utilization of robotics/ai in logistics · utilization of robotics/ai in logistics nittsu research...

Utilization of robotics/AI in logistics

Nittsu Research Institute & Consulting, Inc. (NRIC)

October 2016

1.The concept of the use of robotics 2.Autonomous systems in logistics and manufacturing

1.The concept of the use of robotics

Copyright (C) 2016 Nittsu Research Institute and Consulting, Inc. Proprietary & Confidential 2

(1) NRIC: Who we are

• 50-year old research institute specialized in logistics

• Subsidiary of Nippon Express, Japan’s largest 3PL

• Contribute and help improvement in logistics industry

• Employees: 75


3PL employee

Management

Trade terms, operation

Cargo volume forecasting

MHE research & advice

Emerging markets research

Inside warehouse improvement

Inventory reduction

Delivery network optimization

Warehouse location optimization

Cross border trade trial

Market entry research & advice

Shock, temp./humidity tests

Packing test

Carbon dioxide reduction

Research

Consulting

Education

Engineering

“L”ogistics

Goods needed, amount needed, place needed

Network (node and link)

Demands for more labour force

Same day, Pin-point time delivery, Redelivery

Shortening lead-time

Need for additional services

Increasing demand for high quality

Avoid the industry due to lots of overtime work

Low pay compared to other industries

Harsh work environment

Stricter driving licensing policy implemented

（2）Environment: Conflicting forces in logistics industry


Population aging rate(above 65 years old)

Total population

：between 65 and 74 years old：75 years of age or older ：between 15 and 64 years old ：between 0 and 14 years old

Results Estimation×10,000

（Population）

Wage of Road freight business

Average Wages

￥314,054/month

￥297,233/month

97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13

×10,000Yen

Declining and aging population

Low and decreasing pay in logistics industry

Increasing unpopularity/obstacles

In a long-term, Japan may have to shift to the European model, but what would be the

social impact in near future?

How does the role of the man change?

(3) Difference: Human or Machine?


The main constituent of the work

is machine, human supports

machine

Advantages Easy to standardize

Operator experience not needed

Easy to educate

Decrease in labour force

Easy to collect data

Disadvantages Handle-able goods is limited

Difficult to change work method

Large initial investment

The main constituent of the

work is human, machine

supports human

Advantages Relatively easy to handle variety of

goods (shape, size)

Work method can be changed

flexibly

Small investment

Disadvantages Depends on operator’s capability

Difficult to standardize

Education needed

Work load gets smaller, but does not

get to zero.

Weakness for standardization?

(3) Difference: Human or Machine?


Unpacking of air cargo

Loading to the container

In Japan, wearable machine is preferred…….Because of easy introduction

Experiments performed by NRIC

Reference: In the case of Europe


HAL Suit Stuttgart Exo-Jacket

Robot suit for supporting and enhancing its wearer’s physical abilities

Lower limb and full body type

Developed by Cyberdyne Inc. and the University of Tsukuba

Lightweight suit to support the back and arm

Scheduled for market introduction in about 2018

Developed by Fraunhofer IPA

© NRW Japan K.K.

(4) Robotics in Logistics（Human Substitute Robot)


Unspecified

Third-party

Uncontrollable

Logistics

Specified

“Homemade”

Controllable

Manufacturing

Goods

Product Design

Plan

Manufacturing difficultyWe should consider how to take advantage of the human ability

Key Point(in logistics):Machine･･･”Shape” ”Strength” “Center of Gravity” recognition of the object

that can not be estimatedHuman････Operation the machine can not determine

And Putting more difficult task than PickingPicking ⇒ Recognition of the objectPutting into storage ⇒ Recognition of the yard environment

(4) 10 years from now,

• What will be the view of logistics workplace?

• What kind of innovations are expected?

• How should we be prepared for the changes?


?

We will continue to explore

２.Autonomous systems in logistics and manufacturing


© Fraunhofer • Seite 11

Tim Brandl

October 6th 2016

AUTONOMOUS SYSTEMS IN LOGISTICS AND MANUFACTURING


Fraunhofer-Gesellschaft, the largest organization for applied research inEurope

Wertheim/Bronnbach

Mannheim

Karlsruhe

Pfinztal

Ettlingen Stuttgart

Freiburg

Kandern

Efringen-Kirchen

Alzenau

Würzburg Bayreuth

Erlangen, Nürnberg, Fürth

Regensburg

Straubing

Augsburg

Weßling

München, Garching

Freising

Prien

Holzkirchen

Bremen

Teltow

WildauPotsdam-Golm

Cottbus

BremerhavenHamburg

Kassel

Frankfurt

Darmstadt

Rostock

Oldenburg

Hannover

Braunschweig

Goslar

Göttingen

MünsterLemgo

PaderbornDortmund

Schmallenberg

Gelsenkirchen

Duisburg

Oberhausen

Köln

Euskirchen

AachenWachtberg

Bonn, Sankt Augustin

Kaiserslautern

Saarbrücken

St. IngbertSulzbach

Leipzig

Chemnitz

Freiberg

Dresden, Moritzburg

Zittau

Magdeburg

Halle

Schkopau

Leuna

Itzehoe

Lübeck

ErfurtJena

HermsdorfIlmenau

Berlin

Sulzbach-Rosenberg

Remagen

67 institutes and independent research units

More than 24,000 staff €2.1 billion annual research budget totaling

2/3 of this sum is generated through contract research on behalf of industry and publicly funded research projects

1/3 contributed by the German federal and Länder governments in the form of base funding

International cooperation

Institute

Other Facilities

Headquarter


1981

Founded260

Scientists

250Doctoral candidates

and student assistants

Cooperations include:HSG St. Gallen (Switzerland),

Georgia Tech (USA),Lissabon (Portugal),

Shanghai (China), Rio de Janeiro (Brasil),

Istanbul University (Turkey)

25million €

Revenue

Thereof 54% from Industry, trade and services

The Fraunhofer Institute for Materialflow and Logistics IML –Data and Facts


On the road to Industrie 4.0: Automation

Complete automation of physical processes

Human flexibility as the key for adaptability and changeability in Industrie 4.0

Autonomization on the basis of smart and decentralCyber-Physical Systems

INDUSTRIE

≠ 4.0 =


Industrie 4.0: The necessity for the change of management

Conventional Automation Pyramid

Organized hierarchically Deterministic System modification through

customizing Application-specific solutions

Flexible and changeable Highly decentralised Non-deterministic Multi-Agent-System Cloud-based Situation-specific solutions

Transformationof management

Migration ofCyber-Physical Systems

ERPStrategy

Command LevelPlanning

Operational LevelControl & Monitoring


Sample technologies for Industrie 4.0

3D Printing

Swarm IntelligenceIntelligent Containers AutoID-Applications

Automated PlanningLogistic Drones


Cellular Transport Systems

Example Self-Organisation & AutonomyResearch Project SMART FACE

Intelligent ContainerSolution for low-

volume manufacturing with many variants

http://www.internet-der-dinge.de/en/projects/smart-face.html

Smart Face Demonstrator

http://www.internet-der-dinge.de/en/projects/smart-face.html


Contact

Tim Brandl

(Representative for Fraunhofer IML)

Business Development Unit

NITTSU Research & Consulting, Inc. (NRIC)

E-Mail: [email protected]

Internet: https://www.iml.fraunhofer.de/en.html

Dr. Fumihiko Inoue

Senior Consultant

Advanced Technology Unit

NITTSU Research & Consulting, Inc. (NRIC)

E-Mail: [email protected]

Internet: https://en.nittsu-soken.co.jp/

mailto:[email protected]

https://www.iml.fraunhofer.de/en.html

mailto:[email protected]

https://en.nittsu-soken.co.jp/

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