Industrial digital ecosystems: Predictive models and architecture development issues

Abstract The concept of digital ecosystem (DES) is widely used in autonomous manufacturing process control and the development of complex, stable, interactive, self-organizing and reliable management systems for various industries. The paper offers a concept of DES control system architecture based on predictive models. For estimating and predicting the state of resources in production processes, an approach is developed using data mining based model generation. The paper also reviews the current state of research in the field of DES and their applications in supply chain management (SCM).

[1]  Bruce Ratner,et al.  Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data , 2011 .

[2]  Michael P. Wellman,et al.  Modeling Supply Chain Formation in Multiagent Systems , 1999, Agent Mediated Electronic Commerce.

[3]  Matthew Fuller,et al.  Media Ecologies: Materialist Energies in Art and Technoculture (Leonardo Books) , 2007 .

[4]  Michael P. Wellman,et al.  Decentralized Supply Chain Formation: A Market Protocol and Competitive Equilibrium Analysis , 2003, J. Artif. Intell. Res..

[5]  Asif Akram Value Creation in Digital Ecosystem – A Study of Remote Diagnostics , 2013 .

[6]  Alexander Suleykin,et al.  Distributed Big Data Driven Framework for Cellular Network Monitoring Data , 2019, 2019 24th Conference of Open Innovations Association (FRUCT).

[7]  Alexandre Dolgui,et al.  Data Mining-Based Prediction of Manufacturing Situations , 2018 .

[8]  George Rzevski Intelligent Multi-agent Platform for Designing Digital Ecosystems , 2019, HoloMAS.

[9]  Elizabeth Chang,et al.  Digital Ecosystems A Next Generation of the Collaborative Environment , 2006, iiWAS.

[10]  Marie-Hélène Abel,et al.  Moving from Digital Ecosystem to System of Information Systems , 2016, 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[11]  M. Porter Competitive Advantage: Creating and Sustaining Superior Performance , 1985 .

[12]  Joseph Sarkis,et al.  Blockchain technology and its relationships to sustainable supply chain management , 2018, Int. J. Prod. Res..

[13]  Margherita Pagani,et al.  Digital Business Strategy and Value Creation: Framing the Dynamic Cycle of Control Points , 2013, MIS Q..

[14]  Dmitry Ivanov Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic , 2020, Annals of operations research.

[15]  Alexander Suleykin,et al.  Industrial track: Architecting railway KPIs data processing with Big Data technologies , 2019, 2019 IEEE International Conference on Big Data (Big Data).

[16]  J. Chataway,et al.  Knowledge Ecologies and Ecosystems? An Empirically Grounded Reflection on Recent Developments in Innovation Systems Theory , 2009 .

[17]  Alexandre Dolgui,et al.  Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain† , 2019, Int. J. Prod. Res..

[18]  Claudia Loebbecke,et al.  Commoditized digital processes and business community platforms: new opportunities and challenges for digital business strategies , 2013 .

[19]  Suresh P. Sethi,et al.  A survey on control theory applications to operational systems, supply chain management, and Industry 4.0 , 2018, Annu. Rev. Control..

[20]  Stefan Seuring,et al.  A review of modeling approaches for sustainable supply chain management , 2013, Decis. Support Syst..

[21]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[22]  Alexandre Dolgui,et al.  A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0 , 2020, Production Planning & Control.

[23]  Benoît Iung,et al.  Challenges for the cyber-physical manufacturing enterprises of the future , 2019, Annu. Rev. Control..

[24]  Salil S. Kanhere,et al.  ProductChain: Scalable Blockchain Framework to Support Provenance in Supply Chains , 2018, 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA).

[25]  Dmitry Ivanov,et al.  Researchers' perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management , 2020, Int. J. Prod. Res..

[26]  Elizabeth Chang,et al.  An Integrative view of the concept of Digital Ecosystem , 2007, International Conference on Networking and Services (ICNS '07).

[27]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[28]  Geoffrey C. Bowker,et al.  Information ecology: open system environment for data, memories, and knowing , 2007, Journal of Intelligent Information Systems.

[29]  Alexandre Dolgui,et al.  Manufacturing modelling, management and control: IFAC TC 5.2 past, present and future , 2020, Annu. Rev. Control..

[30]  João Barata,et al.  Mobile supply chain management in the Industry 4.0 era: An annotated bibliography and guide for future research , 2017, J. Enterp. Inf. Manag..

[31]  Boris V. Sokolov,et al.  Reconfigurable supply chain: the X-network , 2020, Int. J. Prod. Res..

[33]  Nataliya N. Bakhtadze,et al.  Knowledge-Based Models of Nonlinear Systems Based on Inductive Learning , 2016 .