AUSSENWIRTSCHAFTAUSTRIA - WKO.at

AUSSENWIRTSCHAFT AUSTRIA

Webinar | 24.11.2020 | 10:00 CET

CHINA: SUPPLY CHAIN

INNOVATIONEN FÜR KMU

Das Webinar beginnt in Kürze

2

SPRECHER

Dr. Michael Berger

Wirtschaftsdelegierter

AußenwirtschaftsCenter Peking

Rainer Schmitz

Vice President

4flow China

AUSSENWIRTSCHAFT AUSTRIA

CHINA: SUPPLY CHAIN INNOVATIONEN FÜR KMU

Webinar | 24.11.2020 | 10:00 CET

3

Fragen? Jederzeit eingeben, Antwort am Ende

des Vortrags

Eine Sequenz nochmals sehen? Spannende Passage

verpasst? Kein Problem: Es gibt eine

Aufzeichnung. Schauen Sie doch mal auf

youtube.com/user/aussenwirtschaft vorbei!

Die Präsentation erhalten Sie nach dem Webinar

Technische Schwierigkeiten? Techniksupport im

Chat

Was Sie technisch wissen sollten…

4

AGENDA

1. Begrüßung und Hinweise

2. Rückblick auf die Supply Chain Herausforderungen durch Covid

3. Lösungen und Best Practices für eine für innovativere, resilientere Supply Chains

4. Q&A

KMPG Umfrage 2020 mit 315 CEOs: What are your greatest risks to growth?

Jänner 2020: 2% aller befragten CEOs antworteten „Supply Chain Risk“

August 2020: 18% aller befragten CEOs antworteten „Supply Chain Risk“

(zweit wichtigsten Risikos nach „Talent Risk“)

Supply Chain Themen, die an Bedeutung gewinnen werden:

Stripping complexity and cost out of supply chains

Building end-to-end visibility

Investing in automation and other advanced technologies

Building agility into the network of suppliers and partners

5

Supply Chain Management wird wichtiger

Ziel: Österr. Einkaufsleiter in den verarbeitenden Industrien dabei zu unterstützen, robuste Supply Chains in Asien zu bilden & Abhängigkeit von einzelnen Lieferanten und Lieferländern zu reduzieren (dual sourcing)

Branchenreport: Sourcing-Situation in China und Asien (für ausgewählte wichtige Produktgruppen)Aufzeigen von Entwicklungen/Risiken und Empfehlungen für Alternativländer zu China in den verschiedenen Produktgruppen

Workshops: mehrere Workshops in Wien im 1. Quartal 2021 geplant

Firmenspezifische Analysen: Ausgewählte Konsulenten bieten begünstigte Einzelberatungen für Unternehmen zu deren individuelle Beschaffungssituation an

Ansprechpartner: AUSSENWIRTSCHAFT Industry/Machinery/Material,Mag. Eric Savoye, E [email protected], T +43 5 90 900-3727

6

Hinweis zum Strategieprojekt: Global Value Chains

der AUSSENWIRTSCHAFT AUSTRIA

© 4flow | publicPage 7 11/24/2020

4flow is your partner for supply chain management and logistics

Optimizing the entire supply chain

About 4flow Our business model

Supply chain expertise Global presence

Hier stehen ein bis zwei

Zeilen Text

Hier stehen ein bis zwei

Zeilen Text

team members

globally

projects annually

around the world

650+ 200+ 16offices in Europe,

Asia, North America

and South America

year of

foundationHier stehen ein bis zwei

Zeilen Text

customers

globally

300+ 2000 100%management-

owned

Supply chain

consulting

4flow consulting

Supply chain

software

4flow software

Supply chain

services

4flow management

Supply chain research 4flow research

Transport

network

design

Cost

optimization

SCM

processes

Site

engineeringDigitization


The spread of the coronavirus resulted in multiple challenges for global

supply chains

Global

supply chains with

all end-to-end functions

Network

Transportation

Intralogistics

Inventory

Organization

❯ Inbound

❯ Outbound

❯ Overseas

❯ …

❯ Raw materials

❯ Work in progress

❯ Finished goods

❯ …

❯ Plants

❯ Warehouses

❯ Cross docks

❯ …

❯ Productivity

❯ Organization structure

❯ Span of control

❯ …

❯ Putaway

❯ Picking

❯ Empties

❯ …

Shutdowns &

closings

Limited

capacities & delays

Reduced

workforce

Supply

shortages

Working

models


The current crisis shows critical disruptions but also offers significant

potential to improve on more resilient supply chains in future

Challenges

and risks

Chances

Network performance

❯ Supply shortages of critical

parts

❯ Insufficient inventories

❯ Unilateral network setup

Limited capacities

❯ Transportation disruptions

and delays

❯ Production stoppages

❯ Increased logistics and

production costs

Operations

❯ Lack of transparency

❯ Unreliable information

❯ Unclear demand situation

❯ Wrong forecasting

Organization

❯ Lack of flexibility

❯ Functional focus

❯ Unclear responsibilities

Supply chain

reconfiguration

❯ Optimized supply chain

setup

❯ Increased network maturity

❯ Improved flexibility

Capacity

management

❯ Strategic security of

required capacities

❯ Flexible capacities

New

technologies

❯ Gain speed in digitization

❯ Enhance IT infrastructure

❯ Integration of supply chain

partners

Collaboration

❯ Internal and external

collaboration

❯ Strategic partnerships


Best practices for innovative and resilient supply chains in uncertain times

Increasing digitization and utilizing innovative applications

Smart factories and

automated warehouses

Integration and operational

intelligence:

Business analytics and

process automation

Big data and

machine learning

Creation of knowledge from

large amounts of data and

independent

performance improvement

Robotic process

automation (RPA)

Increasing process

efficiency and

quality in your organization


❯ The real value add lies in the smart integration of feedback loops between automation and analytics

Smart factories and automated warehouses

Taking intralogistics and production to the next level through analytics

Process

automation

Business

analytics

Layers Integration and operational intelligence

Goods receiving Warehouse Picking Material staging Production Goods issue

Warehouse

management

system (WMS)

Transport

control

system (TCS)

Manufacturing

execution

system (MES)

Warehouse

analytics

❯ Inventory optimization

❯ Demand forecasting

❯ Ad-hoc order processing

Transport

analytics

Production

analytics

❯ Internal traffic optimization

❯ Battery charging patterns

❯ Autonomous vehicle control

❯ Dynamic order scheduling

❯ Smart replenishment

❯ Intelligent maintenance



Concrete examples of business analytics value-add in intralogistics

Use case description Value add and business impact

Picking demand forecasting

❯ Integration of order picking system

(pick-by-vision) and analytics (tableau)

❯ Increase short-term demand forecast

accuracy and optimize picking routes

Efficiency increase and cost reduction

❯ Shorter picking times and higher picking efficiency

❯ Reduced labor demand in order preparation

❯ Real-time transparency on picking efficiency

-20%

Intelligent vehicle routing

❯ Implementation of a transport control

system to increase transparency

❯ Controlled internal material flows and

real-time track & trace

Higher transparency and service level

❯ Shorter transport order lead times

❯ Optimal transportation equipment selection

❯ Higher production flexibility and service level

+11%

Continuous stock optimization

❯ Reduction of inventory through

integrated automation and analytics

❯ System integration (SAP, Power BI)

and continuous parameter optimization

Lower inventories and more space

❯ Higher storage density, less space consumption

and less cost of tied-up capital

❯ Continuous ABC/XYZ analysis and direct

feedback loop into warehouse control systems

-27%

Cost

Service

level

Inventory

level

https://www.tableau.com/de-de/solutions/supply-chain-analytics


Business case

Solution B


Step-sequenced selection and evaluation of suitable technologies

Requirements evaluation

Technology Requirements Investment

Hardware

Software

Integration costs

License fees

Service costs

Operating costs

¥

¥

¥

¥

¥

¥

¥

Overall costs ¥

FTE

Operating costs

Process costs

- ¥

- ¥

- ¥

Overall savings ¥

Target planningDetailed planning

Concept planning

Technology overview Qualitative analysis Quantitative analysisTechnology selection

Flexibility

Scalability

Transparency

Flexibility

Scalability

Transparency

Feasibility Feasibility

Infrastructure

❯ Greenfield vs. brownfield

❯ IT infrastructure: Compatibility and

integrability, interfaces, open

standards

❯ Building infrastructure: e.g.

necessity of navigation elements

and communication technology,

consideration of safety regulations

Solution A


Big data - Creating of knowledge from large amounts of data

Analytics tools can be used to deal with various problems

Methods Applications

Identification of process

gaps: process mining❯ High performance tool sets to analyze big data

❯ Advantages

Very large data

quantity

Complex

computations

and diagrams

Reliability and

robustness

Data plausibility: Automated

data checks and correction

methods

Illustration of material and

information flows:

clustering/aggregation

Estimation of missing

master data: Mathematical-

statistical methods


Machine learning - Independent and continuous performance improvement

Recognition of patterns as well as regularities in existing data sets

Machine learning basics Applications

General applicabilityHigh compatibility with existing

processes.

Big data processingEnables the analysis of data that

cannot be processed manually.

Advanced forecastsFaster and more reliable forecasts

allow for better planning.

Proactive behaviorQuickly adapts to changing data and

new underlying conditions.

Ability to autonomously interpret

problems and recognize patterns

Dynamic algorithms incorporating

new data into existing models to

improve performance

Holistic view of

structured and

unstructured

data

Flexibility

towards new

conditions

Supporting the

analyst by

independently

recognizing

data patterns

Advantages for data processing


Big data and machine learning

Use cases along the supply chain

Procurement

❯ More precise requirements

planning by improving sales

forecasting

Transportation management

❯ Determination of ETA using

❯ Telematics data

❯ Traffic prediction

❯ Efficient dock assignment

❯ Personnel and resource planning

Risk management

❯ Early recognition of environmental,

political and social risks

Business management

❯ More agile management through increased

transparency along the supply chain

❯ Decision-making support for location and

distribution planning

Dispatch

❯ Track and trace

❯ Increase transportation

mode utilization

Cloud

❯ Increased transparency & data quality

through decentralized data storage

External data sources

❯ Social media or weather data to

recognize trends early and better

assess customer needs

Forecasting

❯ Increased accuracy through

❯ Data exchange across

companies

❯ Early prediction of trends

Serialization

❯ Analysis of

product and

part data on

article level

Staff & resource planning

❯ Increased accuracy through

❯ More precise forecast

❯ Prediction of maintenance

Warehouse management

❯ Optimized processes and layout through

simulation

❯ Better inventory and operations

management due to increased transparency


Use case – Improved forecasting with machine learning

Reducing the risk of stock-outs

❯ Application: Fashion items are transported from East Asia

to Germany by sea or air freight

❯ Challenge: Often it is realized only at short notice which

orders use expensive air freight

❯ Objective: Detect air transportation at an early stage and

take measures to prevent it

Prediction of transportation mode Model development and optimization

Orders Risk profile

machine

learning

Determine

measures

Reduce air

freight

Historical

transportation data

Supply chain and

process analysis

Master data

In total, 78k

orders

Sea and air

freight

Classification models for estimating air

freight risk

❯ Random forest

❯ Support vector machine

❯ Boosting algorithms

10% 11%

25% 2%

0%52%

high

medium

low

No Yes

Shipped via air freight

Ris

k o

f air

fre

igh

t


Use case – Development of recommender system

Increasing production utilization

❯ Application: 20,000 different parts are produced on 442

different machines around the world.

❯ Challenge: For most parts, only very few machines are

explicitly known to be able to produce them (and vice

versa). This leads to bottlenecks and low utilization.

❯ Objective: Determine further possible part to machine

combinations to allow better production planning with

improved utilization and higher overall throughput.

Part-machine-matching Model development and optimization

Part-

machine-

matching

Recom-

mender

system

Better

planning

Better

utilization

Historical part-

machine-matching

Recommender

Systems

Overall Project Scope

System

based on

known part-

machine-

matching

Extension for

parameter

entry

(new parts)

Calculation of

„Top 4“-

supply chain

scenarios

Automated

collaboration

in the

production

network

Reflect

production

cost on

different

machines

Level 1 Level 2 Level 3 Level 4 Level 5

25,000 known combinations

Master data

Parts

CAD files

Machines

❯ Collaborative filtering:

❯ Memory based

❯ Model based

❯ Content based

systems


Robotic process automation (RPA) improves productivity and quality

RPA implementation is still in early stage within supply chain management

What is RPA? Degree of RPA implementation

Automated technological approach in which software

robots imitate humans in the execution of repetitive, rule-

based processes

Repetitive Rule-based

Electronic

data input

Multiple IT

systems

Stable

Time-

consuming

Manual

Few

exceptions

RPA-suitable processes

Banking and insurance

Telecomm. and IT

Industry

Supply chain

Despite its significant potential, RPA implementation in

logistics and supply chain management is still in its early

stages.


Robotic process automation (RPA) improves productivity and quality

Realizing efficiency potential in freight management

Background Solution

❯ Actual process: Freight volumes recorded in system

deviate from real freight volumes, which must be identified

and manually adjusted in the system

❯ No automation: Employees manually carry out process

across various systems

(TMS, xls, Outlook)

❯ Automation using RPA (implementation time: 3 weeks)

❯ Implemented target process: A robot carries out the

comparison and adjustment process in the system and

automatically generates a report.

❯ Outcome

Automation Process times Error rate

0%>95 % -65%


Building innovative and resilient supply chains

Important points to consider when starting first lighthouse initiatives

Process

identification

Complexity

Know-how

Project setup

Change and

acceptance

Key dimensions Your takeaways

Identify the right process for a lighthouse project

Select a suitable scope with a manageable complexity

Invest in external know-how for concept and ensure knowledge transfer

Set up of agile project structure with an experienced and competent team

Facilitate change by continuously communicating the new direction

22

Dr. Michael Berger

Wirtschaftsdelegierter

AußenwirtschaftsCenter Peking+86 10 85 27 50 [email protected]

Noch Fragen? Zeit für Q&A!

Rainer Schmitz

Vice President

4flow China

+86 136 8172 4557

[email protected]

AußenwirtschaftsCenter Peking

No. 37 Maizidian Street Chaoyang District

100125 Beijing, China

4flow China

304-305 T1 Building, SCG Parkside

868 Ying Hua Road, Pudong New District

201204 Shanghai, China

4flow Austria

Ungargasse 64-66

1030 Vienna, Austria

4flow Headquarters

Hallerstrasse 1

10587 Berlin, Germany

Elisabeth Moritz

Manager

4flow Austria

+43 664 2511004

[email protected]

Kontaktdaten

Documents

AUSSENWIRTSCHAFTAUSTRIA - WKO.at