Abstract
The increased integration of global supply networks with a reduction in stock and time buffers has raised their vulnerability to disturbances (i.e., events) that erode efficiency and competitiveness. The complex interrelationships in these networks lead to a cascade of knock-on effects that affect company performance in varying degrees. Hence, causality analysis is the key for early identification of critical events. To this end, a dynamic model is developed in this contribution. It describes the basic cause-effect relationship between events, their knock-on effects and company-internal performance indicators. To make the complexity of this task manageable, an effects-based classification of events is delineated from the EPCIS format. The utility and functionality of the model is illustrated on two examples. It is proposed that it serves as a basic outline for the logic foundation of the discovery component of supply chain event management (SCEM) systems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bearzotti LA, Salomone E, Chiotti OJ (2012) An autonomous multi-agent approach to supply chain event management. Int J Prod Econ 135(1):468–478
Blackhurst J, Craighead CW, Elkins D, Handfield RB (2005) An empirically derived agenda of critical research issues for managing supply-chain disruptions. Int J Prod Res 43(19):4067–4081
Chopra S, Sodhi MS (2004) Managing risk to avoid supply-chain breakdown. MIT Sloan Manage Rev 46(1):53–62
Curtin J, Kauffman RJ, Riggins FJ (2007) Making the ‘most’ out of RFID technology: a research agenda for the study of the adoption, usage and impact of RFID. Inf Technol Manage 8(2):87–110
EPC Global (2007) EPC Information Services (EPCIS). Version 1.0.1 Specification. September, 2007
Gaonkar R, Viswanadham N (2004) A conceptual and analytical framework for the management of risk in supply chains. In: Proceedings—IEEE international conference on robotics and automation pp 2699–2704
Günthner W, Meißner S, Conze M, Fischer R (2010) Stand und Entwicklung des RFID-Einsatzes in der Automobillogisitk: Ergebnisse einer empirischen Studie. RFID Anwenderzentrum, München
Guha-Sapir D, Vos F, Below R, Ponserre S (2011) Annual disaster statistical review 2010. Centre for Research on the Epidemiology of Disasters. Universite´ Catholique de Louvain, Brussels
Gunasekaran A, Patel C, Tirtiroglu E (2001) Performance measures and metrics in a supply chain environment. Int J Oper Prod Manage 21:71–87
Gunasekaran A, Patel C, McGaughey RE (2004) A framework for supply chain performance measurement. Int J Prod Econ 87(3):333–347
Holweg M, Pil FK (2005) The second century: reconnecting customer and value chain through build-to-order moving beyond mass and lean in the auto industry, vol 1. The MIT Press, Cambridge
Hopp W, Spearman M (2007) Factory physics, 3rd edn. Irwin McGraw-Hill, NY
Jüttner U (2005) Supply chain risk management: Understanding the business requirements from a practitioner perspective. Int J Logistics Manage 16(1):120–141
Knickle K (2001) Supply chain event management—the next best thing to supply chain perfection. AMR Research Outlook, Boston
Lee HL, Whang S (2000) Information sharing in a supply chain. Int J Technol Manage 20:373–387
Lee HL, Padmanabhan V, Whang S (1997) Information distortion in a supply chain: the bullwhip effect. Manage Sci 43(4):546–558
Liu R, Kumar A, van der Aalst W (2007) A formal modeling approach for supply chain event management. Decis Support Syst 43(3):761–778
Nyhuis P, Wiendahl HP (2006) Logistic production operating curves—basic model of the theory of logistic operating curves. CIRP Ann Manuf Technol 55(1):441–444
Otto A (2003) Supply chain event management: three perspectives. Int J Logistics Manage 14(2):1–13
Özbayrak M, Papadopoulou TC, Akgun M (2007) Systems dynamics modelling of a manufacturing supply chain system. Simul Model Pract Theory 15(10):1338–1355
Radjou N, Orlov LM, Nakashima T (2002) Adapting to supply network change. Tech. rep., Forrester Research
Sterman JD (2000) Business dynamics: systems thinking and modeling for a complex world, vol 53. Irwin McGraw-Hill, NY
Straube F, Vogeler S, Bensel P (2007) RFID-based supply chain event management. In: RFID Eurasia, 2007 1st Annual, pp 1–5
Tang CS (2006) Perspectives in supply chain risk management. Int J Prod Econ 103(2):451–488
Tribowski C, Goebel C, Guenther O (2009) EPCIS-based supply chain event management—a quantitative comparison of candidate system architectures. In: International Conference on complex, intelligent and software intensive systems, pp 494–499
Wagner SM, Bode C (2006) An empirical investigation into supply chain vulnerability. J Purchasing Supply Manage 12(6):301–312
Wagner SM, Neshat N (2010) Assessing the vulnerability of supply chains using graph theory. Int J Prod Econ 126(1):121–129
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Heinecke, G., Köber, J., Kunz, A., Lamparter, S. (2013). Modeling the Basic Cause-Effect Relationship Between Supply Chain Events and Performance. In: Kreowski, HJ., Scholz-Reiter, B., Thoben, KD. (eds) Dynamics in Logistics. Lecture Notes in Logistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35966-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-35966-8_13
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35965-1
Online ISBN: 978-3-642-35966-8
eBook Packages: EngineeringEngineering (R0)