© 2019 FUJITSU© 2019 FUJITSU

Tööstuse

digitaliseeri-

mine ja toetus-

võimalused

Digitaliseerimise trendid ja

praktilised näited

Marek Sokk, AS Fujitsu Estonia

Head of Consulting & Professional Services

© 2019 FUJITSU

Global Trends in Manufacturing

Cost reduction

Increased competition

Reaching new customer

groups and revenue

areas

Manufacturers from the

APAC region, in

particular, consider this

a major topic

Customer centricity

Process efficiency

Optimization of supply

chain

Need to use intelligence

captured (data usage)

Complex IT systems

“Always on” approach

New business models

implementations

Increased globalization

Slow global demand for

manufactured products

Political and Economic

Workforce concerns

Asset Tracking concerns

Cost reduction and

efficiency pressure

Changing and

demanding customer

needs

Market Challenges Drivers for IT investment CIO Business Priorities

IBM Watson is deeply linked

to manufacturing

HPE and GE partnered to

deliver new IoT solutions

Siemens is focusing on

product design and factory

automation with a significant

investment in PLM software

Bosch was considered a

I4.0/IoT leader in the

German market.

Competitor Highlights

By the End of 2021, 25% of Global Manufacturers Will Apply Machine Learning to Data

Across Product Development, Supply Chain, Manufacturing, and Service for More Rapid

Decision Support, Improved Quality, Differentiated Products, and Innovative Business

Models.* IDC Manufacturing Insights for Innovation Acceleration

© 2019 FUJITSU

Our Focus Areas for ManufacturingImproving Efficiency

Increasing productivity per person

Factory uptime & minimizing downtime

Lowering costs

Being Effective

Manufacture those items the markets asks for

Managing the full product lifecycle

How to effectively reach consumers and getting their

feedback

Business Agility

Legacy systems holding back change

IT not enabling new business models

Health and Wellbeing

Being attractive to the next generation workforce

Avoid health problems in the future

Predictive

Maintenance

EHSProcess

Automation

Supply Chain

Optimization

Manufacturing

Operations

Big Data

AnalyticsQuality

ManagementAsset

TrackingAsset

Tracking

Invoice

Automation

Predictive

Maintenance RPA

© 2019 FUJITSU

Revolutionizing quality control

processes

accurate

agile

consistent

efficient…

with F|AIR

© 2019 FUJITSU

Applying F|AIR to a Wind Turbine Manufacture

Ultrasonic quality inspection generates massive

amount of blade scan data for Quality Control

Scan data evaluation process now completely

automated

High gains in time efficiency, enabling skilled

operators to focus on the important part of the data

Cuts inspection times for windmill turbine blades from

six hours to just one and a half hours

‘By adopting Fujitsu’s ground-breaking AI technology it takes only a quarter of the time previously required to perform an inspection’ Kenneth Lee Kaser, Head of SCM,

Siemens Wind Power

6 © 2019 FUJITSU

How Artificial Intelligence enables a new generation of automated Quality Control

Benefit Automated Optical

Inspection (AOI)

Artificial

Intelligence

Learns from images

instead of programmed rules

Fast adaptation to changing

specifications / product variation

Replicates worker judgement

Highlights anything unexpected

Easily applied to more complex

image types (i.e. ultrasound)

Classifies type of defect

Complex Defects

Measurable Defects

Angle

WidthAOI

Artificial Intelligence

Placement

Jagged

EdgeScratch

Curvature

Discoloration

AOI

Artificial Intelligence

© 2019 FUJITSU

Use Case: Applying FAIR to high quality

manufacturing

Adding Machine Learning to train

from microscopic images of defects

Using x-ray / ultrasound scans to

accurately determine (rare) source material

quality

Fully automated scanning of large

amounts of quality data to detect

issues

Inspect 3d images

of various

components

Apply F|AIR to aid with

non-destructive testing

© 2019 FUJITSU

Predictive Maintenance

Business Driver: In industry it is

estimated that early identification and

fixing of problems before they occur

can save 40% in maintenance costs

Predict and prevent failures in equipment, assets or

infrastructure early to ensure early intervention achieving

Operational cost reduction and efficiency gains

Companies benefit from:

Increases the running hours of the analyzed assets

Reducing the human intervention for maintenance

tasks

These benefits apply across any equipment/asset

(i.e wind turbine, hydraulic presses, ATMs, gas pipelines)

This mainly applies to Manufacturing, Utilities and

Transport sectors.

Predictive

Maintenance

Past

Breakdowns

Analysis

Predict Present

or Oncoming

Failure Events

Predict Future

Breakdowns

Increase

Running

Time

Reduce

Operational

Costs

Smart

Planning

Introduction

Predictive

Maintenance

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Predictive Maintenance – Digital

Ear

PALES GEARBOX GENERATOR

ANALYSIS OF THE 3 MAIN COMPONENTS OF THE WIND TURBINE

PAST BREAKDOWNS ANALYSIS PREDICT FUTURE

BREAKDOWNS

INCREASE RUNNING TIME REDUCE OPERATIONAL COSTSSMART PLANNING

BUILD RECOMMENDATION

ENGINE

Custom-designed sound sensors ,purpose-build

cloud-based analytics platform. Fujitsu

developed a technique that identified and

isolated APU sound signatures

© 2019 FUJITSU

Digital Annealer opens up new possibilities

Digital Annealer

Brain-Like

computer

Q u a n t u m

computer

G e n e r a l - p u r p o s e

c o m p u t e r

New digital circuit architecture inspired by quantum phenomena

Solve combinatorial optimization problems much faster than conventional computer

More practical than quantum computer

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Digital Annealer

A new computer which draws

inspiration from the principles of

quantum phenomena

Solve complex combinatorial

optimization problems

Partnered with 1QBit for software,

and opened a research laboratory

at the University of Toronto

Enable breakthroughs in various areas

11

Chemical and Pharma

New materials Drug design

Financial

Portfolio Optimization Arbitrage

Supply Chain

Delivery plan Job scheduling

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Use case | Fast Job Shop Rescheduling

Production jobs are sequences of

operations on machines

Scheduling due to conditions

(operation order, duration, …)

Minimize overall production time

Fast replanning in dynamic

factory

Solvable by annealing

Manufacturing

Operations

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Use case | Workforce optimization

Workforce planning in Factories and Distribution Warehouse

Condition example

• Fulfil each shift work request

• Minimize number of workforce

• Maximum workload per day: 1 shift

• No working across days

• 2 Days off per week

7days, 4 shift/day → 34 workers

DA optimized work shift plan with 29 workers

and secured 5 workers for other tasks

Supply Chain

Optimization

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Use case | Robot Positioning Optimization

Welding robots “visit” seam

location

Seam can be welded in 2

directions

Find best welding directions and

optimal roundtrip between

endpoints

2𝑛−1 𝑛 − 1 ! possibilities

Solvable by annealing

Manufacturing

Operations

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Solving Digital Business

problems through co-

creation

The Fujitsu

Human Centric

Experience

Design program…

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Where Co-Creation takes place

People(desirability)

New Value

Technology(feasibility)

Business(viability)

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Let’s take our first small steps

together

© 2019 FUJITSU

?

Q&AWe’d love to take your

questions…

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