 emissions, which is the original purpose.
Company A may be averse to sharing performance and design information outside
the company, which may lead to customer information, or it may be necessary toplace restrictions on the region where the server is located even if it is stored on thecloud.
On the other hand, depending on the scale of Company A, it may be difficult for
Company A to develop, implement, and maintain CO2 emissions conversion tables
and calculation programs on its own, and to prove the validity of the calculation
results.
Therefore, in the following, we discuss data ownership in the case where all primarydata necessary to calculate CO2 emissions are shared from Company A to Company
B, and where the scope of data utilization at each of Company A and Company B is
set to be simple.
Suppose that the CFP for reporting to Company C, the finished product manufacturer,
is sent to Company C at the time of delivery of components, by Company A's
instruction, separately specifying the user, purpose of use, etc. within Company C. In
other words, it is not assumed that the CFP is automatically shared from Company B
to Company C at any time at the time of CFP calculation.
(C) 2024. Industrial Value Chain Initiative26

4.3 Considering data ownership■The case where all primary data necessary to calculate CO2 emissions are sharedfrom Company A to Company B, and where the scope of data utilization at each ofCompany A and Company B is set to be simple.
Information (0) are stored not only while Company A is using Company B's system, butalso for a certain period of time after Company A has finished using the system, in case theneed arises to verify the calculation process.
Information (1), (2), and (3) are stored in Company B's system for a certain period of timeafter the CFP for each part or each manufacturing order is calculated, in case a need arises
to verify the calculation process. Intermediate data used in the process of calculation is also
handled in the same way.
Information (0), (1), (2), (3), CFP for each part and manufacturing order, as well as theirderivatives, are contractually specified for the region where the server storing them islocated.
The CFP for each part or manufacturing order, as well as its derived data, is stored on
Company B's system and is used only to respond to requests for display and downloading
to Company A.
If intellectual property of a better calculation and display method arises, the intellectual
property rights, i.e., patents, copyrights of programs, etc., are retained by Company B.
Based on these rights, Company B may provide the CFP calculation function to othercustomers other than Company A.
Company A can freely use the CFP for each component or manufacturing order shared from
Company B to Company A, i.e., viewer display results and downloaded contents. However,Company A will not reverse engineer the proration program or conversion table.
(C) 2024. Industrial Value Chain Initiative27

5. Acknowledgements
We would like to express our sincere appreciation to themembers of the smart manufacturing scenario WG. The
smart manufacturing scenario are the subject of ourstudy for the preparation of this documents.

Judging the quality of high-speed precision press
work✓WG 6E02 " How to Connect for Mass Customization "and✓WG 7E03 " Affordable data collaboration for small andmedium-sized manufacturing companies ".

(C) 2024. Industrial Value Chain Initiative28

Authors and Members
Advanced Study Group Members as of July 12th, 2024

Tadamitsu Asaka, Ace of Advanced Connecting, Support member
Hideo Ikeda, KOBE STEEL, LTD., Full member; Vice chairperson
Haruyuki Ishikawa, Huawei Japan K.K., Full member
Nobuyuki Ogura, ogura consulting, Individual member
Tsuyoshi Kobayashi, Business Engineering Corporation, Support member
Hiroyoshi Sato, ADS Tech, Support member
Takaaki Tanaka, Okuma Corporation, Full member
Yasuyuki Nishioka, Hosei University, Academic member
Shinji Hirokado, Denkahimakukougyou Co., Ltd., Full member
Tomohiko Maeda, FUJITSU LIMITED, Full member
Yasuo Matsuoka, DPMS's, Full member
Toshiko Matsumoto, Hitachi Solutions, Ltd. Full member; Chairperson
Hiroshi Yamamoto, IHI Corporation, Full member
Yoko Yoshimoto, Mitsubishi UFJ Research and Consulting Co., Ltd.,
Contributing member(C) 2024. Industrial Value Chain Initiative29

About Trademarks
Company names, service names, and product names are trademarks orregistered trademarks of their respective companies.

(C) 2024. Industrial Value Chain Initiative30

Data ownership and its description in specific examples
Publisher Industrial Value Chain Initiative
President Yasuhuki Nishioka
4-3-28-302 Kudan-kita, Chiyoda-ku, Tokyo 102-0073, Japan
Mail: office@iv-i.org
URL: https://iv-i.org
Date of publication

July 31st, 2024

List priceNot for sale

Reproduction or printing without permission of the publisher is prohibited.

(C) 2024. Industrial Value Chain Initiative

31