SciTePress - Affiliation Profile

Research.Publish.Connect.

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Affiliation

St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 39, 14 Line, St. Petersburg, Russia Federation

Context (please use complete words only)

Field

Sort by

2 Papers

Product Configuration Automation: Digital Transformation Platform and Case Study

Topic: Digital Transformation Strategies of Industrial Enterprises

Authors: Alexander Smirnov ¹ , Alexey Kashevnik ¹ , Nikolay Shilov ¹ , Nikolay Teslya ¹ , Mikhail Petrov ¹ , Mario Sinko ² , Jens Arneving ² , Michael Humpf ² and Thorsten Kolmer ²

Affiliations: ¹ St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg, Russia , ² Festo SE & Co. KG, Esslingen, Germany

Digital Object Identifier: 10.5220/0011524100003329

Proceedings of IN4PL 2022, ISBN: 978-989-758-612-5

Quick Abstract

The paper discusses more than 10-year experience in product configuration automation based on number of joint projects between an academic institution and an industrial partner. During the last years, the research and development in the era of digital transformation has enabled a shift from conventional company business processes to digital business processes. In the paper we present several business processes that have been successfully automated what in turn has significantly decreased the product configuration time as well as errors caused by the human factor. The following business processes are covered: identcode (product code) related procurement, product segmentation, delivery class specification, supply chain & production management; online & offline sales, and customer guidance. We present the developed platform to support these business processes that consists of 10 workflows. We have integrated the developed workflows to the company production processes and show that business process execution time in average has decreased by 2 times and for some processes by more than 10 times. At the moment, the developed platform is being successfully used by the company.

Smart Mobility Support for Vehicle-based Tourism: Theoretical and Technological Foundations

Topics: Decision Support Systems; Geographic Information Systems; Driver Behavior Analysis; City Mobility and Ecodriving; Big Data and Vehicle Analytics; Information Systems and Technologies

Authors: Sergey Mikhailov ¹ , Alexey Kashevnik ¹ , Alexander Smirnov ¹ and Vladimir Parfenov ²

Affiliations: ¹ St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 39, 14 Line, St. Petersburg, Russia Federation , ² ITMO University, Russia Federation

Digital Object Identifier: 10.5220/0011074100003191

Proceedings of VEHITS 2022, ISBN: 978-989-758-573-9

Quick Abstract

Vehicle-based tourism becomes more and more important in the era of the pandemic. Tourism management is an important challenge for the tourist region development. The construction of a personalized attraction visiting route for tourists with personal vehicles has a great impact on the tourist flows. The authors propose theoretical and technological foundations for smart mobility support of vehicle-based tourists. We propose to predict tourist preferences by using deep neural networks for the prediction model’s implementation and demonstrated 70-80% accuracy in training on completed tourist trips to St. Petersburg, Russia. The tourist route attractiveness prediction was used to assess the constructed route quality. The attraction attractiveness and attendance prediction together with potential tourist trajectory prediction were used for attraction selection process personification. The obtained results can be used in smart mobility support systems to improve the travel experience.