Abstract
The Beer Distribution Game (BDG) is a widely used experiential learning simulation game aimed at teaching the basic concepts around Supply Chain Management (SCM). The goal in this problem is to minimize inventory costs while avoiding stock-outs –hence the players face the dilemma between storage and shortage. Human players usually get confused giving rise to significant inefficiencies in the supply chain, such as the Bullwhip Effect. This research paper shows how artificial agents are capable of playing the BDG effectively. In order to solve the dilemma, we have integrated supply chain processes (i.e. a collaborative functioning) through the Drum-Buffer-Rope (DBR) methodology. This technique, from Goldratt’s Theory of Constraints (TOC), is based on bottleneck management. In comparison to traditional alternatives, results bring evidence of the great advantages induced in the BDG by the systems thinking. Both the agent-based approach and the BDG exercise have proved to be very effective in illustrating managers the underlying structure of supply chain phenomenon.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Note that, according to BDG assumptions, it is not possible to elevate the bottleneck. It is an external constraint, which is beyond the supply chain’s sphere of influence. Hence the bottleneck will not be broken, and the continuous improvement cycle is reduced to three steps.
- 2.
Youngman (2009) elaborated an excellent guide that can be consulted to get more detail in the implementation process.
- 3.
This replenishment method is based on a basic periodic review system for issuing orders depending on incoming demand and inventory position, aimed at bringing this position up to a defined level. See Chen et al. (2000) for more detail of this widely studied policy.
References
Chaharsooghi SK, Heydari J, Zegordi SH (2008) A reinforcement learning model for supply chain ordering management: an application to the beer game. Decis Support Syst 45:948–959
Chen F, Drezner Z, Ryan JK, Simchi-Levi D (2000) Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times and information. Manage Sci 46:436–443
Costas J, Ponte B, De la Fuente D, Pino R, Puche J (2015) Applying Goldratt’s theory of constraints to reduce the bullwhip effect through agent-based modeling. Expert Syst App 42:2049–2060
Cox JF, Spencer MS (1998) The constraints management handbook. Lucie Press, Boca Raton
Dardenne A, Lamsweerde A, Fichas S (1993) Goal-directed requirements acquisition. Sci Comput Prog 20:3–50
Gilbert N (2007) Agent-based models. Sage Publications, London
Goldratt EM (1990) Theory of constraints. North River Press, Croton-on-Hudson, New York
Goldratt EM, Cox J (1992) The goal—a process of ongoing improvement. North River Press, Croton on-Hudson, New York
Goodwin JS, Franklin SG (1994) The beer distribution game: using simulation to teach systems thinking. J Manage 13:7–15
Jairman WE (1963) Problems in industrial dynamics. Mit Press, Cambridge
Kaminsky P, Simchi-Levi D (1998) A new computerized beer game: a tool for teaching the value of integrated supply chain management. The Production and Operations Management Society, Miami
Kimbrough SO, Wu DJ, Zhong F (2002) Computers play the beer game: can artificial agents manage supply chains? Decis Support Syst 33:323–333
Lee H, Whang S (1999) Decentralized multi-echelon supply chains: incentives and information. Manage Sci 6:475–490
Lee H, Padmanabhan V, Whang S (1997) Information distortion in a supply chain: the bullwhip effect. Manage Sci 45:633–640
Moyaux T, Chaib-draa B, D’Amours S (2004) An agent simulation model for the quebec forest supply chain. Lect Notes Comput Sci 3191:226–241
Ponte B, De la Fuente D, Pino P, Rosillo R, Fernández I (2013) Supply chain management by means of simulation. Polibits 48:55–60
Ponte B, Pino R, Fernández I, García N, Monterrey M (2014) Multiagent model for supply chain management. In: Managing complexity. Springer, London, pp 233–240
Simatupang TM, Wright AC, Sridharan R (2004) Applying the theory of constraints to supply chain collaboration. Supply Chain Manag 9:57–70
Sterman JD (1988) Deterministic chaos in models of human behaviour: Methodological issues and experimental results. Syst Dynam Rev 4:148–178
Sterman JD (1989) Modeling managerial behaviour: misperceptions of feedback in a dynamic decision making experiment. Manage Sci 35:321–339
Strozzi F, Bosch J, Zaldívar JM (2007) Beer game order policy optimization under changing customer demand. Decis Support Syst 42:2153–2163
Taguchi G, Chowdhury S, Taguchi S (2000) Robust engineering. Mc Graw-Hill, New York
Wilensky U (1999) NetLogo. In: The center for connected learning and computer-based modeling. Available via http://ccl.northwestern.edu/netlogo/. Cited 2 Jan 2015
Wooldridge M (2000) Reasoning about rational agents. MIT Press, Cambridge
Wu HH, Chen CP, Tsai CH, Tsai TP (2010) A study of an enhanced simulation model for TOC supply chain replenishment system under capacity constraint. Expert Syst App 37:6435–6440
Wu HH, Lee AHI, Tsai TP (2014) A two-level replenishment frequency model for TOC supply chain replenishment systems under capacity constraint. Comput Ind Eng 72:152–159
Youngman K (2009) A guide to implementing the theory of constraints (TOC). Available via http://www.dbrmfg.co.nz/. Cited 14 Feb 2015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this paper
Cite this paper
Costas, J., Ponte, B., de la Fuente, D., Lozano, J., Parreño, J. (2017). Agents Playing the Beer Distribution Game: Solving the Dilemma Through the Drum-Buffer-Rope Methodology. In: Amorim, M., Ferreira, C., Vieira Junior, M., Prado, C. (eds) Engineering Systems and Networks. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-45748-2_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-45748-2_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45746-8
Online ISBN: 978-3-319-45748-2
eBook Packages: EngineeringEngineering (R0)