automatic information retrievement for exporting services

Cross-cultural business conference 2021

Steyr, 06.05.2021

Automatic information retrievement for exporting services

First project findings from the development of an AI based export

decision supporting instrument

Aufreiter, David MA MBA

(University of Applied Science Upper Austria; Forschungs und Entwicklungs GmbH)

TAKING COOPERATION FORWARD 2

AGENDA

ProsperAMnet SERVICE EXPORT

RADAR

METHODOLOGY

RESEARCH

PROCESS

FINDINGS/LEARNI

NGS


PROSPERAMNET – RESEARCH PROJECT

Progressing Service Performance and Export Results of Advanced Manufacturers Networks

Project duration: 1.4.2019 – 31.3.2022

Financing: Interreg Central Europe

Webpage: www.interreg-central.eu/prosperamnet

www.prosperamnet.eu

Facilitators (Clusters/Chamber of Commerce) Higher Education Institution Country

Business Upper Austria – Mechatronic Cluster LP: University of Applied Sciences Upper Austria Austria

TGZ Bautzen Innovation Centre (Cluster) University of Passau, Centre for Market Research Germany

Innoskart Nonprofit Ltd.University of Szeged – Department of Informatics

(Natural Language Processing Group)Hungary

University of Economics in Bratislava Slovakia

South Bohemian Chamber of Commerce Czech Republic

Friuli Innovazione Research and Technology

Transfer CentreItaly

Chamber of Commerce and Industry of Slovenia Slovenia

http://www.interreg-central.eu/prosperamnet

http://www.prosperamnet.eu/


KEY FOCUS OF THIS PROJECT

The aim of the project is to support Advanced Manufacturers(AMs) in Central Europe to become excellent in their servicebusiness!

Provision of a multilingual tool (monitor) for improvingthe internal service performance of AdvancedManufacturers in Central Europe.

available online: www.ise-monitor.eu/monitorwww.prosperamnet.eu

Monitor

Provision of a multilingual Artificial Intelligent tool(radar) and a linking support structure for boostingglobal service export performance of AdvancedManufacturers in Central Europe.

Radar

•Building of a transnational innovation network for a sustainable support of Advanced manufacturers in Central Europe and a facilitated access for SME toresearch and innovation

Network

http://www.ise-monitor.eu/monitor

http://www.prosperamnet.eu/


AIM OF THE PAPER

• What market information is needed to support the decision making on

the service export of Advanced Manufacturer?

• How can this respective information be collected with the help of

Artificial Intelligence?

Document a roadmap for progressing towards an Artificial Intelligence-based market informationsolution.

Research questions


QUESTIONS/PROBLEMS/PARAMETERS RELEVANT FOR

SERVICE EXPORT OF MANUFACTURING COMPANIES


AIM OF THE PAPER

• What market information is needed to support the decision making on

the service export of Advanced Manufacturer?

• Which services should the manufacturer offer in a specific country?

• With which partners could the manufacturer collaborate?

• Which of the manufcaturers‘ competitors are active in the specific

target market and which services do they offer?

• How can this respective information be collected with the help of

Artificial Intelligence?

Document a roadmap for progressing towards an Artificial Intelligence-based market informationsolution.

Research questions


SERVICE EXPORT RADAR

We develop a tool…

• for advanced manufacturing companies: NACE

• 26 Manufacture of computer, electronic and optical products

• 27 Manufacture of electrical equipment

• 28 Manufacture of machinery and equipment n.e.c.

for the export of services

through relevant informationen

based on Artifical Intelligence (AI).


SERVICE EXPORT RADAR

The idea

Before a company exports its services to a new market, the readiness of the market has

to be tested. The Service Export Radar is intended to identify opportunities and

possibilities and support companies in their decision making.

The benefits

• Market information, taking into account the specific situation of the company as

well as the industry and country-specific information in order to facilitate market

policy decisions

• Focus on Advanced Manufactureres, who export / want to export services.

• Results are based on Artificial Intelligence solutions,

• Free!

Data from millions of companies in 10 countries

• The results are based on websites of million of companies and from

many databases

• Companies from Austria, Czech Republic, France, Germany, Great

Britain, Hungary, Italy, Slovakia, Slovenia, USA


APPLICATION AND DEVELOPMENT

- Which countries are investigated in the Radar?


USE CASE – COMPETITOR ANALYSIS


HOW? – METHODOLOGY

Identification of market players

in 10 countries

supervised/unsupervised/hybrid

AI-approach

National/International data-

bases

Investigation of the

servitization level of all

individual market player

hybrid

AI - approach

> 1.800 annotations

1 2

www


ANNOTATION SCHEME

General information:

• Company name

• URLs

• Country

• NACE code

• Revenue

• Number of employees

• Does the website has a product

and/or service page

• Customer segments

• Customer references

Taxonomy of industrial

service offerings

(Baines et al. 2013; Gebauer et al. 2010; Homburg,

Fassnacht, and Günther 2003; Mathieu 2001a; Mathieu

2001b; Partanen et al. 2017; Oliva and Kallenberg 2003)

1: pre-sales services

11: product demonstrations

12: customer seminars

2: product support services

21: warranty

22: technical user training

23: customer consulting and support by phone

24: testing, test rigs, quality assurance

3: product lifecycle service

31: installation services

32: repair service

33: spare parts

34: maintenance

35: retrofit, modernization, upgrades

4: R&D services

41: research service

42: prototype design and development

43: feasibility studies

5: operational services

51: project management

52: service for operating the product for the customer

53: service for operating customer’s processes

6: financial services

61: pay-per-use

62: instalment payment

63: leasing

64: rental system


RESEARCH PROCESS

Adapted research project process (Block and Block 2005)


LEARNINGS / FINDINGS

• Precision!

• Definition of the scope of the service categories

• In the way of working of annotators

• Misconceptions and misunderstandings lead to „noise“ in the

machine learning algorithms

• Domain Knowledge!

• Few well-trained annotators are best

• Learning curve

• Transfer of knowledge

• Individual annotator-specific errors are more easily uncovered,

discussed and fixed

• Regular communication and feedback

• Annotations strongly depend on the interpretation of the text

and language on the webpages


PROJEKTPARTNER

• Aufreiter, David MA MBA

• DI Ehrlinger, Doris

• FH-Prof. Mag. Stadlmann, Christian PhD

• FH-Prof. DI Dr. Überwimmer, Margarethe

• Biedersberger, Anna M.Sc

• Korter, Christina MA

• Dr. Mang, Stefan


LITERATURE

• Baines, Tim, Howard Lightfoot, Palie Smart, and Sarah Fletcher (2013), “Servitization of manufacture,”

Journal of Manufacturing Technology Management, 24 (4), 637–46, DOI: 10.1108/17410381311327431.

• Block, Martin P. and Tamara S. Block (2005), Business-to-business market research: [a value-based

approach], 2. ed., 1. [print.]. Mason OH u.a.: Thomson.

• Gebauer, Heiko, Bo Edvardsson, Anders Gustafsson, and Lars Witell (2010), “Match or Mismatch: Strategy-

Structure Configurations in the Service Business of Manufacturing Companies,” Journal of Service

Research, 13 (2), 198–215, DOI: 10.1177/1094670509353933.

• Homburg, Christian, Martin Fassnacht, and Christof Guenther (2003), “The Role of Soft Factors in

Implementing a Service-Oriented Strategy in Industrial Marketing Companies,” Journal of Business-to-

Business Marketing, 10 (2), 23–51, DOI: 10.1300/J033v10n02_03.

• Mathieu, Valérie (2001), “Product services: from a service supporting the product to a service supporting

the client,” Journal of Business & Industrial Marketing, 16 (1), 39–61, DOI: 10.1108/08858620110364873.

• Mathieu, Valérie (2001), “Service strategies within the manufacturing sector: benefits, costs and

partnership,” International Journal of Service Industry Management, 12 (5), 451–75, DOI:

10.1108/EUM0000000006093.

• Oliva, Rogelio and Robert Kallenberg (2003), “Managing the transition from products to services,”

International Journal of Service Industry Management, 14 (2), 160–72, DOI: 10.1108/09564230310474138.

• Partanen, Jukka, Marko Kohtamäki, Vinit Parida, and Joakim Wincent (2017), “Developing and validating

a multi-dimensional scale for operationalizing industrial service offering,” Journal of Business &

Industrial Marketing, 32 (2), 295–309, DOI: 10.1108/JBIM-08-2016-0178.


CONTACT

David Aufreiter MA MBA

FH OÖ Forschungs & Entwicklungs GmbH

Studiengang Global Sales and Marketing

Tel.: + 43 5 0804 33610

E-Mail: [email protected]

automatic information retrievement for exporting services

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