scholarly journals Competence Development within Hybrid Value Creation - Need-based Competence Development for the Successful Implementation of Hybrid, Data-Driven Business Models

2021 ◽  
pp. 143-160
Author(s):  
Sascha Stowasser ◽  
◽  
Nicole Ottersböck
Keyword(s):  
Production Process ◽  
Value Creation ◽  
Business Models ◽  
Competence Development ◽  
Data Driven ◽  
Successful Implementation ◽  
Smart Services ◽  
Hybrid Data

Digitalization and the increasing technical possibilities of networking machines and products as well as the use of large amount of data in the hole production process offer companies the opportunity to establish new, so-called hybrid business models. This enables them to provide customers data-driven, smart services in addition to their physical products, create more value and strengthen their competitiveness. The hybridization of value creation is accompanied by numerous changes and new competence requirements in companies, which need to be shaped socio-technically. In the AnGeWaNt project, such hybrid business models were developed and implemented in three companies. The article describes the approach to analyzing and shaping changes and competence requirements that arise in companies as a result of digitalization and hybridization.

Industry 4.0 initiatives in EU countries: experience for Ukraine

2020 ◽  
pp. 65-70
Author(s):  
Nataliya Ryvak ◽  
Anna Kernytska
Keyword(s):  
Industry 4.0 ◽  
Business Models ◽  
Industrial Revolution ◽  
Successful Implementation ◽  
Industrial Complex ◽  
Interconnected System ◽  
Industrial Transformation ◽  
Eu Countries ◽  
European Experience ◽  
High Level

In this paper, digital technologies development was analyzed as the basis for the so-called “fourth industrial revolution” with the potential for the qualitative transformation of the Ukrainian economy based on EU countries’ experience. Industry 4.0 is a new control chain over the entire chain of creating value throughout the product lifecycle. When developing an economic policy, it is important to pay attention to Industry 4.0. It increases productivity, produces new, better, and individualized products, and implements new business models based on “undermining” innovations. A comparative analysis of national initiatives I4.0 with their characteristics according to the main dimensions, including funding, focus, direction, was conducted. Particular attention was paid to considering deterrents to the successful implementation and enforcement of the I4.0 initiative in European countries. The factors of successful implementation of I4.0 initiatives in the EU countries were analyzed. Drawing on the analysis of the European experience of digital transformations in industry and national economies in general, the necessity of critical focus of such transformations in Ukraine was highlighted, and the need for state support of industrial transformation was substantiated. The emphasis was placed on the cooperation development between stakeholders within the implementation of Industry 4.0 – it is necessary to create national and regional 4.0 platforms, following the example of EU countries, which would bring together government institutions, businesses, and academics. The successful positioning of the Ukrainian modern industrial complex on the world markets depends on the high level of the interconnected system providing factors that characterize its development process. Considering the influence of a list of inhibiting factors on implementing the country’s industry accelerated development, a set of measures needed to transform Ukraine’s industry based on European experience was substantiated.

Hybrid-Data Approach for Estimating Trip Purposes

2021 ◽  
pp. 036119812110184
Author(s):  
Xiaoling Luo ◽  
Adrian Cottam ◽  
Yao-Jan Wu ◽  
Yangsheng Jiang
Keyword(s):  
Transportation Systems ◽  
Data Driven ◽  
Trip Purpose ◽  
Additional Information ◽  
Point Of Interest ◽  
Average Accuracy ◽  
Hybrid Data ◽  
Added Benefit ◽  
Data Source ◽  
High Level

Trip purpose information plays a significant role in transportation systems. Existing trip purpose information is traditionally collected through human observation. This manual process requires many personnel and a large amount of resources. Because of this high cost, automated trip purpose estimation is more attractive from a data-driven perspective, as it could improve the efficiency of processes and save time. Therefore, a hybrid-data approach using taxi operations data and point-of-interest (POI) data to estimate trip purposes was developed in this research. POI data, an emerging data source, was incorporated because it provides a wealth of additional information for trip purpose estimation. POI data, an open dataset, has the added benefit of being readily accessible from online platforms. Several techniques were developed and compared to incorporate this POI data into the hybrid-data approach to achieve a high level of accuracy. To evaluate the performance of the approach, data from Chengdu, China, were used. The results show that the incorporation of POI information increases the average accuracy of trip purpose estimation by 28% compared with trip purpose estimation not using the POI data. These results indicate that the additional trip attributes provided by POI data can increase the accuracy of trip purpose estimation.

scholarly journals Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter - a Case Study

2020 ◽  
Vol 53 (2) ◽  
pp. 11692-11697
Author(s):  
M. Hotvedt ◽  
B. Grimstad ◽  
L. Imsland
Keyword(s):  
Data Driven ◽  
Flow Meter ◽  
Hybrid Data

scholarly journals ProCAVIAR: Hybrid Data-Driven and Probabilistic Knowledge-Based Activity Recognition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 146876-146886
Author(s):  
Claudio Bettini ◽  
Gabriele Civitarese ◽  
Davide Giancane ◽  
Riccardo Presotto
Keyword(s):  
Activity Recognition ◽  
Data Driven ◽  
Knowledge Based ◽  
Probabilistic Knowledge ◽  
Hybrid Data

Extracting useful signals from flawed sensor data: Developing hybrid data-driven approaches with physical factors

2020 ◽  
Vol 185 ◽  
pp. 116282
Author(s):  
Cheng Yang ◽  
Glen T. Daigger ◽  
Evangelia Belia ◽  
Branko Kerkez
Keyword(s):  
Data Driven ◽  
Sensor Data ◽  
Physical Factors ◽  
Hybrid Data

scholarly journals Conceptualising value creation in data-driven services: The case of vehicle data

2021 ◽  
Vol 59 ◽  
pp. 102335
Author(s):  
Christian Kaiser ◽  
Alexander Stocker ◽  
Gianluigi Viscusi ◽  
Michael Fellmann ◽  
Alexander Richter
Keyword(s):  
Value Creation ◽  
Data Driven ◽  
Vehicle Data
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