Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 200

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

A multi-agent based system with big data processing for enhanced supply chain agility

Open access
Giannakis, Mihalis, Louis, Michalis·2016·Journal of Enterprise Information Management
DOI 10.1108/jeim-06-2015-0050
Open this paper in CoChat — free account

Abstract

Purpose: Decision support systems are becoming an indispensable tool for managing complex supply chains. The purpose of this paper is to develop a multi-agent-based supply chain management system that incorporates big data analytics that can exert autonomous corrective control actions. The effects of the system on supply chain agility are explored. Design/methodology/approach: For the development of the architecture of the system, a sequential approach is adopted. First three fundamental dimensions of supply chain agility are identified – responsiveness, flexibility and speed. Then the organisational design of the system is developed. The roles for each of the agents within the framework are defined and the interactions among these agents are modelled. Findings: Applications of the model are discussed, to show how the proposed model can potentially provide enhanced levels in each of the dimensions of supply chain agility. Research limitations/implications: The study shows how the multi-agent systems can assist to overcome the trade-off between supply chain agility and complexity of global supply chains. It also opens up a new research agenda for incorporation of big data and semantic web applications for the design of supply chain information systems. Practical implications: The proposed information system provides integrated capabilities for production, supply chain event and disruption risk management under a collaborative basis. Originality/value: A novel aspect in the design of multi-agent systems is introduced for inter-organisational processes, which incorporates semantic web information and a big data ontology in the agent society.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1108/jeim-06-2015-0050

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Research works printed but not linked: 47

  1. 2007, Modeling agility of supply chain, Industrial Marketing Management, 36, 443, 10.1016/j.indmarman.2005.12.004 · DOI · Paper page
  2. 2003, The impact of ERP on supply chain management: exploratory findings from a European Delphi study, European Journal of Operational Research, 146, 284, 10.1016/S0377-2217(02)00550-7 · DOI · Paper page
  3. 2012, Applications of agent-based models for optimization problems: a literature review, Expert Systems with Applications, 39, 6020, 10.1016/j.eswa.2011.12.015 · DOI · Paper page
  4. 2012, An autonomous multi-agent approach to supply chain event management, International Journal of Production Economics, 135, 468, 10.1016/j.ijpe.2011.08.023 · DOI
  5. 2001, The semantic web, Scientific American, 284, 28
  6. 2014, The use of semantic web technologies for decision support – a survey, Semantic Web, 5, 177, 10.3233/SW-2012-0084 · DOI
  7. 2005, A survey on the recent research literature on ERP systems, Computers in Industry, 56, 510, 10.1016/j.compind.2005.02.004 · DOI · Paper page
  8. 2009, The organisational antecedents of a firm’s supply chain agility for risk mitigation and response, Journal of Operations Management, 27, 119, 10.1016/j.jom.2008.09.006 · DOI · Paper page
  9. 2015, A mixed-method approach to extracting the value of social media data, Production and Operations Management, 25, 568
  10. 2001, An integrated model for the design of agile supply chains, International Journal of Physical Distribution & Logistics Management, 31, 235, 10.1108/09600030110394914 · DOI · Paper page
  11. 2000, Supply chain migration from lean and functional to agile and customized, Supply Chain Management: An International Journal, 5, 206, 10.1108/13598540010347334 · DOI · Paper page
  12. 2004, Mitigating supply chain risk through improved confidence, International Journal of Physical Distribution & Logistics Management, 34, 388, 10.1108/09600030410545436 · DOI · Paper page
  13. 2002, Software agents for bioprocess monitoring and control, Journal of Chemical Technology and Biotechnology, 77, 761, 10.1002/jctb.631 · doi:10.1002/jctb.631
  14. 1999, Systems design framework for the extended enterprise, Production Planning and Control, 10, 3, 10.1080/095372899233370 · doi:10.1080/095372899233370
  15. 2000, Agent-oriented supply chain management, International Journal of Flexible Manufacturing Systems, 12, 165, 10.1023/A:1008195614074 · DOI · Paper page
  16. 2015, An adaptive multi-agent system for cost collaborative management in supply chains, Engineering Applications of Artificial Intelligence, 44, 91, 10.1016/j.engappai.2015.05.002 · DOI
  17. 2006, Living Supply Chains: How to Mobilize the Enterprise Around Delivering What Your Customers Want
  18. 2011, A multi-agent based framework for supply chain risk management, Journal of Purchasing and Supply Management, 17, 23, 10.1016/j.pursup.2010.05.001 · DOI · Paper page
  19. 2012, Understanding the role of logistics capabilities in achieving supply chain agility: a systematic literature review, Supply Chain Management: An International Journal, 17, 438, 10.1108/13598541211246594 · DOI · Paper page
  20. 2013, A multidisciplinary approach to supply chain agility: conceptualization and scale development, Journal of Business Logistics, 34, 94, 10.1111/jbl.12012 · DOI · Paper page
  21. 2013, The PORSCE II framework: using AI planning for automated semantic web service composition, The Knowledge Engineering Review, 28, 137, 10.1017/S0269888912000392 · DOI
  22. 2006, Enterprise integration using the agent paradigm: foundations of multi-agent-based integrative business information systems, Decision Support Systems, 42, 48, 10.1016/j.dss.2004.09.011 · DOI
  23. 2007, MACE-SCM: a multi-agent and case-based reasoning collaboration mechanism for supply chain management under supply and demand uncertainties, Expert Systems with Applications, 33, 690, 10.1016/j.eswa.2006.06.015 · DOI
  24. 2011, A multi-agent model for the reasoning of uncertainty information in supply chains, International Journal of Production Research, 49, 5737, 10.1080/00207543.2010.524257 · doi:10.1080/00207543.2010.524257
  25. 2012, A multi-agent system using iterative bidding mechanism to enhance manufacturing agility, Expert Systems with Applications, 39, 8259, 10.1016/j.eswa.2012.01.160 · DOI
  26. 2015, Classifying systemic differences between software as a service-and on-premise-enterprise resource planning, Journal of Enterprise Information Management, 28, 808, 10.1108/JEIM-07-2014-0069 · DOI
  27. 2008, A framework of e-SCM multi-agent systems in the fashion industry, International Journal of Production Economics, 114, 594, 10.1016/j.ijpe.2007.09.010 · DOI
  28. 2004, Study on multi-agent-based agile supply chain management, International Journal Advanced Manufacturing Technology, 23, 197
  29. 2007, A study on multi-agent supply chain framework based on network economy, Computers and Industrial Engineering, 54, 288
  30. 2008, Global supply chain risk management strategies, International Journal of Physical Distribution and Logistics Management, 38, 192, 10.1108/09600030810866986 · DOI · Paper page
  31. 2012, A multi-dimensional view of agent-based decisions in supply chain management, Communications of the IBIMA, 2012, 1
  32. 2013, Big Data: A Revolution that Will Transform How We Live, Work, and Think
  33. 2012, A multi-agent architecture for reverse logistics in a green supply chain, International Journal of Production Research, 50, 2396, 10.1080/00207543.2011.581003 · DOI
  34. Moyaux, T., Chaib-Draa, B. and D’Amours, S. (2006), “Supply chain management and multi agent systems: an overview”, in Chaib-draa, B. and Müller, J.P. (Eds), Multiagent Based Supply Chain Management, Springer Berlin Heidelberg and Chicago, IL, pp. 1-27.
  35. 2012, Hybrid simulation based optimization approach for supply chain management, Computers & Chemical Engineering, 47, 183, 10.1016/j.compchemeng.2012.06.045 · doi:10.1016/j.compchemeng.2012.06.045
  36. 2014, A multi agent-based service framework for supply chain management, Procedia Computer Science, 32, 53, 10.1016/j.procs.2014.05.397 · DOI
  37. Parunak, H.V. (1999), “Industrial and practical applications of DAI”, in Weiss, G. (Ed.), Multi Agent Systems – A Modern Approach to Distributed Modern Approach to Artificial Intelligence, The MIT Press, Cambridge, MA and London, pp. 337-421.
  38. 2001, International supply chain agility: tradeoffs between flexibility and uncertainty, International Journal of Operations & Production Management, 21, 823, 10.1108/01443570110390507 · DOI · Paper page
  39. 2007, Creating the customer-responsive supply chain: a reconciliation of concepts, International Journal of Operations & Production Management, 27, 1144, 10.1108/01443570710830575 · DOI · Paper page
  40. 2006, Applications of agent-based systems in intelligent manufacturing: an updated review, Advanced Engineering Informatics, 20, 415, 10.1016/j.aei.2006.05.004 · DOI · Paper page
  41. 2009, Initiating an state of the art system for real – time supply chain coordination, European Journal of Operational Research, 196, 635, 10.1016/j.ejor.2008.03.008 · DOI
  42. 2006, The antecedents of supply chain agility of a firm: scale development and model testing, Journal of Operations Management, 24, 170, 10.1016/j.jom.2005.05.002 · DOI · Paper page
  43. 2008, Achieving supply chain agility through IT integration and flexibility, International Journal of Production Economics, 116, 288, 10.1016/j.ijpe.2008.09.002 · DOI · Paper page
  44. 2013, Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management, Journal of Business Logistics, 34, 77, 10.1111/jbl.12010 · DOI · Paper page
  45. 2013, An ontology based approach to organize multi-agent assisted supply chain negotiations, Computers & Industrial Engineering, 65, 2, 10.1016/j.cie.2012.06.018 · doi:10.1016/j.cie.2012.06.018
  46. Wooldridge, M.J., Jennings, N.R. and Kenny, D. (1999), “A methodology for agent-oriented analysis and design”, Proceedings of the Third International Conference on Autonomous Agents, pp. 69-76. · DOI · Paper page
  47. 2000, Applying multi agent technology to supply chain management, Journal of Electronic Commerce Research, 1, 119

Cited by

See all 200

Metadata

Authors
Giannakis, M., & Louis, M.
Venue
Journal of Enterprise Information Management
Publisher
Emerald
Published
2016-09-12
Volume
29
Issue
5
Pages
706-727
ISSN
1741-0398 (ISSN-L)
DOI
10.1108/jeim-06-2015-0050
OpenAlex
W2508962850
Cited by
200
References
2
FWCI
30.98
Type
journal article
Language
en
Open access
green
Licence
www.emerald.com/insight/site-policies (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A multi-agent based system with big data processing for enhanced supply chain agility. https://app.cochat.ai/papers/10.1108/jeim-06-2015-0050
The paper
Giannakis, M., & Louis, M. (2016). A multi-agent based system with big data processing for enhanced supply chain agility. Journal of Enterprise Information Management. https://doi.org/10.1108/jeim-06-2015-0050

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.