Agent Based Modelling of Milk and its Productions Supply Chain and Bullwhip Effect Phenomena (Case Study: Kerman)

Document Type : Case Study

Authors

1 Department of Agricultural Economics, Faculty of Agricultural, University of Jiroft, Jiroft, Iran

2 Department of Agricultural Economics, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Simulating decision-making process of agents of supply chain is affected by various economic and social factors. In this study it is tried to use the agent based simulation which is a conceptual and computational model and investigates the behavior of the agents of the milk and its products supply chain using data for the year 2016 by emphasizing on dairy farmers against the policy increasing raw milk prices. In this regard, bullwhip effect is one of the important issues raised in supply chain. The present study investigates the presence or absence of bullwhip effect in the milk supply chain and its products, using moving average method and order-up-to-level R. Improvement in supply chain performance is one of the major issues in the current world. Lack of coordination in the supply chain is the main drawback of supply chain that many researchers have proposed different methodologies to overcome it. In addition, the application of agent-based simulation has been investigated in order to improve performance indicator in supply chain. The results obtained from the present study indicated that there is the bullwhip effect in the supply chain, then, the agent managed supply chain has been proposed in order to improve one of the performance indicators in the supply chain. Using centrality in decision-making in the supply chain and agent-based simulation, the results indicated that bullwhip effect is reduced or eliminated for the dairy products. Such results highlight the importance and high potential of agent-based simulation in improving the performance indicator of chain.

Keywords


Ajitha, S., Kumar, T.V. and Rajanikanth, K. (2018). Multi-Agent Based Food Processing Supply Chain Management. International Journal of Engineering Research & Technology (IJERT). NCSE'14 Conference Proceedings.
Behdani, B. (2012). Evaluation of paradigms for modeling supply chains as complex socio technical systems. Proceedings of the 2012 Winter Simulation Conference, pp. 1-15.
Blanciforti, L. and Green, R. (1983). An Almost Ideal Demand System Incorporating Habits: An Analysis of Expenditures on Food and Aggregate Commodity Groups, The Review of Economics and Statistics, No (3), pp. 511-515.
Blanciforti, L. and Green, R.(1983). An Almost Ideal Demand System Incorporating Habits: An Analysis of Expenditures on Food and Aggregate Commodity Groups, The Review of Economics and Statistics, Vol. 3, pp. 511-515.
Bonabeau, E. 2002.Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, Vol. 99(22), pp. 7280-7287.
Bray, R.L. and Haim, M.(2012). Information transmission and the bullwhip effect: an empirical Investigation. Management Science, Vol. 58, pp. 860- 875.
Casti, J.L.(1997). Would-be worlds: How simulation is changing the frontiers of science, John Wiley& Sons Inc: Central bank of Islamic Republic of Iran. 2015. Dairy Price Index for Urban and Rural Households. Various publications.
Dehghan Dehnavi, H. and Mashhadizadeh Ardakani, R. (2011). Investigating the bullwhip effect and the main factors impacting development in the textile industry. Third National Conference on The textile and clothing Engineering Conference, Yazd. Islamic Azad University, Yazd Branch.
Dominguez, R. and Cannella, A. (2020). Insights on Multi- Agent Systems Applications for Supply Chain Management. Sustainability, Vol. 12, pp. 1-13.
 Etebari, F., Abedzadeh, M.and Khoshalhan, F.(2011). Investigating Impact of Intelligent Agents in Improving Supply Chain Performance. International Journal of Industrial Engineering & Production Research, Vol. 22, pp. 63-72.
Faryadras, V. and Jiran, A. (2015). Economic Analysis and Effectiveness of Guaranteed Purchasing Policy of Institute for Planning Research, Agricultural Economics and Rural Development, pp. 55-74.
 Fox, M.S. and Barbuceanu, M.R. (2002). Agent-oriented supply chain management. International Journal of Flexible Manufacturing Systems, Vol. 12, pp. 165-188.
Garge, S. and Srinivasan, S. (2014). Bull whip Effect Reduction through Multi agent system Using Jade Tool. International Journal of Advanced Research in IT and Engineering, Vol. 3, pp. 1-15.
Green, R. and Alston, J.M.(1990). Alston. Elasticity’s in AIDS Models. American Journal of Agricultural Economics, Vol. 2, pp. 442-445.
Heath, S.K., Buss, A., Brailsford, S.C. and Macal, C.M. (2011). Cross-paradigm simulation modeling: challenges and successes. In Proceedings of the 2011 Winter Simulation Conference, pp. 2788-2802.
Helal. M. R. (2007). A Methodology for integrating and synchronizing the system dynamics and discrete event simulation paradigms. In Proceedings of the 25th international conference of the system dynamics society, Vol.3, pp. 1-24.
Hubbard L.J., Dawson, P.J. and C.R. Scott, C.R. (2007). Estimating the unit costs of producing oilseed rape in England. Journal of farm Management, Vol. 12, pp. 709-718.
Hubbard, L.J. and Dawson. P.J. (1987). Management and Size economies in the England and Wales dairy sector, J. Agri. Econ, Vol. 38, pp. 27-38.
Jager, W.(2007).  Modeling consumer behavior. Ph.D. thesis. Groningen University.
Jennings, N. R.(2000). On agent-based software engineering, Artificial intelligence, 117: 277-296.
Kerman Agricultural Organization. (2015). Agricultural Statistics letter (2014). Planning and Economic Department of Information and Communication Technology Center.
Kumar, M. and Keswani, B. (2016). Reducing Bullwhip effect of Supply Chain by applying Multi-agent having Fuzzy thinking. International Journal of Recent Research Aspects, Vol.  3, pp. 109-115.
Macal, C.M. and North, M.J. (2008). Agent-Based Modeling and Simulation: ABMS Examples. IEEE. Proceeding of the 2008 winter simulation conference.
McCrae, R.R. and Costa, P.T. (2003). Personality in Adulthood: A Five-Factor Theory Perspective. 2nd ed. New York: Guildford.
Moradi, S., Nasirzadeh, F. and Golkhoo, F. (2015). A hybrid SD–DES simulation approach to model construction projects. Construction Innovation, Vol. 15, pp. 66-83.
Movahedi, R. and Zolfaghari, F. (2011). Analyzing the role of financial factors in two categories of Bullwhip Effect in Supply Chain. Journal of Industrial Engineering., Vol. 2,pp. 199-208.
Naghavi, S., Karbasi, A.R., Daneshvar Kakhki, M. and Roozmand, O.(2017). Investigating Bullwhip Effect in Multi-Stage Milk and its Products supply chain. Journal of Agricultural Economics, Vol. 2, pp. 115-133.
 Nazari, L. and Aghaei, A..(2012). Measure the bullwhip effect phenomenon in a three-stage supply chain with more than one product. Journal of Industrial Engineering, Vol. 46, pp. 105-117.
 North M.J. and Macal, C.M. (2007). Managing business complexity: Discovering strategic solutions with agent based modeling and simulation. Oxford University Press.
Norvig, P. and S.J. Russell, S.J. (2010). Artificial Intelligence: A Modern Approach. Prentice Hall.
Oudendag, D., Hoogendoorn, M. and Jongeneel, R. (2014). Agent-Based Modeling of Farming Behavior: A Case Study for Milk Quota Abolishment. Springer International Publishing Switzerland.
Parsaiyan, S., Amiri, M., Azimi, P. and Taghavifard, M.T.(2019). Designing a Green Closed-loop Supply Chain Simulation Model and Product Pricing in The Presence of a Competitor. Industrial Management Studies, Vol. 17(52), pp. 153-202.
Pedram, Y. and Sepehri, M. (2013). Simulation of discrete events using Anylogic software, Marandiz Publications, Mashhad.
Razavi Hajagha, S., H. Akrami, L. Olfat. (2012). Investigating the effect of combination of forecasts for the bullwhip effect in multilevel supply chains. Journal Management Improvements, Vol. 4(18), pp. 96-113.
Roozmand, O., Ghasem-Aghaee, N., Hofstede, G.J., Nematbakhsh, M.A., Baraani, A.and Verwaart. T. (2012). Agent-Based Modeling of Consumer Decision Making Process based on Power Distance and personality. Knowledge-Based Systems, Vol. 24, pp. 1075-1095.
 Sahay, N. and Ierapetritou, M.)2013(. Supply Chain Management Using an Optimization Driven Simulation Approach. Published online September in Wiley Online Library, 12.
Schieritz, N. and Grobler, A.)2003(. Emergent structures in supply chains-a study integrating agent-based and system dynamics modeling. In System Sciences. Proceedings of the 36th Annual Hawaii International Conference.
 Shapiro, J.F.)1999(. On the connections among activity-based costing, mathematical programming models for analyzing strategic decisions, and the resource-based view of the firm. European Journal of Operational Research, Vol.118, pp. 295-314.
Shen, W., Wang, L. and Hao, Q. (2006).Agent-Based Distributed Manufacturing Process and Scheduling: A state- of- the –Art Surve. IEEe Transaction on system, man, And Cybernetics-Part C: applications and Reviews, Vol. 36, pp. 563-577.
Sumari, S R., Ibrahim, N.H., Zakaria, A.H. and Hamid A. H.(2013). Comparing three-simulation model using taxonomy: system dynamic simulation, discrete event simulation and agent based simulation. International Journal of Management Excellence, Vol. 1, pp. 54-59.