The effect of supply chain noise on the financial performance of Kanban and Drum-Buffer-Rope: An agent-based perspective

Abstract Managing efficiently the flow of products throughout the supply chain is essential for succeeding in today's marketplace. We consider the Kanban (from Lean Management) and Drum-Buffer-Rope (DBR, from the Theory of Constraints) scheduling mechanisms and evaluate their performance in a four-echelon supply chain operating within a large noise scenario. Through an agent-based system, which is presented as a powerful model-driven decision support system for managers, we show the lower sensitivity against variability and the higher financial performance of DBR, which occurs as this mechanism improves the supply chain robustness due to its bottleneck orientation. Nonetheless, we prove the existence of regions in the decision space where Kanban offers similar performance. This is especially relevant taking into account that Kanban can be implemented at a lower cost, as DBR requires a higher degree of information transparency and a solid contract between partners to align incentives. In this sense, we offer decision makers a methodological approach to reach an agreement when the partners decide to move from Kanban to DBR in a bid to increase the overall net profit in supply chains operating in a challenging noise scenario.

[1]  John D. Leeth,et al.  The Simulation Model , 1995 .

[2]  Bruce Potter Necessary but Not Sufficient , 2010, IEEE Secur. Priv..

[3]  John H. Blackstone,et al.  The evolution of a management philosophy: The theory of constraints , 2007 .

[4]  Durk-Jouke van der Zee,et al.  A Modeling Framework for Supply Chain Simulation: Opportunities for Improved Decision Making , 2005, Decis. Sci..

[5]  Usha Ramanathan,et al.  Performance of supply chain collaboration - A simulation study , 2014, Expert Syst. Appl..

[6]  Lisa Scheinkopf,et al.  Theory of Constraints and Lean Manufacturing: Friends or Foes? , 1999 .

[7]  Per Hilletofth,et al.  Hybrid simulation models - When, Why, How? , 2010, Expert Syst. Appl..

[8]  Muris Lage Junior,et al.  Variations of the kanban system: Literature review and classification , 2010 .

[9]  Charles M. Macal,et al.  Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation , 2007 .

[10]  Gérard P. Cachon Supply Chain Coordination with Contracts , 2003, Supply Chain Management.

[11]  Barbara Messing,et al.  An Introduction to MultiAgent Systems , 2002, Künstliche Intell..

[12]  大野 耐一,et al.  Toyota production system : beyond large-scale production , 1988 .

[13]  Hasan Selim,et al.  A multi-agent system model for supply chains with lateral preventive transshipments: Application in a multi-national automotive supply chain , 2016, Comput. Ind..

[14]  Lorraine R. Gardiner,et al.  Drum‐Buffer‐Rope and Buffer Management: Impact on Production Management Study and Practices , 1993 .

[15]  John Davies,et al.  The theory of constraints thinking processes: retrospect and prospect , 2008 .

[16]  H. William Dettmer,et al.  Beyond Lean Manufacturing : Combining Lean and the Theory of Constraints for Higher Performance , 2002 .

[17]  Hasan Selim,et al.  A Multi-objective, simulation-based optimization framework for supply chains with premium freights , 2017, Expert Syst. Appl..

[18]  David R. Nave,et al.  How to compare six sigma, lean and the theory of constraints , 2002 .

[19]  Computer Staff,et al.  The Machine That Changed the World , 1992 .

[20]  V. J. Mabin,et al.  The performance of the theory of constraints methodology: Analysis and discussion of successful TOC applications , 2003 .

[21]  J. Sterman,et al.  Systems thinking and organizational learning: Acting locally and thinking globally in the organization of the future , 1992 .

[22]  James F. Cox,et al.  The Poker Chip Game: A Multi-product, Multi-customer, Multi-echelon, Stochastic Supply Chain Network Useful for Teaching the Impacts of Pull versus Push Inventory Policies on Link and Chain Performance , 2006 .

[23]  Stephen M. Disney,et al.  Measuring and avoiding the bullwhip effect: A control theoretic approach , 2003, Eur. J. Oper. Res..

[24]  P. J. Weeda,et al.  A framework for quantitative comparison of production control concepts , 1989 .

[25]  John B. Kidd,et al.  Toyota Production System , 1993 .

[26]  Hokey Min,et al.  Selection of management accounting systems in Just-In-Time and Theory of Constraints-based manufacturing , 2003 .

[27]  J. S. Goodwin,et al.  The Beer Distribution Game: Using Simulation to Teach Systems Thinking , 1994 .

[28]  Peter T. Ward,et al.  Lean manufacturing: context, practice bundles, and performance , 2003 .

[29]  Marc Lambrecht,et al.  Buffer Stock Allocation in Serial and Assembly Type of Production Lines , 1990 .

[30]  David de la Fuente,et al.  Holism versus reductionism in supply chain management: An economic analysis , 2016, Decis. Support Syst..

[31]  Ramesh Sharda,et al.  Model-driven decision support systems: Concepts and research directions , 2007, Decis. Support Syst..

[32]  Xun Wang,et al.  Inventory management for stochastic lead times with order crossovers , 2016, Eur. J. Oper. Res..

[33]  Bhakti Stephan Onggo,et al.  Agent-Based Simulation Model Representation Using BPMN , 2013 .

[34]  Bob Sproull The Ultimate Improvement Cycle: Maximizing Profits through the Integration of Lean, Six Sigma, and the Theory of Constraints , 2009 .

[35]  Terry P. Harrison,et al.  SISCO: An object-oriented supply chain simulation system , 2006, Decis. Support Syst..

[36]  P. Hines,et al.  Learning to evolve: A review of contemporary lean thinking , 2004 .

[37]  Katsuhiko Takahashi,et al.  Comparing reactive Kanban and reactive CONWIP , 2002 .

[38]  J. P. Hofer,et al.  Problems in industrial dynamics , 1966 .

[39]  Terry P. Harrison,et al.  The Bullwhip Effect—Impact of Stochastic Lead Time, Information Quality, and Information Sharing: A Simulation Study , 2004 .

[40]  Thomas Neitzert,et al.  TQM, TPM, TOC, Lean and Six Sigma - evolution of manufacturing methodologies under the paradigm shift from Taylorism/Fordism to Toyotism , 2009 .

[41]  Rambabu Kodali,et al.  A critical review of lean supply chain management frameworks: proposed framework , 2015 .

[42]  A. Huber,et al.  Service-level performance of MRP, kanban, CONWIP and DBR due to parameter stability and environmental robustness , 2008 .

[43]  Zach G. Zacharia,et al.  DEFINING SUPPLY CHAIN MANAGEMENT , 2001 .

[44]  J. Ravichandran Six-Sigma Milestone: An Overall Sigma Level of an Organization , 2006 .

[45]  Combining Lean , Six Sigma , and the Theory of Constraints to Achieve Breakthrough Performance , 2009 .

[46]  David de la Fuente,et al.  The value of lead time reduction and stabilization: A comparison between traditional and collaborative supply chains , 2018 .

[47]  Jack P. C. Kleijnen,et al.  EUROPEAN JOURNAL OF OPERATIONAL , 1992 .

[48]  George L. Nemhauser,et al.  Handbooks in operations research and management science , 1989 .

[49]  Qingqi Long,et al.  An agent-based distributed computational experiment framework for virtual supply chain network development , 2014, Expert Syst. Appl..

[50]  John Blackstone,et al.  Theory of Constraints , 2010, Scholarpedia.

[51]  J. Rajini,et al.  Integration of lean, Six Sigma and theory of constraints for productivity improvement of mining industry , 2018 .

[52]  Jose M. Framinan,et al.  Inventory record inaccuracy – The impact of structural complexity and lead time variability , 2017 .

[53]  Shie-Gheun Koh,et al.  Comparison of DBR with CONWIP in an unbalanced production line with three stations , 2004 .

[54]  J. J. Dahlgaard,et al.  Measuring lean initiatives in health care services: issues and findings , 2006 .

[55]  Scott E. Page,et al.  Agent-Based Models , 2014, Encyclopedia of GIS.

[56]  Jeff Cox,et al.  The Goal: A Process of Ongoing Improvement , 1984 .

[57]  David de la Fuente,et al.  Exploring the interaction of inventory policies across the supply chain: An agent-based approach , 2017, Comput. Oper. Res..

[58]  James P. Womack,et al.  Lean Thinking: Banish Waste and Create Wealth in Your Corporation , 1996 .

[59]  Dean C. Chatfield,et al.  Returns and the bullwhip effect , 2013 .

[60]  R. Kaplan,et al.  strategic learning & the balanced scorecard , 1996 .

[61]  Jose M. Framinan,et al.  On returns and network configuration in supply chain dynamics , 2015 .

[62]  Pablo A. Miranda,et al.  A simulation model of a coordinated decentralized supply chain , 2015, Int. Trans. Oper. Res..

[63]  U. Netlogo Wilensky,et al.  Center for Connected Learning and Computer-Based Modeling , 1999 .

[64]  Pius Achanga,et al.  Critical success factors for lean implementation within SMEs , 2006 .

[65]  C. Pegels The Toyota Production System , 1984 .

[66]  Brian Hambling,et al.  User Acceptance Testing: A step-by-step guide , 2013 .

[67]  T. Simatupang,et al.  An integrative framework for supply chain collaboration , 2005 .

[68]  Gérard P. Cachon,et al.  Supply Chain Coordination with Revenue-Sharing Contracts: Strengths and Limitations , 2005, Manag. Sci..

[69]  Satya S. Chakravorty,et al.  A comparative study of line design approaches for serial production systems , 1996 .

[70]  Matthias Holweg,et al.  Supply chain simulation - a tool for education, enhancement and endeavour , 2002 .

[71]  Daniel T. Jones,et al.  The machine that changed the world : based on the Massachusetts Institute of Technology 5-million dollar 5-year study on the future of the automobile , 1990 .

[72]  T. Simatupang,et al.  Applying the theory of constraints to supply chain collaboration , 2004 .

[73]  Kent L. Beck,et al.  Test-driven Development - by example , 2002, The Addison-Wesley signature series.

[74]  Dimitris Apostolou,et al.  Enabling situation awareness with supply chain event management , 2018, Expert Syst. Appl..

[75]  Julio Puche,et al.  Systemic approach to supply chain management through the viable system model and the theory of constraints , 2016 .

[76]  José Luis Pérez TOC for world class global supply chain management , 1997 .

[77]  Jayashankar M. Swaminathan,et al.  Modeling Supply Chain Dynamics: A Multiagent Approach , 1998 .

[78]  Danny Berry,et al.  Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain , 1999 .

[79]  Taiichi Ohno,et al.  Toyota Production System : Beyond Large-Scale Production , 1988 .

[80]  Mahesh Gupta,et al.  Comparing TOC with MRP and JIT: a literature review , 2009 .

[81]  Itir Z. Karaesmen,et al.  Decision making in the beer game and supply chain performance , 2013 .

[82]  David de la Fuente,et al.  Applying Goldratt's Theory of Constraints to reduce the Bullwhip Effect through agent-based modeling , 2015, Expert Syst. Appl..

[83]  Stephen Michael Disney,et al.  The effect of vendor managed inventory (VMI) dynamics on the Bullwhip Effect in supply chains , 2003 .

[84]  Yahia Zare Mehrjerdi,et al.  The collaborative supply chain , 2009 .

[85]  Anthony L. Patti,et al.  A comparison of JIT and TOC buffering philosophies on system performance with unplanned machine downtime , 2008 .

[86]  Ying-Chuan Chen,et al.  Comparing kanban control with the theory of constraints using Markov chains , 2007 .

[87]  Ann Lehman,et al.  JMP start statistics : a guide to statistics and data analysis using JMP , 2012 .

[88]  Katharina Wagner Problems In Industrial Dynamics , 2016 .