Digital Transformation and Processes Innovation

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Engineering".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 1364

Special Issue Editors


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Department of Mechanical Engineering, ISEP–School of Engineering, Polytechnic of Porto, 4200-072 Porto, Portugal
Interests: tribology; coatings; manufacturing processes
Special Issues, Collections and Topics in MDPI journals

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Guest Editor

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Guest Editor
Department of Mechanical Engineering, ISEP—School of Engineering, Polytechnic of Porto, 4200-465 Porto, Portugal
Interests: industrial management; lean; six-sigma; processes improvement; safety; sustainability
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute of Mechanical Engineering and Industrial Management (INEGI), School of Engineering, Polytechnic of Porto, 4200-465 Porto, Portugal
Interests: operational research; decision support models; supply chain management; logistics; transportations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The digitalization of processes has gained great prominence in research essentially due to the added advantages it provides in improving efficiency and automating control. Furthermore, digital transformation implies a deeper analysis of installed systems, leading to the identification of gaps in processes, which, in turn, can give rise to innovations in the way these processes are conducted. The need imposed by the market to make processes more flexible implies that there is a strong innovation component, which can be accelerated by digital transformation. Therefore, this Special Issue aims to bring together cutting-edge work in the investigation of new methodologies for accelerating digital transformation and innovation in processes, both industrial and in the services sector. Original research or review work, properly structured, will be welcome, as well as case studies of success in the development of digital transformation or innovation in processes with a view to increasing efficiency.

Dr. Francisco J. G. Silva
Prof. Dr. Luís Pinto Ferreira
Prof. José Carlos Sá
Dr. Maria Teresa Pereira
Guest Editors

Manuscript Submission Information

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Keywords

  • digital transformation
  • innovation
  • processes
  • Industry 4.0
  • smart manufacturing
  • manufacturing
  • services

Published Papers (2 papers)

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Research

31 pages, 706 KiB  
Article
A Mechanistic Study of Enterprise Digital Intelligence Transformation, Innovation Resilience, and Firm Performance
by Guangsi Zhang, Xuehe Wang, Jiaping Xie and Qiang Hu
Systems 2024, 12(6), 186; https://doi.org/10.3390/systems12060186 - 24 May 2024
Viewed by 281
Abstract
Enterprise Digital Intelligence Transformation is based on the Digital Conversion of information and process service upgrading, further touching the enterprise’s core business, with the goal of building a new business model of Digital Intelligence Transformation at a higher level. Based on dynamic capability [...] Read more.
Enterprise Digital Intelligence Transformation is based on the Digital Conversion of information and process service upgrading, further touching the enterprise’s core business, with the goal of building a new business model of Digital Intelligence Transformation at a higher level. Based on dynamic capability theory, this paper conducts an in-depth study on the mechanism of enterprise Digital Intelligence Transformation and firm performance. This paper selects manufacturing companies listed in China’s Shanghai and Shenzhen A-shares from 2013 to 2022 as the research sample, and analyzes and tests the sample data using empirical research methods in order to explore the actual impact of Digital Intelligence Transformation on firm performance, including the specific pathways of action and moderating effects. This study helps enterprises to positively face the megatrend of Digital Intelligence Transformation and upgrading and the challenges it brings, and to grasp the new opportunities in the digital era. This study finds that enterprises carry out digital empowerment transformation and development strategies, and implement information Digital Conversion, service upgrading, and Digital Intelligence Transformation to promote firm performance to different degrees. Enterprise innovation resilience has a mechanism effect between information digitalization conversion enterprise performance and process service upgrading enterprise performance. The higher the environmental uncertainty, the greater the positive contribution of information digitalization to firm performance. Digital Conversion is the base and service upgrading is the process. The current sample enterprises have limited years of data collection, and most of them have only carried out the strategic implementation of Digital Conversion or servitization, and have not reached the high-level stage of digital and intellectual transformation. Therefore, it is found that enterprise innovation resilience has not yet shown a significant role mechanism effect between digital–intelligent transformation and enterprise performance at present. And environmental uncertainty has not yet shown a significant moderating effect in the stage of Servitization Upgrading and digital–intelligent transformation. The marginal contributions of this paper are mainly reflected in the following: (1) This study introduces the dynamic capability theory to explore the role of Digital Intelligence Transformation (Digital Conversion + service upgrading) on enterprise performance. (2) This paper investigates the role of Digital Intelligence Transformation in influencing the performance of enterprises from the Digital Conversion and service upgrading phases, and enriches the relevant role and regulatory mechanisms. (3) This study provides new ideas and strategic suggestions on Digital Intelligence Transformation for enterprises with different factor intensities, at different stages of development, and in different regions. Full article
(This article belongs to the Special Issue Digital Transformation and Processes Innovation)
10 pages, 450 KiB  
Article
Minimization of Costs with Picking and Storage Operations
by Cristina Lopes and Ana Oliveira
Systems 2024, 12(5), 158; https://doi.org/10.3390/systems12050158 - 1 May 2024
Viewed by 671
Abstract
This work presents two mixed-integer programming models that intend to minimize the costs of the picking and storage operation through better planning and organization of the places occupied by the products in the warehouse. A large customer that stores frozen goods in a [...] Read more.
This work presents two mixed-integer programming models that intend to minimize the costs of the picking and storage operation through better planning and organization of the places occupied by the products in the warehouse. A large customer that stores frozen goods in a Portuguese cold chain logistics company was selected for the analysis of the allocation of the products in the warehouse and of the corresponding outbound movements. Data with 8525 movements that occurred during 2021 were collected for 228 different product references. For this case study, the products that had a picking place in the initial scenario now have pallets with all the goods in the reserve area, and vice versa. The mathematical models were permitted to obtain savings for the logistics operator costs of around 30.9%. The proposed models can, in the future, be applied in other warehouse scenarios to companies in completely different sectors of activity. Full article
(This article belongs to the Special Issue Digital Transformation and Processes Innovation)
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