Conceptual Agent based Modeling in Supply Chain: An Economic Perspective

Document Type : Research Article

Authors

1 Department of Management, Faculty of Management and Accounting, University of Tehran college of Farabi, Qom, Iran.

2 Department of management of Tarbiat Modarres university

Abstract

Abstract:
The implementation of government legislation, social responsibility, environmental concerns regarding the reduction of waste, hazardous material and other consumer residuals have made the competition between the firms stricter than ever and nowadays firms that want to survive need a more productive and innovative approach toward the financial aspects of their businesses.his paper presents a conceptual supply chain model integrating financial and physical flows. The main idea of this paper is to identify financial tools and show the usefulness of them in order to increase the competitiveness of the supply chain as a whole in the volatile markets. In doing so, we consider the role of a financial service provider and try to incorporate the effects of supply chain finance concept on the working capital i.e. liquidity of the member entities. So, the main contribution of this research is to address an innovative approach to model financial flows in supply chain and to introduce the financial tools (supply chain finance practices) in the supply chain framework employing Economic Value Added and cash-to-cash cycle as performance measures and finally devise a conceptual agent-based model and show how agent based modeling can be beneficial in this field.

Keywords


Anwar, T. (2004). The search for meaningful measures of working capital. Building an Edge. Today’s Insights for Tomorrow’s Financial Institutions. Archives IBM.
Avci, M. G., & Selim, H. (2017). A Multi-objective, simulation-based optimization framework for supply chains with premium freights. Expert Systems with Applications, 67, 95-106. 
Axelrod, R. (1997). Advancing the art of simulation in the social sciences. In Simulating social phenomena (pp. 21-40): Springer.
Badell, M., Romero, J., Huertas, R., & Puigjaner, L. (2004). Planning, scheduling and budgeting value-added chains. Computers & chemical engineering, 28(1), 45-61. 
Bahri, M., St-Pierre, J., & Sakka, O. (2011). Economic value added: a useful tool for SME performance management. International Journal of Productivity and Performance Management, 60(6), 603-621. 
Bals, C. (2018). Toward a supply chain finance (SCF) ecosystem–Proposing a framework and agenda for future research. Journal of Purchasing and Supply Management. 
Betts, A. (2010). Collaborative financing: the wave of the future. Financial-I Trade & Supply Chain Handbook. 
Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(suppl 3), 7280-7287. 
Cao, E., & Yu, M. (2018). The bright side of carbon emission permits on supply chain financing and performance. Omega. 
Carnovale, S., Rogers, D. S., & Yeniyurt, S. (2018). Broadening the perspective of supply chain finance: The performance impacts of network power and cohesion. Journal of Purchasing and Supply Management. 
Cronie, G., & Sales, H. (2008). ING Guide to Financial Supply Chain Optimisation. ING Wholesale Banking. 
Dominguez, R., Cannella, S., & Framinan, J. M. (2015). On returns and network configuration in supply chain dynamics. Transportation Research Part E: Logistics and Transportation Review, 73, 152-167. 
Dunbar, K. (2013). Economic Value Added (EVA TM): A Thematic-Bibliography. The Journal of New Business Ideas & Trends, 11(1), 54. 
García-Flores, R., & Wang, X. Z. (2002). A multi-agent system for chemical supply chain simulation and management support. Or Spectrum, 24(3), 343-370. 
Gavirneni, S., Kapuscinski, R., & Tayur, S. (1999). Value of information in capacitated supply chains. Management science, 45(1), 16-24. 
Golpîra, H., Zandieh, M., Najafi, E., & Sadi-Nezhad, S. (2017). A multi-objective, multi-echelon green supply chain network design problem with risk-averse retailers in an uncertain environment. Scientia Iranica. Transaction E, Industrial Engineering, 24(1), 413. 
Gomm, M. L. (2010). Supply chain finance: applying finance theory to supply chain management to enhance finance in supply chains. International Journal of Logistics Research and Applications, 13(2), 133-142. doi:10.1080/13675560903555167
Guillen, G., Badell, M., & Puigjaner, L. (2007). A holistic framework for short-term supply chain management integrating production and corporate financial planning. International Journal of Production Economics, 106(1), 288-306. 
Hammami, R., Frein, Y., & Hadj-Alouane, A. B. (2009). A strategic-tactical model for the supply chain design in the delocalization context: Mathematical formulation and a case study. International Journal of Production Economics, 122(1), 351-365. 
Hennah, D. J., & internationale, C. d. c. (2013). The ICC Guide to the Uniform Rules for Bank Payment Obligations: International Chamber of Commerce.
Laínez, J. M., Puigjaner, L., & Reklaitis, G. V. (2009). Financial and financial engineering considerations in supply chain and product development pipeline management. Computers & chemical engineering, 33(12), 1999-2011. 
Lin, F.-r., Sung, Y.-w., & Lo, Y.-p. (2005). Effects of trust mechanisms on supply-chain performance: A multi-agent simulation study. International Journal of Electronic Commerce, 9(4), 9-112. 
Long, Q. (2014). An agent-based distributed computational experiment framework for virtual supply chain network development. Expert Systems with Applications, 41(9), 4094-4112. 
Longinidis, P., & Georgiadis, M. C. (2011). Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertainty. International Journal of Production Economics, 129(2), 262-276. 
Longinidis, P., & Georgiadis, M. C. (2013). Managing the trade-offs between financial performance and credit solvency in the optimal design of supply chain networks under economic uncertainty. Computers & chemical engineering, 48, 264-279. 
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of management review, 20(3), 709-734. 
Meng, Q., Li, Z., Liu, H., & Chen, J. (2017). Agent-based simulation of competitive performance for supply chains based on combined contracts. International Journal of Production Economics, 193, 663-676. 
More, D., & Basu, P. (2013). Challenges of supply chain finance: A detailed study and a hierarchical model based on the experiences of an Indian firm. Business Process Management Journal, 19(4), 624-647. 
Nickel, S., Saldanha-da-Gama, F., & Ziegler, H.-P. (2012). A multi-stage stochastic supply network design problem with financial decisions and risk management. Omega, 40(5), 511-524. 
O'Hare, G. M., & Jennings, N. (1996). Foundations of distributed artificial intelligence (Vol. 9): John Wiley & Sons.
Pfohl, H.-C., & Gomm, M. (2009). Supply chain finance: optimizing financial flows in supply chains. Logistics research, 1(3-4), 149-161. 
Poirier, C. C., & Bauer, M. J. (2000). E-supply chain: using the Internet to revolutionize your business: how market leaders focus their entire organization on driving value to customers: Berrett-Koehler Publishers.
Ponte, B., Costas, J., Puche, J., De la Fuente, D., & Pino, R. (2016). Holism versus reductionism in supply chain management: An economic analysis. Decision Support Systems, 86, 83-94. 
Popa, V. (2013). The financial supply chain management: a new solution for supply chain resilience. Amfiteatru Economic, 15(33), 140. 
Protopappa-Sieke, M., & Seifert, R. W. (2010). Interrelating operational and financial performance measurements in inventory control. European Journal of Operational Research, 204(3), 439-448. 
Puigjaner, L., & Guillén-Gosálbez, G. (2008). Towards an integrated framework for supply chain management in the batch chemical process industry. Computers & chemical engineering, 32(4), 650-670. 
Ramezani, M., Kimiagari, A., & Karimi, B. (2015). Interrelating physical and financial flows in a bi-objective closed-loop supply chain network problem with uncertainty. Scientia Iranica. Transaction E, Industrial Engineering, 22(3), 1278. 
Sargent, P. (2006). Financing the chain. Logistics Europe, 14(5), 40-42. 
Seifert, R. W., & Seifert, D. (2011). Financing the chain. International commerce review, 10(1), 32-44. 
Singh, R., Salam, A., & Iyer, L. (2005). Agents in e-supply chains. Communications of the ACM, 48(6), 108-115. 
Stern, J. M., Stewart, G. B., & Chew, D. H. (1995). The EVA®financial management system. Journal of applied corporate finance, 8(2), 32-46. 
Supply Chain Council. (2014). Supply-chain operations reference-model version 10.0. Overview of SCOR version, 5(0). 
Swaminathan, J. M., Smith, S. F., & Sadeh, N. M. (1998). Modeling supply chain dynamics: A multiagent approach. Decision sciences, 29(3), 607-632. 
Tavan, D. (2012, 1 July). Trade Finance: Strenghtening the Chain-SCF. The banker.
Utomo, D. S., Onggo, B. S., & Eldridge, S. (2018). Applications of agent-based modelling and simulation in the agri-food supply chains. European Journal of Operational Research, 269(3), 794-805. 
Van der Vliet, K., Reindorp, M. J., & Fransoo, J. C. (2015). The price of reverse factoring: Financing rates vs. payment delays. European Journal of Operational Research, 242(3), 842-853. 
Wandhofer, R. (2013). Citie Perspective Regulatory. Citie Perspectives, 4.
Wang, Q., Batta, R., Bhadury, J., & Rump, C. M. (2003). Budget constrained location problem with opening and closing of facilities. Computers & Operations Research, 30(13), 2047-2069.