Fujitsu’s Monozukuri Strategy · Fujitsu’s Monozukuri Strategy Akira Ito Ken Takizawa Akihiko Miyazawa Fujitsu has expanded its activities to the development department and other

10 FUJITSU Sci. Tech. J., Vol. 53, No. 4, pp. 10–21 (July 2017)

Fujitsu’s Monozukuri Strategy

Akira Ito Ken Takizawa Akihiko Miyazawa

Fujitsu has expanded its activities to the development department and other supporting de-partments in the form of the Fujitsu Production System (FJPS) since the introduction of the Toyota Production System (TPS) in the production department in 2003. These activities are firmly based on the principle of using Fujitsu’s own information and communications tech-nology (ICT). The digitization of human activities and things is progressing in parallel with advances in ICT and the Internet of Things (IoT), and the analysis and prediction of conditions in the near future using such a wide variety of data is becoming a reality. These developments point to a major transformation in the entire Monozukuri (Japanese way of manufacturing) process. Fujitsu envisions smart Monozukuri as a higher order of Monozukuri that intercon-nects departments and plants as well as suppliers, partners, and customers via a virtual environment. This paper describes the current state of Fujitsu’s Monozukuri platform based on ICT activities that have come to support the evolution of Monozukuri and a next-generation smart Monozukuri platform.

1. IntroductionStarting out as a manufacturer of telecommu-

nications equipment, Fujitsu has grown to become a Monozukuri (Japanese style of manufacturing) enter-prise as well as an information and communications technology (ICT) solutions vendor. Fujitsu has ad-vanced its Monozukuri by utilizing its own ICT. Creating a virtuous cycle between Monozukuri processes and solutions has been a major strength of Fujitsu. At the same time, Fujitsu has come to offer its customers Monozukuri ICT solutions using as reference the en-vironments, tools, and know-how that it has fostered through the effective use of ICT.

This special issue introduces Fujitsu’s approach to constructing a smart Monozukuri platform, advanced technologies supporting that platform, and practical cases of innovative Monozukuri. In this paper, we sur-vey the current state of Monozukuri at Fujitsu, describe Fujitsu’s activities toward smart Monozukuri, and dis-cuss the future outlook for Monozukuri.

2. Monozukuri in the Fujitsu GroupIn this section, we describe the Monozukuri plat-

form that has come to support the evolution of the Fujitsu Production System (FJPS) while touching upon Monozukuri in the Fujitsu Group.

Since establishing the Monozukuri Development Unit in 2003 to enhance Monozukuri processes, Fujitsu has been expanding activities promoting innovation in Monozukuri based on FJPS (Figure 1). In addition to “evolution of development, production, and manu-facturing technology,” we have been working on three themes: “production process innovation,” “supply chain process innovation,” and “development process innovation.”

These activities promoting comprehensive in-novation in Monozukuri including human-machine harmonized production have been a driving force be-hind the ongoing development of FJPS. The following describes each of the above activities.

2.1 Production process innovationFujitsu is promoting innovation activities with

“just-in-time” and “jidoka (automation with a human

11FUJITSU Sci. Tech. J., Vol. 53, No. 4 (July 2017)

A. Ito et al.: Fujitsu’s Monozukuri Strategy

touch)” as two main preconditions of the leveling pro-cess described later. In the area of jidoka, the idea is not simply to save on labor but to create techniques and mechanisms that can respond to changes in prod-uct types and volumes in an agile manner.1) Just-in-time production

Streamlining, single-piece fl ow, and the pull sys-tem are being achieved on the Fujitsu production line. These changes are helping to prevent stagnation in the production process and wasteful overproduction and to improve just-in-time production. In addition, thorough implementation of standardized operations makes it easier to visualize and correct anomalies and to expand the range of mixed production. This, in turn, enables the construction of a production line capable of re-sponding to fl uctuations in production volumes without drops in effi ciency. This approach to shortening lead times and reducing costs is progressing daily in FJPS.2) Jidoka

Fujitsu is promoting jidoka based on the concept of a “production line enabling human-machine harmo-nization.” The following measures are needed to deal effi ciently with changes in the types and volumes of products through human-machine harmonization. • Simplification of setup operations at time of prod-

uct switching (versatility)This measure includes automatic generation

of robot operations, autonomous judgment of work status, automatic generation of work plans, and human-machine communication.• Greater degree of freedom in dividing up work

between human and machines

This means expanding the range of jidoka targets such as the handling of fl exible objects, connector join-ing, soldering, and 3D picking.

These measures include the detection of changes such as fl uctuation in lot characteristics and gradual degradation of equipment, that is, the autonomous ab-sorption of changes and fl uctuations. They also include the absorption by machines (robot systems) of fl uc-tuations in production volumes that have traditionally been dealt with by changing the number of production personnel.

In the above way, Fujitsu has come to place im-portance on two key axes: expansion of the domain capable of being automated by robot systems and improvement in the level of autonomy on the produc-tion line. One successful example of this approach is Fujitsu’s multi-function assembly robot system, which can automatically change between six types of tools and perform multiple types of assembly without a switching setup.3) Use of ICT at production sites

The use of ICT at production sites has been pro-gressing with the aim of shortening the production preparation period. For example, the digital environ-ment of FUJITSU Manufacturing Industry Solution Virtual Product Simulator (VPS), a digital tool using 3D modeling, can be used to evaluate the ease of as-sembly and maintainability of products and to perform process design, prepare work instruction manuals, etc. In addition, the digital environment of Global Protocol for Manufacturing (GP4), which enables production-line simulation, can be used to design

Figure 1Expansion of activities promoting innovation in Monozukuri.

2003 2006 2017

Production process innovation

Developmentprocess innovation

Supply chainprocess innovation

Evolution of development, production, and manufacturing technologies

1935

Application of ICT

FJPS reflecting

Fujitsu Monozukuri

12 FUJITSU Sci. Tech. J., Vol. 53, No. 4 (July 2017)


product production lines. Production equipment and products that have been digitized using model-based development techniques are also being used to test production equipment and equipment-control software.

2.2 Supply chain process innovationIn the supply chain, Fujitsu is promoting activities

that can suppress fluctuation in materials and facilitate efficient Monozukuri through a two-stage process of leveling off the receipt of orders and shipments by sales and operations planning (S&OP) in the sales depart-ment and leveling off the submittal of orders in the production department.1) S&OP activities

Originating as production innovation in the production department, S&OP activities are aimed at improving Monozukuri by expanding the FJPS concept to suppliers and customers and the supply chain itself. These activities are driving improvements in the opera-tions department and sales department with the aim of leveling off the receipt of orders. Promoting innovation with the sales department is helping to eliminate the stagnation of information transmission through S&OP activities. Constructing a mechanism that can link up with the sales department—even in relation to informa-tion on business negotiations that have yet to produce an order—and updating that information as needed and thereby obtaining an understanding beforehand of large-scale negotiations, sets a foundation for achiev-ing production leveling while meeting delivery dates.2) Promotion of production leveling

Production-leveling activities in the production department are progressing through coordination be-tween the operation and manufacturing departments, and we have been promoting several measures such as production of fixed orders ahead of schedule.

For example, the operations department ex-ecutes a plan-do-check-act (PDCA) cycle with the aim of meeting targets in agreement with the manufacturing department by constructing leveling logic that takes into account the production line configuration (as in mixed production of various types of products on one production line) and by visualizing the level of leveling. The manufacturing department, in turn, works to re-duce production-related factors that hinder leveling off in processes that perform lot-based production because

of equipment-related constraints, such as by making lot sizes smaller through improvements in setup opera-tions. In this way, improvements are being sought on a daily basis so that Monozukuri can be performed with-out a drop in production efficiency even in the event of fluctuating production volumes.

2.3 Development process innovationThe Flexible Technical Computing Platform (FTCP)

supports development work in the Fujitsu Group from product planning to product design and analysis.1) Integration and sharing of design know-how

(FTCP 1.0)Fujitsu set up the FTCP (Figure 2) in 2007 as a de-

velopment environment for company-wide integration. It provides a mechanism for interconnecting a variety of tools and in-house systems essential for product devel-opment, and in this capacity, it has produced significant effects in cost reduction, quality improvement, and greater design efficiencies. In short, FTCP integrates design technologies that are needed from design to production (such as computer-aided design [CAD], computer-aided engineering [CAE], product data man-agement [PDM], design rules, and parts information), and promoting the use of these technologies within the Fujitsu Group has accelerated the consolidation and sharing of design know-how. The early introduction and in-house use of advanced technologies via FTCP has given birth to a virtuous cycle that enhances the technologies available for use and facilitates the con-solidation of new forms of know-how. 2) Extensive testing functions

Testing functions include design rule checks (DRCs) based on design standards and a variety of sim-ulation techniques for electromagnetic field analysis and other purposes. There are about 300 DRC testing functions that include a design for manufacturability (DFM) function for evaluating productivity plus know-how for making design work more efficient. Checks at the design stage can be executed in real time, and design errors can be corrected on the spot, which has the effect of greatly reducing the amount of reworking. Simulation, meanwhile, can be used to verify whether designs are meeting standards. The FTCP enables ef-fective operation of DRC and simulation functions.3) Development and application of an engineering

cloud



In 2010, with the aim of compiling and managing development know-how within the Fujitsu Group in an integrated manner, Fujitsu began development of an engineering cloud (“Engineering Cloud”) to place all design technologies (tools, standards, libraries, design data, etc.) in the cloud and promote a shift to a compre-hensive design environment in the cloud rather than on the designer’s terminal. Application of this cloud has been expanding in conjunction with usage trials. In general, desktop virtualization tools must deal with many issues including screen responsiveness since CAD and CAE require highly detailed line drawings and high picture quality. For this reason, Fujitsu Laboratories undertook the basic development of proprietary high-speed image compression and transfer technologies, which were then implemented by Fujitsu and applied to its in-house cloud environment.4) Enhancement of design platform (FTCP 2.0)

As the use of simulation in design work came to be accepted, Fujitsu set out to expand its use and re-spond to an even higher level of needs. In the case of commercially available analysis software, calculation speed tends to saturate as the degree of parallelism in computational processing increases, which has been a major problem in terms of cost and performance. To

solve this problem, Fujitsu developed solver software, FS-Solver, that prevents a reduction in calculation speed even with increasing model scale and then went on to construct an original analysis environment that can accommodate the development of all sorts of products from ubiquitous devices to large-scale serv-ers. This made it possible to perform a wide range of model analysis from models of individual printed circuit boards (PCBs) to large-scale models of entire prod-ucts. Additionally, in parallel with the above, Fujitsu developed preprocessing (creation of models) and post-processing (visualization of calculation results) techniques for improving effi ciency in analysis work. In this way, Fujitsu has been able to contribute to making a whole series of analysis tasks more effi cient from the incorporation of design data to model creation, highly parallel calculation, and results display (Figure 3).

3. Smart MonozukuriAdvances in ICT and the Internet of Things (IoT)

combined with the digitization of human activities and things are enabling the use of diverse types of data as useful information. The analysis and prediction of conditions in the near future using such a wide vari-ety of data are becoming a reality, and the possibility

Figure 2FTCP development platform.

DRC: Design rule checkPCB: Printed circuit board

Fujitsu Group Development Platform(integrates know-how in development departments)

Flexible Technical Computing Platform (FTCP)

Structural toolsLSI tools Electrical toolsFujitsu FujitsuFujitsu Company A/B Company A/BCompany A/B

Verification technology/DRC

Electrical DRC

... DRC

... manufacturing check

Logical connection check (LvS)

... check

Design standards/Library environment

Parts libraryAnalysis

modeling library

Material properties

library

Design standards

Simulation

Electrical

Signal integrity

Power integrity...

Structural

Thermal-fluid

Reduction of sound...

LSILogic/Delay

...

Strategic toolsWiring support tools

... solver

RVEC*

Knowledge management

Automatic analysis toolsfor collective knowledge

Data/Knowledge/Know-howautomatic systematization tools

LSI CAD 3D CADLSI CAD PCB CAD PCB CAD 3D CAD

* RVEC (Remote Virtual Environment Computing, pron. “re-vec’ ”) is a display acceleration technology that improves the operational responsiveness of screen transfer.



of making radical changes to the entire Monozukuri process is coming into view. This is digital innovation targeting Monozukuri. Against the above background, we next describe the form of the next-generation smart Monozukuri conceived by Fujitsu.1),2)

3.1 What is possible with smart Monozukuri?Fujitsu’s objectives with smart Monozukuri from

the viewpoints of customers, society, in-house devel-opment sites, and production sites are summarized in Figure 4. • Customers: Make your dreams come true.• Society: Contribute to the environment as a social

mission.• Development sites: Continuously pursue lower

costs, higher quality, and greater value.• Production sites: Use “only what is needed when

needed.”In Figure 4, the “things” deemed necessary to

achieve the form desired for these four objectives are defi ned on the layer labeled “Things,” and the tech-nologies for achieving these “things” are defi ned on the layer labeled “Technologies.” Here, the core tech-nologies for achieving this layer of technologies are digitization technologies.

3.2 Approach to achieving smart MonozukuriFujitsu’s approach to achieving smart Monozukuri

is shown in Figure 5. In the fi gure, “projection” and “connect (synchronize)” are keywords that best refl ect this approach to achieving effi cient, technology-based Monozukuri dictated by an appropriate level of quality. In this approach, development sites leverage common platform technologies including production and manu-facturing technologies plus ICT to develop and test products and production in a digital environment in ac-cordance with product attributes. Production sites use IoT to interconnect people and a variety of facilities and robots. This process “projects” the digital model created in a development site to a production site. Meanwhile, the state of production sites evolving with genba-ryoku (on-site competence) is immediately fed back into the digital model through IoT technology. With our smart Monozukuri, we aim to promote high-speed evolution in manufacturing through short-cycle verifi cation in the digital environment, high-speed refl ection at the production sites, and integration of genba-ryoku with digital technologies such as ICT and IoT.

Fujitsu is achieving the “things” shown in Figure 4 through smart Monozukuri by integrating the initia-tives shown in Figure 6 at these sites. In the following, we describe the current state of six of these initiatives.

Figure 3Massively parallel analysis solver supporting large-scale models.

FS-Solver (Thermal Flow/Structural)

Parallel calculations scalability

(a) Performance comparison

Create analysis model

Preprocessing

FS-Solver (Thermal Flow/Structural)

-

Calculate and output results

Engineering Cloud environment (Linux)

(b) Analysis process flow

Structuraldesign

Board design

PCB CAD

3D CAD

Mesher(translator)

Create mesh

FS-Solver

Parallel processing

Display results

Post-processing

Calc

ulat

ion

spee

d ra

tio

No. of CPU cores

10,000

1,000

100

10

11 10 100 1,000 10,000

Thermal-fluid analysis (company A)

Structural analysis (company B)

Thermal-fluid analysis (company C)



Figure 4What is possible with smart Monozukuri?

Human-machine harmonization(human-centric)

•Order-made Monozukuri

•Co-creation with customers and other companies •Quick/Inexpensive

•“My factory” (design – production)•Give form to “things”

•Open innovation sites

•Plants with zero facility failures

•Enhanced product maintenance/service

•Automatic product design

•Monozukuri with “0” defects

•Dynamic allocation • Production-line allocation • Line reconfiguration

•People-friendly plants

Development sites

Customers

•Automatic reflection of improvements

•Acquisition of know-how in a virtual environment

•“Things” production

•Lower costs

•Short-lead-time development

•Optimal design quality

•Monozukuri navigation

•Maintenance/Use of design assets

•Accelerated personnel development

•Flexible production • No setup switching • Support of IoT fluctuations

•“Virtual Obeya” conferencing

•Autonomous/ Intelligent robots

•Fault/Defect prediction

•SCM modeling/ simulation

•Microfabrication•Container fabrication •Rapid manufacturing

•Tool/System linking

•Event sensing

•In-line self-test

•Process auto-generation

•Large-scale coupled analysis

•Rapid prototyping

•Work navigation•DF/RF synchronization

•Authentication, security

•Wiring-support engine

•RVEC

•Telexistence

Opt

imal

SCM

(onl

y w

hat i

s ne

eded

whe

n ne

eded

)

Make your dreams come true

Opt

imal

pro

duct

dev

elop

men

t(c

ost,

qual

ity, v

alue

)

Production sites

Contribute to the environment

•Virtual environment UI

•Real-time analysis

•AR/VR

•CAD/CAE

•Standards/Libraries

•Memory sharing

•Strategic tools

•Model-based development

•Production/Manufacturing simulation

•DMU/DRC

“Things”

Society

•Green design•Green Monozukuri

Technologies

DF: Digitalized factoryRF: Real factoryAR: Augmented realityVR: Virtual realityDMU: Digital mock-upSCM: Supply chain management

Figure 5Approach to achieving smart Monozukuri.

Projection

Connect (synchronize)

TPSDigital factory

Development/Verification environmentDigital product

Customers, maintenance services, product development,

production technologies

Production siteReal product

Real factory

On-site capabilities + IoT system

Common base technologies + ICT infrastructure



1) Virtual ObeyaVirtual Obeya (where obeya means “large room”

in Japanese) is a forum constructed in digital space to enable consumers, retired specialists, suppliers, ven-dors, etc. at sites around the world to communicate and participate in Monozukuri. It aims for a Monozukuri process that links design and production sites and that promotes the sharing of product development informa-tion without being limited by place and time.2) Design automation

The functionality, performance, and quality required of products have become increasingly sophis-ticated, and a variety of issues such as heat generation, electromagnetic compatibility (EMC), enclosure design, low-voltage design, and noise have become increas-ingly intertwined. Fujitsu is working to solve these complex and composite issues through a One Platform concept that integrates the CAD/CAE tools used in elec-trical and structural systems and that achieves design automation (Figure 7). The following technologies related to this platform are also being developed.• Composite analysis (coupled analysis)

The need for introducing coupled analysis tech-niques for simultaneously simulating the composite elements of heat, electricity, and structure has been recognized, and in-house trials have begun.• System linking using web API

In addition to coordinating in-house systems related to product development such as FTCP and product data management (PDM), it will also become important to link up the systems of all product-related departments: marketing, design, manufacturing, logis-tics, and so on. To this end, the plan is to standardize the procedures for calling the database tools managed by each department as a Web API and to link up related departments so that each can create even more ad-vanced and superior services.• Advanced design/testing techniques using AI

Operating FTCP in the Engineering Cloud environ-ment enables the history of design data and design processes to be stored as engineering chain manage-ment (ECM) information. In addition, an environment for extracting supply chain management (SCM) infor-mation stored in the production department by a web

Figure 6Map of smart Monozukuri initiatives.

Production allocation adapting to

environmental changes(6) Dynamic allocation

Human-machine harmonization line, on-site improvement activities,operators, production equipment

ProductionDesign

Real-time tuning of manufacturing/testing

conditions(5) Monozukuri navigation

Procurement Logistics MaintenanceProduction

preparations

Dig

ital

Real

Engineering Cloud

Sales

Grasping quality and degradation factors

Virtual ease-of-assembly

testingVPS/MFG

Virtual production line design/testing

GP4

Minimization of machine-downtime effects

Detection of predictors

Visualization

Engineering Cloud Portal　RVEC

Virtual prototyping/

testingVPS/DMU

Analysis tools

CAE

Design tools

CAD

Reinforce computer resources

ICT infrastructure

Expansion of Engineering Cloud domain

Creation and prior testing of training data

Robot simulation

Flexible response to changes in type/quality,robust to disturbances

(3) Autonomous manufacturing system

Digital model enhancement

(4) Reflection of on-site improvements

Effective use of design assets and wisdom

(2) Design automation

Remote inter-site communication

(1) Virtual Obeya

Event sensing by IoT platform

Planning

R&D



API can be constructed, and machine learning as one instance of practical artifi cial intelligence (AI) can be applied to design automation.3) Autonomous manufacturing system

Fujitsu is developing autonomous/cooperative technologies to enable a robot system to “feel,” “think,” and “behave” on its own. This includes automatic tracking of variations in production lots and of aging deterioration of the robot system itself. An automatic generation system for robot programs and an automatic generation system for image-recognition programs3) are also being developed. The automatic generation system for robot programs enables operators to specify robot actions with simple operations. It also can ab-sorb differences in specifi cations among different types of robots so that operators can perform their work with-out having to worry about those differences.

This technology helps to minimize the labor as-sociated with setup switching and work rearrangement and can lower the barriers to introducing robots.4) Automatic reflection of on-site improvements

made by peopleThis technology uses IoT to automatically and

immediately refl ect information such as operator

movement, equipment operations, and operation tim-ing in the corresponding digital factory. The digital factory can thus be brought increasingly closer to the real factory as the latter evolves (Figure 8).5) Monozukuri navigation

The idea of this initiative is to merge various types of information from the production site with the prod-uct design model, support the analysis of defects and

Figure 7One Platform.

SI analysis

FTCP electrical CAE

PI analysis

FTCP electrical CAE

Electromagneticfield analysis

FTCP electrical CAE

Circuit design

FTCP electrical CAD

Board design

FTCP electrical CAD

Structure

FTCP structural CAE(pre/post)

Thermal-fluid

FTCP structural CAE(pre/post)

3D verification

FTCP structural DMU

3D design

Structural CAD

Structural analysis

Front-end GUIs

*3D-CAD data is transmitted to DMU

Back-end services

Electrical noise analysis

PCB design Structural/Thermal-fluid analysis

Structural design

AI: Artificial intelligence

Thermal-fluid analysis solver

Structural analysis solver

DRC engine

AI engine

Parallel automatic processing engine Individual/Group management

Know-how sharing

Structural CADElectrical analysis Electrical CAD

Figure 8Automatic refl ection of on-site improvements.

On-premise GPS Wearable sensorImage tracking

Integrated detection of operator movement/posture, task extraction

Digital factory Real factory



their causes using data mining and statistical analy-sis, and construct a model for predicting the state of production.

This prediction model can then be used to tune production facilities in real time and provide improve-ment guidelines to the development and production sites (Figure 9). 6) Monozukuri dynamic allocation

The confi guration of a smart supply chain is shown in Figure 10. In this process, information as-sociated with each type of business is constructed as a data model in the corresponding business cloud, and links are formed between these clouds. All production

information can therefore be shared even if some systems or facilities are not linked up. As a result, the stagnation of things and information can be elimi-nated, which enables not only a shortening of lead times and reduction of inventory but also the constant monitoring of differences with the prediction model. This information can be shared instantly among all the business clouds, which means that measures can be taken immediately on detecting situations that involve risk.

The purpose of dynamic allocation is to coordi-nate different production plants as demand fl uctuates by linking production information from different pro-duction lines and by performing load adjustment and delivery-date negotiations through the help of simula-tions. Another purpose is to coordinate order receiving/placing information through the mutual exchange of parts-supplier process-status information and one’s own production-planning information. In this way, a mechanism for linking production information be-tween companies can be created so that production fl uctuations that take into account a supplier’s parts-production conditions can be absorbed, and parts procurement can be leveled out.

Improving the accuracy of demand prediction is one means of achieving dynamic allocation. To this end, Fujitsu has constructed a demand-prediction mechanism that uses not only past results and infor-mation on major business negotiations but also applies machine learning and big data analysis that combines

Figure 9Monozukuri navigation.

PredictionmodelDevelopment site

Production site

Design model

Statistical model

• Parts variation• Facilities operating conditions• Know-how

• Design information

Real-time tuning

Manufacturing-state prediction, improvement guidelines

Manufacturing-state prediction, improvement guidelines

・Analysis・Mining・Learning

Improvement

Improvement

Figure 10Dynamic allocation.

Logistics informationInventory status

Product informationProduction plan

BCP, BCM BCP: Business continuity planningBCM: Business continuity management

Production line specifications

Parts movementinstruction

Productioninstruction

Production line

Production line

Production line

Customer 1

Customer 2

Parts storage

Parts storage

Parts storage Customer n

...



estimates from current negotiations and product con-fi guration information. Compared with conventional techniques based on past results, this mechanism achieves high accuracy in demand prediction.

4. Future outlookThe Industrie 4.0 initiative in Germany and the

Industrial Internet Consortium centered about the United States are well underway, and in Japan, the Robot Revolution Initiative (RRI) based on Japan’s Robot Strategy and activities promoting manufacturing business reform through IoT have begun in earnest. Fujitsu has been contributing to these activities as well as to the Industrial Value Chain Initiative (IVI), a private-sector effort.

In step with these trends, Fujitsu will con-struct a “digital Monozukuri platform” to serve as a hub connecting the digital and real worlds through a next-generation Monozukuri infrastructure. A conceptual diagram and the confi guration of this

digital Monozukuri platform are shown in Figures 11 and 12, respectively. This platform interconnects all Monozukuri processes and the Monozukuri fi eld, digital fi eld, and market/customers. In this way, it facilitates links with suppliers and partners and enables immedi-ate feedback from customers.4) Going forward, Fujitsu seeks to promote this digital Monozukuri platform as a de facto standard through the IVI and RRI and to even-tually establish it as an international standard.

The following measures can be viewed as keys to achieving a digital Monozukuri platform.1) Construct a data distribution infrastructure

This means constructing an environment that en-ables uniform access to data and information from the upstream to the downstream.2) Provide a plug-in method for diverse services and

establish data formatsTo further accelerate design work, there is a

need for a plug-in method for using services in the data distribution infrastructure and for data formats to

Figure 11Conceptual diagram of digital Monozukuri platform.

Monozukuri fieldDigital field

Development/Production/

Logistics management

Manufacturing system

Enterprise levelPLM

Production line

Production facilities

Management level

Operation level

Control level

Equipment level

Market/Customers

• Design information management• Technology information management• Know-how management

• CAD/CAE• Virtual conferencing• Cloud

Plant production simulation

Line simulation

• Robot simulation• Machine simulation

• ERP • Production management

• MES • Energy management

• SCADA • Monitoring/Control

PLC

• Robots• Automated machinery• Sensors

1,000 ms or less

100 ms or less

10 ms or less

1 ms or less

Market/Needs Operation information

Digi

tal

Mon

ozuk

uri p

latfo

rm

Procure-ment

PlanningService process

Desi

gn p

roce

ss

Re

aliz

atio

n pr

oces

s

Shipment

Feedback path

Com

mun

icat

ion

path

Control path

PLM: Product lifecycle managementERP: Enterprise resource planningMES: Manufacturing execution system

SCADA: Supervisory control and data acquisitionPLC: Programmable logic controller



enable effi cient data exchange among different types of services.3) Perform real-layer control from the digital layer

This will require the formation of control paths from the digital world to the real world and feedback paths from the real world to the digital world to support in real time the different requirements of each level in Figure 11, from PLM to production facilities. In terms of feedback paths, there will be a need not only for feed-back on each level but also feedback from upper and lower levels.4) Form communication paths with the field

This measure involves the formation of paths for accessing outside information and communicating with customers, suppliers, and partners as well as com-munication paths on the digital layer to enable secure connections with the platforms of other companies.

5. ConclusionIn this paper, we examined the evolution of

Monozukuri in the Fujitsu Group, described the current state of Fujitsu’s Monozukuri platform that has sup-ported that growth, and discussed the future outlook for Monozukuri at Fujitsu. Going forward, the plan is to make “smart Monozukuri” a reality through a

next-generation Monozukuri platform and to promote digital innovation in Monozukuri. Fujitsu aims to real-ize its vision of a Human Centric Intelligent Society in the fi eld of Monozukuri and to make far-reaching con-tributions to the advancement and expansion of the manufacturing industry.

References1) J. Matsueda et al.: Internal Initiatives Aiming to Realize

Smart Monozukuri. FUJITSU, Vol. 66, No. 4, pp. 81–88 (2015) (in Japanese).

http://img.jp.fujitsu.com/downloads/jp/jmag/vol66-4/paper11.pdf

2) Fujitsu: Fujitsu Ushers in the Next Generation of Manufacturing with Human-Robot Collaboration.

http://www.fujitsu.com/global/about/resources/news/press-releases/2015/0306-01.html

3) Fujitsu Laboratories: Fujitsu Develops Technology to Automate Construction of Visual-Inspections Programs for Production-Lines.


4) Fujitsu: Fujitsu Begins Sales of Digital Solution “COLMINA” to Create Connected Services in the Manufacturing Industry.


Figure 12Confi guration of digital Monozukuri platform.

Job management Load-balance management/control User management License management Log collection

SCM management

Automated services

Know-how sharing

AI engine

Simulation testing services

DRC engine

Analysis solver

Design information management services

CAD data management

Drawing management

Parts list management

Project management

Robot SI services

Automatic generationof robot motion

programs

Image recognition AI

Production facilities services

...

...

Outside-company services

...

...

ProductionequipmentPLC Robots Sensors

Edge computers

Provide mechanism for linking with various companies and collecting information

Range of PlatformOther companies may provide services on platform

Achieving Monozukuri linking multiple enterprises

Met

aArc

* Factory management

* MetaArc is a digital business platform providing advanced technologies for cloud, mobile, big data, IoT, etc. SI: System integration

Engineering management




http://img.jp.fujitsu.com/downloads/jp/jmag/vol66-4/paper11.pdf



Ken TakizawaFujitsu Ltd.Mr. Takizawa directs production/manu-facturing technology development and the promotion of Monozukuri innovation activities.

Akihiko MiyazawaFujitsu Advanced Technologies LimitedMr. Miyazawa directs integrated devel-opment environments and advanced technology development.

Akira ItoFujitsu Ltd.Mr. Ito directs advanced technology devel-opment and the Monozukuri integrated development environment in Fujitsu’s Service Platform Business.

Documents

Fujitsu’s Monozukuri Strategy · Fujitsu’s Monozukuri Strategy Akira Ito Ken Takizawa Akihiko Miyazawa Fujitsu has expanded its activities to the development department and other