TY - JOUR

T1 - An approach and decision support tool for forming Industry 4.0 supply chain collaborations

AU - Cisneros-Cabrera, Sonia

AU - Pishchulov, Grigory

AU - Sampaio, Pedro

AU - Mehandjiev, Nikolay

AU - Liu, Zixu

AU - Kununka, Sophia

N1 - Funding Information: The work presented has received funding from the European Commission under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 723336 ). Financial support has been provided by the National Council of Science and Technology (abbreviated CONACyT) to Sonia Cisneros-Cabrera (agreement no. 461338 ). We are grateful to the Guest Editor Professor Shenle Pan and five anonymous reviewers for constructive and insightful comments on the paper. We wish to thank Arturo Jimenez, Qudamah Quboa, Tomas Grubhoffer, Jan Rada, and Jan Dyrczyk for their contribution to implementing the TDMS, and Carolyn Langen and Menno Guldemond for their work on providing the risk scores. Also, we thank the DIGICOR project team members for their invaluable input which shaped our work, and Nikolai Kazantsev for his suggestions and discussions. Funding Information: Ms. SONIA CISNEROS-CABRERA (sonia.cisneroscabrera@manchester.ac.uk) is an Information Systems postgraduate researcher at The Alliance Manchester Business School from the University of Manchester. She obtained her Master’s degree (MPhil) in the School of Computer Science from the same University in 2016 with work developed on High-Performance Computing, Big data, and Data Quality subject areas. Previously, Sonia was part of Deloitte Mexico as an internal business technology analyst and part of the IT project management office, just after graduating from the Technological University of Coacalco (Tecnológico de Estudios Superiores de Coacalco) as Computer Systems Engineer. Sonia’s research has been funded by the European Commission (H2020) Programme, the Alliance Manchester Business School, the Engineering and Physical Sciences Research Council (EPSRC) in respect of the EPSRC Network Plus: Industrial Systems in the Digital Age, and by Mexico's National Council of Science and Technology (abbreviated CONACyT). Funding Information: Dr. GRIGORY PISHCHULOV (grigory.pishchulov@manchester.ac.uk) is a Lecturer in Information Systems and Supply Chain Management, and a former Research Fellow in Smart Manufacturing at the Alliance Manchester Business School. His research is focused on problems in supply chain coordination, sustainable supply chain management, data analytics, and use of information technologies in supply chain management. His research has been funded by the Leverhulme Trust, European Commission, and German Federal Government. Funding Information: Dr. PEDRO SAMPAIO (P.Sampaio@manchester.ac.uk) is an Associate Professor in Information Systems and a former business technology consultant of McKinsey & Company. Dr Sampaio has published extensively in the areas of Process Digitalization in Industry 4.0, Applied Deep Learning and Expert Systems for Business Automation and Data Science. His research has been funded by the European Commission (H2020, FP7), Innovate UK, Case Awards from EPSRC (UK) and grants and fellowships from CNPq, CAPES and FAPESP (Brazil). Dr Sampaio currently serves as Deputy Director of the University of Manchester’s Data Visualization Observatory and is the Principal Investigator of a Knowledge Transfer Partnership on Digitalization and Industry 4.0 for Construction Industry Processes Improvement. Funding Information: Prof. NIKOLAY MEHANDJIEV (n.mehandjiev@manchester.ac.uk) is a Professor of Enterprise Information Systems at the Alliance Manchester Business School. He serves as the Director of the University of Manchester’s Data Visualization Observatory, and as the e-Research Coordinator for the Faculty of Humanities within the University of Manchester Digital Futures framework. He researches the transforming role of intelligent technology for the manufacturing and process industries. Mehandjiev has published more than 150 peer-reviewed research outputs and has edited three special issues of international journals. He has supervised 17 PhD students to successful completion and has participated in 25 collaborative projects funded by EC (H2020, FP5/6/7) and EPSRC. His most recent research projects are in collaboration with Unilever, Airbus and BT.

PY - 2021/2/1

Y1 - 2021/2/1

N2 - Industry 4.0 technologies, process digitalisation and automation can be applied to support the formation of supply chain collaborations in manufacturing. Underpinned by information and communication technologies, collaborations of independent companies can dynamically pool production capacities and capabilities to jointly react to new business opportunities. These collaborations may involve a wide range of enterprises with different sizes and scope that individually would not be able to tender for such new business opportunities. To form these collaborative teams, assistive processes and technologies can underpin the effort towards exploring the tender requirements, unbundling the tender into smaller tasks and finding a suitable supplier for each task. In this paper, we present an approach and a tool to support decision making concerning forming supply chain collaborations in Industry 4.0. The approach proposed is unique in integrating industry domain ontologies, assistive human-computer interaction tools and multi-criteria decision support techniques to form team compositions speeding-up the collaboration process whilst maximising the chances of forming a viable team to fulfil the tender requirements. We also show evaluation results involving stakeholders from the supply chain function pointing to the effectiveness of the proposed solution, available as a demo online. (1)(1) http://130.88.97.225:4200 (username: TDMS@uniman.eu; password: uniman).

AB - Industry 4.0 technologies, process digitalisation and automation can be applied to support the formation of supply chain collaborations in manufacturing. Underpinned by information and communication technologies, collaborations of independent companies can dynamically pool production capacities and capabilities to jointly react to new business opportunities. These collaborations may involve a wide range of enterprises with different sizes and scope that individually would not be able to tender for such new business opportunities. To form these collaborative teams, assistive processes and technologies can underpin the effort towards exploring the tender requirements, unbundling the tender into smaller tasks and finding a suitable supplier for each task. In this paper, we present an approach and a tool to support decision making concerning forming supply chain collaborations in Industry 4.0. The approach proposed is unique in integrating industry domain ontologies, assistive human-computer interaction tools and multi-criteria decision support techniques to form team compositions speeding-up the collaboration process whilst maximising the chances of forming a viable team to fulfil the tender requirements. We also show evaluation results involving stakeholders from the supply chain function pointing to the effectiveness of the proposed solution, available as a demo online. (1)(1) http://130.88.97.225:4200 (username: TDMS@uniman.eu; password: uniman).

KW - digitalization

KW - supply chain collaboration

KW - Industry 4.0

KW - decision support systems

KW - interoperability

KW - ontology

KW - Decision support systems

KW - Digitalization

KW - Ontology

KW - Supply chain collaboration

KW - Interoperability

UR - https://www.mendeley.com/catalogue/22cf4d13-98d7-3bac-a3a1-f3488f625ffc/

UR - http://www.scopus.com/inward/record.url?scp=85099544667&partnerID=8YFLogxK

U2 - 10.1016/j.compind.2020.103391

DO - 10.1016/j.compind.2020.103391

M3 - Article

VL - 125

SP - 1

EP - 16

JO - Computers in Industry

JF - Computers in Industry

SN - 0166-3615

M1 - 103391

ER -