More information on the European union is available on the Internet (http://europa. eu). Cataloguing data can be found at the end of this publication.
or a group of countries. 18 The advantage of this method is that such data are available in a comparative format (with some restrictions),
quantitative analyses calculate degrees of specialisation of regional economies on the basis of employment (or value-added) data.
Hence, it is important to match these specialisation data with performance indicators (value added, exports, etc.
It is based on a network analysis using data on job changes between industries, showing proximity between industries in terms of skill sets.
and requires reliable historical statistical data and in depth analysis. Cluster mapping and benchmarking activities are powerful tools for starting the assessment of regional specialisation patterns and comparing statistical findings among regions.
and providing benchmarking possibilities across the EU. It should be stressed that statistical data at the same level of granularity are not always available across the EU and,
therefore, additional efforts should be made by some regions to complement existing data sets by more detailed quantitative and qualitative information.
Collect, if necessary, more detailed statistical data and perform qualitativebased surveys to better understand the dynamics of regional clusters to be used for implementing smart specialisation strategies.
Data from the 2010 Digital Competitiveness report77 reveals that while representing 5%of GDP, ICT drives 20%of overall productivity growth
The deployment of a culture of open data and secured online access, the harnessing of a true digital single market (ecommerce),
The DAE scoreboard provides data and an annual assessment of the performance at EU and Member State level.
For this, solid economic data is necessary. The Commission is in the process of setting up an EU Monitoring Mechanism,
This mechanism will provide EUWIDE and international market data on the demand and supply of KETS,
Collect, if possible, statistical data and perform qualitative-based surveys to better understand the dynamics of CCIS to be used for implementing smart specialisation strategies;
Applicants are invited therefore to explore potentials for synergies with the relevant Managing Authorities in charge of the ESIF programmes in their territory1 A novelty in Horizon 2020 is the Open Research Data Pilot
and reuse of research data generated by projects. While certain Work Programme parts and areas have been identified explicitly as participating in the Pilot on Open Research Data
individual actions funded under the other Horizon 2020 parts and areas can choose to participate in the Pilot on a voluntary basis. The use of a Data Management 1 http://ec. europa. eu/regional policy/indexes/in your country en. cfm HORIZON 2020 WORK PROGRAMME
2014-2015 Innovation in SMES PART 7-Page 4 of 35 Plan is required for projects participating in the Open Research Data Pilot.
Further guidance on the Open Research Data Pilot is made available on the Participant Portal. Mainstreaming SME support especially through a dedicated instrument SME participation is encouraged throughout this work programme and in particular in the priorities Industrial Leadership and Societal Challenges.
SME support will be targeted with the dedicated SME instrument which is a novel approach to support SMES'innovation activities.
For an entrepreneur comprehensive data and performance indicators would allow drawing conclusions whether open innovation is productive
-Collection and analysis of information and data on the application of open innovation in SMES, taking into account different situations in Member States and in specific market segments.
and data accumulated through the coaching engagement. It should also act as a single reference pool
and the services‘enhancing the innovation management capacity of SMES'Furthermore the support provided would secure the quality of the benchmarking by accelerating the inflow of new data sets allowing to replace the oldest data collected in 2008/09.
For key data on the research and innovation landscape in your country, please consult the following link:
Data from a research project SME Policy and the Regional Dimension of Innovation (SMEPOL, see acknowledgements) enable us to assess better
The data presented in Table 2 correspond to the results of other studies (e g.,, Fritsch and Lukas
A statistical analysis of data from a survey on innovation systems in several European regions (Kaufmann and To dtling, 2000b) leads to the conclusion that it is particularly the interaction with science that stimulates more advanced innovation, i e.,
This leads to a situation where certain technological fields are pre-2 All data regarding innovation support are based on the annual reports of the FFF (Forschungsfo rderungsfonds fu r die gewerbliche
According to the survey data the spillover effects to SMES in the region are negligible. In comparison, relations to the centres are more frequent in the category of large firms.
Some key data on Bremen concerning innovation...43 Table 3: Some key data on Tuttlingen concerning innovation...
59 Table 4: Stylized Matrix of Good Practice Elements in Surveyed Regions...62 1 1. Introduction1 This report is part of the work undertaken to realise Work Package 2 within the CRIPREDE project.
which is reflected in statistical data showing a lower number of patents or lower expenses for R&d personnel,
Based on US data, Acs and Audretsch (1990) provided further empirical support for the disproportionate contribution of SMES to innovation.
Data from this study clearly shows that the pattern of patenting is concentrated much more than the distribution of the industry in general.
, data networks) that supported the settlement of foreign enterprises. In Bangalore subsidies were given on an enterprise level (e g.,
Talking of key data, on the one hand the inhabitants'income situation in Bremen is relatively good, for instance.
and the existence of innovation abilities (see also Table 2 for more data referring to Bremen's success
Some key data on Bremen concerning innovation First, there are the general conditions and resources to look at.
But for‘the greater ease of retrieving data,) (we will make the Prato industrial district‘ideally'correspond to the province of the same name,
Some key data on Tuttlingen concerning innovation What is responsible for this success? Firstly, there are some general conditions resp. resources that contribute to it.
when data is not available for that calendar year, in which case the latest prior data was used).
3 DESI scores range from 0 to 1, the higher the score the better the country performance. 4 In the DESI 2015, the low-performance cluster of countries comprises Bulgaria, Cyprus
(2014) 5a4 Open Data Score (0 to 700) 300 (2014) 19 n. a.-378 (2014) 5b1 Medical Data Exchange%General practitioners 12
Only 12%of Hungarian general practitioners exchange medical data electronically, versus 36%in the EU. The same applies to eprescription,
which data that is already known to the public administration is filled pre in the forms that are presented to the user),
More information on the European union is available on the Internet (http://europa. eu). Cataloguing data can be found at the end of this publication.
DG Research and Innovation Economic Analysis Unit Data: DG Research and Innovation, Eurostat, Member State Notes:(
which data are available). Hungary has had a participant success rate of 20.4%in FP7 close to the EU average of 21.5,
DG Research and Innovation Economic Analysis Unit Data: DG Research and Innovation, Eurostat, OECD, Science Metrix/Scopus (Elsevier), Innovation Union Scoreboard Notes:(
and the latest available year for which comparable data are available over the period 2000-2011.3) Fractional counting method.
DG Research and Innovation Economic Analysis Unit Data: Science Metrix using Scopus (Elsevier), 2010; European Patent Offi ce, patent applications, 2001 2010 Hungary's scientifi c and technological strengths The maps below illustrate six key science
Compiled by Science-Metrix using data from Scopus (Elsevier) Number of publications by NUTS2 regions of ERA countries Health, 2000-2011 Publications (Fractional Counting) 9. 7-4537.4
Compiled by Science-Metrix using data from Scopus (Elsevier) Number of publications by NUTS2 regions of ERA countries Information and Communication Technologies, 2000-2011 Publications (Fractional Counting) 0
Compiled by Science-Metrix using data from Scopus (Elsevier) Number of publications by NUTS2 regions of ERA countries Environment (including Climate Change & Earth sciences), 2000-2011 Publications (Fractional Counting
Compiled by Science-Metrix using data from Scopus (Elsevier) Number of publications by NUTS2 regions of ERA countries Biotechnology, 2000-2011 Publications (Fractional Counting) 0. 0-98.6
Compiled by Science-Metrix using data from Scopus (Elsevier) Number of publications by NUTS2 regions of ERA countries Automobiles, 2000-2011 Publications (Fractional Counting) 0. 0-13.9
Compiled by Science-Metrix using data from Scopus (Elsevier) Number of publications by NUTS2 regions of ERA countries Security, 2000-2011 Publications (Fractional Counting) 0. 0-26.3
DG Research and Innovation Economic Analysis Unit (2013) Data: Innovation Union Scoreboard 2013, Eurostat Note:(
1) Based on underlying data for 2009,2010 and 2011. The graph above shows that, in Hungary,
DG Research and Innovation Economic Analysis Unit Data: OECD Notes:(1) High-tech and medium-high-tech sectors are shown in red.'
DG Research and Innovation Economic Analysis Unit Data: COMTRADE Notes:‘‘Textile fibres & their wastes'refers only to the following 3-digit subdivisions:
DG Research and Innovation Economic Analysis Unit Data: Eurostat, DG JRC ISPRA, DG ECFIN, OECD, Science Metrix/Scopus (Elsevier), Innovation Union Scoreboard Notes:(
which compatible data are available over the period 2000-2012.2) EU average for the latest available year.
DG Research and Innovation-Economic Analysis Unit Data: DG Research and Innovation, Eurostat, Member State Notes:(
DG Research and Innovation-Economic Analysis Unit Data: DG Research and Innovation, Eurostat, OECD, Science Metrix/Scopus (Elsevier), Innovation Union Scoreboard Notes:(
and the latest available year for which comparable data are available over the period 2000-2011.3) Fractional counting method.
DG Research and Innovation-Economic Analysis Unit (2013) Data: Innovation Union Scoreboard 2013, Eurostat Note:(
1) Based on underlying data for 2009,2010 and 2011.0.527 0. 612 0. 543 0. 000 0. 100 0. 200 0. 300 0. 400
DG Research and Innovation-Economic Analysis unit Data: OECD Notes:(1) High-tech and Medium-High-tech sectors are shown in red.'
DG Research and Innovation-Economic Analysis unit Data: COMTRADE Notes:""Textile fibres & their wastes"refers only to the following 3-digits subdivisions:
DG Research and Innovation-Economic Analysis Unit Data: Eurostat, DG JRC-ISPRA, DG ECFIN, OECD, Science Metrix/Scopus (Elsevier), Innovation Union Scoreboard Notes:(
which compatible data are available over the period 2000-2012.2) EU average for the latest available year.
see Section 1. 3 and Annex I for more information about data sources. For data analysis, descriptive and analytical statistical methods were used,
data related to orders (received or placed) is exchanged typically in a paper-less way between the ERP systems of the two companies trading with each other.
the survey found that about a third of the medium-sized and more than 40%of the large firms in the sector use Electronic Data Interchange (EDI.
CRM (customer relationship management) systems, a comprehensive software to capture, storage and analysis customer data in an integrated way, is used not yet widely in the TLS sector (see Section 3. 6. 3).%Companies*accepting
The self-assessment of firms to what extent their data exchanges with business partners are conducted electronically, however, suggests that enterprises in Europe
24 ITMS (Intermodal Transportation Management System) 8 4 RFID (Radio frequency identification Device) 7 2 Data Exchange mostly electronically 13 13 WMS (Warehouse Management
The analysis used data from the EU KLEMS Productivity and Growth Accounts2 (macro-data) as well as from the E-business Survey 2007 (micro-data.
Regressions based on the micro-data from the E-business Survey 2007 aimed to explore links between ICT usage,
Policy implications The empirical study findings (micro-data and case study analysis, macroeconomic analysis) lead to the conclusion that the following issues are particularly relevant for policy considerations (see Section 6. 3:
This chapter is mainly based on survey data from the Sectoral E-business Watch. Chapter 4 assesses the impact of the developments described in Chapter 3 on work processes and employment, innovation and productivity,
A qualitative case study approach (Chapter 5) is combined with a descriptive presentation of quantitative survey data (Chapter 3)
notably by enabling electronic data exchanges between a company and its customers, suppliers, service providers and business partners.
System and application software €76 billion IT services Consulting, implementation and operations management €140 billion Carrier services Fixed voice telephone and data services, mobile telephone
The maturity of ICT-based data exchanges between businesses and their suppliers and customers, fostered by progress in the definition
but it requires the agreement between the participants on electronic standards and processes for data exchange.
Data and information sources The study is based on a mix of data sources and methodologies, including primary data collection, desk research and case studies. More specifically,
information was collected from the following sources: Sectoral E-business Watch Survey (2007: The TLS sector was one of five sectors covered by the Sebw Survey of 2007.
data for 1970-2005 are available for the former EU-15 EU MS and for the US,
while data from 1995-2005 are available for 10 new MS that joined the EU in 2004.
The data sources that have been used to create the EU-KLEMS data series are large based on series from the national statistical institutes (e g. investment series),
Due to the broad range of sources used and data limitations in these sources, the level of detail in the EU-KLEMS database varies across countries, industries and variables. 16 Case studies:
This constitutes the first and most basic step in data presentation. The requirement for this step is that micro-data have been aggregated
and that weighting has been applied. Weighting is an important issue for data presentation, as unfortunately it is understood not well by many users of data.
However, weighting is necessary, as due to stratified sampling the sample size in each size-band is not proportional to the population numbers.
If proportional allocation had been used, the sample sizes in the 250+size-band would have been extremely small,
It combines micro-data analysis (using data from the E-business Survey 2007) and macrodata analysis (using the EU-KLEMS Growth and Productivity Accounts).
of having comparable e-business data and results for the land transport and logistics sectors, due to their huge importance in the European competitiveness and future economic challenges of the region.
data set to describe freight e-maritime Sustainable quality & efficiency Continuous bottleneck exercise Freight transport logistics personnel and training Improving performance Benchmarking intermodal terminals Promotion
and best practices Statistical data Simplification of transport chains Simplification of administrative compliance Single transport document Liability Security FREIGHT TRANSPORT LOGISTICS ACTION PLAN Framework for the ITS Regulatory Framework for the standardisation
including GPS, real-time engine-health monitoring and wireless data. RFID-enabled supply chains are beneficial in the following ways:
This would require standardisation efforts towards a single platform for applications, data and interconnectivity. In the one hand, the EU is investing considerable public funds in these systems
clean and energy efficient mobility of people and goods. 53 There are many practical examples that would help the Working group to collect data as is the case of the FRIDA solution developed
and (iii) data acquisition technologies. 55in what concerns the identification technologies, firms may appeal to bar-coding or to RFID.
As regards data communications technologies, firms may appeal to the electronic data interchange (EDI), the Internet, the Value Added Network amongst others.
Nowadays, as regards the data acquisition technologies, the firms usually deal with a large amount of goods
and data which means that data collection and exchange are critical for logistics information management and control.
Good quality in data acquisition can help firms deliver customers'goods more accurately and efficiently.
for customers, the information and communication systems convert data into information, in order to facilitate managerial decision making. The author argues that information is a resource to be used for decision and
as data is not available..Source: Eurostat, Community survey on ICT usage and e-commerce in enterprises.
and Wireless LAN) medium to share data and other resources. In the TLS industry, as shown in Exhibit 3. 1-6,
%Remote access means that employees can access data from the company's computer system remotely, e g. when working from home or travelling.
Some cost savings are due to utilising a single network to carry voice and data especially where users have existing underutilised network capacity that can carry Voip at no additional cost.
Sectoral e-Businesswatch (Survey 2007) Based on the data from the TLS Sebw Survey 2007, where companies were asked
Sectoral e-Businesswatch (Survey 2007) 3. 3 Standards and interoperability 3. 3. 1 Types of e-standards used Electronic Data Interchange (EDI
-9) Small (10-49) Medium (50-249) Large (250) Data for TLS total weighted by employment (read:"
"firms representing x%of employment"),data for size-bands in%of enterprises. Source: Sectoral e-Businesswatch (Survey 2007) Figures related to the TLS sector shows (Exhibit 3-3-1) that about a third of mediumsized companies and more than 40%of large firms
The widespread adoption of XML as a common data language is giving B2b integration the critical mass it needs for rapid growth.
Interoperability refers to the"ability of two or more systems to exchange data and to mutually use the information that has been exchanged."
Existing travel and transport information services often lack data outside a limited range of general facts.
having built defined system architecture, a data management module and a localisation algorithm, as well as having developed information services for multimodal forms of transport.
the system architecture and the data models available to everyone. The WMS can also benefit from such a development style.
and data models. mywms will be coded object oriented and in the programming language JAVA. Free software products will be used for the development and operation.
the master data management and the possibility to enter and process storage and retrieval orders.
or transforming data into a seemingly unintelligible form, which involves the user's secret or private key,
Freight transport Logistics Data are weighted by employment (read:""firms representing x%of employment"in the sector.
which implies the integration of business applications and data with the Internet and with the systems of the company's trading partners.
(or attempt to integrate) all data and processes of an organisation into a unified system.
A key ingredient of most ERP systems is the use of a unified database to store data for the various system modules.
because if a business partner does not have an ERP system, the exchange of data in standardised,
some conclusions can be drawn from the presented data: one tenth of the TLS sector firms use a fleet control system,
data related to orders (received or placed) is exchanged typically in a paper-less way between the ERP systems of the two companies trading which each other.
This enables the automated processing of data during all transaction phases (request for quotations/proposals,
Data exchange between ERP systems represents the most sophisticated form of ebusiness. However, due to the low rate of adoption of ERP systems in the TLS sector (see Section 3. 4. 1),
Exhibit 3. 4-4 Data exchange with business partners Companies characterising their typical data processing and exchange with business partners as Transport & Logistics Sector"mostly verbally""mostly in paper based format
Sectoral e-Businesswatch (Survey 2007) As expected, most of the companies where data with business partners is processed
and exchange data with business partners mostly electronically (Exhibit 3. 4-4). An example of a transport document management system E-business in the transport
if a company depends on a third party to provide the data, it is very important to get a clear E-business in the transport
Freight transport Logistics Micro (1-9) Small (10-49) Medium (50-249) Large (250+)Data for TLS total weighted by employment (read:"
"firms representing x%of employment"),data for size-bands in%of enterprises. Base: all companies.
extensible mark up language (XML), electronic data access (EDA) and the Internet. To meet customer demand for seamless, comprehensive and reliable information on which to base business decisions today,
companies must integrate data from the many sources involved in a customer's supply chain.
Data on SCM usage in the TLS sector in Europe are analysed in Section 3. 5. 1 of this Report.
Warehouse management systems utilize Auto ID Data Capture technology such as barcode scanners, mobile computers, wireless LANS and potentially RFID to efficiently monitor the flow of products.
Once data has been collected, there is either batch synchronisation with, or a real-time wireless transmission to a central database.
Logistics Micro (1-9) Small (10-49) Medium (50-249) Large (250+)TL (USA) Data for sector totals
data for size-bands in%of firms. Source: Sectoral e-Businesswatch (Survey 2007) Case studies about WMS in France Geodis Group The case study about Geodis Group (see Section 5. 7) shows a typical example for automated
and their automation, reducing the need for repeated data entry and reduces errors and delays.
Interaction can be through a variety of channels, such as web pages, email, automated phone (Automated Voice Response AVR) or SMS. Analytical-analysis of customer data for a broad range of purposes (for example,
Logistics Micro (1-9) Small (10-49) Medium (50-249) Large (250+)TL (USA) Data for sector totals
data for size-bands in%of firms. Source: Sectoral e-Businesswatch (Survey 2007) The relatively low diffusion of CRM technology within micro and small firms should not come as a surprise:
According to the survey data, large TLS enterprises are currently increasing focus on ICT issues, as they have started introducing more advanced ICT solutions such as eprocurement systems, WMS, SMS, CRM systems and so on.
For the analysis of the impact on productivity and links with the skills base, EU-KLEMS data have been used. 109 The analysis of links between ICT adoption
and value chain characteristics is based on micro-data from the E-business Survey 2007. The"structure-conduct-performance"paradigm Economic literature suggests that the ongoing diffusion of ICT
and considering the data that are available for a given sector in EU-KLEMS and from the Sectoral E-business Watch surveys.
or not (Section 4. 1. 4). The empirical analyses in sections 4. 1. 2 till 4. 1. 4 will be based on data from the EU KLEMS project.
and Development Centre (GGDC) in March 2007 does not allow the retrieval of the data for land transport activities
and related services can be employed as an argument in favour of looking at the data at a somewhat higher aggregation level than the one envisaged for the qualitative research reported elsewhere in this text. 112 NACE 1. 1 defines the transport and logistics services sector
which the necessary data was available over the period from 1995-2004. It can be seen that the contributions of the different components vary greatly among the member countries studied.
own calculations 4. 1. 3 ICT impact on labour productivity growth Labour productivity growth in the transport and logistics sector The EU KLEMS data contains consistent
annual data for a subset of the EU-27 (typically the EU-15 or less.
Data on labour input are available in terms of labour productivity, employment, average hours worked per employee and total working hours.
we have estimated a stochastic production possibility frontier (SPF) model by using a panel data set for 14 EU member states for
which all necessary data was defined available as EU-14). 114 Specifically, we have used the error component model suggested by Battese and Coelli (1992),
Data requirements include gross production value, total intermediate inputs, total working hours, ICT-capital stock, non-ICT capital stock and total working hours,
"which cannot be measured by means of the data on ICT-investment available in the database. 116 t-values above 2 assure by a rule of thumb this 5%-signficance threshold of the test. 117 For medium-skilled labour the estimated
or university degree the use of e-collaboration tools (such as SCM or other applications to share information about inventory levels with business partners) to share data with business partners The analysis is conducted at the micro-level,
using data from the E-business Watch Survey 2007. Internal capacity Knowledge stock and skills found a firm's absorptive capacity to adopt new technologies (Cohen and Levinthal
The hypothesis is tested on the basis of data from the E-business Survey 2007. In order to focus the analysis only on ICT-enabled innovations,
E-business in the transport & logistics industry 109 The hypothesis is tested on the basis of data from the E-business Survey 2007.
and the use of electronic data and information exchange between business partners, a probit regression was run.
The hypothesis is tested on the basis of data from the E-business Survey 2007. The analysis focuses only on
and analysis of data, assuming that one variable is dependent upon another single independent variable (simple regression) or several independent variables (multiple regression).
The hypothesis is tested on the basis of data from the E-business Survey 2007. The dependent variable controlling for organisational changes is based on companies'answers to the questions of
and the use of electronic data and information exchange between business partners, an ordered logit regression was run. 123 Exhibit 4. 2-5 reports the results of the regression.
E-business in the transport & logistics industry 114 The hypothesis is tested on the basis of data from the E-business Survey 2007.
The hypothesis is tested on the basis of data from the E-business Survey 2007. The dependent variable accounting for the change of a firm's market share can take one out of three values:
The hypothesis is tested on the basis of data from the E-business Survey 2007. The dependent variable can take a value 1 if a company outsourced any of its business activities in the last 12 months,
Innovation, market structure, value chain The analysis based on the E-business Survey 2007 data allows identifying the driving forces of ICT and its impact on selected business dimensions.
and data on passengers, sales figures, etc. for consolidation, settlement and result evaluation of services and operations.
Without an automated and computerised information system it would be impossible to generate this type of operation data to satisfy these client demands.
and aggregates data from its various facilities, for example bus information form the ticket machines, ticket systems onboard the busses and ticket offices,
and analyse the data in real-time to support the management of its daily operations. The system has different application modules for different operations and type of centres.
Data flows are integrated with the accounting, analysis, planning and control systems of the company. The accounting information from the different company centres are integrated in the central servers.
Data are transmitted via regular broadband network connections (ADSL) with a download speed of 8mbit/s in the central offices and 3mbit/s in the rest of the centres.
A practical example of applications enabled by the company-wide integration of data flows is a new timetable
which manages the timetables of all individual workers and automatically links these data to the payroll application.
The company has future project for the implementation of GPS systems and Wifi implementation onboard the buses for data transfers in the garages.
These processes can be accomplished much faster and based on much more accurate data, which facilitates decision making in operational management.
It is now possible to make backup copies of all the relevant data from the central servers,
avoiding duplicating task and minimising the risk of losing important data. The system allows eliminating existing duplicated processes
and to allow AIT having visibility on the whole activity through the availability of complete data. Current ICT solutions implemented are a company web site
in order to get detailed data on their traffic and activity from the current system in 2003,
Data entry of a transport file: At the beginning of the transport process a specific transport file is created directly in the system including all information related to the transport order.
Depending on the data that is entered at the beginning, transport purchase orders are created automatically. This is possible through the interconnection of the system with the global e commerce shipping platform INTTRA,
This module also allows preparing all the elements of a future transport file (data directly usable
The most important benefit is achieved probably through the availability of detailed data of the activity allowing AIT today to have clear visibility on profitability of the different transports.
and no data is available for the period before the solution implementation. There is no doubt that the solution allows AIT today to save time,
where the data for one year is stored to optimise the respond times of the system. Another database contains all data elder than one year
and a third database contains all information for the infocentre, allowing providing the Business figures
and analysis. It is very important to consider the correct maintenance of the databases including cleaning the data,
extracting the data from the operating database to keep the response time of the system optimised.
The integration of the systems enables the centralisation and optimisation of all the data and operations of the sales area.
as it can also be used to dispose of the needed statistics and data for: activity and service management;
the data is integrated directly into the internal CEMAT transport management system. CEMAT sends a pre-filled bill of lading document to the customer
The terminal employee enters the data into the CEMAT transport management system and gives the signed paper to the customer (or his driver).
and data is rekeyed into the CEMAT transport management system. It is necessary that both, the customer and CEMAT, store paper documents.
Increased efficiency, due to the elimination of data duplication, the electronic management of the transport documents and the automatic delivery of documents.
Higher speed in transmission of data to the customer. Improved production mode in the terminals, with the elimination of manual processes leading to less errors
and financial Data analysis of this data allows the company to take efficient decisions for the continuous modernisation of the railways.
and market data about the passenger railway activities. 5. 5. 2 E-business activities The decision to implement an e-business solution was taken in 1993.
as well as a statistics/reporting system (xselldata) and a data maintenance warehouse (xsellwarehouse). For a nationwide system, a unitary hardware and standard software architecture has been chosen to offer long-term benefits, like improved technical support, technical operating services and reduced TCO (Total Cost of Ownership.
and software) as well as the design and provided real data to determine the transaction volumes needed to operate such a system.
The lack of data available before its implementation as well as its recent deployment does not allow the company to quantify the benefits achieved so far
Thanks to the availability of detailed data about destinations, passenger categories, distance choices etc. delivered by the system,
This service allows an easy data entry for the customer and a better order planning and management for Fret SNCF.
the data is sent to the Web platform where it is available to customers. For the ordering process, the customer consults the transport catalogue on the portal
The Web platform automatically sends the data to the internal order management system. The ordering process is composed of a couple of events like reservation of resources, departure of wagons, delivery, incident etc.
The order management platform communicates data for each transport phase to the Web platform allowing the customer to follow up on the order.
The production mode E-business in the transport & logistics industry 152 needed to be adapted by deploying processes that allow the improvement of data quality
and is a driving force for improving data entry quality. Today, the e-services portal is operational
and publish the data on the website. 5. 6. 3 Impact The e-services solution affects the whole company and its working processes:
A company investing in such a system should analyse precisely the different data creation processes and the quality of data produced.
If data quality is adapted not to the level required the processes should be adapted, the employees trained and the quality of information improved.
and updated data, improved inventory management thanks to the visibility of exact stock variations and logistics and in a greater ability to serve the customer by reducing cycle times.
Data accuracy and inventory accuracy both improve. Mistakes are pushed to an absolute minimum. Deliveries are timely,
yard management, automated data collection, automated material handling and equipment management. The resulting increase in process efficiency has translated into sizable labour efficiencies.
and Hupac ensures the efficient exchange of data with clients, terminals, service providers and other operators through several information technology tools.
an software that manages transport data in real time, coordinating all phases of intermodal traffic. GOAL is connected to e-train, an innovative GPS train control system.
In 2006 the project concerning the automatic integration within Goal of data originating from external systems such as clients,
and process data coming from peripheral terminals which do not use Goal. The second part of the solution is e-train, a new satellite-based train monitoring system that covers most of Hupac's traffic network.
Another advantage with GPRS is that you are priced on the data volume and therefore this is less E-business in the transport & logistics industry 163 expensive.
putting them together into a load by taking into account all different factors and constraints and entering the whole data into the computer.
'The sales and nominal ledger data is transferred from the‘Truck Business'solution by Transaction Broker automatically at the press of a button.
in order to get the data from the suppliers according to the requirements defined by ABX group. Another future development planned is the extension of the existing solution through an SMS (Short Message Service) solution that should automate some of the information exchange with the suppliers
and will allow Saima Avandero to automatically integrate data received into the database. The solution has been operational for over 5 years
One employee is assigned to manage the supplier relationships on a full-time basis. The quality of data received from the suppliers is an important issue for Saima Avandero..
Sometimes the quality of data is poor and sometimes data is not even available at all.
Since Saima Avandero works with a multitude of small suppliers (sometimes up to five suppliers for one single transport) it is very difficult to get them engaged to provide the requested data (either through the common system or in any other way.
The biggest issue faced by Saima Avandero, is to provide a homogenous level of data quality to the customers.
For some transport, the quality of data is very good while for others it is inexistent.
Another important issue is the additional work generated by the solution. The manual work generated by the data collection from suppliers
and entry into the system is costly and time consuming. In conclusion, the impact of the solution implementation is expected not as well as
Some strategic customers for whom the company puts many efforts in place to maintain the data quality at a high level are satisfied
The negative impacts in this case can be summarised in additional manual work to comply with the poor quality of data generated by the overall solution as well as poor customer satisfaction.
Solutions that are aimed at providing data to customers encounter huge issues if the quality of information cannot be guaranteed in cases where a company depends on a third party to provide the data it is very important to get a clear commitment of this third party on the respect
and correct application of processes deployed with the solution. The case of Saima Avandero, cooperating with a multitude of small transport suppliers seems to represent a typical example of the Italian transport market where lot's of small transport companies are coexisting.
Even if this solution should improve the current situation by reducing the manual data entry of information provided
It should be based on timetable data from the existing travel planner tool. Another important requirement was that the suppliers could meet time and budget constraints.
and download data. The rollout is still ongoing and 90%of targeted vehicles are equipped with the solution today.
technical groups, training groups, data capture/testing groups, evaluation groups. Once a specific task for which the group was set up was finished the group was dissolved
Passenger information data can be accessed using a dialogue-oriented user menu on a WAP-compatible mobile phone (Wireless application protocol.
The data administration and evaluation system is used for the evaluation and report generation of data recorded during operations.
The operators are able to gain an overview of complex daily activities, evaluate these and respond when needed.
The analysis of historical data is of great interest for Trafikanten and allows them to provide detailed reporting on all activities to the Public transport authorities.
There is one person in charge of filling the data at each transport company working for the PTA.
and FRIDA is fed automatically with the data. Every 6 months the operators send the data about miles ran
and fuel used for each vehicle. The system calculates the pollution generated. Each time an operator buys a new vehicle the data is entered into the system.
The information is stored centrally in the system and organising authorities have full access to the information as well the operators.
New functionalities that have been developed are the connection to the FRIDA system via mobile devices (through offline data synchronisation
to ease the process of data collection. Värmlandstrafik checks the vehicles on a regular basis to make sure that they are in good condition.
and the data directly fed into the system. The solution allows today counting any vehicle that runs in Sweden whatever kind of vehicle is used:
which is being used in a way that makes it possible for SLTF to compile national data about the vehicles concerning environmental discharges, increased accessibility for disabled persons etc.
The data provided by FRIDA supports the Swedish PTAS today in achieving an important objective
*Data weighted by employment("firms representing%of employment in the sector expect that ICT will have a high/medium impact on Source:
Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India, Journal of Productivity Analysis, 3 (1-2), 153-169.
An assessment using multiple survey sources and linked data. European commission. European commission (2006)" Keep Europe Moving".
Evidence From Panel Data, Information systems Research, Volume 10, Issue 2, 134-149. Hossain, L, . and Wigand, R. T. 2003).
The E-business Survey 2007 methodological notes Background and scope The Sectoral E-business Watch collects data relating to the use of ICT and e-business in European enterprises by means of representative surveys.
Automated data exchange (Project 1)/ E-business with customers and suppliers (Project 2) C: e-Standards and interoperability issues (Project 1) D:
and manually processed exchanges with business partners had been substituted by electronic data exchanges. Some questions were filtered,
data collection and reporting focus on the enterprise, defined as a business organisation (legal unit) with one or more establishments.
confidence intervals Statistics vary in their accuracy, depending on the kind of data and sources. A'confidence interval'is a measure that helps to assess the accuracy that can be expected from data.
The confidence interval is estimated the range of values on a certain level of significance. Confidence intervals for estimates of a population fraction (percentages) depend on the sample size, the probability of error,
data are presented normally in both ways, except for data by sizebands. These are shown in%of firms within a size-band,
where employment-weighting is implicit. 131 The EU-7 are composed of those countries which were covered by the survey.
To ensure data comparability, only interviews from these countries are included in the aggregated"total"values.
Confidence intervals for employment-weighted data are highest for the steel industry, due to the small number of observations and because this sector's structure makes it more sensitive to data weighting
(i e. large firms dominate in a comparatively small population). Employment-weighted data for this industry therefore have lower statistical accuracy than for the other sectors.
The calculation of confidence intervals is based on the assumption of (quasi-)infinite population universes. In practice, however, in some industries and in some countries the complete population of businesses consists of only several hundred or even a few dozen enterprises.
For this analysis a panel-data approach was used because of the low number of countries sampled.
The only way a cross-section approach could be used would be by pooling industry and country data.
frontier for an industry across all countries, we obtain a multicountry data panel with a common stochastic production possibility frontier.,
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