Simphony Supply Chain Simulator: a simulation toolkit to model the supply chain of construction projects

Abstract

The supply chain management (SCM) field of research has developed a valuable set of concepts and strategies to incorporate the core competencies of suppliers into industrial systems, a trend followed by the construction industry. Research into quantifying the benefits of implementing SCM in construction has been very limited; the literature for the construction industry generally discusses how SCM concepts can be adopted, or what problems and challenges inhibit such adoption, without analyzing and quantifying the effects of these techniques on an actual construction project. No analytical approach or special-purpose simulation tool has yet been developed to facilitate such quantitative analysis for construction projects. This paper presents a construction supply chain simulation toolkit that is capable of modeling different supply chain problems and is compatible with other construction simulation tools. A detailed simulation model of the effects of supply chain issues on the productivity of a real-life construction project, constructed using this toolkit, is also presented.

Keywords

productivity analysis project management simulation models supply chain management systems management tunnel construction

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References

Sahin F. and Robinson EP Information sharing and coordination in make-to-order supply chains . J Oper Manag 2005; 23: 579 598.

Forrester JW Industrial dynamics. Waltham, MA: Pegasus Communications, 1961.

Simchi-Levi D. , Kaminsky P. and Simchi-Levi E. Designing and managing the supply chain: Concepts, strategies and case studies , 2nd ed. New York : McGraw-Hill, 2003.

Newhart DD , Statt KL and Vasco FJ Consolidating product size to minimize inventory levels for a multistage production and distribution systems. J Oper Res Soc 1993 ; 44: 637 644.

Voudouris VT Mathematical programming techniques to de-bottleneck the supply chain of fine chemical industries. Comput Chem Eng 1996; 20: 1269 1274.

Nozick LK and Turnquist MA Inventory, transportation, service quality and location of distribution centers . Eur J Oper Res 2001; 129: 362 371.

Melachrinoudis E. and Min H. The dynamic relocation and phase-out of hybrid two-echelon plant/warehousing facility: a multiple objective approach . Eur J Oper Res 2000; 123: 1 15.

Chopra S. , Reinhardt G. and Dada M. The effect of lead-time uncertainty on safety stock. Decis Sci 2004; 35: 1 24.

Arcelus FJ , Kumar S. and Serinivasan G. Retailer’s response to alternate manufacturer’s incentives under a single-period, price-dependant, stochastic demand framework. Decis Sci 2005; 36: 599 626.

10.

Tiger AA and Simpson P. Using discrete event simulation to create flexibility in APAC supply chain management. Global J Flex Syst Manag 2003; 4: 15.

11.

Sen W. , Pokharel S. and YuLei W. Supply chain positioning strategy, integration, evaluation, simulation and optimization. Comput Ind Eng 2004; 46: 781 792.

12.

van der Zee DJ and van der Vorst Jgaj. A modeling framework for supply chain simulation. Decis Sci 2005 ; 36: 65 95.

13.

Fleisch E. and Tellkamp C. Inventory inaccuracy and supply chain performance: a simulation study of a retail supply chain. Int J Product Econ 2005 ; 95: 373 385.

14.

Hwarng HB , Chong CSP , Xie N. and Burgess TF Modeling a complex supply chain: Understanding the effect of simplified assumptions . Int J Product Res 2005; 43: 2829 2872.

15.

Chatfield DC SISCO and SCML-software tools for supply chain simulation modeling and information sharing. PhD, Pennsylvania State University, 2001.

16.

Terzi S. and Cavalieri S. Simulation in the supply chain context: a survey. Comput Ind 2004; 53: 3 16.

17.

Jiang A. , O’Brien W. and Issa R. Construction supply chain performance management. International Conference of Computing in Civil Engineering, Cancun , Mexico, 2005.

18.

Vaidyanathan K. and O’Brien W. Opportunities for IT to support the construction supply chain. Proceedings of the Fourth Joint International Symposium on Information Technology in Civil Engineering. Nashville, Tennessee, 2003 .

19.

Cox A. and Ireland P. Managing construction supply chain: the common sense approach. Eng Construct Architect Manag 2002; 9: 409 418.

20.

Palaneeswaran E. , Kumaraswamy M. and Thomas S. Formulating a framework for relationally integrated construction supply chain. J Construct Res 2003; 4: 189 205.

21.

Walsh K. , Hershauer J. , Tommelein I. and Walsh T. Strategic positioning of inventory to match demand in a capital projects supply chain. J Construct Eng Manag 2004; 130: 818 826.

22.

Tommelein ID , Akel N. and Boyers JC Capital projects supply chain management: SC tactics of a supplier organization. Construction Research Congress in Construction . Honolulu : Hawaii, 2003.

23.

Chen F. , Drezner Z. , Ryan JK and Simchi-Levi D. Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times and information. Manag Sci 2000 ; 46: 436 443.

24.

Chatfield DC , Kim JG , Harrison TP and Hayya JC The bullwhip effect-impact of stochastic lead time, information quality, and information sharing: a simulation study. Production and Operations Management. 2004; 13: 340 353.

25.

Dejonckheere J. , Disney SM , Lambrecht MR and Towill DR The impact of information enrichment on the bullwhip effect in supply chains: a control engineering perspective. Eur J Oper Res 2004; 153: 727 750.

26.

Ryan JK Analysis of inventory models with limited demand information. PhD, Evanston University, 1997.

27.

AbouRizk S. , Ruwanpura J. and Fernando S. Special purpose simulation template for utility tunnel construction . Proceedings of the 1999 Winter Simulation Conference , 1999.

28.

Al-Battaineh HT , AbouRizk S. , Tan J. and Fernando S. Productivity simulation during the planning phase of the Glencoe Tunnel in Calgary, Canada: a case study. Proceedings of the 2006 Winter Simulation Conference, 2006.