Sage Journals: Discover world-class research

Abstract

The supply chain is a key element of successful operation of businesses in a turbulent economic situation. Swift management of delivery of raw materials and finished products while keeping costs as low as possible and maintaining proper customer service is becoming as vital as the quality and price of a product when gaining competitive advantage. This leads companies to a wide search for the best strategies that allow efficient management of the supply chain. The basic problem, however, is the occurrence of the so-called Forrester effect (also known as the bullwhip effect). It involves intensified transposing of changes in demand onto the execution of product flow in supply chains. The aim of this article is to develop a manner to reduce the Forrester effect using the consensus method. The first part of the article analyzed the current state of knowledge on the discussed problem. Then it presented basic elements of the developed prototype of a SCM system and defined the meth-od for reducing the Forrester effect using a consensus algorithm. The final part of the article de-scribes the way to conduct an experiment that involves verifying the consensus algorithm and analyzes the results of the verification and their influence on the reduction of the Forrester effect.

Keywords

Supply chain management Forrester effect consensus methods

Get full access to this article

View all access options for this article.

References

Rutkowski

, Best Practices in Logistics and Supply Chain Management. The Case of Central and Eastern Europe, In: Global Logistics and Distribution Planning. ed. Waters

. Kogan Page. London, 2010, 13–28.

Sitek

and Wikarek

, Cost optimization of supply chain with multimodal transport, Proceedings of the Federated Conference on Computer Science and Information Systems. https://annals-csis.org/proceedings/2017/drp/pdf/80.pdf., 2012, 1057–1064.

Forrester

J.W.

, System dynamics: A major breakthrough for decision makers, Harward Business Review 36(4) (1958), 37–66.

Moyaux

, Chaibdraa

, D’Amours

, Supply Chain Management and Multiagent Systems: An Overview, In Chaibdraa

, Müller

J. P.

. eds. Multi-agent-based supply chain management (p. V–X). Springer. Heidelberg, 2006.

Jacoby

, Guide to Supply chain Management, The Economist & Profiled Books, London 2009 pp. 19.

Hudnurkar

and Rathod

, Collaborative supply chain: Insights from simulation, International Journal of System Assurance Engineering and Management 3(2) (2012), 122–144.

Zolfpour-Arokhlo

, Selamat

and Mohd Hashim

S.Z.

, Route planning model of multi-agent system for a supply chain management, Expert Systems with Applications 40(5) (2013), 1505–1518.

, Fundamentals of supply chain management, Dr. Dawei Lu & Ventus Publishing ApS. bookboon.com, 2011.

Dyk

, Lenar

, Applying negotiation methods to resolve conflicts in multi-agent environments, In Zgrzywa

(red.), Multimedia and Network Information systems. MISSI 2006, Oficyna Wydawnicza PWr. Wrocław. 2006.

10.

Barthlemy

J.P.

, Dictatorial consensus function on n-trees, Mathematical Social Science. nr 25, 1992.

11.

Hernes

, Knowledge Integration Method for Supply Chain Management Module in a Cognitive Integrated Management Information System, in Computational Collective Intelligence. 8th International Conference, ICCCI 2016, Halkidiki, Greece, September 28-30, 2016. Proceedings, Part I, 2016, vol, 9875, 81–89.

12.

Nguyen

N.T.

, Advanced Methods for Inconsistent Knowledge Management, Springer-Verlag London, 2008.

13.

Hernes

and Sobieska-Karpińska

, Application of the consensus method in a multiagent financial decision support system, Information Systems and e-Business Management 14(1) (2016), 167–185.

14.

Christopher

, Logistics and supply chain management. creating value-adding networks, Third Edition, Financial Times Press, Paerson Education Limited 2005.

15.

Chern.

C.C.

, Chou

T.Y.

and Hsiao

, Assessing the efficiency of supply chain scheduling algorithms using data envelopment analysis, Information Systems and e-Business Management 14(4) (2016), 823–856. doi: 10.1007/s10257-015-0301-8

16.

Hudnurkar

and Rathod

, Collaborative supply chain: Insights from simulation, International Journal of System Assurance Engineering and Management 3(2) (2012), 122–144.

17.

García

F.T.

, García Villalba

L.J.

and Portela

, Intelligent system for time series classification using support vector machines applied to supply-chain, Expert Systems with Applications 39(12) (2012), 10590–10599.

18.

Podobnik

, Petric

and Jezic

, G., An Agent-Based Solution for Dynamic Supply Chain Management Journal of Universal Computer Science, 14(7) (2008), 1080–1104.

19.

Kovalchuk

, Multi-agent decision support system for supply chain management, University of Essex, 2009.

20.

Farrell

and Loffredo

, A Simple Agent for Supply Chain Management, Department of Computer Science The University of Texas at Austin, 2006.

21.

Wolley

A.W.

, Chabris

C.F.

, Pentland

, Hashmi

and Malone

T.W.

, Evidence for a Collective Intelligence Factor in the Performance of Human Groups, Science 330(6004) (2010), 686–688.

22.

Nguyen

N.T.

, Processing inconsistency of knowledge in determining knowledge of collective, Cybernetics and Systems: An International Journal 40(8) (2009), 670–688.

23.

Hernes

and Nguyen

N.T.

, Deriving Consensus for Hierarchical Incomplete Ordered Partitions and Coverings, Journal of Universal Computer Science 13(2) (2007), 317–328.

24.

Raja

M.A.Z.

, Khan

J.A.

and Qureshi

I.M.

, Evolutionary Computational Intelligence in Solving the Fractional Differential Equations, In Intelligent information and database systems, N.T.:Nguyen, M.T. Le, J. Swiatek, (eds), Lecture Notes in Artificial Intelligence 5990 (2010), 231–240.

25.

Pirasteh

, Jung

J.J.

and Hwang

, Item-Based Collaborative Filtering with Attribute Correlation: A Case Study on Movie Recommendation, Intelligent information and database systems, Edited by: N.T. Nguyen, B. Attachoo, B. Trawinski, K. Somboonviwat, Lecture Notes in Artificial Intelligence, 8398 (2014), 245–252.

26.

Sobieska-Karpińska

and Hernes

, Distance function between structure of variants in consensus determining process in supply chain management systems,, Ekonometria 3 (41), Wydawnictwo UE we Wroclawiu. Wrocław, 2013, 131–140.

27.

Hernes

, A Cognitive Integrated Management Support System for Enterprises, In Computational Collective Intelligence. Technologies and Applications. 6th International Conference, ICCCI 2014, Seoul, Korea, September 24-26, 2014. Proceedings 8733 (2014), 252–261.

Reduction of a Forrester effect in a supply chain management system

Abstract

Keywords

Get full access to this article

References