CLUJ-NAPOCA| MARCH 2015

Forderung und Angebote der

KÜHNE LOGISTICS UNIVERSITY

an eine digitalisierte Welt

Prof. Dr. Thomas Strothotte

Präsident, KLU

KÜHNE LOGISTICS UNIVERSITY, HAMBURG

BSc Mangement

MSc Global Logistics

MSc Management

Executive MBA

Logistics & Leadership

English

International Student Body

17 Professors

2 Departments

Logistics/SCM

Management &

Economics

Research-oriented

Excellent Ranking Results

International Faculty

„Educating the leaders of tomorrow“

2

WHO WE ARE

3

German level of university education

taught in English

50% international students

Full-time and part-time degree programs

Wide range of international partner

universities

Extensive corporate ties

Excellent career support and personal

development

EXCELLENT RANKING-RESULTS

Teaching:

Top tier among all German management faculties

Research:

No. 1 in research output per professor among all private Business

Schools in Germany, Austria and Switzerland

No. 3 in research output per professor of all 100 German management

faculties

4

GROWING NETWORK OF PARTNERS WORLDWIDE

5

Wirtschaftsuniversität WU Wien

KU Leuven

Technical University of

Denmark

Ecole de Management

Strasbourg

IGR-IAE, Université de Rennes 1

MEL, Erasmus University

TU Eindhoven

Chalmers University,

Gothenburg

USI, Lugano

Koç University

Özyegin University

(Examples, overall list contains

more than 50 partners

worldwide)

IPADE

Mexico City

Universidad

de los Andes

Universidad de

Montevideo

S.P. Jain

Institute of

Management

& Research

XLRI

Tongji

Univ.

Pyeong-

taek

Univ.

Higher School of Economics (HSE)

Moscow / St. Petersburg

National University

of Singapore (NUS)

COMPANIES HIRING OUR INTERNS

6

OUR RESEARCH AREAS

• Humanitarian logistics

• Maritime logistics

• Green logistics

• Sustainable operations

• Warehousing

• Service logistics

• Reverse logistics

• Intermodal transportation

• Transport management

7

• Intercultural management

• Leadership

• Risk management

• Pricing & revenue strategy

• Marketing & innovation management

• Service management

• Finance

• Infrastructure Economics

Logistics & SCM Management & Economics

MSC | FACTS & FIGURES

8

Degree Master of Science (MSc) in Global Logistics / Management

Target group Holders of Bachelor degree (BSc) in business, economics,

industrial engineering, computer science, mathematics

Duration 2 years (4 semesters), full time

Class size max. 30

Language English

Structure On campus lectures in semester 1, 2 and 4

Integrated internship / study course abroad in semester 3

4-months Master Thesis in semester 4

Start Annually, September 1

Admission

requirements

Recognized university degree (min. Bachelor)

Proficiency in English language (TOEFL, etc.)

Credit points 120 ECTS credits

Tuition fee 11.000 Euro per year (total 22.000 Euro)

CAREER OFFICE & ALUMNI RELATIONS

9

Anne

Rehmet

Ina Renz

OUR FACULTY

10

NEW PROFESSORS 2015

11

Associate Professor of Logistics and Supply Chain Management

• Asvin Goel

Assistant Professor of Managerial Accounting

• Alexander Himme

Assistant Professor of Freight Transportation: Modelling and Policy

• Hanno Friedrich

Associate Professor of Information Systems/ Computer Sciences in Logistics

• André Ludwig

PHD-PROGRAM

• Cumulative theses (3 papers)

• 4-year program

• Funded by scholarships

• Possible fields: Leadership, Operations Research, Supply Chain Management, Supply Chain

Strategy

12

13

KLU AND DIGITALIZATION:

SUPPLY CHAIN ANALYTICS

“SUPPLY CHAIN ANALYTICS

LEVERAGES DATA AND STATISTICAL

METHODS TO CREATE NEW INSIGHTS

AND TO IMPROVE UNDERSTANDING

FOR BETTER DECISION MAKING IN

ALL ACTIVITIES ACROSS THE SUPPLY

CHAIN.”

Source: KLU research report „Supply Chain Analytics – Gaining value from data-based decision making”

RECENT TRENDS TOWARDS ANALYTICS CREATED HUGE

EXPECTATIONS WITH RESPECT TO BENEFITS OF USING ADVANCED

METHODS AND DATA

15

Recent White Papers and Books on Big Data and Analytics

BUT WHAT IS BIG DATA?

THE AMOUNT OF DATA THAT IS BEING STORED IN VARIOUS

MEDIA HAS BEEN GROWING EXPONENTIALLY SINCE THE TURN

OF THE CENTURY

1 EB = 10006bytes = 10

18bytes =

1000 petabytes = 1 million

terabytes =

1 billion gigabytes.

IDC PROJECTS THAT THE GENERATION OF STORABLE DATA

WILL GROW TO APPROXIMATELY 40,000 EXABYTES BY THE END

OF THIS DECADE

WHAT IS „BIG DATA“?

18

DATA SOURCES AND TYPES IN SUPPLY CHAIN ANALYTICS

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Social Networks

(e.g. Facebook, Google)

ERP systems

(e.g. SAP, Oracle)

Data archives

(e.g. primary and secondary)

Location data

(e.g. GPS, mobile)

Scans

(e.g. Barcode, RFID)

Data Sources Examples

Sensors

(e.g. camera, temperature)

Internet

(e.g. homepage, other websites)

Sales, capacities, inventories,

production plans, supplier quotes, …

Inventory information, time-stamps, …

Quality, yield, process status, …

Crowd behaviour, product perception via

text mining,

Truck locations, consumer locations,

inventory locations, …

Weather information, financial status,

consumer surveys, competitors promotions, …

Consumer click streams, access statistics,

consumer comments, …

WHY DO WE NEED BIG DATA?

TYPICALLY ANALYTICS IS CLASSIFIED INTO THREE LAYERS THAT

EXTEND FAR BEYOND TYPICAL REPORTING

Source: Adapted from Davenport and Harris 2007, Competing on Analytics

Ease o

f A

nalysis

Co

mp

etitive A

dvan

tag

e

Stochastic Optimization

Optimization

Predictive modeling

Forecasting

Simulation

Alerts

Query/drill down

Ad hoc reporting

Standard reporting

Prescriptive

„What to do?“

Predictive

„What will happen?“

Descriptive

“What happened?“

What will happen if … ?

What if these trends continue …?

What could happen …?

What actions are needed …?

What exactly is the problem …?

How many, how often, where, …?

What happened?

How can we achieve the best outcome

including the effects of variability?

How can we achieve the best outcome?

ALL STAGES OF THE SUPPLY CHAIN CAN BENEFIT FROM

SIGNIFICANTLY ADVANCED ANALYTICSSELECTIO

N

Supply Chain Design

Sourcing

Cost modeling

to identify cost

drivers

Quantification

of benefits

from spend

pooling

Automatic

analysis of

contract

compliance

…

Manufacturing Warehousing Transportation

Scheduling of

energy-

intensive

production

Statistical

quality control

and tolerance

optimization

Lot sizing and

scheduling

considering

cost and

inventories

…

Picking zone/

warehouse

space allocation

Worker to

picking zone

allocation based

on efficiency

Automatic stock

relocation in

high rack

storage areas

…

Truck vs. rail

transportation

mode

selection

Real time

routing and

ramp

allocation at

warehouses

Delivery

scheduling in

line with

consumer

patterns

…

2 3 4 5 Point-of-Sale

Out-of-stock

detection and

prevention

Shelf space

optimization

Channel/store

allocation of

goods

maximizing

service

Retail employee

scheduling

…

6 Consumer

Credit rating to

define

payment terms

offered

Return

projection to

calculate out-

standing

inventory

Product

recommendatio

ns based on

purchase

history , Fraud

7

Sales, Inventory and Operations Planning1

Supplier risk management

and incoming goods

projection

Staff/asset planning

and workload

optimization

Inventory projection

and scenario planning

Forecasting accuracy

evaluation and

optimization

Product Design

Source: KLU research report „Supply Chain Analytics – Gaining value from data-based decision making”

LET‘S LOOK AT WELL KNOWN SUPPLY CHAIN PROBLEM:

HOW TO IDENTIFY STOCK OUTS IN THE SUPERMARKET

Reasons

Buys nothing

Substitutes products

Postpones purchase

Buys product somewhere else

Customer reactions

Poor inventory management

Poor order management

Inaccurate forecasting

Goods in backroom storage room

Items misplaced in store

Lost sales

Lost customer good will

Decline in market share

Financial impact

APPROACH TO DETECT OUT-OF-STOCK SITUATIONS: MANUAL

1/4

Manual approach

Additional employee checks shelves

frequently and refills them if

necessary

Easy implementation

No infrastructure required

Pro‘s

Con‘s

High labor costs

Random process

dependent on

employee

productivity

APPROACH TO DETECT OUT-OF-STOCK SITUATIONS: RFID 2/4

RFID approach

RFID chips are attached to the

products

RFID scanner detects if products are

removed or from the shelf or

misplaced in the store

Greater control over shelf

inventory

Constant real time

updating

Pro‘s

Con‘s

Requires additional

IT infrastructure and

initial investments

Expensive RFID tag

on every item

APPROACH TO DETECT OUT-OF-STOCK SITUATIONS: OPTICAL

3/4

Optical approach

Optical camera attached to the

shopping cart scans the shelves on

customer tour and sends

information regarding empty spots

to the branch manager

High efficiency and

accuracy

Standardized process

Pro‘s

Con‘s

High initial

investment costs

Requires additional

IT infrastructure

SOURCE: McKinsey, Yinzcam

APPROACH TO DETECT OUT-OF-STOCK SITUATIONS: ANALYTICAL

4/4

Analytics approach

Calculate dynamic frequency of

having a SKU in a shopping basket

and estimate probability of having

no SKU in X subsequent shopping

baskets

High efficiency and

accuracy

Standardized process

Pro‘s

Con‘s

In-stock situation

Out-of-stock situation

SKU in

shopping basket

SKU in

shopping basket

SKU in

shopping basket

SKU on average

in every 50th

basket

SKU still not in

150th basket

?

KLU RESEARCH TOPIC TOGETHER WITH TCHIBO

EX-POST AND EX-ANTE WEATHER INFORMATION ALLOWS FOR

PRECISE WORKFORCE PLANING IN THE WAREHOUSE

Better staff planning with

lower idle times

Less overtime and

unplanned additional night

shifts

Less waiting time for

customers (24 hours

dispatch target)

No need for staff

exchange across

departments

Better planning of 3PL

transport capacity

…

June

24

June

25

June

26

Thu Fri Sat

June

27

Sun

12.5h

0.0mm

25.5°C

12.5h

0.0mm

24.0°C

4.5h

3.0mm

18.5°C

2.5h

4.0mm

16.5°C

Baseline

sales

forecast

Sales

forecast

with

weather

Source: KLU research project „Online sales on a rainy day“ by Kai Hoberg and Sebastian Steinker

Workforce

required

on Mon

Benefits from Forecasting

Accuracy Improvement

Note: Data sanitized

Weather

forecast

on Thu

ASSESSING SUPPLY CHAIN RISKS THROUGH DATA

VISUALIZATION

Full motion

visualization of

hurricanes moving

across the Atlantic

Assessing weather

induced supply chain

risks

Increases and

decreases in size and

frequency over time

Understand and

predict weather

patterns

Implemented in many

statistical packages

Source: SAS blog, http://marketanomaly.com/

Visualization of

hurricane movements

YOUR CONTACT

Professor Thomas Strothotte, PhD (McGill), MBA (Columbia)

president@the-klu.org

Kühne Logistics University

Großer Grasbrook 17 | 20457 Hamburg | Germany

Phone: +49 40 328707-0

Fax: +49 40 328707-109

www.the-klu.org