The article presents a computer simulation-based model for analysis of supply chain resilience, which allows determining
and verifying the generally valid principles, capabilities and ways for building long-term resilience of global
supply chains against serious disruptions. The model is created on the basis of a supply chain from automotive industry
and contains the main logistics flows used by present automotive producers. Any real automotive supply
chain can be modelled as a combination of these logistics flows. The model was created in DOSIMIS-3®
 and verified
using experimental data. The performed simulation demonstrates a significant decrease of the supply chain performance
in case of serious disruption occurrence.Web of Science54470670

Besta, Petr

Karkula, Marek

Lenort, Radim

Staš, David

Wicher, Pavel

DSpace at VSB Technical University of Ostrava

703METALURGIJA 54 (2015) 4, 703-706 P. WICHER, D. STAŠ, M. KARKULA, R. LENORT, P. BESTAA COMPUTER SIMULATION-BASED ANALYSIS OF SUPPLY CHAINS RESILIENCE IN INDUSTRIAL ENVIRONMENTReceived – Primljeno: 2014-07-10Accepted – Prihvaćeno: 2015-03-15Preliminary Note – Prethodno priopćenjeISSN 0543-5846METABK 54(4) 703-706 (2015)UDC – UDK 629.3: 681.1: 005.93: 004.4=111P. Wicher, P. Besta, R. Lenort, Faculty of Metallurgy and Materials Engineering, VŠB – Technical University of Ostrava, Czech Repub-lic D. Staš, Škoda Auto University, Czech Republic, M. Karkula, AGH University of Science and Technology, PolandThe article presents a computer simulation-based model for analysis of supply chain resilience, which allows deter-mining and verifying the generally valid principles, capabilities and ways for building long-term resilience of global supply chains against serious disruptions. The model is created on the basis of a supply chain from automotive in-dustry and contains the main logistics flows used by present automotive producers. Any real automotive supply chain can be modelled as a combination of these logistics flows. The model was created in DOSIMIS-3® and verified using experimental data. The performed simulation demonstrates a significant decrease of the supply chain perfor-mance in case of serious disruption occurrence.Key words: supply chain, resilience, computer simulation, industrial environmentINTRODUCTIONLeanness was the leading approach to supply chain management in past years [1], but today’s supply chains must face volatile environment with a wide spectrum of factors causing their disruption. New tools for increasing the supply chain resilience must be developed for that reason. One of the promising tools, which can be used for that purpose, is computer simulation. The aim of this ar-ticle is to design a computer simulation-based model for analysis of supply chain resilience, which allows deter-mining and verifying the generally valid principles, capa-bilities and ways for building long-term resilience of global supply chains against serious disruptions.LITERATURE REVIEWSupply chain resilience is defined as follows - it is: (1) the ability of a system (supply chain) to return to its original state or to move to a new, more desirable state after being disturbed [2], (2) the ability to bounce back from large-scale disruptions [3], (3) being better posi-tioned than competitors to deal with – and even gain advantage from - disruptions [4], (4) the ability to main-tain output close to the potential one in the aftermath of shocks [5]. The main idea of these definitions is to cre-ate such a supply chain that is not vulnerable to serious disruptions. According to the World Economic Forum (WEF) [6], the most important ones include: natural disasters, extreme weather changes, conflicts and politi-cal troubles, terrorism and sudden radical changes of demand.There is only a limited number of research works dealing with the computer simulation-based analysis of supply chain resilience. The major ones include re-searches [7 – 11]. On the basis of these studies, it can be deduced that [12]: (1) there is only a limited number of research works focused on a specific simulation model for supply chain resilience analysis, (2) the existing models work especially with disruptions that do not cause a long-term reduction in the performance of the entire supply chain, (3) simulation frameworks and models are not created for the purpose of strategic deci-sion-making in supply chains, (4) the models simulate only selected parts of the supply chain and include only a very limited number of subjects.DESIGNED MODELThe authors of this article have developed the initial version of computer simulation-based model to elimi-nate the above presented shortcomings of the existing simulation models.The model takes into consideration the crucial di-sruptions identified by the WEF. These disruptions do not affect only the selected links in the chain (concrete manufacturers, suppliers, distributors and customers), but the entire areas. According to the nature of disrupti-on, it will affect areas defined geographically, political-ly or economically. That is why the model works with a high degree of aggregation. Each element of the model represents the supply chain links in a given area (e.g., the element of Source contains a group of suppliers of Northeast Europe). It’s an original and unique feature of the proposed model which hasn’t been used in any other simulation-based model so far. It allows modelling the whole length of the supply chain, not only a small part of a real supply chain. Due to the nature of WEF disrup-704P. WICHER et al.: A COMPUTER SIMULATION-BASED ANALYSIS OF SUPPLY CHAINS RESILIENCE IN INDUSTRIAL... METALURGIJA 54 (2015) 4, 703-706tions, the model is designed in such a way to analyse supply chain resilience in a long-term horizon (decades) and to support decision-making of a strategic nature.The model is created on the basis of a supply chain from automotive industry, because: (1) the automotive industry is crucial to Europe‘s prosperity [13], (2) the automotive industry is a representative of global supply chains (worldwide), which contain all kind of elements, from a supplier of steel materials and other components, trough manufacturing plants, to a distribution network, (3) these supply chains are affected by all major disrup-tions defined by the WEF, (4) the automotive industry is the leader in supply chain management.The model was created in DOSIMIS-3 ® (dynamic, stochastic, and discrete event simulation tool) – see Fig-ure 1. The model contains the main logistics flows used by present automotive producers: (1) sourcing by means of hub and spoke or cross docking system – the branch of Manufacturing (M) – Transport 5 (T5) – Storage 1 (S1) – Transport 1 (T1) and 2 (T2) – Source 1 (So1) and 2 (So2), (2) direct sourcing – the branch of Manufactur-ing (M) – Transport 3 (T3) and 4 (T4) – Source 3 (So3) and 4 (So4), (3) distribution to transmarine destinations – the branch of Consumption 1 (C1) – Transport 8 (T8) – Storage 3 (S3) – Maritime transport (MT) – Storage 2 (S2) – Transport 6 (T6) – Manufacturing (M), (4) direct distribution – the branch of Consumption 2 (C2) – Transport 7 (T7) – Manufacturing (M).Any real automotive supply chain can be modelled as a combination of these logistics flows.The model uses JIT supply chain strategy. The indi-vidual links in the supply chain can be arranged in a series or in a parallel form. A disruption of a link in the series part of the supply chain will reduce the perfor-mance of this whole section.The model is balanced as far as its capacity is con-cerned (capacity is equal to demand). The whole capacity of the supply chain is 500 000 tonnes per year. The simu-lation step is one week and the simulated period is 20 years. The capacity of the elements and the performance of the whole chain are measured in tonnes per week.A serious disruption can appear in each element of the model (including Consumption) with the same dis-ruption parameters (parameters were selected on the basis of [13]): (1) disruption periodicity (time interval between disruptions) is set according to the exponential distribution where mean value is 4 years, (2) disruption time period (time interval between the disruption begin-ning and capacity recovery) varies from 1 to 3 months, according to uniform distribution, (3) disruption capac-ity loss (the number of units lost at the outset of the disruption) is assumed in the amount of 100 % (total capacity loss), (4) disruption profile (the shape of the disruption capacity loss from the beginning to the end) is represented in Figure 2. The model uses the loss of unrealized production caused by a disruption as a supply chain performance measure. This loss is represented by unsold units.EXPERIMENTS AND RESULTSThe performed simulations demonstrate a signifi-cant decrease of the supply chain performance. The ideal performance, i.e. without disruptions, is 10 mil-lion tonnes per 20 years. When disruptions were includ-ed and simulation was run 30 times (to get statistically significant results), the supply chain performance (see the OC column in Table 1) is on the average 7 612 thou-sand tonnes per 20 years (app. 76 % of the ideal perfor-mance). Such a significant decrease is caused especially by the used JIT supply chain strategy. A disruption in any element causes stoppage or substantial limitation of the other supply chain elements. The performance of the manufacturing element from selected simulation run is presented in Figure 3 in order to demonstrate this fact. Figure 1 The initial model in DOSIMIS 3®Figure 2 Used disruption profile705P. WICHER et al.: A COMPUTER SIMULATION-BASED ANALYSIS OF SUPPLY CHAINS RESILIENCE IN INDUSTRIAL...METALURGIJA 54 (2015) 4, 703-706Full performance decrease is caused by a disruption ap-pearing directly in this element. The other falls are re-lated to disruptions of other elements.The performance of the selected network elements is shown in Table 1.CONCLUSIONThe designed model allows using the execution of the simulation experiments to examine the effect of the changes in the model structure (elements and linkages), the model input parameters, and the change of the logis-tics strategy on the supply chain performance. The ac-quired results can be used in two ways: (1) to determine the generally valid principles of building resilience of supply chains. The conclusions related to supply chain density, supply chain complexity, and supply chain node criticality can serve as an example [6], (2) to check the possibility of using the specific capabilities and ways to increase the resilience of the modelled supply chain. The implementation of the individual capabilities and ways in the model are reflected in the change of the structure or the parameters setting of the model. For ex-ample, the establishment of a close cooperation among the various network elements will reduce the disruption time period or the disruption capacity loss, which will result in an increased performance of the supply chain. It will be possible to use the acquired decrease in the loss of unrealized production to compare it with the in-vestments necessary to build the given capability or way and to make the final decision on its implementa-tion.Both of these outcomes will facilitate strategic deci-sion support in designing new resilient supply chains or in increasing the resilience of existing supply chains in a longer time horizon.AcknowledgementThe work was supported by the specific university research of the Ministry of Education, Youth and Sports of the Czech Republic No. SP2014/81 and by the Inter-nal Grand Agency of ŠKODA AUTO, a.s. No. IGA/2012/3.REFERENCES[1] A. Samolejova, R. Lenort, M. Lampa, A. Sikorova, Speci-fics of Metallurgical Industry for Implementation of Lean Principles, Metalurgija 51 (2012) 3, 373-376.[2] M. Christopher, C. Rutherford, Creating Supply Chain Re-silience through Agile Six Sigma, Critical Eye (2004), 24-28.[3] Y. Sheffi, J. Rice, A Supply Chain View of the Resilient Enterprise, MIT Sloan 47 (2005) 1, 8-41.[4] Y. Sheffi, Building a Resilient Supply Chain. Harvard Bu-siness Review. Supply Chain Strategy 1 (2005) 8, 1-4.[5] R. Duval, J. Elmeskov, L. Vogel, Structural Policies and Economic Resilience to Shocks. [online] OEECD Econo-mics Department Working Paper 567, 2007. [cit. 11.7.2014]. Available from: http://ssrn.com/abstract=1002508 [6] World Economic Forum: Building Resilience in Supply Chains: Report. Switzerland: World Economic Forum, 2013.[7]  M. Falasca, C. Zobel, D. Cook, A Decision Support Fra-mework to Assess Supply Chain Resilience.  Proceeding, 5th International ISCRAM Conference, Washington, 2008, pp. 596-605.[8]  S.A. Melnyk, A. Rodrigues, G.L. Ragatz, Using Simula-tion to Investigate Supply Chain Disruptions, Supply Figure 3 Performance of the Manufacturing elementTable 1 Performance of selected elementsSelected elements OC C1 C2 M S1 So1 So2 So3 So4Performance / thou-sand tonnes/ 20 yearsWithout disruption 10 000 5 000 5 000 10 000 5 000 2 500 2 500 2 500 2 500With disruption 7 612 3 805 3 807 7 612 3 663 1 844 1 819 1 976 1 972Diff erence / thousand tonnes 2 388 1 195 1 193 2 388 1 337 656 681 524 528Percentage of max. performance 76,12 76,10 76,14 76,12 73,26 73,76 72,76 79,04 78,88706P. WICHER et al.: A COMPUTER SIMULATION-BASED ANALYSIS OF SUPPLY CHAINS RESILIENCE IN INDUSTRIAL... METALURGIJA 54 (2015) 4, 703-706Chain Risks, G. A. Zsidisin, B. Ritchie (ed). Springer Science and Business Media, New York, 2009, pp. 103-122.[9]  H. Carvalho, A. P. Barroso, V. H. Machado, S. Azevedo, V.C. Machado, Supply Chain Redesign for Resilience using Simulation. Computers & Industrial Engineering 62 (2012) 1, 329-341.[10] I. L. Nunes, S. Figueira, V. C. Machado, Combining FDSS and Simulation to Improve Supply Chain Resilience. Deci-sion Support Systems – Collaborative Models and Appro-aches in Real Environments: Lecture Notes in Business Information Processing. 121, (2012), 42-58.[11]  A.J. Schmitt, M. Singh, A Quantitative Analysis of Disrup-tion Risk in a Multi-echelon Supply Chain, International Journal of Production Economics 139 (2012) 1, 22-32.[12] R. Lenort, E. Grakova, M. Karkula, P. Wicher, D. Staš, Model for Simulation of Supply Chain Resilience, Procee-ding, METAL 2014: 23rd International Conference on Me-tallurgy and Materials. Ostrava, 2014, TANGER, 7 p.[13] Automotive industry [online]. European Commission. [cit. 11.7.2014]. Available from: http://ec.europa.eu/enterprise/sectors/automotive/index_en.htm.Note:  The responsible translator for English language is Petr Jaroš (English Language Tutor at the College of Tourism and Foreign Trade, Goodwill - VOŠ, Frýdek-Místek, the Czech Republic)

A computer simulation-based analysis of supply chains resilience in industrial environment

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A computer simulation-based analysis of supply chains resilience in industrial environment

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